If you can still hear me, I guess, we can kick off the discussions. Thank you all for dialing in. First disclaimer, this is the first time I am having a poster session in a virtual conference. So we're going to be a little bit improvising here. It's definitely different than just standing next to a real poster and having people passing by. On my side, I am part of the mathematical modeling and artificial intelligence team of KPMG Switzerland. And I'm based in Zurich. And well, this session is about the way we approach optimization problems. As I was mentioning before, in the past few years, Python has matured a lot, both from an probabilistic programming perspective, but also for what concerns the integration and the interfaces to solvers such as, for example, grubby, but also open source solvers. Both grubby py and pyomoc would do a good job. I think that from an application perspective, our methodological approach is coming definitely something viable. There's a lot less glue code that needs to be put together to have a model up and running. So I think this is probably a reasonable way to approach the Caesar science problems in a context of a high uncertainty. Do you have any question or shall I just skim through the content of this poster? All right, I saw a sign of feedback, but are you able to also speak? Or have you been cut off? Yes, can you hear me now? I can hear you, yes. Yeah, because I have to activate the microphone. I didn't want to make confusion with voice, so I silenced it. It's okay, that's helpful. I still need to get some feedback from time to time if I'm still there, if I'm going in the right direction, so please don't be shy. Okay, thank you. Yeah, so I think the feedback was if it's okay if we go through the content of this session and then perhaps if you have some questions. Anytime 53 to interrupt me. All right, so the idea is that we often work in on optimization problems and optimization problems can be, for example, well find the optimal price for a portfolio of products. In this particular use case, we have helped a company, an industrial distributor, a company that purchases industrial goods in Asia and resells them in Europe, find the optimal price. And the definition of the opening price, there was a price that was maximizing the revenues after the application of transportation and storage costs. And interestingly enough, we started from the bottom, and we have built what is called a company value drivers. We tried from a modeling perspective to break down their target metric, or North Star metric, I was also called in some other context, into its components. The optimal price is the result of the calculation of the revenues and the calculation of the optimization cost of the transportation costs. The transportation costs are based on a shipping strategy. The shipping strategy itself depends on some optimization parameters that include volume, how much you would like to transport when there are valid times with transportation and storage costs. In this setup, the client had to approach different manufacturers that had different factories, then goods had to be shipped close to the cost, and from the cost had to be shipped to by both to Europe, to distribution centers and from distribution centers to the client. And the question was, well, how do we deal with uncertainty? And one way to deal with uncertainty is that when you're running an optimization problem, you have to factor in information from distribution of the optimization parameters. And one way to define the estimate of the distribution is by having a generative model. The generative model takes in some input. Input can be internal data, such as, for example, historical sales data or information about the marketing promotional strategy, but also external data. Micro-economic, for example, the price of competitors or micro-economic. You can have some macro-economic indicators, for example, unemployment. You can have some information about commodity prices. And there, here, what you build is a generative model that speeds out the distribution. In this case, over volume, you might have a second model that focuses on the transportation and the storage costs. From a business perspective, this helps because stakeholders that come from the business can have a look at the structure of this model and somehow understand how things have been calculated. They understand the revenues. They understand what drivers led to the revenues. They understand the transportation costs, what drivers led to transportation costs. And this is one of the first advantage. The second advantage is that you're not bound to distributions that are easy to handle analytically. You can have the classical Gushan, but what you can have is you can have a partial distribution. And then you're able also to have, for example, a catastrophe and rare events model in build in your model. We said that the optimization parameters are produced with probabilistic programming. I don't know if you are familiar with the concept of probabilistic programming. But the main takeaway is that what you want to have is a generative model that takes in priors in a vision sense about the drivers. You can use some data and combine them to produce a distribution of the optimization parameters. I guess one of the interesting thing from a Python perspective is that the frameworks have matured a lot. You can have a PMC. You can have Pyro. You can have eWords. They come with pros and cons, but depending on your needs and modeling preferences, it's really a lot easier to set up a probabilistic programming. A probabilistic program for the key optimization parameters. I guess also that the topic is really interesting at the moment because I feel that variational inference made a big step forward. From a computational perspective, it's an existing algorithms at the moment are a lot more efficient than what we had five, six years ago. Debugging is a lot easier. Modern frameworks come with all the tools you need to follow the traces in the probabilistic program so that you are able to see what happens under the hood and debug things. And once you have the optimization parameters, what you can do is you can take your results. For example, you can get an approximation and an estimation of the key optimization parameters. And you can plug the data into a... In this case, what we had was a mixed integer linear program. And from there, you can calculate an optimal solution. For example, how we should transport goods from point A to point B. And you can have either a robust or a stochastic programming setup. I think that from an implementation perspective, stochastic programming with this methodological approach comes out right out of the box. It's also if depending on the number of variables and the complexity of your optimization problem, you might also want to recode your problem as a robust optimization problem by defining the deterministic equivalent of your problem. And one of the frequently asked questions there is, well, do you really need all these probabilistic programming to write your deterministic equivalent? My answer generally is yes, you need to get there. You need to get the feeling for, for example, the first moments of an optimization parameter so that then you can encode it as a deterministic equivalent using, for example, an elliptic formulation. And then when it comes to the optimal solution, I guess the things probably you realize that rather quickly are relatively straightforward because what you have is the transportation cost, given the volume estimated at a given price, you have the price, you have the volume, you have a transportation cost and there's not really a lot more than that in this current use case. I guess the exciting part is really the combination of probabilistic programming and the robust or stochastic programming. Any questions so far? No for me, Mattia. Good. Did I scheme to the content too fast? No, it's okay, it's okay. Are you working on these topics yourself, in your daily business or daily life? I personally work in a quantitative financial world. So not exactly that, but I'm interested in also this topic that it is more related to industrial world, I guess. Well, I started off my career as a quote in an investment bank and I transitioned out of finance and went to consulting. Fine. Are you Italian? Yeah, you heard it from the accent, I guess. Yes, yes. Although I am currently based in Zurich, probably I saw that. Yes, yes. We have a good team here. We also have a few Italians in the team. If Italians at the moment are everywhere, we are spreading out really fast. Yes, it's true. On your side, when it comes to your daily life, I guess, you are solving a lot of optimization problems in the Markovitz sense, right? Not so much because in actual financial quantitative strategies, optimization doesn't work as expected or as the books said, say or write, because a financial world is characterized by a lot of unexpected events in a fat-tailed magnitude. So optimization is not so cheap, optimization from an economic point of view, because when you optimize, for example, an active strategy in the markets, if inputs are not what you put in the model, the results can change drastically. So we work personally in a more robust sense than I optimize the sense. Right? Well, yeah, I think it's very interesting the conversation. Yeah, well, I think one thing that is interesting is that you can get the optimization parameters by Monte Carlo. Yes. By inference. And in both setups, it's fine to get to use fat-tailed distributions. Yes, yes. There are, there was a couple of use cases where we have, in the generative model, we have estimated an optimization parameter by having also a couple in the middle. Okay. So what you have, you have your priors here, and then you have a couple of models here, and then we were getting what we were interested in here, that then we were in turn feeding into the optimization. Yes. I think the methodological approach is really quite flexible. My question is, when you were talking about robust, what do you mean? Because when I, when I, when you would say robust optimization, I really think about robust optimization, but maybe in your domain. Yes, yes. Because in my domain, robust means essentially that the capital allocation, so the capital that is invested in the current day in the markets is a function of the current volatility of the markets. So robust, it means more, more a sort of risk management, risk management dependency or risk management function in the sense that you have to, you have to follow the estimated risk of markets, the estimated potential volatility of markets. So the model has to change in the, in this sense. So robust means a risk management optimized tool, not an outcome optimized tool, but a risk, a risk metric optimized tool. Okay, I understand, I understand. Yeah, it's very interesting. It's, and then why I guess what, yeah, and then it's a capital allocation on different assets. Yes. For this reason, the first I said to you the fact that the Markowitz framework is not used anymore in in applied finance because it focuses more on expected returns, but the estimation of expected returns is the confidence interval are very big compared to the risk metrics. So we focus more on the risk measures risk metrics than the expected returns. Okay, it's very, very interesting. The only thing I didn't remember what it's called about the last thing I did in between the field that was implementing some entropy portfolio location. It was based on the work of Meucci, but that pretty much as far as the things. I think it was even 10 years ago. Yeah, wow, time flies. Is there somebody else in the Boston? Yeah, I have a question. Please. Yes, about your destruction of the model, was it like a, did you design it in a hierarchical manner. It wasn't really going to your combination of the demand estimation and the macroeconomic model or just if you could describe the structure a bit more. Yes. In this particular setup, what we have is really a modular architecture where we have two models. One is the demand estimation where we want to estimate demand for a product at a given price point. So we want to draw the price quantity curve. And that particular model in this case was an ensemble of trees. Alongside that, we had a model that was looking only at the macroeconomic indicators, and it was trying to estimate the transportation and storage costs. And that model was another ensemble. And the output there was on one hand we had as an optimization parameter we had the volume that was the output of the blue box. And then we had the macroeconomic model that was producing transportation and storage costs. We have the tree that in in the optimization, which is the pink purple box here. We also know that the optimization parameter in the mixed integer linear program were actually estimated distributions of the volume of lead times transportation and storage. And I think that all these are helps when it comes to understanding the structure of the model and breaking out the things from a business perspective is easier, because there were also different department different subject matter experts involved. Yeah. Actually, if I think your question is quite interesting and as a, as a team, we will travel around and we will work with different companies. We see that in across different industries from from retail to chemicals to pharma. We see that many companies have started applying time series analysis to them to forecast the things transitioning from pencil and pen or Excel to to have some some models. The companies are a little bit more sophisticated. They were trying to neural networks but the less the messages companies are now having a lot of models built by different functions. You could have for example, functions from the commercial department that work on revenue forecasting, then you have manufacturing but focusing on cost of manufacturing. You have like tons of models that are spitting out on a daily basis, tons of forecast with different purposes and goals. So the way we see probabilistic programming is also a way to combine existing models into one big model that builds on a value driver street. This is what articulates from a north star metric. In this case, we were trying to optimize the price as revenue is less cost but most of the companies are primarily interested in, for example, earnings, and then earnings can be broken down as as part PNL items. PNL items can be further broken down. And in there, in that value driver street with probabilistic programming, you can combine existing generative or discriminative models. Okay. Makes sense. Yeah, it makes sense. And yeah, I'm a bit new to this bachelor student in data science. So when you said the ensemble model or ensemble structure. What exactly does that entail? Yeah, so what what we had he was whether it was either random forest or some causing or random forest, for example, gradient boosted trees. Okay. The idea is that you can have a one regression tree, but then you can have plenty of them and each tree contributes his own forecast and then the model combines all these are over forecast produced by the regression trees into one final forecast. Okay. All right, yeah. Okay, that makes sense to me. Thanks. Yeah. I mean, the random forest or tree ensembles are really, let's say the Swiss army knife of the data scientist in the industry. Yeah. Okay. Thank you. Thanks. Thanks for asking, asking questions. Anybody else. Yes, I have another question. Sorry. No, that's, that's fine. And it's good that you feel the awkward silence. I just said that it's the first time I do this poster session in a visual conference so I don't really know how this goes. When you're like having a proper poster session in the physical you have your thing where people come to grab coffee and it's easy to chit chat. I just, you know, I don't want to have been, I have impressions sometimes. Yeah, I got disconnected or something. Yeah, sorry. No, it's fine. Yeah, I'm also a bit like new to this online video chat. I normally don't do a haven't done too much video conferencing. I guess we have to get used to it. But yeah, I have a question about the drivers, because I'm, I'm also currently working on an internship and I'm also applying Bayesian modeling as well, Bayesian machine learning. Yep. But it's more like in marketing and sales context. So I was curious as to how do you identify your key drivers. It was mostly through like domain knowledge or if you used like a, you say a Cartesian, like Cartesian grid search or how you determine the most important or most salient drivers. Yeah, it's, it's a very it's a mix of both. Let's say, let's say that over over them over the years as a team, we have put together in a repository, a time series data store. Where we keep track. I'm talking now about external drivers. So for example, price intelligence, micro economic data from for example, sector views or micro economic indices, which you can pull from for example, the Bloomberg or Reuters, or capital like you so we have a number of data, data sources, we consolidate the time series in a time series data store. We have a lot of them. And with that, but we have a good of starting point. And then, when, when, when we approach a new client, we have we organize a workshop and we get the different, different subject matter experts from different areas of the company. So what do you think drives your business. And then if we don't have that particular driver, we try either to find it or to proxy. And then what what you end up having is, is that it's a data store that contains tons of external drivers, then you have to connect that to their, if you to their earpiece system.  At least most of our clients are with SAP.  And then probably you're going to have also other, other silos in a company where you can find information about for example market. And then once you have all these, the question is, from a modeling perspective, how do you read out the signals that don't matter, or from a modeling perspective, there is also the question of, if you have signals and signals don't matter or signals go integrate or correlated does this work. So you have to understand a little bit the model you're using and the hands of these situations. Okay. I think what maybe if if you're looking at the side of the topic, one other thing that I think it's very interesting from my perspective is the featureization of marketing and promotional data. So if I just to spend a couple of words of these without going too much in detail, I wonder what I see is that most of the companies, sort of like know when they send, for example, or start promotional campaigns online. But they don't really know from a quantitative perspective how they're doing it. So what what what I've been doing for in a number of projects is helping clients, for example, go through their email campaigns or tweeters or LinkedIn and a lot and extract features out of that. One feature, for example, could be how many characters, then they are like a matrix with describe the complexity of the language. You can see online that they, how they are analyzing and comparing the performance or one of them, the complexity of the language used by different US presidents from Trump to JFK. And then what you can you're going to build also then features around the promotional campaign. Yeah, I've seen that also, like, there's some initiative in the company I'm working to do that. But the problem is there's like not enough data scientists. Yeah, they should hire a consultant. Yeah, but yeah, it's, it's pretty exciting but I've also noticed the research itself, it's still a bit. I mean, at least what I've, I've seen is still a bit immature in terms of how it's exactly to quantify the impact of these new marketing outreach, you know, for attribution models and things like that. Yeah, attribution models are, well, the one attribution model are at the moment still an alchemy. I think see many companies that, for example, sort of like a random guess with the attribution strategy that they have in place. First touch, a second touch and touch. Yeah, from a modeling perspective, I think, if you look at the fly machine, definitely a field but it's arrived before for innovation. Yeah. And yeah. Oh, sorry, go ahead. No, I want the, I thought I heard somebody saying something of the background. No, never mind. Sorry. Anyway, any, any other question? Hello, yes. We are actually doing something very similar in our company but from the buyer side, let's say because if I understand correctly, this is for the selling site, like you produce a product and then to put the optimal price on the product eventually, right? Well, in this specific case, we had a man in the middle. It was a company that was buying from the manufacturer and then selling as a distributor to a company that would in turn resell it. Yeah, it was and let's say rather not vertically constituted industry but we have used a very similar modeling approach in, for example, fashion and the luxury where it's a company that manufactures and then sells B2C with their own stores and B2B to stores that go to them in turn resell. Yeah. Because what we noticed is very often there is specific demands actually when, yeah, you have to get suppliers or so for your current production but then only the microeconomic model is the variable input. And I was wondering the next step in the optimization, how you use this because I missed that part of the presentation a bit with the stochastic programming during the optimization step. Is it kind of an iterative process or how do you really take into the model for optimizing these Bayesian outputs of the machine learning from the step before? Yeah, good point. So I guess you're asking what happens in the purple pink box. Yeah, because we use actually the same solver I think and yeah I didn't know that this is possible or how to do that. This is a very good question and I think, I mean on my side I hope I'm very interested about the topic so I'm happy, I would be happy also to connect after this poster session. But there are two different ways to go. So what you have is, as I understand that you have a solver that does for example, this has the capacity to solve mixed integer linear programs. Yes. We work a lot with Gruby. I guess you have with Gruby. If you're not with Gruby, you're with C-Plex or we are with something open source. Then once you're there, so you can start with a standard mixed integer linear program. But now what you have is for your optimization parameters, you don't have your point estimate, but you have a distribution. So one way to do, to solve the problem is to encode the problem with robust optimization where you write a deterministic equivalent of your MIP program taking into account the information that you can extract. For example, the first and second moments from the distribution. So the idea of robust optimization is something that comes from engineering internationally. Imagine you have to build a bridge. It has to be able to load the 10 cars at the same time. How do you optimize? One optimization parameter is like how much weight goes on the bridge. And the robust optimization says, well, you have an uncertainty set. And the uncertainty set is how much I expect these 10 cars to weight plus a safety. The stochastic programming approach is for every, you can, for example, run a Monte Carlo simulation. So you are approximating by Monte Carlo your distribution and then you say, sorry, from an optimization perspective, you want your MIP program to satisfy the constraint for all the points in the data set. And if you like, you can set it up as in a way where constraints can also be satisfied from a soft sense, soft constraints or improbability sense. Yes. So you have a chance of breaking the constraints. But then since it's from a Monte Carlo, hopefully the most important ones you will most of the time not violate because there's so many of these samples. Well, what you want to say, in the robust optimization perspective, what you have is a constraint, which is a hard constraint on the uncertainty set. In stochastic programming, you can use your data points. And then you can rewrite your data points from the simulation. And then you can encode a program where you can use the information on the fully characterized probability distribution so that you can recode your mixed integer program in a way where you can have, for example, you can tune your utility function on one hand. And on the other hand side, you can say you can also by having information of fully characterized probability distribution set a constraint in a way which is satisfied, soft to satisfy. So the constraints holds 80% of the time. Yeah, yeah. That sounds very interesting. Thanks a lot. Thanks. Yeah, it's a, it's a quite, quite a broad, broad subject. Yeah. I'm not sure I covered everything in a very rigorous way. There's some hand waving the way I presented. I just was trying to communicate the, let's say, the big picture while trying not to choke. But, but if you like, this is something that I'm very quite passionate about. So we can touch base after the after the session. Yes, gladly. Thanks a lot. My pal is here. Um, any other, any, any other, any other question. I also questions on the discord. I'm going to look at them later. I make you. Hi. We say here. Yeah, hi, hi, how are you doing. Yeah, fine. How are you. I'm fine. Fine. It's been quite a quite an interesting poster session so thanks for dialing by the way. Yeah, not to be competitive but another track is very quiet while you are buzzing with the questions. Another poster track is quiet. Good. Good. Well, no, I do really appreciate the questions because no, it's been a very good, good audience. Yeah, quick question. I was going through the references were provided at the right bottom corner. Yeah. Found somewhere that your company helps in organizing the sports events. How is this possible? Can you, can you add some color to your, to your question like how would the, how would the problem setup look like? Not sure. There was a reference PDF file to study. Use case, scheduling of sporting events. Yep. KPMG has strong credentials in the scheduling of sporting events in Switzerland and internationally. Yes, this is, yeah, we have been doing sports schedules for quite a while. My, my question was, was about what, we have a few minutes I have to stay also longer in the channel if this is possible, but what would you like, would you like me to give you like an idea of how we structure model, or are you interested in some specific No, this is the first time I heard that machine learning can be used for scheduling the events. Just an overview. I'm not a programmer. I'm from electronics perspective. Okay, yeah, I understand the question. So generally, most of the schedules that we have tackled. The scheduling problem was an operation research problem. So what we're talking about the mixed integer linear program. We are also by the way, we also presented at the scientific conference about how we do schedule optimization with the mixed integer linear program and five or six months ago. Perhaps if you like after after this session I can, I can send you the material. Yeah, please. That is the question. And where does a machine learning come into play. I think there in the sports industry we have a very similar setup, where we use a generative models to estimate the probability distributions of a key optimization parameters. For example, one of the classic use cases that we had with sports organization is they would like to understand demand better web demand is a number of viewers for an event when the competition is streaming and the number of attendance that will be able to join physically. So what what we do is we take a historical data about the competition about the event sales data for example streaming data with the partners, such as for example, I don't know the YouTube's equivalent and what what we do there is we build a model that says hey, this is what we expect them on to be. And then based on that and based on other constraints such as the availability of a location, the cost of rent a location because often that also is is varying. Yeah, we have a mixed integer program. Mixed integer linear program that then produces the answer. It really makes sense. Very happy means this is the first time where I heard how to use machine learning before organizing something. Yeah, it's a very, yeah, no, I understand it. So it's something relatively new. The classical approach is a deterministic mixed integer linear program. And one, one other element which is interesting is that, well, most of the time you end up having a not just one solution, but you end up having several solutions several optimal solution on the frontier. And this is because when when it comes to scheduling one of the biggest challenges is that you generate is a multi objective organization. You have KPIs like for example, travel, but you also have KPIs related to the fairness. And the getting into fairness is a very tricky problem. Fairness can be quantified in different ways. For example, if you have an event where the competition sees home and away games, what you want to have is a schedule where as much as possible you have a partner home away, home away, home away. But now with the sports federations are looking at also other factors, such as for example, the estimation of the impact of travel on the players on the athletes. And this is again a machine learning topic where you start from certain information about the traveling and you try also to the sports team now are really getting into the quantified self perspective. So they are using devices to track physically physiological data about the players. And based on that, and information about the traveling schedules, as federations asking for example, what is the impact of, for example, the jet lag. All right. So this is where we stand with respect to sport foundations. If you ping me on even on LinkedIn, if possible, I'd be happy to share some material with you. I will definitely do I took you off the track from your posters. You gave a very pretty good insight. Very happy. Thanks. Thanks. I definitely would like to learn about the mathematics. It's most often we use machine learning with algorithms which are pre written completely defined and all our mathematics will be helpful. I'll get to learn from you. Sounds good. Happy to stay in touch. I like these events because we are also an opportunity to network. Yes. Cool stuff. Cool. I think I'm, I'm out of time but nobody is is pinging me to stop. So if I, if you guys, if you guys or girls or ladies or have other questions. Just off the record and on a lighter note. This is the best session what I have seen from the morning. You can interact, right? Others were little passive. Yeah, look, it's, yeah, there's, yeah, there's, there's a interaction element which is, which is interesting. It's all other top tracks are one sided means questions will be collected in the charts and answered at the end. We have a regular talks. We have a regular talks as well, but this, this time I wanted to try out something new, something new, which is being there and presenting on a poster for Thanks. See you. Yeah. Hello. Yeah, hi. Hi. So where can we find any material about your, I would say your presentation, like, even like concrete, what concrete examples. Do you have like, like, I would say like project to share about the topic, for example. Um, I have put something super high level here. And if you click, you can see something, something more. But and, but I also have been given some talks and I have presentations and material online. I have quite some stuff on my, on my LinkedIn. And, but I can also share the things with you on the discord channel, perhaps. Okay, yeah, would be great. So I was given a chat about talk about kind of like this very same model setup and use case at the grubby days. And I believe that they have the grubby as put something online. Yeah, this is my, my full talk about these are busy use case, but I'm going to share the link with the video as well. Okay, thanks a lot. Thank you. Thank you. And yeah, look guys connect with me happy to stay in touch. Share ideas. It doesn't really have to be on the bar stuff though. So if you have just just run on questions. Okay, I think we're all going to add the only Indian Adobe. Perfect. Perfect. Thanks. Thank you. Thanks for the presentation. My, my pleasure, my pleasure. Bye. Actually, I think I'm out of time, but I'm going to, I'm going to be reachable on this card. Yeah, thank you. And enjoy the event. Bye bye.


Poem 1 of Renaissance and Other Poems This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information or to volunteer, please visit LibriVox.org Recording by Linda Liu Renaissance and Other Poems by Edna St. Vincent Malay Renaissance All I could see from where I stood Was three long mountains and a wood I turned and looked another way And saw three islands in a bay So with my eyes I traced the line Of the horizon, thin and fine Straight around till I was calm Back to where I'd started from And all I saw from where I stood Was three long mountains and a wood Over these things I could not see These were the things that bounded me And I could touch them with my hand Almost I thought from where I stand And all at once things seemed so small My breath came short and scarce at all But sure the sky's big I said Miles and miles above my head So here upon my back I'll lie And look my fill into the sky And so I looked and after all The sky was not so very tall The sky I said must somewhere stop And sure enough I see the top The sky I thought is not so grand I most could touch it with my hand And reaching up my hand to try I screamed to feel it touch the sky I screamed and lo infinity came down And settled over me Forced back my scream into my chest Bent back my arm upon my breast And pressing of the undefined The definition on my mind Held up before my eyes a glass Through which my shrinking sigh did pass Until it seemed I must behold The immensity made manifold Whispered to me a word who's sound Deafened the air for worlds around And brought un-muffled to my ears The gossiping friendly spheres The creaking of the tinted sky The ticking I saw and heard and knew at last The how and why of all things Past and present and forevermore The universe cleft to the core Lay open to my probing sense That, sickening, I would feign pluck thence But could not Nay, but needs must suck Had the great wound and could not pluck my lips away Till I had drawn all venom out Ah, fearful pawn For my omniscience paid I toll In infinite remorse of soul All sin was of my sinning All atoning mine And mine the gall of all regret Mine was the weight of every brooded wrong The hate that stood behind each envious thrust Mine every greed Mine every lust And all the while for every grief Each suffering I craved relief with individual desire Craved all in vain And felt fierce fire about a thousand people crawl Perished with each Then mourned for all A man was starving in Capri He moved his eyes and looked at me I felt his gaze I heard his moan And knew his hunger as my own I saw at sea a great fog bank Between two ships that struck and sank A thousand screams the heavens smoked And every scream tore through my throat No hurt I did not feel No death that was not mine Mine each last breath that Cry met an answering cry From the compassion that was I All suffering mine and mine its rot Mine pity like the pity of God Aw awful weight Infinity pressed down upon the finite me My anguished spirit like a bird Beating against my lips I heard Yet lay the weight so close about There was no room for it without And so beneath the weight lay I And suffered death But could not die Long had I lain thus Craving death When quietly the earth beneath gave way And inch by inch So great at last had grown the crushing weight Into the earth I sank till I Full six feet underground did lie And sank no more There is no weight can follow here However great From off my breast I felt it roll And as it wet my tortured soul Burst forth and fled in such a gust That all about me swirled the dust Deep in the earth I rested now Cool as its hand upon the brow And soft its breast beneath the head Of one who was so gladly dead And all at once and over all The pitying rain began to fall I lay and heard each pattering hoof Upon my lowly facet roof And seemed to love the sound far more Than ever I had done before For rain had hath a friendly sound To one who six feet underground And scarce the friendly voice or face A grave is such a quiet place The rain I said is kind to come And speak to me in my new home I would I were alive again To kiss the fingers of the rain To drink into my eyes a shine Of every slanting silver line To catch the fresh and fragrant breeze From drenched in dripping apple trees For soon the shower will be done And then the broad face of the sun Will laugh above the rain-soaked earth Until the world with answering mirth Shakes joyously And each round drop Rolls twinkling from its grass-blade top How can I bear it buried here While overhead the sky grows clear And blue again after the storm A multi-colored, multi-form Beloved beauty over me That I shall never, never see again Spring-silver-autumn-gold That I shall never more behold Sleeping your myriad magics through Clothes the polkered away from you Oh God, I cried, give me new birth And put me back upon the earth Upset each cloud's gigantic gourd And let the heavy rain down-poured In one big torrent set me free Washing my grave away from me I ceased and threw the breathless Hush that answered me The far-off brush of herald wings Came whispering like music down the vibrant strings Of my ascending prayer And before the wild winds whistling lash The startled storm clouds reared on high And plunged in terror down the sky And the big rain in one black wave Fell from the sky and struck my grave I know not how such things can be I only know there came to me A fragrance such as never clings To ought-save happy living things A sound as of some joyous elf Singing sweet songs to please himself And through and over everything A sense of glad awakening The grass, a tip-toe at my ear Whispering to me I could hear I felt the rain's cool fingertips Brushed tenderly across my lips Layed gently on my sealant side And all at once the heavy night Fell from my eyes and I could see A drenched in dripping apple-tree A last long line of silver rain A sky grown clear and blue again And as I looked, a quickening gust The wind blew up to me And thrust into my face A miracle of orchard breath And with a smile I know not how such things can be I breathed my soul back into me Ah, up then from the ground sprang I And hailed the earth with such a cry As is not heard say from a man Who has been dead and lives again About the trees my arms I wound Like one gone mad I hugged the ground I raised my quivering arms unhigh I laughed and laughed into the sky Till at my throat a strangling sob Caught fiercely On a great heartthrob Sent instant tears into my eyes Oh, God, I cried No dark disguise can error hereafter hide from me Thy radiant identity Thou canst not move across the grass But my quick eyes will see the paths Nor speak, however silently But my hushed voice will answer thee I know the path that tells thy way Through the cool eve of every day God, I can push the grass apart And lay my finger on thy heart The world stands out on either side No wider than the heart is wide Above the world is stretched the sky No higher than the soul is high The heart can push the sea and land Farther away on either hand The soul can split the sky in two And let the face of God shine through But east and west will pinch the heart That cannot keep them pushed apart And he who's soul is flat The sky will cave in on him By and by End of poem one Poem two Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St. Vincent Malay Interim A room is full of you As I came in and closed the door behind me All at once A something in the air Intangible Yet stiff with meaning Struck my senses sick Sharp unfamiliar odors Have destroyed each other's rooms Dear personality The heavy scent of damp venereal flowers The very essence hushed the stilt of death Has strangled that habitual breath of home Whose expiration leaves all houses dead And where so where I look as hideous change Save here Here It was if a weed choked gate had opened And I had stepped into some long forgot Enchanted Strange Sweet garden of a thousand years ago And suddenly thought I have been here before You are not here I know that you are gone I will not ever enter here again And yet it seems to me If I should speak Your silent step must wake across the hall If I should turn my head That your sweet eyes would kiss me from the door So short a time to teach my life Its transposition to this difficult Anomic custom key The room is as you left it Your last touch As pressure Knowing not itself as saintly Hallows now each simple things And glorifies And glows between the dusts, grey fingers Like a shield applied There is your book Just as you laid it down Faced to the table I cannot believe that you are gone Just then it seemed to me I almost laughed to think How like reality The dream had been Yet knew before I laughed And so was still That book Outspread just as you laid it down Perhaps you thought I wonder what comes next And whether this Or this will be the end So rose and left it Thinking to return Perhaps that chair When you arose And passed out of the room Rocked silently a while Air it again was still When you were gone forever from the room Perhaps that chair Stirred by your movement Rocked a little while Silently to and fro Are the last words your fingers wrote Scrawled in broad characters Across a page in this brown book I gave you Here your hand Guiding your rapid pen Moved up and down Here with a looping knot You crossed a tee And here another like it Just beyond these two eccentric ease You were so small And wrote so brave How strange it seems out of all words These are the words you chose And yet a simple choice You did not know you would not write again If you had known But then it does not matter And indeed If you had known there was so little time You would have dropped your pen And come to me And this page would be empty And some praise other than this Would hold my wonder now Yet Since you could not know And it befell that these are the last words Your fingers wrote There is a dignity some might not see in this I picked the first sweet pea today Was there an opening bud beside it You left until tomorrow All my love All the things that withered And you came not back That day you filled the circle of my arms That now is all my empty life That day That day you picked the first sweet pea And brought it into shape I recall with terrible distinctness How the smell of your cool gardens Drifted in with you You held it up for me to see And flushed because I looked not At your flower But at your face And when behind my look You saw such unmistakable intent You laughed and brushed your flower Against my lips You were the fairest thing God ever made I think And then your hands above my heart Drew down its stem into a fastening And while your head was bent I kissed your hair I wonder if you knew The loved hands Somehow I cannot seem to see them still Somehow I cannot seem to see the dust In your bright hair What is the need of heaven When earth can be so sweet If only God had let us love And show the world away Strange cancelings Must ink the eternal books When love crossed out Will bring the answer right That first sweet pea I wonder where it is It seems to me I laid it down somewhere And yet I am not sure I am not sure Even if it was white or pink For then it was much like any other flower to me Save that it was the first I did not know that if I had known But then it does not matter Strange how few after all said and done The things that are of moment Few indeed When I can make of ten small words A rope to hang the world at you And I am as the little juice I can when it slacks So the bulls tighten to a thought Here, let me write it down I wish to see just how a thing like that Will look on paper, and you And I have you now no more How can you run so straight across the page Beneath the weight you bear How can you fall apart When such a theme has bound together And here after aid and trivial expression That I have been so hideously dignified Would God that tearing you apart Would tear the thread I strung you on Would God, oh God My mind stretches asunder On this merciless rack of imagery Oh, let me sleep a while Would I could sleep And wake to find me back In that sweet summer afternoon with you Summer Does summer still by the calendar How easily could God, if he so willed Set back the world a little turn or two Correct its griefs And bring its joys again We were so holy one I had not thought that we could die apart I had not thought that I could move And you'd be stiff and still But I could speak And you'd perforce be dumb I think our heartstrings were Like warp and wolf And some firm fabric Wolven in and out Your golden filaments and fair design Across my duller fiber And today the shining strip is rent Exquisite fine pattern is destroyed Part of your heart aches in my breast Part of my heart Lies chilled in the damp earth with you I have been torn in two And suffer for the rest of me What is my life to me And what am I to life A ship whose star has guttered out A fear that in the deep night starts awake perpetually To find its senses strained against the taut strings of the quivering air Waiting the return of some dread core Dark Dark Is all I find for metaphor All else were contrast Save that contrast wall is down And all opposed things flow together into a vast monotony Where night and day And frost and thaw And death and life are synonyms What now What now to me are all the jabbering birds And foolish flowers that clutter up the world You are my song Now let discord scream You are my flower Now let the world grow weeds For I shall not plant things above your grave The common balm of the conventional woe for its own wound A mid-sensations rendered negative by your elimination The elimination stands today Certain unmixed The element of grief I sorrow And I shall not mock my truth with travesties of suffering Nor seek to effigy its incorporeal bulk And little rye-faced images of woe I cannot call you back And I desire no utterance of my immaterial voice I cannot even turn my face this way or that and say My face is turned to you I know not where you are I do not know if heaven holds you Or if earth transmutes body and soul You into earth again But this I know Not for one second space shall I insult my sight With visioning such as a credulous crowd So eager I behold self-conjured in the empty air Let the world wail Let drip its easy tears My sorrow shall be dumb What do I say? God God pity me My gone mad that I should spit upon a rosary My become so shrunken What do God I too might feel that frenzied faith Whose touch makes temporal the most endearing grief Though it must walk a while As is its want with wild lamenting What I too might weep Where weeps the world and hangs its piteous wreaths For its new death Not truth But faith it is that keeps the world alive If all at once faith were to slacken That unconscious faith which must I know Yet be the cornerstone of all believing Birds now flying fearless across Would drop and tear to the earth Fishes would drown And the all-governing reams would tangle in the frantic hands of God And the worlds gallop headlong to destruction Oh God, I see it now In my sick brain staggers and swoon How often over me flashes this breathlessness of sudden sight In which I see the universe enrolled before me like a scroll And read thereon chaos and doom Where helpless planets roll dizzily round and round and round and round Like tops across a table Gathering speed with every spin To waver on the edge one instant Looking over and the next To shudder and lurch forward I am weary doubt it is too much Though you were dead again Poem three of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St. Vincent Malay A Suicide Cursed a life I will live with thee no more Thou has mocked me, starved me Beat my body sore And all for a pledge that was not pledged by me I have kissed thy crust And eaten sparingly that I might eat again And met thy sneers with deprecations And thy blows with tears I, from thy glutted lash, glad, crawled away As I spent passion for a holiday And now I go Nor threat, nor easy vow Of tardy kindness can avail thee now with me When sphere and faith alike are flown Only I came and I depart alone And know not where nor under whom I go But that thou canst not follow me, I know Thus I, the life, and ceased But through my brain my thought ran still Until I spake again But I go not as I came No trace is mine to bear away of that old grace I brought I have been heated in thy fires Bent by thy hands Fashioned to thy desires, thy mark is on me I am not the same, nor ever more shall be As when I came Ashes am I of all that once I seemed In me all sunk that left And all that dreamed is wakeful for alarm O shame to thee For the ill change that thou hast wrought in me Who laughed no more nor lift my throat to sing Ah, life, I would have been a pleasant thing To have about the house when I was grown If thou hadst left my little joys alone I asked of thee no favor save this one That thou wouldst leave me playing in the sun And this thou didst deny Calling my name insistently until I rose and came I saw the sun no more It were not well so long on these unpleasant thoughts to dwell Need I arise tomorrow and renew again my hated task But I am through with all things save my thoughts and this one night So that in truth I seem already quite free and remote from thee I feel no haste and no reluctance to depart I taste merely with thoughtful mean and unknown drought That in a little while I shall have quaffed Thus I to life and ceased and slightly smiled Looking at nothing And my thin dreams filed before me one by one till once again I set new words unto an old refrain Treasures thou hath never hath been mine Warm lights in many a secret chamber shine of thy gaunt house And gusts of song have blown like blossoms out to me that sat alone And I have waited well for thee to show if any share were mine And now I go Nothing I leave and if I ought attain I shall become unto my own again Thus I to life and ceased and spake no more But turning straight away Saw a certain door in the rear wall Heavy it was and low and dark Away by which none error would go That other exit had And never knock was heard there at Bearing a curious lock Some chance had shown me fashion faultily Where life held content the useless key And great course hinges thick and rough with rust Whose sudden voice across the silence must I knew be harsh and horrible to hear A strange door ugly like a dwarf So near I came I felt upon my feet The chill of acid wind creeping across the sill So stood long time till over me at last came Wearingness and all things out there past to make it room A still night drifted deep like snow about me And I longed for sleep But suddenly, marking the morning hour Bade the deep-throated bell within the tower Startled I raised my head and with a shout They'd holed upon the latch and was without Ah, long forgotten, well-remembered road Leading me back unto my old abode My father's house There in the night I came and found them feasting And all things the same as they had been before A splendor hung upon the walls And such sweet songs were sung As echoing out a very long ago Had called me from the house of life I know So fair their raiment shone I looked in shame on the unlovely garb in which I came Then straight away at my hesitancy mocked It is my father's house I said and knothed And the door opened To the shining crowd tattered and dark I entered Like a cloud, see no face but his To him I crept And father I cried And clasped his knees and wept Ah, days of joy that followed All alone I wandered through the house My own, my own, my own to touch My own to taste and smell All I had lacked so long and loved so well None took me out of sleep Nor hushed my song Nor called me in from the sunlight all day long I know not when the wonder came to me Of what my father's business might be And wither fared on what Aaron spent The tall and gracious messengers he sent Yet one day with no song from dawn till night Wondering I sat and watched them out of sight And the next day I called And on the third asked them if I might go But no one heard Then, sick with longing I was at last and went unto my father In thy vast chamber whereon he for so many years has sat Surrounded by his charts and spheres Father, I said Father, I cannot play the harp that thou didst give me And all day I sit in idleness While to and fro about me Thy serene grave servants go And I am weary of my lonely ease Better a perilous journey overseas away from thee than this The life I lead To sit all day in the sunshine like a weed That grows to naught I love thee more than they who serve thee most Yet serves thee in no way Father, I beg of thee a little task To dignify my days Tis all I ask Forever But forever this tonight Child My father's voice replied All things I fancy Hath the desire to me thou hast received Have prepared for thee within my house This spacious chamber Where are delicate things to handle and to wear And all these things are thine Dost thou love song My minstrel shall attend thee all day long Or sigh for flowers My fairest gardens stand open as fields to thee on every hand And all thy days this word shall hold the same O pleasure shall thou lack that thou shalt name But ask for task He smiled and shook his head Thou hadst thy task And latest it by He said End of poem The door Of Renaissance and Other Poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Lu Renaissance and Other Poems By Edna St. Vincent Malay God's World I cannot hold thee close enough Thy winds, thy wide grey skies Thy mist that roll and rise Thy woods this autumn day That ache and sag and all but cry with color That gaunt crack to crush To lift the lean of that black bluff World World I cannot get thee close enough Long have I known a glory in it all But never knew I this Here such a passion is That stretches me apart Lord I do fear thou's may the world too beautiful this year My soul is all but out of me Let fall no burning leaf Prithee Let no bird call End of poem Poem 5 Of Renaissance and Other Poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Lu Renaissance and Other Poems By Edna St. Vincent Malay Afternoon on a hill I will be the gladdest thing under the sun I will touch a hundred flowers and not pick one I will look at cliffs and clouds with quiet eyes Watch the wind bow down the grass And the grass rise And when lights begin to show Up from the town I will mark which must be mine And then start down End of poem Poem 6 Of Renaissance and Other Poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Lu Renaissance and Other Poems By Edna St. Vincent Malay Sorrow Sorrow like a ceaseless rain Beats upon my heart People twist and scream in pain Dawn will find them still again This is neither wax nor way Neither stop nor start People dress and go to town I sit in my chair All my thoughts are slow and brown Standing up or sitting down Little matters Or what gown or what shoes I wear End of poem Poem 7 Of Renaissance and Other Poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Lu Renaissance and Other Poems By Edna St. Vincent Malay Tavern I'll keep a little tavern Below the high hills crass Wherein all grey-eyed people May set them down and rest There shall be plates of plenty And logs to melt the chill There shall be grey-eyed people Who happen up the hill Their sound will sleep the traveler And dream his journey's end But I will rouse at midnight The falling fire to tend I, to the curious fancy But all the good I know Was taught me out of two grey eyes A long time ago This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Lu Renaissance and Other Poems By Edna St. Vincent Malay Ashes of Life Love has gone and left Love has gone and left me And the days are all alike Eat I must and sleep I will Would that night were here Ah, the lie awake and hear the slow hour strike Would that it were day again Was twilight near Love has gone and left me And I don't know what to do What you will is all the same to me But all the things that I begin I leave before I'm through There's little use in anything As far as I can see Love has gone and left me The neighbors knock and borrow Life goes on forever Like the gnawing of a mouse And tomorrow and tomorrow There's this little street And this little house End of Poem Poem 9 Of Renaissance and Other Poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Lu For Renaissance and Other Poems By Edna St. Vincent Malay The Little Ghost I knew her for a little ghost That in my garden walked The wall is high, higher than most And the green gate was locked And yet I did not think of that Till after she was gone I knew her by the broad white hat By the dear ruffles round her feet By her small hands that hung In their lace mitts, austere and sweet Her gowns white folds among I watched to see if she would stay What she would do She looked as if she liked the way I let my garden grow She bent above my favorite mint With a conscious garden grace And now there was no hint Of sadness in her face She held her gown on either side To let her slippers show And up the walk she went with pride The way great ladies go And where the wall is built in new And is of ivy bear She paused, then opened and passed through A gate that once was there Ten of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St. Vincent Millay Kin Desara Am I Kin Desara That so oft falls the knocker of my door Neither loud nor soft But as long accustomed under sorrow's hand Marigolds around the step And rosemary stand And then comes sorrow And what does sorrow care For the rosemary or the marigolds there Am I Kin Desara Are we Kin That so oft upon my door Oh, come in And the poem Poem 11 Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St. Vincent Millay Three songs are shattering One The first rose on my rose tree But it bloomed and shattered During sad days when to me Nothing mattered Grief of grief has drained me clean Still it seems a pity no one saw It must have been very pretty Two Let the little bird sing Let the little lambs play Spring is here and so tis spring But not in the old way I recall a place where a plum tree grew There you lifted up your face And blossoms covered you The little bird sing and the little lambs play Spring is here and so tis spring But not in the old way Three All the dogwood blossoms are underneath the tree Airspring was going Ah, spring is gone There comes no summer to the like of you and me Blossom time is early But no fruit sets on All the dogwood blossoms are underneath the tree Browned at the edges Turned in a day And I would with all my heart They trimmed a mound for me And weeds were tall in all the paths That led that way End of poem Poem 12 Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St Vincent Malay This shroud Death I say My heart is bowed unto thine Oh mother This gown will make a shroud good as any other Eye that would not wait to wear My own bridal things In a dress dark as my hair Made my answerings Eye tonight That till he came Could not Could not wait In a gown as bright as flame Held for them the gate Death I say My heart is bowed unto thine Oh mother This red gown will make a shroud good as any other End of poem Poem 13 Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St Vincent Malay The dream Love If I weep It will not matter And if you laugh I shall not care Foolish am I to think about it But it is good to feel you there Love In my sleep I dreamed of waking White and awful the moonlight reached Over the floor And somewhere Somewhere There was a shutter loose It screeched Swung in the wind And no wind blowing I was afraid and turned to you Put out my hand to you for comfort And you were gone Cold Cold as dew Under my hand the moonlight lay Love If you laugh I shall not care But if I weep It will not matter It is good to feel you there End of poem Poem 14 Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St Vincent Malay Indifference I said For love was laggared Or love was slow to come I'll hear his step And know his step When I am warm in bed But I'll never leave my pillow Though there be some As would let him in And take him in with tears I said I lay For love was laggared Or we came not until dawn I lay and listened for a step And could not get to sleep And he found me at my window With my big cloak on All sorry was the tears Some folks might weep End of poem Poem 15 Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St Vincent Malay Which wife She is neither pink nor pale And she never will be all mine She learned her hands in a fairy tale And her mouth on a valentine She has more hair than she needs And the sun is a woe to me And her voice is a string Of colored beads Or steps leading into the sea She loves me all that she can And her ways to my ways But she was not made for any man And she never will be all mine End of poem Poem 16 Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St Vincent Malay Blight Hard seeds of hate I planted That should by now be grown Rough stalks and from thick stamens Poisonous pollen blown And odors rank unbreathable From dark Corolla's throne At dawn from my damp garden I shook the chilly dew The thin bows locked behind me That sprang to let me through The blossoms slept I sought a place Where nothing lovely grew And there When day was breaking I knelt and looked around The light was near The silence was palpitant with sound I drew my hate from out my breast And thrust it in the ground Oh ye so fiercely tended Ye little seeds of hate I bent above your growing Early, noon, and late It are ye drooped and pitiful I cannot rear ye straight The sun seeks out my garden No nook is left in shade No mist nor mole nor mildew Endures on any blade Sweet rain slants under every bow Ye falter and she fades End of poem Poem 17 Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St. Vincent Malay When the year grows old I cannot but remember When the year grows old October, November How she disliked the cold She used to watch the swallows Go down across the sky And turn from the window With a little sharp sigh And often when the brown leaves Were brittle on the ground And the wind in the chimney Made a melancholy sound She had a look about her That I wish I could forget The look of a scared thing Sitting in a net How beautiful at nightfall The soft spitting snow And beautiful the bear boughs Rub it to and fro But the roaring of the fire And the warmth of fur And the boiling of the kettle Were beautiful I cannot but remember When the year grows old October, November How she disliked the cold When the poem, poem 18 Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St. Vincent Malay Sonnet One Thou art not lovelier than lilacs No Nor honeysuckle Thou art not more fair Than small white single poppies I can bear thy beauty Though I bend before thee Though from left to right Not knowing where to go I turn my troubled eyes No more here nor there Find any refuge from thee Yet I swear So has it been with mist With moonlight so Like him who day by day Unto his drought of delicate poison Adds him one drop more Till he may drink unharmed The death of ten Even so Even nearer to beauty Who have quaffed each hour More deeply than the hour before I drink And live What has destroyed some men End of poem Poem 19 Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St Vincent Malay Sonnet 2 Time does not bring relief You all have lied Who told me time would ease me of my pain I miss him in the weeping of the rain I want him at the shrinking of the tide The old snow's melt from every mountainside And last year's leaves are smoke in every lane But last year's bitter loving must remain Heaped on my heart And my old thoughts abide There are a hundred places where I fear to go So with his memory they brim And entering with relief some quiet place Where never fell his foot Or shown his face I say There is no memory of him here And so stands Tricket So remembering him Poem 20 Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St Vincent Malay Sonnet 3 Mindful of you, the sodden earth in spring And all the flowers that in the springtime grow And dusty roads and thistles And the slow rising of the round moon All throats that sing the summer through And each departing wing And all the nests that the baird branches show And all winds that in any weather blow And all the storms that the four seasons bring You go no more on your exultant feet Up paths that only missed in morning new Or watch the wind or listen to the beat Of a bird's wings too high in air to view But you were something more than young and sweet In fact, in the long year remembers you Poem 21 Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St. Vincent Malay Sonnet 4 Not in this chamber, only at my birth When the long hours of that mysterious night were over And the morning was in sight I cried But in strange places Step and birth I have not seen Through alien grief and never shall one room Who in so many rooms first saw the light Child of all mothers Native So is no warmth for me at any fire today When the world's fire has burned so low I kneel Spending my breath in vain desire At that cold hearth which one time roared so strong And straightened back in wariness And longed to gather up my little gods and go End of poem 22 Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St. Vincent Malay Sonnet 5 If I should learn In some quite casual way That you are gone Not to return again Read from the back page of a paper say Held by a neighbor in a subway train Held at the corner of this avenue And such a street So are the papers filled A hurrying man Who happened to be you At noon today And happened to be killed I should not cry aloud I could not cry aloud Or wring my hands in such a place I should but watch the station lights rush by With a more careful interest on my face Or raise my eyes And read with greater care Where to store first And how to treat the hair At home 23 Of Renaissance and other poems This is a LibriVox recording All LibriVox recordings are in the public domain For more information or to volunteer Please visit LibriVox.org Recording by Linda Liu Renaissance and other poems By Edna St. Vincent Malay Bluebeard Sonnet 6 This door you might not open And you did So enter now And see for what slight thing you are betrayed Here is no treasure hid No cauldron No clear crystal mirroring the thought for truth No heads of woman slain for greed like yours No writhings of distress But only what you see Look yet again An empty room Cobwebbed and comfortless Yet this alone out of my life I kept unto myself Last any know me quite And you did so profane me when you cracked Unto the threshold of this room tonight That I must never more behold your face This now is yours


Hi, this is Sean with Protected Trust again, and today I wanted to talk about a feature that is available in most Office 365 tenants that you can use to improve your email security posture without a whole lot of configuration. In the Microsoft 365 Defender Portal, which you would get to by going to security.microsoft.com as a global admin in your tenant. If you then go to email and collaboration and policies and rules and then threat policies, you'll see that you have a number of policies that are available in here to do things like anti-fishing, anti-spam, anti-malware, depending on the licensing that you have available in your tenant. If you have Microsoft Defender for Office 365, then you will also have these additional policies for safe attachments and safe links. But today the thing that I wanted to talk about are the preset security policies. Preset security policies are available to all Office 365 tenants that have either exchange online protection or any of the licenses that include Microsoft Defender for Office 365 such as Microsoft 365 E5 licenses, or you can also get Defender for Office 365 as a standalone license that you add on to other licenses. But even with exchange online protection, you have the preset security policies available. You just don't have all of the same features that are available. So to begin with, I'm just going to go in and look at the standard protection preset security policies. So in order to enable it, I have to select this option here for manage protection settings. And when I do that, it's going to take me through a wizard. It's very, very simple to set up. And by doing this, any organization that has a exchange online protection or those additional licenses that I was talking about, you can go in and set up custom policies. I'm going to back back out to that for a second. And so if we went into threat policies and anti-fishing, anti-spam, like if we went into our anti-spam policies and went and looked at our anti-spam inbound policy, there are policies that you can go in here and configure. But the settings here, there's a lot of settings to configure, and a lot of people just don't want to deal with going in and setting up all these settings. That's where the preset security policies are really useful. So you can just go in and create, basically enable the standard protection policy for everyone in your organization. And it gives everyone a fairly decent baseline of protection against spam and fishing. And just basically bad content coming into your tenant over email. So here I'm going to go again to manage protection settings. And I'm going to just kind of like walk through the wizard real quick. It's pretty simple. In this case, we're going to apply protection to all recipients. And if I wanted to, I could exclude some users. But in this case, I'm not. I'm just going to apply it to everybody. Since the tenant that I'm in right now has Defender for Office 365, there are some additional features that are available here. Realistically, here at Protected Trust, we would recommend if you have Defender for Office 365, if you're paying for those additional licensing, then you should probably go in and enable the custom security policies and go in and set those things up. The convenience here is that you don't necessarily have to do that. So, but since this tenant does have Defender for Office 365, I will have a few additional settings that are available. So in this case, I want to apply that protection to the previously selected recipients. So again, since in the previous step, we selected everyone in the organization, this is going to apply protection to everyone as well. The next steps, it's going to ask me about impersonation protection. So add email addresses to flag when impersonated by attackers. So this setting, what it does is if we have anyone in our organization, so we receive an email from the outside world and it's impersonating a specific user in our organization, then we want to handle that email differently than we would an email that comes in as anyone else. For example, in this case, our president of this company, Patty Fernandez, we're going to make her have this setting here. So I think it's Patty. So we're going to add Patty Fernandez because she is the president of this company. You know, people might email others within the organization, spoofing her email and or pretending to be her by creating a Gmail account that has Patty Fernandez as the display name, for example. This setting helps protect against that kind of situation. And anyone in our organization who is targeted in that fashion, so we maybe regularly receive emails where that user is being impersonated, then this setting is where we would help prevent and control that kind of attack in our organization. Again, this is additional configuration that we only have in this organization because this organization has that additional licensing for Microsoft Defender for Office 365. But still, it's good stuff, good stuff to set up. So protected custom domains, you can add additional domains. So, you know, we could have our own domain. In this case, it was just contoso.com. But maybe we also have a partner like maybe protected. You know, maybe we want to make sure that emails that come from protected trust aren't impersonated, for example. So we could have basically, you know, partners here, people that we regularly do business with and most importantly, their email is set up correctly. So whenever they send email, they have things set up like SPF and DMARC and DKIM signing and all of those things. So if we're 100% positive that this partner has their email set up correctly, then it would be a good idea to add them to this list. But I wouldn't recommend that you go overboard with this and it's something where you can only add 50 domains to this list. So you kind of have to, you know, be picky and choosy when you're adding them to it. So the next setting would be trusted senders. So if you have an organization that you intentionally use to impersonate your users and you've run into problems receiving emails from that organization, maybe you have like a constant contact address or maybe there's a, actually a really good example is like a Gmail account. So like Patty Fernandez, for example, if she wants to be able to email herself from her own, you know, personal email account, then, you know, maybe we need to add that sender.  So what that would allow, by adding that as a trusted email address, so she wouldn't have difficulty emailing herself from that personal email account. It would still make it into the tenant. So anyway, that's how you would enable trusted email addresses. And all of those settings, after that Defender for Office 365 protection setting there, all of those settings are the ones that are only available if you have that additional licensing. Otherwise you would pop right to the next setting, which is the policy mode.  So in this case, I'm gonna go ahead and turn it on. So this is, gives us a little screen where we can review and confirm any of those changes that we've made when we confirm that it's gonna enable that policy in our tenant. So we'll see in the tenant, now standard protection is on. There's an option here where we could turn it on or off if we wanted to. Also, if we go into, just back into emailing collaboration and policies and rules and threat policies again, and then any of these policies, so we go and look in anti-fishing, for example, we will now have the standard preset security policy as a policy that's available in this list. So, okay. Now say if we have a user in our organization who is maybe more problematic, maybe they get a lot of spam, maybe they're more likely to fall for phishing attempts. Maybe just we just wanna protect them a little more than our other users. So if we go back into our preset security policies, you'll see that there is also an option for a strict protection preset security policy. So again, if we go to manage protection settings, in this case, I wouldn't recommend that you use the strict protection policy against all of the users in your organization. In my opinion, it tends to be too strict for general business, but it's something that you could certainly enable for specific users in your organization if they are more prone to being problematic than other users. So in this case, we're gonna pick on Adele, and we're just gonna add her to the list again, because this tenant has the Office 365 licenses. We're going to have some additional settings here. Again, I'm gonna use previously selected recipients, so it only applies to the users that we selected in the previous step. Again, that was just this one user Adele. Gonna say next. It's gonna walk us through the next settings, impersonation protection. Again, I would recommend that we set things up here, the same as we did in the other ones. So we're gonna protect for emails that look like they come from Patty Fernandez. We might wanna add our own domain to this list, or any important partner domains. If we have trusted senders, in this case, Patty's probably not gonna email Adele, so I'm not gonna bother adding her to this policy. But if we had any trusted senders, we could add them to this list as well. Again, I'm just gonna turn the policy on and confirm, and that's it. That's all it really takes to set up what is a decent baseline of email security for your organization. It's just a few clicks, and there you go.  It's certainly easier than going in and enabling a custom anti-spam policy, though we do recommend doing so if you do have the licensing to do so. And that's pretty much it. So very simple, very easy way for you to improve your email security. Have a great day.