So for those folks out there that are in the helping professions that didn't know they were autistic or neurodivergent, or that they were in a neurodiverse relationship, you are not alone. Because we don't learn about these things in school. And so we don't recognize why we're having the differences we're having. Yeah. Sure, sure. And as I said, it's definitely something that is underrepresented in terms of the online content that you can see.


Everybody forced here ripping 2022 fanatics memorabilia silver box break number five random hit style Silver box right here, I'll leave it here bring up items over one at a time There's three of them three spots on the break will type in the hits as we go Do the random at the end to see who gets what? Good luck the last silver box of tonight. I'm sure they're all sold out for tomorrow by now There's no way there's any available. These have been too much fun, man There's only three spots in a break. So here we go There's inside the box. There's so many boxes guys. I'm just gonna go with What is that? All right, we'll start with that. Yeah, I'm telling you So this stuff is nuts This is so yeah There's no way all right guys. Let's get started with the first hits What?  This is a silver box. This is silver. Yeah, there's no way there's spots I do we've been pulling we pulled like a Luca Jersey earlier. Are we got a basketball trying to find the name on top? Dude no way this basketball is in my hands right now This is a dual auto Luka Donchich Dirk Novitsky Silver, Inc. Luca and Dirk Yeah, buddy This box right here. This has to be a mistake. We're about to do here Dirk and Luca do a lot of baby. I Don't know everybody is about in this break is about to walk away. I Did fanatics make a mistake because My god, I seriously can't believe this is here right now I don't know where the Hologram is but you got this bad boy in the box. Oh my god Luca and Dirk do a lot of basketball says silver right there. So what it says?   I need to open it first.  We got a signed replica baseball helmets of Aaron judge Aaron judge That's right you guys there it is There's no way this is sick. Maybe they intentionally loaded this box. I have no idea Yeah, buddy Aaron judge signed a replica helmet That's right. Oh And the best is yet to come.  That one also comes with a stand. It's kind of loose This one is a lot less safely packaged as the right, but it'll be okay We'll probably honestly we'll probably package this differently. There's no way we're shipping Oh my god, you guys This this is a good warm-up for the diamond I would say For the diamond level boxes. We've got a signed football of none other than Tom Brady Tom Brady signed football Yes, yes, yes ladies and gents there it is Are you freaking kidding me? Luca Dirk signed basketball Aaron judge replica helmet and a Tom Brady signed football With the Super Bowl 39 logo on it. Oh my god.  Oh my god Yes, you guys everybody to crush it in the random Congrats to the three that bought into this this box. Oh my goodness. Oh I don't understand Oh my god. Oh My goodness Good luck on the random three times or not three times But the three names three hits each the same amount of times here and they'll put them side-by-side to see who's getting what? This is this is break five. This is break five. I I am just shocked right now. I Am shocked We're gonna randomize five times just L's in the diamond box Five times on the names five times on the hits will put them side-by-side Whoo one two three four and five Steven C. Down to Ronald are Five times on the hits one two three four five The deluca dirt ball down to the Brady ball How is that in one box, man? I don't know. I'm trying to run up through my head right now wide that would happen That must have been intentional. Oh My god Steven C with the deluca and dirt ball Jimmy C with the Aaron judge helmet and Ronald are with the Brady football Yep diamonds up next


Hi everyone. Good afternoon. Thank you for coming. Maybe you can call your friends some room Okay, so I represent Delivery which is an eight-year-old startup based out of Gurgaon. I'm not sure how many of us have heard about it Show fans anyone who's about Excellent, okay, so delivery was named the startup startup of the year in August A few weeks back. We'll be getting the award in two weeks. I think We're into logistics. We're India's largest second largest logistics company now our main business bread and butter comes from delivering e-commerce shipments from Wherever you order from to your doorstep. For example, if you order a shipment from Amazon About 25% of them are outed through us and go to your doorstep We are also into a bunch of other B2B services for example distribution Full full truckload warehousing and and the works. So we provide end-to-end logistic services for Customers not only in India, but now also in other countries in South Asia We have opened up an office in the US. We're expanding into markets in the Americas and Europe as well Okay, so that's delivery In terms of volume we do about half a million shipments a day We are about to celebrate our 500 millionth shipment in two weeks So that's gonna be a milestone The sale season is up. So it's a good time. We we are hitting around a million shipments a day these days So our systems are put under pressure. I've been on the call since morning That's a quick intro about delivery I am Kabeer Ustogi. I lead the data science team there I'll use this opportunity to tell you about what key problems we are working on I'll also focus on some technical details about the problem on the board But also we'll go through Why we are doing what we're doing build some intuition as to what are the main challenges that we face as a Logistics company and how machine learning helps us Okay, so I'll start with a brief introduction of our Network or operations so deliveries and operations company largely What we do is simple As soon as you place an order we pick it up from the warehouse It could be Amazon Flipkart Minthra whoever you order from We take it to our processing center where we sort it depending on where the shipment needs to go to From there it goes to a transportation hub, which it could be an airport or a major truck hub that we operate From there it either sits on a truck or an airplane goes to another hub Which is the destination hub from there it goes to some delivery center, which is equivalent of a local post office From there you have bikers who deliver to your doorstep so this is essentially what we do now My my PhD was in optimization algorithms, so I did this I finished my PhD back in 2013 And I joined delivery in 16 after a three-year stint as a professor Teaching optimization techniques and statistics when I joined delivery. This was like Disneyland for me, right? this bunch of optimization problems because we're dealing with a huge network our network consists of around 4,000 nodes Hundreds and thousands of edges At any given time you have about 10,000 or trucks running 30,000 bikers It's a massive network. So there's a lot of room for optimization here Starting with network design problems facility location problems truck scheduling problems Shipment allocation problem vehicle routing problem and the works The problem is that none of these really work if you don't understand your ecosystem that you're operating in quite well So all of these techniques if you don't understand your ecosystem is garbage in garbage out, right? So you can do all sorts of optimization. I did my PhD in machine scheduling actually I was very kicked about when I came to know these problems I even have a big book on machine scheduling which was part of my thesis springer offered to Publish it as a book. They were a kind although the readers were not kind enough the book sold only The book sold only a few hundred copies If it had a love story probably even more But the next one would be a love story Yes, so the royalties was slim. I thought it'll make me rich, but no Anyways, so at delivery I wanted to you know solve these problems at scale It was very good opportunity for me. However, we got stuck here So the ecosystem it's very complex as I said It starts with we need location intelligence if you are delivering 6700,000 shipments a day. We are touching 700,000 Addresses every day, which we need to physically find deliver and so on so you need to have a strong location intelligence You need to build customer intelligence right customers are finicky. I I'm not available on Sundays. I'm not available nine o'clock. I'm taking a shower. I don't have cash Come back into ours Which makes these things very tough to do We operate a fleet of 10,000 vehicles and 30,000 bikers a lot of intelligence Goes in there as well to coordinate everything We have 4,000 odd facilities and finally On the order of a million shipments a day So that is our entire ecosystem along with a lot of other Uncertain elements which we need to deal with Now given these things together once we have a very good understanding of these elements Then our optimization models can work because then you know what you're dealing with the hardest ones to To actually build intelligence on as it turned out was location In other countries when I was doing consulting work as a professor in the UK That wasn't a problem at all Because location address data in the UK or most western countries is very clean. It's very structured Post codes in the UK give you the lat long of the address within 200 meters 90 percent of the times In India, that's not the case addresses come in all shapes and forms these are Pictures that we took on on on a trip to old Delhi in Chandni Chowk I'm not sure who has who's from Delhi here, but Chandni Chowk is a very Congested neighborhood very unstructured. It's it's the oldest part of Delhi So the first address I have is 1880 slash 13 hyphen m hyphen 16 The one next doors call themselves as simply m 15 without the the other nice things The ones next door was 1858 w hyphen 2 Now with this sort of unstructuredness in the dresses It becomes very hard for us to understand what the customer really means Moreover addresses in smaller towns are even fancier right people people write addresses with Names of their neighbors next to Gupta G's house night next next to dr. Sharma's house next to the the the old one there All sorts of things you have workflows as well. I'll I'll skip to an example You have workflows like on the weekend delivered to my house on the week they deliver to my parents house Because that's where I spend my weekends There's a very nice threat which we found in in one of our shipments, right? This was I Have masked the necessary details here because the man seems dangerous but What he says is house number xx village xx Post office police station district Jalandhar rural Punjab I want all products which I had already been ordered Should be original. Otherwise, I take strict action against you because I am CID officer from Punjab When I last time order current watch the mirror and second harm And arm was broken Adam poor Punjab Pin code so the guy has been quite diligent writing everything right this there's pretty much everything we need Pin code city Brilliant address, but so many things that we don't need so Oh, yeah, yeah, I can't make this up. I mean Yeah, yeah, so Okay, so the the good clients I don't I don't want to name the good clients But you know the the good ones they have a proper structure to type in the address the smaller ones don't care right they they they want you to order you type in any crap and they'll Give it to us and we are expected to deliver so there are there others with with Nice words with greetings to your family and so on So Yeah, so dresses are painful Now pin codes in India don't work The median size of a pin code in India is 90 square kilometers As I said in the UK Average is around 200 meters So pin codes hardly give you any ability to localize an address Moreover 10% of the pin codes are wrong people don't know their pin codes Pin codes are something which are passed on from generations. You know your grandfather tells your father your father tells you There's there's no way to know what the pin code of your house is I'm not sure I've been working in this area for a while The postcode gives the post office gives some recommendation But then they open a new post office as cities expand and then there's none. There's nothing in the news You have to trust your father really So we notice that 10 to 15 percent of the pin codes are wrong Also addresses come with very nice spelling errors one of my favorite ones is and here you asked Which is one edit away from east and one edit from west and Combined that with the wrong pin code and there you go So That's why we have 30,000 bikers. I mean if this was cleaner, I'm sure we could do with less Also people right besides Very subjectively it could be next to one kilometer away five kilometers away wherever And of course we've gone through this So yeah, what is needed so my job here is to make sense of this So that once I know the address once I know the lat long then I can use all my nice optimization techniques to optimize my way there But we got stuck in the very very first chapter So there are few solutions out there which help us So in the news recently the government of India has been trying many different ways to standardize addresses There's plus codes from Google there's robocords from some consortium from MIT E-lock from map mind. Yeah, what three words they per all sorts problem with these things is that They are hashes of Geocode geocode is a lot latitude and longitude right so you must know your geocode and then it converts it into some hash which is an Alphanumeric code in what three words it's it's memorable three words and so on But at the end of the day you must know your geocode to identify a hash the problem is people don't know their geocodes It's very hard. A lot of people don't know how to use maps Pinpoint your geocode on the map is very hard. My wife has three degrees. I asked her to do this. She couldn't do it We ran an experiment We asked about half a million people to do this over Web link that we send them the average error on pinpointing the geocode was around one kilometer So which is worse than the noisy addresses that I have Okay, so we decided to build a bearer system Something which does not rely on geocode. So we decided to learn from whatever we have so We built a system from scratch which is able to disambiguate the addresses and predict the light a latitude longitude polygon, etc of of the address That's How it looks like that's the front end of our service so I typed in an address real address with misspelling What our system is able to do is it's able to predict, okay find out that it's Raheja Atlantis Notice the spelling is incorrect at the top. It's able to find out that it's in sector 31, which is a missing piece of information at the top Gurgaon, Haryana India, so it's able to predict the entire hierarchy of where this address is likely to be and predict polygon of where Raheja Atlantis is If you double-click on sector 31, it gives you the polygon for sector 31 and Tells you where exactly that house is expected to be which is the blue bubble there Okay, so this is what we've built I'll talk about how this helps us how we went on went about building this and so on But this exercise is very important because it helps us bring structure to the address So you type in any crap at the top it will disambiguate it and provide a structure So if we get very very good at this Then I really don't need the geocode from the people I can get their address and I'm able to Structurize it and give it back to them that look this is your actual address Perhaps you can use it the next time Okay, so speaking of polygons Polygons are nothing but boundaries of the localities That we are operating in Now why it's very important for us to build polygons is Okay for pin codes. It's it's it's easier right because pin codes are government-defined entities However, localities are not localities. Okay a lot of cities in India very old people have colloquial names writing localities, right? And next to my neighborhood there's a next to my locality There's a neighborhood called Char Dukan, which was found it was named because there were four shops back in the 60s And people started calling the entire neighborhood Char Dukan The government doesn't know that there's this it's Char Dukan, right and you won't find it any legal document But people write it way fondly So we need to figure out what people are writing and where those entities are so it gives a digital Existence to localities and pin codes often because pin codes are changing so even though the government prescribes them We don't know much about them Once you know what exactly your locality is where it is how big it is it you can do analytics on top of it, right? This this is a very good example. I'm not sure if the colors are a nicely visible here, but The black polygon here is that of a pin code in Gurgaon The smaller polygons that you see are that of localities, okay Now these are the polygons that we've been ourselves part of the steam was actually Vivek Who was with us? Until last year. He's moved on to Neustadt now, but thanks Vivek for I'll be quoting a bunch of your stuff here So so the green guys are the ones where we believe that our delivery time is somewhere between Zero to six minutes the yellow guys are the ones where our delivery time is six minutes to 12 minutes The red guys are the ones are where our delivery time is 12 minutes plus So have knowing these polygons is able we are able to do better analytics On how our field executives are moving on the ground What are the speeds they are in when they're in this polygon versus when they're in that polygon? And this helps us also determine the incentives that we want to give to our delivery boys and so on So this is about The polygons help us track our field executives as well, right because if I expect a delivery in sector 31 And he's marking something in sector 44. I can easily geofence the location and figure out What's what's wrong if something is a miss and so on And finally once I exactly know where the place is I can go back to my optimization techniques and Reduce the number of field executives. I need from 40,000 to 20,000 and I pay my salary. That's All right So to build all of this the sources of data so what we have is a Lot of address data as I mentioned we've delivered close to 450 million Shipments so far so we have 450 million strings of addresses which people have written Along with that we have a lot of location data that we've captured from mobile devices of our field executives So we have Around 450 million Addresses along with that around 250 million would have been tagged with an accurate geocode When the delivery boy goes to deliver along with that we have a lot of open source data from open street maps and so on So that's the data we are talking about In our estimate so far I we have delivered to around 85 million unique households the definition of unique is is fuzzy because The same person may write this the same address in 10 different ways Although we have an engine to unify them, but you know, it can't be 100% sure so 85 million addresses I just like the upper bound of what we have All right, so How do we do this so that's the data now I'll talk about what we really do with that data There are two broad steps to it. The first type is that I first want to understand How our localities and cities in India structured What are the states in India what are the cities in India? What are the localities in each city is including the ones like Chardu Khan So the first thing is we build a graph of all the localities This is totally unsupervised One way to do this is do this manually But that will take a lot of time, right? I mean India is a very big country with so many cities Delivery is live in 2000 plus cities. Each city on average has around 10,000 localities So you can do the math. It's it's it's hard to do build this manually So what we did was we started to build a build a graph Which was trained on all the addresses that we see 400 million of them And the graph looks like this is a snippet of the graph, right? So India at the top states I miss the city there But okay, the city is supposed to be good now The locality is there within the locality sub locality is within sub locality is towers and etc Now how this works is that given an address string. I tokenize each each word I build n grams from them and I put it in in a graph Bases how those n grams are related to each other in the address, right? so if I see sector 31 and Huda market in the same address I Am tempted to put them together and join them with an edge All right, if I see Rahid Atlantis and tower a written very frequently together. I join them in an edge So my graph essentially has three very important components One is we for each node. We have the probability that this node actually exists As I said because it's unsupervised and people write all sorts of crap in the address your your graph is expected to have all sorts of noisy nodes, so there's one probability which tells you what is the Probability of this node to actually exist the second one tells you The weight of this edge That the probability that Huda market is indeed a child of sector 31 And the third one tells you whether two nodes are actually similar or not because sec 31 and sector 31 maybe two nodes in my graph, but they're actually the same and We must know that they're the same so that we can merge them later, right? so Although this deserves an entire Presentation and a paper in itself on how we actually do this, but I'll I'll quickly just give away brief Intuition and move on So we learn these these three scores Bases a lot of features that we develop for for example the features to know whether an address whether a node is valid or not could be Simple things like the length of the that address the frequency the number of times people have written that address The geo codes which are associated with that the the size of the polygon associated with that So a bunch of features go into that we have a lot of label data That we have gotten labeled by some manual annotators We can train our models using that and derive these probabilities Using some machine learning model Okay, so the second part now the first part only tells me what entities exist in India, but they don't tell me where they are So now I'm talking about What this topic was it's supposed to be I want to now generate the polygons for each of those nodes in my graph, right? Which will tell me more about where they are How big they are how small they are what are they geographically close to each other and so on So this is a high-level workflow the several pieces that go in The first piece is obviously an input address comes in we search the graph for that input address That search looks something like this, right? So that's an input address it searches the graph gives you the list of nodes present in the in the address So this is a graph search that we do again This deserves an entire session to itself, but again, I'll skip knowing that it does its job. Well Once we know the node or the set of nodes present in the address For example in the in the previous address the set of nodes were a rajat lante sector 31 Good gown, etc So I know those set of nodes associated with the address the second thing the third thing I do is I go and deliver to that address The field executives goes and captures the lat long of that address when they use their app the next thing I must do is Validate whether the lat long is correct or not because oftentimes the lat long is not correct the field executives You know, they've had a long day. They they want to take a break smoke a cigarette So when they're smoking a cigarette, they you know punch all of them together They go back home punch it together at home and so on so there's some again There's some order which predicts assigns a score to each of the entries made by the delivery executive again It deserves an entire session to itself What I'll move on So the ones which we believe are clean so around half of them So we do as I said 500,000 deliveries a day We accept around 3000 data points from that 300,000 data points from that 40% we reject basis Some anomalies that we detect in that data So the ones which are clean So for a given node we aggregate all the clean points and then you generate The polygon for that. So that's the main intuition is any questions here and this part is important. So we can pause to No, it's not it's actually not a very big graph. So we don't really It's less than a million. Sorry No, no, no, we're we're not we tried using graphical databases where it's a standard psql and We're trying to use RDF and other things but so far a pure skill works for us, but it gives us bad performance So we're working to do better there So the time that it takes for us to search the graph is around 100 milliseconds right now We believe if you do a better job at choosing the right database 100 milliseconds for a single core So the polygons would What do you mean by fuzzy G code? Sorry No, the ones we rejected we have strong evidence to believe that they are nowhere close to Where they're supposed to be so we reject them all together If we have a sparse data if we end up rejecting everything then of course We run the model with a lower accuracy and you know We use the points which have lesser lesser accuracy as I mentioned we we provide a probability to each point So right now the threshold is at some some level But if we get very few points there we can reduce the threshold the quality of that polygon will be worse But it's better than having nothing So this spatial data is actually with all of this it's become a structured now right because this is a latitude Longitude some address string along with the node IDs that we've generated So it's need to actually we don't need to know Don't need to but I'd like to hear more about What what's Where your thought is coming from and we can discuss it offline. So not really so Sure. Okay. So point and polygon sort of things. Yes Correct. Yes for that. Yes. We we do have Certain libraries which do it very fast. I don't recall which libraries we use but someone I'll figure that answer for you and let you know I'll do that. So that's the next part of the that's that's the last box, which is what the talk talk is about Yeah, I'll I'll get to that by the way a node should have its own unique polygon But yes, there could be overlapping polygons if that's what you mean. So we'll get to that so once we go and physically deliver so we capture the geocode from the field executive and This is just a manual data point that we capture which is used in all subsequent steps All right so Now the final question on how do we create these polygons? So what we have is we have a certain node ID. Let's say that node ID sector 31 and for sector 31 we have all clean data points that we believe are taken legitimately and Now the job is to create some Boundary for sector 31, but it's challenging right because Even though we do do some Pre-processing on the data steps. It could still be quite noisy. There are multiple reasons where it could be noisy The field executive could have made a manual error as I said while smoking a cigarette and we may not have caught it The ones we catch we are definitely sure that they are invalid but the bunch of them we can't catch When you go and deliver do delivery in a building. There's bad GPS signal although, you know, the Jeep the guy has done a good job, but because it's Turning to the closer cell tower and so on you could get away bad geocode The other thing that would screw up is that my node identification could be wrong So when I was calling adfix, which is the graph search tool that we have it's possible that it screws up It does screw up in three to four percent cases That it could identify the wrong node that happens many times when people do write very bad addresses, right? They write their locality and they also write the locality next to theirs Just they're trying to be helpful But but it doesn't really help so so they try and put as much information Which confuses our systems in about two to three percent cases as a result you see something like this so the red ones are the ones where We believe that the node ID is let's say sector 31 the green ones are the ones where the node ID is something else sector 40 Let's say So you do see some you know criss-crossing here. So you see some red ones here. You see some Green one somewhere else. Okay. This is a bad example But the intuition here is that you have a mix of dots and crosses and you want to draw Decision boundary to separate sector 30 and sector 40 a node is a Locality or a city This stuff right so it each of these are nodes so it could also be the rooftop For example, if someone has delivered to plot 5 in hood a market many times We would have a note for that as well But normally door numbers are quite noisy so people write door numbers in all shapes and forms Okay, so the intuition here is that we need to create some decision boundary So that we are able to physically separate sector 30 and sector 40 I like this example a lot because in this case decision boundaries are actual boundaries On a map so it's it's very you know cool. Yes So the red polygon is after the decision boundary was created So what we first have is just the red dots and the green dots and Then we created the then we ran some ML algorithm which some classifier which you know divided the space into two I'll we'll come to this in a bit. It'll be covered. If not, we'll take it again Okay, now let's formulate it as a machine learning problem So what we have is our feature vector is the latitude and longitude and the label is the Locality or the node ID which we predicted from our graph search So to get the label we have done a lot of work basically To get this was easy, but to get the label we had to build that entire graph search it and so on once we have this in this form We should be able to apply any non-linear classifier To build a nice decision mark to separate those two planes Here The output that we're expecting could be two types to answer a question It could be overlapping locality polygons or non overlapping This becomes the overlapping bit are harder because again in this case, it's not a clear separation between two You could have labels which are you could have the same feature vector with Multiple labels right which which obviously makes things harder For the sake of intuition I'll stick to the non overlapping polygons to get a sense of non overlapping polygons think of pincodes all pincodes are expected to be mutually exclusive Okay, so what we do now is Ashore in the earlier picture we have certain red points certain purple points We try and create some decision boundary in our case we tried using a KNN Decision trees neural networks something as simple as KNN works fine with case around somewhere close to 15 What we do is we divide the entire city into a small grids The centroid of the grid we find the closest K neighbors The closest K neighbors are those points the blue points and the red points the nodes associated with the blue points are the majority of the nodes associated with the the neighbors become the response the predicted label for my Grid now once I I know the predicted label for each grid all I need to do is merge all of them together and get a clean polygon So this one is the polygon for the pin code 6 double zero double zero six, which is somewhere in Bombay, I think Sorry Okay Now we do some post-processing we run concave hull library on the existing point so that we clean up all the empty points And we get a tighter polygon just to give you a sense of how Accurate these polygons are I have drawn drawn the red one here is from Google Maps and The green one here is from authentic government sources, which we believe is the source of truth At least for pin polygons the government sources can be relied upon In terms of precision recall Recall can be seen here as if you assume the green one to be the source of truth Recall can be seen as the fraction of the green polygon covered by the blue polygon, right? and precision is the fraction of the red dots Divided by sorry the the fraction of the blue dots divided by the red dots And the total dots within that so the red dots compromise your precision basically so less of the red dots in your polygon More will be the precision Okay, so these are improvements and that's the final polygon we get I can do better at fine-tuning That's when the road networks come in so locality polygons are you know naturally would expect that a road divides a locality locality a from locality B So instead of going for arbitrary grids like we did earlier We can choose grids divided by road segments. So we take we took OpenStreetMaps entire road database Vivek did a lot of work here And we got all possible grids that the road intersections created and Again, you use a simple KNN sort of techniques Take the centroid of this grid check the K nearest neighbors and assign the label accordingly This gives us a cleaner boundary as as seen here So you see that it does follow the roads perfectly. You still see some jaggedness here. That is because of the noise And we're trying to work on other techniques maybe to reduce that noise So this is how to get better final slide here is a few observations and a few weaknesses of this The first is that we need to look for a classifier which is Fairly nonlinear right the model nonlinear the better the curves of our polygon will be you don't want very linear algorithms or Even there's something like a decision tree doesn't work very well because it does have a linear separation between For overlapping Localities this is quite painful because in overlapping localities. What you want to do is For a given dot there could be two possible labels right a given dot could also be in sector 31 and Also be in a sub locality within sector 31 to take this example I may have a polygon for this hotel or what's this called brigade area something like that, right and It's this location also lies in Rajah Jeenagar So the same X comma Y has two labels. So when I'm training The classifier the predicted Label I'll take not just the top one, but the top two in this case Yes, so now often because the early technique works only if all polygons are mutually exclusive But that's not real life right you have hierarchical structures So in that case every point may have multiple labels, which means you may want to output multiple labels Now the problem with multiple labels is that if you take the top two labels coming out Also a lot of noise may creep in So again that needs to have a lot of fine-tuning when it comes to these models and we are working on this So this one is an example of locality polygons. Hence you see some noise there Localities by nature are not mutually exclusive. There's a lot of overlapping Or a lot of parent-child Relationships in codes by nature are mutually exclusive. So we didn't face that much problem with pin codes But for localities we do Finally the final caveat here is that all of these polygons are what people think their localities are for example when a person writes I'm in Sector 31 we believe that he's in sector 31, right? I mean It's possible that these localities the polygons that we have may be smaller than actual government defined Polygons because the they belong to only our data set what we see is only those areas where people are ordering from Where we're delivering to and so on. So even if the government says the polygon for 6 0 0 0 6 is 30 square kilometers We may only have about 15 square kilometers of it because that's where people live All right, that's that's it for me. You can have more information on our egg blog and for Thank you any questions So we do build polygons for pretty much every city we deliver to However for the purposes of a presentation the cleanest ones we had for For bigger cities because we have cleaner data there for our internal purposes. We don't need to put it on a slide So we we they're jagged. They're bad-looking, but they work Your second question was about blacklisted areas. What does that mean? Yes, that goes into the Analytics part that I spoke earlier about right there could be areas where In general customers are harder to deliver to or there are too many returns or there's simply too much violence or abuse Whatever so I can classify that the harder part here is how do I identify which areas what once I do that? Then I can do all sorts of analytics and Assign attributes to my localities even addresses Yeah, yeah, yes So that depends on the use case. So if I'm looking for something, okay I want to see my returns over the last three months in this area. I fetch that data and and and build it Thank you


Citing Articles in MLA 9 This video demonstrates how to cite articles in MLA 9 and explains where to get help if you have more questions about MLA citations. First, gather your information. You'll need the author name or names, the title of both the article and the source where it was published, such as a magazine, journal or newspaper, the volume and issue if applicable, the publication year and day and month if available, the page numbers, and a digital object identifier DOI or URL if available. You can use the video citing an article on the BC Libraries YouTube channel to learn how to find this information for articles in a commonly used library database. Once you have all the pieces, it's time to arrange them into MLA format. First, add the author's name, last name, first name, period. Or, for two authors, last name, first name, comma, and second author's first name, last name, period. For a source with three or more authors, list the first author's last name, comma, first name, comma, at, all, period. After the author or author's names, add the title of the article in quotation marks. Place a period at the end of the title before the end quotation mark. Then, add the publication or sources title in italics followed by a comma. If your source has a volume and issue number, common for journal articles, this information goes after the source title. Add v-o-l, period, followed by the volume number, comma, n-o, period, followed by the issue number, and with a comma. Next, include the publication date. Depending on the type of source, this might include a year, a year and a month, or a day, month, and year. After the publication date, add a comma and then the page range for the article. For articles that span a range of pages, include p-p, period, before the page range. For articles on just one page, use a single letter p, followed by a period, and then the page number, followed by a period. Depending on your source's format, you might then add a URL or DOI if one is available. If your article is from a database, you would also include the database title in italics after the page number and followed by the URL. That's the basic format. For any other article citation questions or to get help constructing citations, visit our citation guide at belviewcollege.libguides.com.mla9.com. Or use the Ask a Librarian page on our website to learn more about getting in touch with a librarian.


Hey guys, welcome back to our channel. My name is Andrea with Food and Mentory Adventures in Food and I am here today with a haul from Kroger.  I spent a little less than $100. I think it was 98 in some change. So I'll start back here with the produce. I grabbed some shredded carrots and celery hearts for a recipe. I also grabbed some broccoli because I just like to have it on hand and it's much cheaper to get the broccoli florets and then cut my own broccoli than to buy the pre-cut broccoli. I grabbed Howard a couple of avocados. He pretty much eats them almost with every single meal. Then I also grabbed some frozen green beans for a recipe and frozen collard greens for a recipe too. Then I grabbed some romaine blend for dinners and then I grabbed a couple of packages of berries. I grabbed these berry berries. I've never seen them in a little three pack like that. I thought it was pretty cute. So you get blackberries, blueberries, and raspberries and then I grabbed some strawberries too. In the frozen section I saw this stewed beef noodle soup. Howard and I really like Asian food and I've never seen this before. So I grabbed this one for Howard to give a try because it looked pretty good and it looked like something he would order and then this was also a new item for me. This is their ramen soup and it was again in the frozen section and it comes with, let me see if you can see it, like a seasoning package, noodles, and all of that. So we're going to be trying that out. I needed some whipped topping and then I grabbed a few gift sets. So their gift sets were on sale with your Kroger card for 50% off. So the Burt bees was probably like 30% off, not 50. But I love Burt bees chapstick. So this is a four pack. I think it was $8 and something and at Walmart this four pack is nine something. So I thought that was a pretty good deal. They also had the fruit flavored Burt bees as well. Burt bees like the, I think it was coconut and maybe watermelon and stuff like that. But I prefer just the classic and then I grabbed this Pantene Gold. You get the hydrating buttercream and then a detangling milk. So this was like $4.99. And then I also grabbed this Teal's bath salts and this was also $4.99. And you get the Relax and Relief Glow and Radiance and Black Elderberry. And the packages are individually 14 ounces each. So I thought that was a pretty good deal. Grabbed some white grape juice and then I also grabbed some ham. So if you spent $25 on your groceries then you got the ham for this price instead of this price. So I think it was like 89 cents a pound or something like that. So I'm gonna throw that in our freezer. Maybe we'll have it for Christmas. I'm not sure but anyway grab that. And then I grabbed just a package of pie crust. These were so hard to find right around Thanksgiving. And I just grabbed a package just to have on hand. And then they were also clearancing these, this cookie dough out as well. I think it was $1.35 and it's the caramel apple. So it has little apple pieces and caramel. So it sounded good to me. Wanted to give it a try. I needed some Lipton Onion Soup mix. And then I also needed some Sharp Cheddar. So I got two blocks of Sharp Cheddar. The Philadelphia Cream Cheese was almost all gone. So I grabbed three boxes. They were on sale for $1.99 each. And then I grabbed this fried rice. I grabbed this for the first time. I think my last Kroger Hall. And I wasn't sure about it because I never had it before. Well we made it and it is really good. Now it does have peas and carrots in there which I do not care for at all. So last time before I cooked the fried rice I just took the peas and carrots out. And this is really good. It has a great flavor. So grab that. We needed some eggs. So I just grabbed some eggs. And then I grabbed this Sargento Provolone Cheese for a recipe as well. I had a coupon for a free package of Sargento. Just needed some chocolate chips. So I got some mini chocolate chips and regular chocolate chips. I grabbed Harrison some Goldfish crackers. So I got him these S'mores. I don't really think he is crazy about chocolate but trying to introduce him to new flavors. So I got these. And then he loves the vanilla cupcake. And then trying these out on him as well. The Mickey Mouse shaped ones. I needed a couple of cans of whole tomatoes for recipes. And then the last thing that I picked up were these Nature Valley mini sandwiches. I have been wanting to try them for a long time. And I was just kind of strolling through the aisles. And I noticed that these were on clearance for I think like 60 cents. And they were originally three something. So I grabbed the very last box so I could give them a try. So this is everything that I picked up on my Kroger haul. I hope you all enjoyed it. If you did please give us a thumbs up. And if you have not subscribed to our channel we'd certainly love to have you. Thanks so much for watching and we'll see you all next time.


People are busy, particularly in this part of the world, I see always categorizing people, that is, good people, bad people, good guy, bad guy. Who's a good guy, who's a bad guy, it's all fixed up. You must experiment this with this.  And then you see at that moment when he's happy, he's a fantastic guy. If you meet him when he's unhappy, if you meet him when he's frustrated, if you meet him when he's in some way unfulfilled, he's a horrible guy. But when he's happy, if you meet him, he's a fantastic guy.


Hello everybody welcome back to another Adobe Illustrator tutorial in this one. We're in Illustrator 2022. It just launched a couple days ago and I want to show you a brand new window or brand new panel. Let's take a look. Head up here to window and when you drop it down you're going to see 3D and materials. Heck yeah and you'll see here that I've got some shapes here and well you don't have to be a rocket scientist to figure out that I made these using this new panel. So I'm just going to delete all this stuff and we're going to start from scratch. Let's go ahead and just grab a, I don't know, let's grab a rectangle and let's start creating a tool. But this time we have the 3D and materials window open. Just going to drag out a simple little rectangle and presto. What you're going to see here under object is we've got a few new options here including things like plane, extrude, revolve and inflate.  Anyways jokes aside guys yeah we've got all these different options so I'm just going to go through them real quick here I'm going to left click on plane not a whole lot going on here but let's take a look at extrude. Extrude is a good one because when I pop the depth up you're going to see now that obviously we've got sort of like a three-dimensional rectangle and if you go in you can start making some crazy changes to it. I've increased the depth I can go in here if I want to add a bevel although it does look a little funny. You can go ahead and change the width of the bevel. Hey that looks pretty cool it looks like a nose but just a heads up yeah you can do all kinds of cool things now inside 3D and materials including bevel inside if you want to make like a drawer but yeah no there's a lot of different options also you can rotate on the x y and z axis and you can do that by just clicking on the selection tool and making changes like here so I can just move it around and you know I can switch it up and down and stuff like that things that you should already probably know unless you're new to Adobe Illustrator then welcome aboard and of course you can make changes to change the shape so you can sort of pull in the edges and give it a more rounded shape and things like that so there are a whole bunch of different options now available in 3D and materials I won't go through all of them because you guys can go ahead and do that yourselves but I'm just going to make a couple of changes oh well I got you yeah that one takes a little while because uh yeah I got big brass big brass ones you know what I'm talking about big swing and brass cylinders but jokes aside yes you can go in and do a whole bunch of different things now I will make another shape here while I got you I'm going to click outside of that one let's go ahead and make a let's make a little circle type thing and I'll make that and then we're going to use the other one here I'm going to show you the inflate option you just left click on inflate and presto you've got a 3D inflation including the lighting and everything that goes with it again you can make minor adjustments to it this doesn't do the whole hell of a lot but again yeah you can change these things do your rotations etc change the different materials if you want to change the lighting and all of this now and is inside 3D and materials also I will make a quick note here that you can go ahead and switch on ray tracing and if you render with ray tracing it will give you a bit of a higher res a little bit of a more a better fidelity in your final product and I will also let you know that under the quality you can go low medium or high so if you want really really good really good look you can go with a high resolution ray trace and then hit render and it will render out a nice sweet looking set of stuff so guys that is the new 3D and materials window it's great it's synod inside adobe illustrator 2022 thank you for watching ton more stuff coming up stay tuned


section 38 of English and Cantonese Dictionary. This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information or to volunteer, please visit LibriVox.org. English and Cantonese Dictionary by John Chambers revised and enlarged by Thomas Kirkman-Dilley, section 38. Rattan-Satang, Rattan-Shavings-Tung-Si, Split-Rattan-Tung-Tiu, Rattan-Cortage-Ching-Tang, as tough as Rattan-Tung-Gung-An. To tie up with Rattan-Datang-Rattle-Ngou, referring to a drum, Ling-Lum-Goo, Death-Rattle-Ce-Hee, O-Tam-Shang-Geng, O-Tam-Yong, Rattlesnake-Hang-Mei-She, O-Fan-Shi-Tou-She, Ravage-Gie-Wai, The Ravages of Smallpox, Dou-Ching-Sing-Heng, Rave-Song-Sum-Oh-Fat-Nam-Wah, Rave-In-Anger-Bou-Nou, Raviled-Luen, Raven-Woo-Nah, Raviness-Hungry-Moh-Duck-Guck, Gritty-Wai-Sick, Rave-In-San-Kok, Ravage-Kyong-Due, Meaning Force, Care, Ravage-Heart, Duit-Sum, Ravage-With-Delight, S'ai-Hee-Yue-Zi-Gi, Ro-Sang-Oh-Sang-Gae, Ro-Meat-Sang-Yong, Ro-Height-Sang-Oh-Pee, Ro-Silk-Sang-Si, Liberation of Raw Fish, Yu-Sang, Ray, Ray-of-Light, S'ai-Guang, Referring to a Fish, Bu-Yu-Oh-Pee-Pah-Sah, The Sun's rays, Yat-Sé-Chu-Ke-Guang-Hee, Razor-Thai-Dou-Oh-Thai-Tou-Dou, Razor-Stroop, Mo-Dou-Pee, Razor-Home, Mo-Dou-Seg, Rich-Dou-Oh-Dou-Duck, Rich-With-The-Hand, O-Dou, Rich-Out, Sun, I-Can-Rich-It, O-O-Duck-Dou, Did-Not-Rich-A-Hundred, Mei-Chu-Yi-Bah, The Desired End is Riched, S'O-Yu-Hang-Ge-Si-Yi-Seng-Gong, The Goods have safely reached their destination, O-Dee-For-Yi-Di-Fou-Tou-Dong, Within Rich-of-A-Gun, Pao-Da-Dou-Shu, O-Of-A-Man's Rich, 人力所不及, Rich-of-A-River, 兩岸的遠近, To-Reach-the-Bottom, 到底, Read-Dok, Read-in-Silence, Horn-or-Thai, To-Read-to-Another, 讀與人聽, To-Read-and-Compare, 對讀, To-Read-Proofs, 改告, To-Read-Praise, is not read as follows, 舉順月, Reading the Rich-of in Morning, 讀禮, To-Read-Silently, that is to oneself, 默讀, Reader of the Grand Secretariat, 內閣士讀學士, Assistant-Reader of the Grand Secretariat, 內閣士讀, Reader of the Hang-Lin, 士讀學士, Sub-Reader of the Hang-Lin, 士讀, He reads well, 佢讀得明白, Readily, 即是, Or, 易易, Readiness, 傲容易, Or, 咁心快手, Readiness of Speech, Readiness of Apprehension, 聰明, Or, Are in Readiness, Ready Money, 燃銀, Ready Made, Real, 真, Real Estate, 實業, His Real Name, 佢真名, My Real Motives, Reality, 實, Or, 真實, In Reality, 實在, A Name without Reality, 有名無實, Really, 果然, Or, 確實, Rim, 國家, Or, 幫國, Or, 幫家, Rim, 一滾子, Rip, 收割, To rip the rice, 割鑊, A Ripper, 收割佬, Or, 割鑊佬, Rare, like a horse, 惡高前提, Or, 裝, Meaning race, 養, Rare pics, 操, Rare, the rare, May, Or, 後變, In the rare, 後面, Or, 後頭, The rare of the army, 軍後, To follow in the rare, 跟後, To attack the rare, 功後, Rare emerald, 三等水師提督, Rare guard, 後衛兵, Reason, 道理, Or, 情理, A reason, 緣故, Reasoning, 理論, Or, 辯論, Reasonable, 有道理, Or, 有理, Or, 合理, We are sure, 裝, someone, 膽, Rebate, Meaning a Groove, 入樓, Rebel, To rebel, 作反, Rebels, 易策, Or, 作反者, Or, 易匪, Or, 反黨, The rebel forces, 亂黨嘅拆軍, Rebellion, 叛亂, Or, 謀反事, To plan a rebellion, 謀反作亂, Rebellion breaks out, 有作亂起來, Rebound, 贊開, Rebuild, 再建造, Or, 再作, Rebuild, 扎成, Or, 扎馬, His father used to rebuke him for his misconduct, 佢嘅父上以唔好行為罵佢, Rebate, 踏倒, Recall, 叫, someone, 返, Recall to mind, 上得返, We can't, 反口, Reset, 退, Or, 吞口, Reset, 收單, On receipt of your answer, 收到你回信之時, The receipt for this account, 收此掃嘅收單, After receipt of this notice, 接圍論之後, I am in receipt of yours of the 10th instant, 我今收到你十步所來嘅信, Receive, 接收, Or, 接到, Or, 受, Receive from a superior, 領, To receive rent, 收租, To receive a letter, 接信, He received that is welcomed me very kindly, 佢待我甚善, Recent, 近似, Or, 近似嘅, Recently, 近來, Receptical, 藏, Or, 房, Or, 藏住, Recipe, 方法, Or, 房子, Recipient, 收物嘅人, Reciprocate, 互相交接, Resite, 換眾, Or, 念出, To recite prayers, 念經, To recite a lesson, 念書, He recited a piece of poetry, 佢念絲數學, Reckless, 刁蠻, Or, 唔打你乜嘢, Reckon, 點數, Or, 算, Reckon accounts, 計數, Reclaim, 攞返, Or, 夠返, Recline, 憤低, Or, 憤, Or, 哀倒, Or, 哀下, Or, 依著, Or, 憑身, Or, 哀住, Recline here, 憤到哂數, Reclose, 再閉, Reclose, 離世獨居, Or, 幽隱者, Or, 獨居修行者, Or, 獨居修道者, Reconition, 借餘功勞, Or, 認, In recognition of his valuable services, 以紀念佢功勞, Reconnaissance, 自認嘅保單, Or, 拒結, He was afterwards released on his own reconnaissance, 佢自立拒結後將佢釋放, Ordered to enter into a reconnaissance, 命佢自立保結, Recognize, 認, Or, 認得, And somebody recognizing my services to him, 多謝我與佢待勞, I felt to recognize him, 我唔認得佢. Recall, 吞, Or, 吞後, Or, 讚起, Or, 挑回, Or, 退步, You record a little, 你退後幾步, Recollect, 記起, Or, 抑起, Or, 回想, Or, 記, Or, 記念. To recollect distinctly, 記得清楚, Unable to recollect, 想唔出, As nearly as I can recollect. To recollect, 盡照我記得, Recommend, 再起手做, Or, 由此再做, Recommend, recommend a person, 舉箭, Or, 補舉, Or, 輔託, Meaning advice, 勸, A friend recommends him, 一個朋友舉箭佢. Recommendation, 箭書, Or, 箭紙, The house courier has no letter of recommendation. 個管店冇舉箭紙, Recompense, 報答, I must recompense your kindness, 我必報答你嘅因, He is recompensed by the fruit of his labor. 舉行事有制得還其功, Reconcilable, 和得翻, Reconcile, 令某人再好, Reconcile, 復活. Reconciliation, 勸和嘅事, Do effect a reconciliation between them, 令佢哋相好如初。 Reconcite, 傲妙, Or, 心傲, Reconnoiter, 打探, Record, 錄, To record, 記, Placed on record, 記載, A record office, 記室, A record book, 登記報, To record daily affairs, 記日事, To record merit, 記功, Official records, 按文, Or, 按券, On record, 再按, Live on record, 全按, Record of one's own actions, 言行錄, Honorably recorded five times, 記錄五次, An act worth recording, 一場錄事, Recount, 述出例, Recover, 得翻, Meaning get well, 病翻, Recreate, 又灑下, Recrimination, 互相攻撤, Or, 互相爭重, Recruit, 添補, Recruit health, 養返下, Mostly recruits, 多係新入營嘅軍事, To recruit the body, 補養身子, Rectify, 整靜, Or, 改靜, Or, 整好. Mistakes can be ratified within 24 hours, 限24點鐘內將錯述改靜, Retolino, 直少的, Ratitude, 義氣, Or, 靜跡, Rector, 教會長, Or, 靜老師, Or, 治理者, Or, 掌教, Rectum, 直腸, Or, 帶腸, Recumbent, 誰賭, Or, 挨住, Recur, 又來, Or, 來服, Recur in speaking, 再講, The idea recurred to my mind. Reminds, 可真有思喜其事。 The veer recurres, 熱靜再發。 Recurrence, 再有, Or, 再遇。 Red 紅 or 珠 as red as a rose 如玫瑰花之紅 Red as blood 如血之紅 Red as fire 如火之邪 Note in red ink 珠牌 Red hot 燒紅 or 火紅 The fire is still red hot 奇異火燒紅 and of section 38 section 39 of English and Cantonese dictionary this is a LibriVox recording all LibriVox recordings are in the public domain for more information or to volunteer please visit LibriVox.org English and Cantonese dictionary by John Chambers revised and enlarged by Thomas Kirkman-Deely section 39 Redeem 熟 or 熟番 Redeem from sin 熟醉 Redound to 跪翻 Redress Redress a grievance 生冤 No redress 無法 To seek redress from the king 求各皇生冤 Backing for the redress of certain wrongs they had suffered 求生理受屈的冤情 Reduce meaning bring 令 or 自洗 Meaning diminish 減 Meaning change 化 or 變 Meaning subdue 打服 To reduce dollars to cents 將銀轉為先使 Reduce the price 減少 Or 減價 Cannot reduce it 不減得 Reduce to extremities 困極 Reduce to ashes 成灰 Reduce to subjection 精平 Reduce in rank 降職 Reduce to starvation 窮之受惡 Reduce expenses 省費用 Redundant 太多冇用 Metaphorically 蛇足 Read 爐位 Or 爐敵 Real and stagger 行動搖擺 Glocally 行得啤啤下 Real to real 訪 Or 以車訪屍 That woman makes a living by reeling silk 個女人訪屍線度日 To real seek from the cocoons 訪出蟬的屍 A real 訪屍的車 A real 訪屍的車 Wheel of threat 線路 Or 車路 Reimbark 再落船 Reconnect 再切法律 Reenter 再入 Or 再進 To reenter the army 再當兵 Reestablish 再切蠟 Reexamine 再查 Reexchange 服役 Or 蟲翼 Reexport 服出口 Refashion 再做 Or 再行 Refaction 補身 Or 點心 Refactory 食點心 for Refer, refer to 指, meaning speak of 講及, meaning apply to 轉長 Or 轉問 To refer to nature 指性而言 To refer to a book 人書 He refers all things to himself 他以各自歸自己 I refer the decision to you 我教與你作主 He refers to you 他指你 Refine 傾煉 Or 煉 Refit 修正 Reflect 反照 Or 射轉 Meaning think 思想 The lights on the bridges are reflected in the water 橋上嘅光反照然光於水面 Their eyes reflect the inward joy 其眼形出其心嘅歡喜 His character reflects great honor on his parents 佢嘅品行致榮於父母 Reformed 變靜 Or 改惡千善 To reform a nation 新聞 Or 花紋 Reformed 變靜 Or 改惡千善 To reform a nation 新聞 Or 花紋 Reformed To reform one's errors 改過去 Reformation 改邪歸政 Refalt 轉邪 Or 說曲 Refrain 戒 Or 忍守 Or 忍口 I refrain from speaking . 我噤口不言 Refresh 翼保 This is the retina, not 太挑剔 I reframed from speaking. 我禁口不言 Refresh 亦保 or 知保 felt refreshed by the sleep. 隨後覺得爽神 refreshment 知保野 or 點心 refuge 躲避處 or 避身所 refund 常懷 refund money 還錢 refuse 推遲 or 唔搵 meaning not receive 唔接 refuse 搏贏 regain 得翻 or 贏翻 regard 顧住 meaning pity sort regardless 唔打理 regard to express regards 問候 regard 鬥算 regenerate 更生 or 重生 or 翻生 regent 涉症 registerize 殺君者 regiment 介口 regiment 營 or 棋 or 一營兵 region 地方 register 記錄 to register 想測 or 註測 register of population 戶口測 to fill in the register 點測 register of shareholders 股份測 to enter on the register 注入測內 to register a letter 擔保訊 register the payment in a book 進掃注入部內 register of the inmates of a house hung up at house doors 門牌測 to register names 登名 or 諸名 it is registered 全安 or 再安 registrar 長安師 registrar general in Hong Kong 安撫華民政務司 regret 識 or 臉識 meaning repent 悔 or 反悔 I regret exceedingly 我心甚不落 not worthy of regret 不足識 his death is regretted by his friends 他死去有人痛識 regular 有次罪 or 依法 or 證 there are now two regular clerks here 同個長功寫字在這間行 as regular as clockwork 如中之行而準 regular troops 證兵 or 兵丁 a regular life 端證行為 regulate 定制 or 整好 or 自 or 收你 to regulate one's life 收身 to regulate the empire 自天下 regulation 彰情 rules and regulations 規條 or 規例 rehearsal 踩起 rain 為王 or 造王 one rain 一王之世 he has rained three years 舉燈為有三年之久 during the reign of Tung Chi 同治連奸 reimbursed 田環與銀 rain 令 pride 悲頭 rain deer 不大陸 reinforce 添寶 or 天兵 or 助以兵 or 助以力 reinstate 叫翻 or 請翻 the king has been reinstated on his throne 國王復活王位 reject 丟棄 or 丟拋 or 拋棄 I reject it is over 我不找他所求 rejoice 喜悅 or 歡喜 or 喜歡 to rejoice in what pleases you 落你之落 or 與你同落 I rejoice to hear 我聞之甚喜 Japan rejoices over her victories 日本國恩得盛將大喜 rejoin meaning answer again 復答 rejoin the 回音 relapse 復陷 relapse of sickness 復病 or 舊病再發 he relapsed into his old habits after amendment 恢改之後再行惡 relapse 雪之 or 廣屬 meaning belong to 屬 to relate legends or stories 廣古 to relate dreams or absurd stories 雪夢話 the deeds which relate to this property 關涉此業的戒指 it does not relate to me 不關我事 relations, five relations 五論 or 人論 relation or relative 親戚 or 相屬 blood relations 骨肉的親 relatives by blood and affinity 親戚 relatives by affinity 因親 relatives of the same surname 親派 relatives of another surname 親倦 or 親寸 the six nearest relatives 六親 the relation of husband and wife 夫妻的情義 the nine relatives 九座 namely great-great-grandfather 九祖 great-grandfather 曾祖 grandfather 祖 father 父 oneself 己身 身子 grandson 孫 great-grandson 曾孫 great-great-grandson 元孫 in relation to 論及 no relation whatever to 毫無干涉 no relation to 無關 relax 放鬆 he relaxed his hold 舉放鬆手 relaxation 輸身 release 釋放 放去 to release a prisoner 釋犯人 relent 悔 or 可憐 or 發慈心 or 發憐心 or 下棄 relevant 關涉 relic 前人守責 or 如物 or 為物 or 聖骨 the most important relic is left 佢所為下緊要嘅物 relic 寡婦 relive meaning relive 浮突 auto-relive 高突 basal-relive 低突 demi-relive 中突 relive relive the poor 救濟 meaning lighten 減輕 relive suffering 救苦 no relief 無救 religion meaning doctrine 教 poor 教門 the religion of Jesus that is Protestantism 耶穌教 Moammadan religion 回回教 Roman Catholic religion 天主教 the religion of the literati that is Confucianism 余教 the religion of Buddha 食教 佛教 the religion of Tao 道教 to adopt a religion 進教 or 逢教 to follow a religion 重教 or 信教 to propagate a religion 全教 to forsake a religion 反教 or 為教 these men have no religion 這些人沒有教門 religious 驚旗 religious books 法門經書 religious societies 教會 or 教門 a most religious people 極神心的人 religious tenets 教條 to lead a religious life 修道 or 習教 relinquish 寫 relish to relish 知味 a relish 味道 or 知味 or 味 may no relish for food 唔試食 relish the flesh of dogs 好食狗肉 relish a joke 月於談笑 relish your words 喜聽理所講 reluctantly 勉強 rely on 依賴 or 試 or 哀矣 powerful friends to rely upon literally to have a big mountain close by 有大山哀 to have something to rely on something to fall back upon 有得哀笨 nothing to rely on 無所依靠 remain 留住 remain over sing end of section 39 section 40 of English and Cantonese dictionary this is a LibriVox recording all LibriVox recordings are in the public domain for more information or to volunteer please visit LibriVox.org English and Cantonese dictionary by John Chambers revised and enlarged by Thomas Kirkman-Deely section 40 remainder 雨聲 or 重有地 he puts the remainder in the bank 其與方有銀行 remount 命 someone 反 or 著 someone 回去 remounts the case for a week 將按額後一個禮拜 remark meaning 試 體見 meaning 死 is remarked 所說的話 a man remarked 有人願 remarkable 非常 or 其意 or 出名的 a remarkable phenomenon 格外出奇的景象也 remedy 自法 or 意志 no remedy 無法 is not easily remedied 係非易解救 remember 既得 or 記住 remember me kindly to my old friends 同我問候有人嘅安 remember me to all your family 同我與虎尚各位請安 remind 提醒 remit 放縱 remit remit meaning forgive meaning send meaning relax song remit a case 解按 remounts 如歲 remounts rate 責論 remorse remorseless 殘忍 remote 軟 or so remove meaning flip 半千 meaning put away 除去 remove from office 除官 to remove the outer shell 除去愛學唔要 re-numerate 陪答 rent 裂 or 扯裂 render meaning return whan meaning make 寧 or 洗 rendezvous 罪處 renew or renovate 更新 or 改新 or 整翻新 renounce rent 租銀 or 租價 to rent 租 meaning torn 爛腰 rent on lease 包租 rent by the month 省租 rent paid in advance 上期租 rent book or 補 meaning go to 去到 to repair a clock 修整鐘 to repair a coat 補衫 to repair to Peking 改前去北京 repair 還翻 or 陪還 repeal 廢除 再做 or 重複 or 再說 repeat a lesson 背書 or 念書 or 念出 to repeat an action 再朝前一樣行事 not to be repeated 不得再為 you repeated what I said 佢將我話說念出來 the man desired to repeat his visit the next day 個人育命日再去探佢個人育命日再去探佢 repeat a lesson 再三 or 數次 repeat a lesson I have heard it said I have been hearing people say repeal 拒住 or 拒絕 or 寒雨 or 防雨 or 抵雨 or 干預 to repeal an enemy 預敵 or 與敵軍抵擋 repent 復仁 repent and mend 復改 repent of faults 復過 repent of sin I sincerely regret having taken his advice 我復仁聽佢嘅勸言 he now repents having assisted that man 佢前幫助個人咁佢恨錯了 he now repents having assisted that man 佢前幫助個人咁佢恨錯了 he tries to make man repent 佢欲勸人知悔 repent repetition 罪女 the repetition of his visits to this man made people suspicious 佢嘅女女往探呢個人令人生意 repent repent or 憂怨 or 怨仗 to repent at property 怨窮 he never repents 永無生怨 replace 補翻 回補 補回 復位 to replace it with one of approved pattern 以合色的以代之 replaced by one of common glass 平常玻璃以代之 replaced human nature by the divine nature 以天性而逆人性 replaced human nature by the divine nature 以天性而逆人性 replant 再重 replenish 滿翻 he replenished his lamp with oil 佢加油於燈 to replenish bodily strength 補氣力 replant 滿盈 reply 回音 not yet replied to my letter 未有答我嘅信 reply in writing 回信 report 風聲 give a report 回覆 referring to a sound globally when we get to the police station I will report to you 到差管時我必品理 to report in person 親品 submit a report 新品 to report in detail 品名 to report to a superior 品購 meaning the petition of 居品者 meaning humbly petition 購品 report on orders received 品名 report 安靜 the greatest confidence is reposed in him 極深信於佢 represent meaning stand for 作為 or 當做 meaning symbolize 建議 represented by a solicitor 原告驚請壯司代辦 they represented to the king 佢哋對皇上說之 repress 禁制 or 押制 to repress oneself 押幾 reprisals to make reprisals 搶返 report reprimand or reprove 扎乘 or 扎扎 reprimand or reprove to the face 赤白 or 免赤 a severe reprimand 隐扎 to reprove oneself 自責 reptile 爛虫 large classes of reptiles 爬虫有四種 republic 民主之國 or 合眾政治之國 repudiate hey to repudiate knowledge of 說不知 to repudiate a wife 出妻 to repudiate death 唔應欠債 repugnant 相反 or 相公 or 易 or 額 repugnant to reason 不合道理 repulse 打退 to repulse the enemy 舉退敵人 repulsive 鬥貓 reputation 明星 or 睇面 repulsive 鬥貓 reputation 明星 or 睇面 a good reputation 美明 or 有睇面 of no reputation 無明星 the reputation of a family 門風 to lose one's reputation 失體面 to bring discredit on one's family's reputation by 門風 to ruin a man's reputation by 人聲望 to seek reputation 求命 request 求請 or 問取 he requests to go on leave 舉高價 or 舉品價 as requested 照所求 cannot assist to your request 唔准你所求 to be in request 有人買 at my request 照我所請 to request earnestly 苦求 to request an audience 請見 requiem 安行 or 甘色 require 需要 difficult matters require steady attention 難事而有恆心 absolutely require 必要 you are required to quit this house 你要由這間屋簽出 to require of 立令 required 報 or 仇答 or 報答 to require evil for good 因將仇報 to require heaven's favor 仇答天恩 to require favor 報恩 rescue 救 or 打救 or 救出 to rescue from fire and water 救於水火 I will rescue you from danger 我救你出險 to the rescue to the rescue 救命 research 考救 resemblance 像樣 or 貌 resemble 似得 or 像似 resembling 由之負 resentment 怨恨 to cherish resentment 抱怨 lifelong resentment 抱恨 留有餘 to reserve 存下 reserve 深沉 reserve and at ease 休下 reserve seats 好出位 reservoir 速披 or 披持 or 水井 or 大水塗 recite 居住 residence 住家 or 宅 or 雨 Mr. Kham's residence 金雨 during our residence in Macau 在澳門居住的時 resign 告退 or 辭職 resign once self to resign the throne 退位 resigns office 舉辭職位 resigns meaning submissive 舒服 resin 重明 or 重明 or 重香 or 重枝 or 重樹膠 resists reserve reserve reserve reserve reserve reserve reserve reserve reserve reserve reserve reserve reserve reserve difficulties, 解難, to resolve doubts, 適疑, it is resolved, 已定, resolved to become a good man, 立意做好人。 Resort to a place, 去到, resort to a person, 轉向, to a thing, etc., 轉洗, a place of resort, 再去, meaning an escape, 去路, a favorite resort for pedestrians, 行人最好散步嘅地。 Resorted to blows, 佢用拳打, result, 響聲, meaning a echo, 回響, or 撞聲, resource, 所依賴, meaning escape, 去路, without resources, 棍手 full of resource, 多方法, or 多計, You are a man of infinite resource, 你有好多心思, at the end of his resources, that is, at the end of his tether, 無法指, our resources are limited, 我哋前人短小, respect, to respect, 驚, or 驚重, meaning regard, 顧。 To obey and respect one's parents, 考驚父母, to respect one's reputation, 顧睇面, the English show great respect to women, 英人甚驚婦女, Chinese respect the aged, 華人驚老, and of section 40, section 41 of English and Cantonese dictionary, This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information or to volunteer, please visit LibriVox.org. English and Cantonese Dictionary by John Chambers, revised and enlarged by Thomas Kirkman-Dilly, section 41. Respectable, 有睇面, respectful, 恭敬, respecting, 論及, 個 自愈, respectable, 恭敬, respectable, 恭敬, respect a, respect 論及, or 自愈, to pay respect, 拜後, respect to, to pay respect, to pay respect, 拜後, Respiration, Full Cup, Respite, Heat-S. Resplendent, 光朗, Resplendent, 光朗, Resplendent, 光朗, Resplendent, 光朗, respond 應答 response 應答 or 複答 no response 無回音 in response to the man's cry 應人所負 responsibility 負荷 or 擔待 or 試問 the responsibility rests on me 為我試問 I will take the responsibility 我擔上身 the office is important the responsibility great 任重責貸 too many responsibilities 責任太多 disavowed responsibility 不肯擔待 legally exonerated from all responsibility 照例可說面一蓋擔待 each on his personal responsibility 各自擔保 responsible 試問 a responsible office 重任 hold me responsible for everything 各試問我 I am not responsible for any risk 我不以外是 I am responsible 我擔任 a responsible government position 重任的差事 I am not responsible for his debts 他所欠的銀不關我事 rest sick or 安息 to rest hit ha or 休息下 or 休息下 the rest 如性 or 如性的 or 其餘 or 還有 the prerequisite may he rest in peace 會向千秋 at rest one thing when the mind is at rest then there is joy someone 執落 to go to rest 去睡 to retire to rest 退息 it rests with me 在我 as to the rest 至於其餘 this matter rests with me 這事歸入我手里 restaurant 走硬店 or 番菜館 rest in place 安息之處 rest institution 陪寶 or 交匯 restive 傲頸 restless 無靈內 or 淹尖 restoration 復匯 the restoration of peace and prosperity 復太平及慶望 restore 挽匯 or 復 meaning safe 救番 restrain 禁制 or 拘束 restrict 限住 to restrict one's movements 限制其舉動 restricted to time 限定日期 restriction 限度 without restriction as to time 但限期 result result in 中歸 result from 出乎 the result 關係 or 效厭 always with the same result 其效厭上是階同 as a result 因為這件事 result in good or 結果 the result in evil or 結果 the result in calamity 將有禍 or resume 開番手 meaning return to 做番 to resume his post 再行理事復任 resurrection 復生 or 翻生 resuscitate 翻生 to resuscitate a quarrel 翻開爭端 retell my say for or 靈脈 retell trade 少犯 without trade 少生意 or 少買賣生意 retain 留番 or 收留 to retain a visitor 留客 to retain by force 拘留 retain under disfavor in office 留任 retic retic a place 克服 retic a prisoner 抓番 retellate 還手 or 報番 retellate evil for evil 以惡報惡 retard 制 or 阻慢 or 整慢 wretch 作偶 retentive memory 好記性 retina 眼望 retinue 跟番 with his retinue retire 退避 or 去 or 歸隱 to retire from office 自事 or 歸田 to retire from business 歸隱 to retire one step 退後一步 it was obliged to retire on half pay 拒退食半諷 he has retired on a pension 拒退即食長量 a retired soldier 告老歸田之兵士 retired 憂政 or 背 retired and elegant 憂瓦 a retired life 隱居 a retired street 偏壁的街 a retired scholar 忍者 or 居事 retirement 正宗過 忍處 or 厭居 high officials retirement into private life retort to retort 還口 retrace 回報 or 後悔 or 返轉去 he retraced his steps 舉返轉頭而去 retract 返口 or 食言 or 返悔 to retract verbal evidence 改口供 i will not retract 我唔食言 i will not retract retreat 退 or 走 a retreat 忍處 to retreat towards the capital 迫退回經食 retreated to the mountains 退入深山 retrench 減省 to retrench expenses 減省費用 retribution 報應 retrieve 得翻 or 贏翻 or 夠 he retrieved his lost reputation 佢得回所失嘅明星 retro c 加回 retrograde 倒行 retrograde motion 易行 retrospect 回顧 or 回事 or 回事 return 返 or 回 or 返去 or 返禮 or 歸 to return a present 遮疑 to return a call 遮報 to pay a digestion call that is after a feast or dinner 遮走 to return home 返去歸 to return a visit 答拜 or 回拜 to return thanks 答因 or 借因 to return an answer 忌回音 to return to 貴 the returns of the population 戶口策 return passage ticket 回頭飄 the return of the year 新春 the return of the seasons 四季復甘 reunite 合翻埋 or 再合 reveal 啟示 revel 鬧走 to revel in the moonlight harvest festival 上月 to revel among flowers 想花 revenge 報仇 or 說恨 to revenge oneself revenue 富銀 or 稅響 or 稅銀 revenue derived from land 田稅 revenue derived from the ground 地稅 customs revenue 關稅 bought of revenue 戶部 revenue department or treasury full form revenue cruiser 執思船 reverberation 迴勢 reverberation of sound 應響 revere 陰叢 or 崇敬 or 鳳敬 or 敬慧 to revere the sacred truth heard there 恭敬所聞的聖道 reverence to love and reverence our parents 敬愛父母 the sages reverenced this heavenly teaching 聖賢敬此天道 reverent meaning honorable 尊 meaning elderly 老 the reverent Mr. Lee 李牧師 reverie 想入神 reverence the reverse 想反 or 對面 to reverse 倒轉 to reverse a sentence 反案 evil is the reverse of good 惡與善相反也 the reverse of a coin 前輩 revert 歸反 review 月 or 簡月 or 平 to review a book 平書 to review essays 平文章 to review troops 月兵 revile 惠罵 or 講壞 I reviled him to his face 我當面罵他一場 revise 修改 or 修淑 revive 復興 or 復生 or 再往 revive the spirits 打醒精神 revoke 反變 or 追回 or 廢 to revoke a law 徐例 to revoke verbal evidence 改口供 revote 背叛 or 叛逆 subdue a revolt 彈押 revoting 齊慘 revolution 周 or 回 revolution in a state being revolve 噔噔轉 or 噔噔轉 or 旋轉 revolve in an orbit 周轉 to revolve mentally fancy the earth revolves 地轉 revolver 連環窗 or 對面笑 reward 想 or 想吸 or 想刺 or 花紅 or 想蜜 or 想刺嘅借物 to gain a reward 得賞 he rewarded my services 舉賞我的功勞 to reward workmen 打賞工人 the good will be rewarded 善人執授賞 a play card offering a reward 賞貼 public offer of a reward 賞借花紅 scale of rewards 賞價 command and reward 鑑賞 rewrite 再寫 or 復寫 reynard 胡理 rhapsodic 史詩的 or 不相連的 rhapsody rhetoric 善言的法 or 善論的理 or 口才 rhetorician 善變的事 or 善言者 or 善論者 room 淡 yellow room 黃淡 rheumatism fong sub 風濕 rhinoceros 西牛 rhinoceros horn supposed to detect poison saikok rhubarb 戴王 or 旺略 rhyme hip one good rhyme 口音律 to match rhymes with another person wǒ xi rhymes in the event tone pěng wǎn to make rhymes 壓 one without rhyme or reason is sometimes loses his temper kù yǒu shì mǒ guu phà luu hǐ lǎi rhyming dictionaries wǎn shu oblique rhymes that is rhymes in the deflected tones zhé gǎn rhymes in successive lines liǎng gǉ wǎn rhymes in alternate lines gan gǉ wǎn rhymes alternating in successive lines gǎ wǎn medial rhymes words of the same sound rhyming together in different tones sèi sēng tōng wǎn and of section 41 section 42 of English and Cantonese Dictionary this is a LibriVox recording all LibriVox recordings are in the public domain for more information or to volunteer please visit LibriVox.org English and Cantonese Dictionary by John Chambers revised and enlarged by Thomas Kirkman Dilley section 42 rīp lǎi or lǎxā guāt rīps tǎi guāt the rīps gan lǎi the long that is through rīps chǎng lǎi the short force of floating rīps duǎn lǎi wǎn rīps lǎi tǎi guāt the rīps generally lǎxā guāt a hands rīp that is useless gǎi lǎi umbrella rīps that is framework zhē guāt the rīps of a fan xīn guāt rebaltery wǎng mǎn chǎo qì rīpon rīpon sīdāi rice hot rice mǎi boiled rice fan growing rice wǒ meaning patty guk old man's rice nò mǎi rice flour mǎi fun rice cakes mǎi běng to eat rice that is to eat a meal generally sèk fan the cover of a rice boiler fan guāi to steam rice zhǔ fan to grow that is plant rice zhōng guǒ he does not know the price of rice that is he is inexperienced gui mǎi jiā he's not worth his rice we say salt sèk sāt mǎi spilled the rice that is spoiled the affair dǒu mǎi broken rice mǎi zǎi water in which rice has been washed mǎi gāng shuì boil your rice that is slender you zhǔ lǐ ge mǎi to buy rice that mǎi to sell rice tiǔ mǎi red rice hǎo guu mǎi rice of the best quality shāng bǎo mǎi rice or patty field wǒ tiān rice grew zhūc to boil rice to a thick grew that is congee bōu zhū thin rice glue zhūng yǎm porridge of rice and pork zhūr yǒu zhū to stock a field with rice seedlings shì tiān shi wǒ rice weevil mǐchōng rice liquor lǎo mǐ zhǎo ordinary soft rice zhīm lǎo rice bird wǒ fā zhè rice paper tōng zǐ rice paper pictures tōng zǐ wǎ rice paper plants that's here papyrifaria tōng chǒu rice pounder a fly which has a work tail-like motion of the body when settled on anything coming in the summer zhōng mǐ gōng gōng rich fu hǎo or fu gui rich man chǎi zhū or chǎi zhū lǒ the god of riches chǎi shǎn or chǎi bǎxīngguān riches that is money will buy even the gods that is everyone has his price chǎi hǎi yī tōng shǎ he who steals the household riches that is that thief of a cook tōu gā chǎi rich and insipid lǒng dāng richly spiced hāng lǒng a rich flavor lǒng mì rich strong wine lǒng zhǒu riches chǎi bǎ rīcati yī yu rīt chǎi liu or lǎt hǐu to rid of care fōngshām or gāi lǒu to rid of trouble tui lǎn to get rid of dāg tui to rid of moss chǎi tǎi riddle meaning sieve, sayi, meaning puzzle mì ask a riddle dǎ mì yi or dǎ gu zǎi or dǎ mì it gave me a riddle to guess kùi dǎ mì gòu ngòu gu riddle with zhōu shēn dǎ chūn or tāo dǎn dǎ mùn shēn the vessel's funnel is riddled with a shot and a shell gòu chèk shūn yīn tōng chūn sān wéi dǎn zǐ pào mǎ sǒo dǎn dǒu chūn lǎn called quite riddled with bullets yī chūn kīn wéi xǎo dǎn dǎ chūn ride, care, or sing to ride a horse, care mǎ or sing dǎi mǎ shāng to ride in a sedan chair chǎo gǎo to ride in a carriage chǎo chē to ride in a rickshaw chǎo chē zǐ to ride at anchor wān pò the vessel rides at anchor xuǎn pào nǎo to ride out a gale dāi fōng chui bù tuu lǎo rich, tiǎo chè rich in the field lai the rich of a hill sān zhè or sān bùi the rich sim on a chinese shoe hái liǎo good luck on raising the rich pole sǎo liǎo dàigat rich pole liǎo zhè no bad boom that is no moral courage mǎo mǎ zhè guat ridicule zhāo xiǎo or shuì chi or hei xiǎo an object of ridicule xiǎo bēn to fall into ridicule yǎn yǎn xiǎo ridiculous or xiǎo riding jacket mǎ quā riding master gào kè xi riding school or kè guǐn rivaled meaning grooved yǎng lǎo right zhè or zheng or guǎn dǎo right side yào right hand yào shǎo a right angle zhè gào a right line zhè xiān all right quite right ngām shài on the right zhòi yào bēn his work is all right qùi hàp zhōu gōng fū he is all right qùi shān zǐ tǎo dāng everything is all right shì shì tǎo dāng in the right hàp dòu lǐ or zhè to put to rights hōn fāng tǎo dāng righteous yì rigid ngāng or gǎn rigid with code dōng dōu ngāng a rigid teacher yǐm shì rigid laws yǐm lǐ rigmarōu wàn suì rigorous yǐm gǎn or zhīm yǐm real xǐu hòu or xǐu qī or gǎn rim yǐn bīn the rim of a cup pui hǎo ring wǎn finger ring gāi zǐ to ring hǎng to ring a bell dǎ zhōng ring finger mōu méng zǐ ring leader tǎo mō or tǎo yǎn ringlet xǐu wǎn ringworm xǐn to get ringworm sāng xǐn red ringworm hǒng wǎn hǐu xǐn a leperous patch gāng qǐn xǐn ringlets yǐzǐzǐ rinse long rinse it clean yǒng gāng zhèo or long gāng zhèo to rinse the mouth xǒu hǒu riot luìn nào or nào xi there was a riot of considerable dimensions yǒu yì dài dǎng yǐn zhī yǔ a formidable riot dài lào yìchèm rip open tāng hǎi rip sǒu ripen on the tree 樹上树 rip fruit 树生果 rip corn 树菊 the fruit is over ripen 齊果太树 as ripe similar to our ruddy as a cherry 树如山楂桃 a ripe scholar 飽和的事 ripen 長树 or 成树 riple 波吻 or 水波吻 or 水吻 rice 上 or 起 or 興起 meaning get up 起身 or 升 or 登 or 發達 or 上達 or 增 or 漲 the flood is rising 水漲 the rising price 增加 the river rises 水氣上升 the river is rising 河水漲起 the tide rises 水潮漲 stocks are rising 古粉漲價 sun rise 日出 or 日升 or 日上 to rise in rebellion 作反 or 作伴 or 伴奕 to rise in full of a country 國嘅輕敗 rise in full in prices 價嘅起跌 to rise in full in office 升降 the rise of an affair 時嘅跟游 rise that is source of a river 河嘅沿 to give rise to 智 or 所以有 or 故有 rise in office 升官 or 高升 rising that is rebellion 作亂 rising and at rest 起居 risk 險 to risk 伴 at his risk 講有意外事歸舉 to run a risk 行險 or 冒險 to risk one's life 拼命 to risk one's property 拼家業 at the risk of his life 冒生命嘅險 willing to risk life and limb 願以生嘅險 to risk one's all 換狂 to risk that in doing it 換死去做 right 禮儀 or 禮問 funeral rights 喪禮 or 壯禮 the board of rights 禮部 office of rights in a Yemen 禮課 the three books of rights 周禮 而禮 and 禮記 hence in the book of rights it is said 禮月 rival 對頭 rivalry 相爭 bitter rivalry 苦爭 rive poor lead arrive 條列 or 條列口 river 河 a river 條河 or 條江 Canton river is called 海 or 條海 a river mouth 河口 river water 河水 up the river 河上 down the river 河下 river source 河源 the source of the river 光號 聲源 the cause of a river 河道一帶所流的水 tributary of a river 知河 river basin 附近河的地 the god of the yellow river 河白 inspector of river police in Canton 河白所 intendant general of rivers that is of inland waters 種毒河道 river to river 鑽釘尾 meaning to man 馬翻 a river 兩頭釘 river a broken bowl 釘碗 river is a tension 令它留意 the river iron plates together 釘埋鐵板 reverence 溪 or 小河 or 山坑 road 準魚 road road high road 官路 or 大道 or 通渠 to make a road 整路 to repair a road 保路 on the right road to fortune 瞻瞻行好運 on the road to prosperity 瞻瞻發達 on the road 路上 a level road 平路 a royal road 遇到 or 遇路 by the road side 路邊 cross roads 十字路 a fork in a road 哀叉路 the meeting of three roads 三叉路口 room 油 roar how or harm roar of thunder 廣廣聲 roar of water 行拜 colloquially pop pop sand roast seal roast in ashes 煨 roast goose 燒鵝 a roasted pig 燒豬 roast beef 燒牛肉 to roast over charcoal fire 炭煨 to roast brown 煨龍 to roast in the embers 煨火煨 end of section 42 section 43 of English and Cantonese Dictionary this is a Librebox recording all Librebox recordings are in the public domain for more information not to volunteer please visit Librebox.org English and Cantonese Dictionary by John Chambers revised and enlarged by Thomas Kirkman-Deely section 43 rob 搶奪 robber 查 rob 譜 robust 裝肥 or 裝見 rock 鵬 rock 盤石 or 帶石頭 to rock 兩邊擺 rock fish 石口公 rocket a rocket 一支起火 falling rocket 九龍到地 a rocket to be caught 砲頭 or 花砲 rocking chair 鹿耳 rock salt 石鹽 rock work 假石山 rocky 蠶岩 or 溪區 or 多個石的 a rocky precipice 石鹽 rod 乾 or 棍 or 棚 the rod of iron 鐵條 to give the rod to the child 摘子 or the montee 網然 or 跨口 or 虛跨 row referring to female deer 腰 or 鹿母 fish grow 魚春 row 擦仔 or 爛仔 or 棍頭 or 小擦 rockery 棍頭嘅事 row to row look row up 捲埋 row as water 滾 a row 捲 to roll together and to unroll 捲書 roll it together 捲埋 or 捲起來 a row of cloth 一卷布 a row of paper 一卷紙 a hot row 熱小慢頭 a row core 點明 the row that is register of names 明布 when the row was called 點明知識 to roll a ball 捲球 to roll the ground smooth 捲泥 to roll ink in printing 捲墨 to fold rolling over 捲落地 to roll tool and throw that is to rock 捲來捲去 to roll with the feet 捲腳捲 a game of rolling cache or corpus 捲牛 roll the blinds up 捲起張臉 to roll the eyes about 捲眼 to throw the embroidered ball that is to choose a husband 拋售球 the ball rolls 球錄 the ship rolls 船載海上搖錄 to roll between the fingers 搓撓 to roll incense sticks 搓香 to roll out the dough 搓開麵粉 capital must be kept rolling 裁員滾滾 roller 剪 or lock a stone roller 石滾 the roller of a map 地圖繼續 rollers that is heavy waves at sea roller romantic to go over to room in server in room 2 as the roman's do 入香隨俗,入水隨彎 Roof 牙杯 河 屋杯 河 牙面 Roof of the mouth 上河 To roof 蓋牙面 This house is roofed with slates. 這一間屋用石板蓋面 Roof to roof, that is to cheek. 欺騙 Roof,房 個,廳 Meaning space 地方 個,廳 A little room 房仔 個,小地方 No room for hope 無可望處 Next room 隔房 Dining room 餐房 Bedroom 分房 A library 書房 Drawing room 客廳 或 人客房 Spare room 限房 This room is occupied. 這一間房有人住 In the room of 袋 My private room 我私家房 No room 無地方 或 無位 Roost 雞齊 To roost 貓 A roost for birds 著鳥齊子的樹 The birds are roosting.  請 你 來 見 我 To walk round 行 過 各 書 To turn round 轉 頭 或 回 頭 To roll round in the hand 搓 圓 To fire three rounds 放 三 響 炮 Round tip 起 或 打 醒 Meaning urge 鼓 舞 To round to fight 激 鬥 To rout a rebellion 激 反 Round 中 人 亂 罪 或 亂 走 或 敗 走 To rout 敗 陣 或 打 敗 Root 道 路 Routine 慣 經 或 規 舉 或 常 規 或 辦 法 The daily routine of work 日 日 所 做 嘅 功 夫 Rove 遊 或 閒 遊 或 去 框 Rover 遊 手 Row, meaning rank 行 過 乱 Plant them in open rows 種 開 一 乱 乱 Arranged in five rows 排 列 五 行 The trees are in rows 樹 列 成 行 A row of houses 一 條 屋 與 相 連 Row, meaning disturbance 鬧 事 或 嘈 炒 或 相 爭 He kicked up a row with his master about his wages 舉 與 東 主 為 人 功 一 事 相 爭 起 例 Row to row 罩 Row ashore 罩 埋 頭 Row across the river 罩 過 海 Row harder 出 力 罩 Rowdy 匪 徒 Row lock 掌 腳 Royal 國 王 或 王 或 與 The royal law 朝廷 律 法 A princess royal 公 主 A royal couple 王 與 王 后 二 人 A royal salute 朝 拜 君 王 Royal authority 皇 帝 嘅 權 Members of the royal family 皇 帝 嘅 親 人 A royal palace 一 座 公 殿 或 王 殿 Royal residence 皇 公 Royal ascent 陰 準 Royalty 皇 道 Emblems of royalty 與 物 Meaning attacks on money payment 報 效 銀 Rub 採 或 採 或 無 採 Rub the hands Nor so To rub with wax and polish 擦 立 To rub down ink 磨 脈 To rub on paint As native painters do 糯友 Wash and rub 洗 抹 To rub off 抹 去 To rub along 一 直 做 去 To rub up meaning polish and brighten 擦 光 To rub with salt 以 鹽 擦 枝 Rubbish 廢 物 或 爛 泥 或 垃 殺 或 發 濟 野 Rubble 慢 石 Ruby 紅 寶 石 Rub 準 如 Rubber 托 或 採 Rub 操 或 武 禮 或 匹 略 或 老 網 A root about 操 路 鏡 屋 Rub work 絕 功 Root language 操 話 A root winter 鹽 東 Rudiments 配 或 操 做 嘅 Rudiments of learning 小 學 Rudiments of grammar 文化 初 皆 To teach the rudiments 啟 無 如 都 如 悔 Referring to a plant 臭 草 Roofle 閉 矮 Roofle 空 圖 Rubble 周 埋 Rub Roofle 爐 口 煎 Rugged 啟 區 或 粒 級 或 鹽 蟬 Ruin 破 敗 或 崩 敗 或 敗 壞 Someone has tried to ruin me 有 人 欲 害 我 Trade is ruined 生 以 壞 To ruin a reputation 拜 明 如 規 舉 或 發 到 To rule 管 理 Rule paper 間 線 The rule of a country 國 制 Rules of a school 管 規 The rule of a family 家 政 To rule the empire 自 天 下 To rule over 管 治 Ruler 主 宰 或 民 目 或 人 目 或 目 師 Defend to an instrument 間 尺 The Taylor's Food Rule 排 前 尺 The Mason's Food Rule 周 通 尺 Rum 借 執 走 Meaning strange 古 路 陽 或 古 怪 或 古 識 的 Rumble 光 Locally 藤 藤 聲 The rumble of thunder 雷 光 Ruminate 翻 草 或 反 熱 Meaning reflect 心 思 思 或 再 三 思 度 Rumage 找 穩 或 撈 亂 Rumor 消 識 或 風 聲 或 流 風 Rumor says that 人 傳 Rumor's got about that 有 人 傳 說 Rum 美 根 或 美 龍 骨 Rumble 潮 或 潮 蒙 蒙 Run 走 或 跑 走 或 奔 走 Run as water 流 Run off 走 路 Arrange with help 我 走 去 坐 佢 To run around 各 潛 Reverse run into the sea 河 流 入 海 The bill has 30 days to run 此 單 行 30 日 交 銀 The waves run high 浪 大 It has rained 3 days running 一 年 三 日 落 雨 I ran short of pens and paper 我 用 完 紙 筆 To fight cocks and run that is raised dogs 鬥 雞 走 狗 In the long run 到底 A run of good luck 遇 得 好 彩 嘅 事 The letter runs 此 信 月 To run a race 鬥 走 To run off 走 脫 To run into 走 入 A run on the bank 向 銀 行 取 銀 Run to an enormous size 發 將 期 身 Run to seat 結 總 Don't run into debt 唔 好 欠 人 錢 To run after 跟 美 To run out That is expire 滿 期 To run over That is overflow 流 出 泥 Run up a flag 升 高 Or 扯 起 To run into pot 洗 盡 講 口 To run wild 繁 生 End of section 43