So welcome to this Birds of a Feather session, focused on people who have what I call specialized questions that you wanna ask about community health and sustainability at scale. And so I'd like to start with just a brief discussion of what kinds of things have occurred at scale and what kinds of projects or groups of repositories that we have looked at at scale. So first of all, what do I mean by at scale? I'm interested in, so when I say at scale, we're usually, I'm in the thousands of repositories, usually 2000 or more, has many as 25,000 repositories that we're trying to create signals for and understand at any one time. And usually these are open source program offices inside of organizations that have these kinds of questions and this diversity of projects. And also companies that are part of the open source community who manage thousands of projects at a time. And I'd like to open a chat discussion since that's how you're able to participate. And just ask, when we say at scale and when we talk, when you decided to participate in this Birds of a Feather session on health and sustainability at scale, what does at scale mean to you? And I believe you can use your chat to get to sort of typing in what at scale means to you. Okay, attendees, apparently you need to raise your hands first in order to be called on. So for those of you who just are joining us, this is a Birds of a Feather session on health and sustainability at scale. I introduced the topic as large scale organizations that either use a number of open source projects in the thousands and wanna understand ongoing health and sustainability of those dependencies at scale or open source software companies that look at tens of thousands of projects that they have an interest in and wanna understand at scale. And I'm throwing out a question, thinking of a Birds of a Feather session being a little bit more conversational. My understanding is you have to raise your hand to ask a question in the chat, but I'm interested in what you think health and sustainability at scale is, what is it to you? Looking to the participants to ask a question, sort of state to you, what is it? What does that scale mean to you? So I don't know what it means that no one's raising their hand in a Birds of a Feather session. Let me try to think about, can, is everybody able, can somebody raise their hand just to make sure that that part of it is working? And so I've got a couple of people who've raised their hand, I don't seem to have the power to do anything with the hand raise. It seems that the raise hand function requires camera and mic not text. Okay, so you can ask, you can answer my question via text without raising your hand, but you can ask, you can speak I think if. Okay, and Dylan, I am not hearing you. That would probably be because I was not talking. Oh, okay, great, thank you for helping to sort out this process. Well, I guess since I'm on the spot as a co-founder of a small startup at scale to us means something with a business substantially more established and with access to much larger resources than what my co-founder, Vinny, and I currently have. It's a little bit, I guess ephemeral, a concept for someone that's still kind of as small and growing as we are, and I guess something that we're still interested in learning more about hence why we were on the call. Yeah, yeah, so that's a, so there are others who are in similar positions that have a sort of interest in understanding what it looks like to be at scale. And let's see, and then I have another, there's another person who accepted. So, all right, so Shriram, I think, are you the other person having a hand raised? I think, okay. Yeah, can you hear me? I can hear you now. I think what's happening is the person who's the host is the person who can give you the control, not me. So Megan's helping us out, so sorry. All right, no problem, I'm glad we can test it. So do you have a, I mean, is your interest in, or is your interest for definition of what at scale means different from what Dylan said? Sorry, I missed part of that, so I didn't get to hear everything Dylan said. I apologize, but I am interested in that kind of scalability for metrics. I am involved in the chaos project. We do an apps ecosystem. It's a work we're changing the name, but it's very much linked to scale the onboarding and various other projects we're doing, not at IT Renew, but at the Canom project. So, yeah, this is something interesting to me. Yeah, so that gets to sort of the question of what chaos is. And Sharon, you're clearly familiar with it. We are a project that provides both definitions of health and sustainability metrics as well as tools that let communities look at the health and sustainability by gathering and analyzing a bunch of data. So the main area where the metrics are developed, I've just put it in chat, and our general community site is just without the slash metrics trailer. And so chaos does these two things. One is sort of try to help define, provide standard definitions or reach consensus in a large group of industry participants about what does it mean? How do we count commits? How do we count newcomers? What are the things that are parameters? Newcomers is a really good example of, and newcomer, what we call it, what I call stickiness. Okay, you get so many newcomers a month. How many of them make a second contribution or a fifth contribution on a periodic basis in that those kinds of metrics can help people who are running large scale communities or startup open source communities determine if the new project that they have is scaling well and also share practices that they've had for scaling the projects as well. What we do with chaos and the tools that we have on chaos, and the tool I work most closely with is called Augur, is we gather, in the case of Augur, up to, I guess, 25,000 is the most we've gathered in a single database of repositories with lots of different information where we can look at relative newcomer participation, newcomer stickiness. We can also look at some of the metrics that we've defined around elephant factor. We have a license scanner that lets you know what licensing risk is, sometimes at scale, understanding licensing is really, really important. And so when you have a large scale enterprise, for example, if you're a startup firm and you have a large number of open source projects and you wanna understand the relative health, one way to do it is to just sort of get a survey of the landscape on some of the basic metrics and statistics, and another way to approach it is to take this giant collection of data that Augur lets you create in a database, and actually, I'm just typing the link to Augur in there, and actually starting to use some of the Jupyter notebooks that we're distributing to analyze things like pull request stickiness. You can also do competitive intelligence. So if you have a bunch of projects you're competing against, that is another way that people define scale. In the case of Shoram, who, am I saying your name correctly, Shoram? Yeah, sorry, the last bit there got crumbled. What was the last thing in the case? Am I saying your name correctly? Can you hear me already? All right, all right. Yeah, yeah, it was just that little bit. You were perfectly clear up until then. So yes, my name is, yeah, it's Shoram, for now it's Shoram, or Shree. I'm mostly known as Shree in the open source world, so. Yeah, okay. Oh yes, yes, Celine pointed out that I mistyped my, I actually mistyped the chaos group in our, on the internet there. So thank you very much, Celine. So the group that Shoram is working with is looking at kind of a, I would call it a orthogonal view of metrics definitions, and it really is kind of organically an at scale problem because in the case of the GNOME community, you have, I don't know, how many different applications for that desktop? We have a large number, and there's part, I would say like, I'm gonna put out a guess here, but there's nine that are part of the release of GNOME, but hundreds of others right there. Right. Yeah, and I would imagine that in the case, so in the case of this sort of orthogonal, specialized desktop app system, it's really hard to keep track of even hundreds of different repositories that are material to the utility or usefulness of that ecosystem, right? Right, right. I will also wanna add that it's not just GNOME, but essentially organizations, yeah, it's basically organizations that are public facing projects, like desktop projects, that are very complex because we're just basically platforms of various types. And so this effort is sort of because it's different from corporate-sponsored projects, but we have similar problems, but we have this additional thing where we deal with the public a lot more closely. And the consumer is using the tools. Right, exactly, exactly. And so how we interact with that public can be somewhat complex because it's not a narrow, I wanna say a cross-section, right? It's basically the Linux enthusiast, which poses an incredible amount of challenges for us at times. Yeah, yeah, yeah. So it's a public-facing environment of highly technical people who are not necessarily contributors. And so part of the responsiveness needs that specialized part of the CAS community has is to understand that there's some alignment between the public satisfaction or dissatisfaction and the developer bandwidth or commitment or ongoing contributions to a particular part of that ecosystem. Yes, exactly, exactly. And it's not just that, but quantifying those interactions through GitLab specifically, since that's been what our communities have been using. But we do a lot of quantitative things, even parts of abusive, like the abusive uses of emojis, right? Or some of the things that we can actually quantify. So we can quantify both negative interactions and positive interactions through emojis or some of these other things. And this is for us ways that these are things that are different that I feel that we have in other more controlled environments is that we don't have control over the people because a lot of them are mainly anonymous, right? So which gives them some freedom to do some free speech. I'll put that gentlemen. Yeah, yeah. Oh, that's, so that's a very different, you know, and I was listening to Dylan earlier. His interest, they're the interest that his, the firm that they're at has is in understanding, you know, what does scale look like and how would they help people understand its health and sustainability at scale? In your case, I think what I hear is that there's this public pressure piece of it. So scale to you means how much sort of help desk type of input can you manage? And then what are some metrics that you can put around that? Are you, is this your group? Is your subgroup looking at sort of those kinds of metrics? Or what are the kinds of metrics that are really critical for something where there's a lot of public input, which is really distinct from the kinds of things that most open source projects are attending to like contributors and commits and things like that. Right, I, well, at the moment, we're still putting together these metrics. Still in the very beginning stages, but we're essentially breaking down the problem into what are the information we're looking for based on roles and various other things. So if you give me a moment, sometime here I can try to put something together based on the work we've done, but I can't, I can't, I can't, I can't immediately. Oh, oh, oh. Oh, oh. First time, first time. I didn't prep myself for that. Yeah, no, I didn't know what I expected you to. So that makes, that makes perfect sense. Are there other folks who have different interests in what scalability is? I see some folks from Ubuntu here. I see a few other academics like myself in the room. Are there other kinds of sort of dimensions of scalability that folks may want to raise their hand and share with us as a, just examples of what brings your interest here. Again, my idea being my birds of a feather, the more we share about our particular challenges that we're facing, I think the more interesting the discussion might be. Okay, hearing none, I do have a chat question. So Selene is really interested if you look to your chat and I'm just sort of providing the sort of sports commentator where you look because it's not always clear in the system. So interested in the SCMS Dylan's working on, but by us so being able to measure community sentiment probably will be huge when it's working. SCMS, I don't know what SCMS is actually an acronym for. When we have Dylan raise his, Dylan, why don't you raise your hand again and see if Megan can give you a little bit more of the floor. And then that way we can, first of all, I don't know what SCMS is. Right, I lost track of that acronym, which is. So yeah, Venya Logan and I are in the process of introducing a new metric system in with the most recent chaos community metrics launch here. And what we went ahead and designed is the social currency metric system that is essentially a way to quantify qualitative data. That's essentially what that is. We are in the process of working with one of the, these students going through the Google summer coding program here that chaos sponsored and in the process of really, I guess, spinning things up and our big question is we have this implementation that is nearing what's essentially a minimum viable product. How do we take that MVP and continue to spin up the number of clients essentially that we can onboard, that we can serve and that we can support as that comes out of production and everything. And the way that we've kind of, the conclusion that we've come to is a sort of passive training program where essentially we do rounds of videos with that we would provide for free. Our big thing with the SCMS is basically being a free supported platform for anyone that's interested in the concept with more intensive lessons kind of locked behind that, that paywall, that being less of training, more of like a consultation sort of thing with more in-depth trainings available to those that choose to go that route. And so that's kind of how part of how we're trying to solve this question of scalability is allowing those that kind of want to DIY this whole thing to purchase basically lessons that will teach us how to do them if they're not getting what they want from the free ones and just trying to be as transparent in how we actually implement this process as possible. So that's kind of what the social currency metric system is and how we're kind of envisioning scalability at this point in time. Yeah, so for those of you not familiar with KS or for whom KS is a new thing, there's really two dimensions of scale that are being talked about. Dylan is talking about the social currency metric. And when social currency metric systems actually a very different way of thinking about scale than what some of the other folks like Shuram was talking about where he has hundreds of projects that he needs to kind of keep track of because there's high demand. That's one sort of category of scale. The social currency metric system is a way of, it's a very different than really every other chaos metric. And so far as most of the chaos metrics work to define a discrete kind of thing like issue comments and provide parameters that can be used to interpret those issue comments. So that any tool that implements the chaos metrics will give you consistent answers and parameters. And it becomes kind of tool agnostic in a very open source way. The social currency metric system in contrast is a very substantial kind of a metric that takes into account. I would say, Dylan correct me if you think this is too elaborate, but I would say probably constitutes 20, the weighting and aggregation of 20 to 25 distinct discrete metrics into a score of sorts to help understand the level of influence that different or the level of social currency that individuals in a particular ecosystem might have. Sorry, I started talking and forgot that I had muted myself. No, I'm sorry. Yeah, no, that's, we do have these, these five main metrics that we look at that we kind of have a 201 implementation that we were in the process of drawing up that breaks these down a little bit more. But some background on the SCMS is it's a result of I am by trade an anthropologist, venue by trade is a communication specialist and an anthropology student as well there. And so we were kind of coming at this from a rather different perspective both in terms of scalability and in terms of what the actual purpose of these metrics are than a lot of chaoses, the other metrics that they support and that they've helped to foster. We look at our five metrics first offer transparency, utility, consistency, merit and trust. And these seem like sentiments that would be more or less impossible to analyze. And they're definitely fairly difficult to do so, but by taking a look at qualitative sources from channels such as Facebook, GitHub comments, support emails and tickets and everything like that, you can get a bit better an idea of how people think, feel and are speaking about the organization that you're attached to. And that's, it definitely is a bit more upfront a little, yeah, I guess diagnostic if you will about the people that are working with the organizations that you're attached to. So is it trying to, so what's the ultimate goal, would you say of that perspective on scalability? So the ultimate goal is our goal at the start of this is to make the internet a better place, which is fairly ambitious, but it takes someone with just crazy enough to think that they can potentially change things around to make things to try and hit that ambition. That's kind of why we're all sitting here today with the various foundations that have coalesced into this very comprehensive organization and subsidiaries. But what our eventual end goal is, is to give businesses and organizations ways to quantify how they can be better serving the organizations surrounding their brand. And that's kind of the end goal here and what scalability for us looks like is essentially being able to take on more organizations. And while ideally we would like to give everyone one-on-one attention and all of that, and that might be kind of a scalability in terms of improving from just Venian myself, that's kind of still part of what we're trying to ask ourselves is what does this, is this a scaling of our company or our technology both? We're still, I wish I had a clearer answer than a shred of the shoulders for you, but we're still at the point where we've been very pedal to the metal with getting everything set up that we don't have as good of an answer for you there as well. So I'm just gonna share with people. So scalability from the perspective of this social currency metric system is really, I think kind of a horizontal scalability problem where there's a lot of different things. And I'm just sharing the proposed metric here so people get an idea and I'll also share it in the chat window. Oops, hang on.  And that's the actual currently proposed social currency system. And you can see that it's just, there's a lot of different data gathered here. It's a fairly complex concept. Would you agree, Dylan? It's definitely very different. I would, it's something that's seen academic use for, especially in the field that Vinny and I met in anthropology for 40, 50 years, but it's definitely a new form of data analysis for kind of more privatized organizations, which thankfully means that it's very different. That doesn't necessarily mean it's unapproachable for those that don't have a background. Right, right. And so, and I think that it's very well articulated here. I think when it comes to implementation, it's more complex, for example, than when you think about scalability, one of the first metrics, the activity metrics that we've all looked at, or many people have looked at historically, might be something like code changes, which is a very fundamental metric that we did a lot of work at the very beginning to define with some clarity in the evolution working group. And it's a very straightforward definition and lasts about a page and really is just about this fundamental idea of volume of coding activity. And I think when I contrast it to what I would call a metric that's trying to scale across a number of things and aggregate and weight and measure them collectively as this concept of social currency. Do you agree with that, Dylan? That that's what it's trying to do? I had a little bit of a disruption in connection there, part of the way through it, but it sounded, from everything that I caught, it sounded pretty accurate. I'm sorry. So yeah, it's a no problem. So it's a multi-dimensional perspective as opposed to a discrete metric. It's talking about standardizing, collecting, analyzing communication traces, which broadly construed could be just about every other metric that is a communication trace. So commit may not be considered a communication trace, for example, but an issue or a comment or a comment on a pull request or a comment on an issue are all types of communication traces. And measuring all of these things could in and of themselves be a metric. So each of these could be in the historical work of chaos over the last three plus years. Each of those individual things that are part of workgroup, the social currency metric system is like a discrete metric. I suppose I would say, and I'll stop sharing my screen for a minute. I've shared the links if you wanna browse around. With that in mind then, and when I think about scalability from the, so there's really two perspectives on scalability that we've heard about so far. One is a complex metric, like social currency. The objective of social currency again is to what level is it trying to understand like where does it help understand, where does it help explain or define the health and sustainability of an online community? It's clearly where I'd call a scaled metric. It's extremely, got a lot of stuff in it. But what does it do to just tell us something about health and sustainability? That's not, I hate to put you on the spot. Just trying to get a little bit of discussion going around that. Not a problem. It's one of those things where the end goal, what it's supposed to do here is it's supposed to be included in like marketing conversations and such or conversations about interesting trends that are being seen in the qualitative data surrounding your company. Essentially say every Friday or bi-weekly, whatever your organization has a meeting where they talk about interesting trends that have been seen in someone's on interesting Facebook thread or a question that really sparked some good conversation and everything. Our goal is to capture the more meaningful conversations that people are having and to narrow in on the useful we call it sentiment, but the useful trends within those conversations in order to pull out what exactly you're doing well as an organization, what you as an organization could be doing better generally what people think and feel about what you do. Which is again, a pretty ambitious goal and something that's going to be fairly difficult to implement at first if you've never done something something like this, it's going to be difficult to kind of wrap your head around and everything, but that's at the end of the day, what it's supposed to do is give you an idea about the sentiment of those that are participating in these branded communities. Shreya and Celine asked a few really interesting questions that folks can see and I'm wondering if either of them want to elaborate or if we should provide an opportunity. Do we have metrics for inclusive language watching for master blacklist, et cetera? And this is a question by Tim at the Octo project at the Intel. One of the things that the chaos project is just doing is a badging project, it's a badging project for all of, I'm just going to side, we'll come back to the other thing, but this is an important question. Diversity and inclusion and using inclusive language like for example GitHub just announced they're going to get rid of the word master in the names of repositories. And so diversity and inclusion badging is a project that's part of chaos supported in part by the Sloan Foundation in Mozilla where the diversity and inclusion working group has defined a set of characteristics that I can't remember. The exact repo for that. But they've defined a set of event-based and participation-based kinds of metrics and the diversity and inclusion metrics are really distinct from the metrics that exist in other metric working groups because they're difficult to measure from electronic trace data. And the badging program is intended to follow the model of the open source, the Journal of Open Source software where there is peer review on every single awarding of a badge. So in some cases, in a lot of cases you can get a badge put onto your project simply by asking, filling out a questionnaire or making sure that certain files exist. In the case of the badging program we're putting together for diversity and inclusion those questionnaires, but there'll also be a peer review of the application for the badge by other members of the community. And the badge will have sort of a lifetime that I don't think has been fully determined yet somewhere between one and three years so that the use of inclusive language, I mean, use of inclusive language watching for words like master and blacklist are certainly part of the discussion in the diversity and inclusion working group. And I encourage you to look into that working group and ask about the badging program there. Now jumping back to some of the concerns. So communities suffer from tribalism at least out in the Linux enthusiast space without completely understanding everything I've read. How do I deal with weaponized campaigns against a project so the choice they want is more trusted? And then also wondering about tribalism and measuring that. Are there tools for visualizing networks within a community? Clashes can be easy to see in some visualizations. So I'd like to, a lot of, so from a birds of a feather perspective I would invite Shiram or Selene to talk a little bit more about those questions. I am able to talk about them for a while, if you like, but I think we have about 10 minutes left in this session. So I'd like to have either Shiram or Selene have an opportunity to perhaps elaborate just a little further on what you mean. And if you don't want to, it's okay.  Okay, so hand raise. Okay, Shiram. Okay, so the desktop space is not exactly logical in terms of how they support. Support is like one would prefer one or the other, but there are people out there who are looking for weakness and will exploit anything to show that, yeah, you got the look of surprise on your face. I live in a world of good intention. So a little bit more about that. So when I do engagement, I'm usually in forums or like Reddit or like on our Linux or if I'm on or we're moderating r slash r cano, since I represent cano, I don't have a KDE representative here, but they will probably about to say that's similar, similar experience. Or in Twitter, there are forces always looking to somehow, I try to get a good word for this, but they're looking to knock us off, right? Because we're generally, at least for cano, we're generally the most installed, the default choice. And so there are a lot of tribal, the sort of tribalism comes in is when there's something wrong, right? They will augment that wrongness to be able to say, this is not the right project. They're trying to influence what should be the default because the project is changing things, right? Changing culture or things like that, whether we put, say something about Black Lives Matter, that was four days of dealing with incredible amounts of negativity in the sense of it. So the sense of the project, I mean, I just want to understand that the project itself is in those cases where the project takes a sort of a view toward diversity and inclusion. Exactly. Are these are the moments where people seek to weaponize that perspective and actually reduce the, try to subvert that project or try to replace that project with a competing one, but it's a, you view that as an opportunistic kind of attack? It is an opportunistic kind of attack. And it's not related to just, our stance on diversity. It could be as simple as a perception and that money was squandered, like a foundation money was financial thing. It doesn't matter if something can be used, it can be politicized. So there's a lot of politicization that happens out in the space because- What is the forum where this politicization takes place and Micah's echoing your sentiments about toxicity? And I think we could all have a beer together about that, but where do you see this actually? Where does it happen? Where does it happen? It happens usually on social media of some sort. I see it a lot on Reddit and there's no rhyme or reason because I mean, yeah, it's Reddit is a very toxic place, but it's also a place that we've actually, we looked at our numbers and we've been increasing, like at least in our own forums who triple more than tripled our subscribers. So, but that and Twitter, I can't make a post by anything related to diversity without having to deal with multiple days of issues with comments and toxicity.  So I'm just gonna, we have, if I understand correctly, three minutes left. I would like anyone that has a specific link that they're willing to share where toxicity takes place in their ecosystem or an ecosystem they're aware of that you don't need to have to call your own out. I think the caste community, we have not discussed this issue so substantively as an obstacle to participation. So this kind of tribal aggression behavior isn't something that frankly, we've looked at it all. Earlier in my own career looking at open source, I did a lot of analysis of networks to under, network analysis and waiting of networks based on communicate both work and talk. And in that work, certainly we could identify the core periphery called extra periphery, contributory. And we can also do certain things like, maybe you should turn off your, yeah, there you go. So there's a lot of network analytic techniques that we can use that I've used in the past to identify types of leadership, types of participation based on the controlled world of how people do things in open source. I'm really interested in, like I heard Reddit, I would love to know about, and I heard Twitter, I'd love to know about subreddits or Twitters or Twitter hashtags or Twitter accounts where this kind of toxic tribalism takes place because I think that combined with analysis of the networks, which we have one Google summer code student focused on this summer could be really helpful for scaling questions. It sounds like scaling is, in this case, once you've scaled, then, and you have a voice and credibility, people have a desire, you become a point of attention. And when you take a stand on something with a social concern, that can cause you problems. It looks like there is a keep the conversation going. Please visit the number two track community leadership OSPO to do on our Slack workspace after the session ends. So this session is recorded. I think the Slack suggestion is a great one with 12 official minutes left, as I understand it, and Megan correct me if I'm wrong, but I believe that we are supposed to end at 510 on this schedule. It would be great if people would be willing to share with us. I will provide my own email address if you don't wanna share something, oops, and I can't even, apparently I'm not able to type my own information or the information of my project. So join that Slack channel, and I think the app ecosystem is interesting. I think some of these issues that we've identified are interesting, and I appreciate this birds with feather session went a completely different direction than I expected. I'm glad that it did. Thank you all so much for participating. And I look forward to connecting with you each in the future. Megan, am I right that this is the, I haven't done it, 510? I'm just trying not to, I don't wanna, the invite that I got was, it was originally scheduled for a bit longer, but I think we ended up at a little, oh yes, a little, correct, but you can go over a little, oh, I can't go over a little. So with communities, only a few hundred people, badly behaving people can be dealt with individually. Thousands more though, it's very difficult. And I think, you know, there's, I don't wanna call out any communities, but certainly, I guess there, I think most of the open source world is aware of some of the communication challenges that occurred in the Node community. Number of years ago, certainly some of the kernel community has had toxicity called out, but it sounds like from what some of you are saying that there are communities that have, that kind of get attacked in a very tribal way. And this is a phenomenon I'm just very interested in knowing more about. And is that Slack channel the best place for the chaos community to kind of look for? People to talk to about that particular part of open source. Does anyone know? You can type it in chat. Okay, and Sherom says, one thing about currency normalizing is about what this kind of issue is about. So a little bit more into that social currency metric myself, but thank you all. I think there have been some really good suggestions in chat and I'm very interested in what Megan shared for the Slack community. I'll just repeat that right here at the bottom. And invite you to participate in the chaos mailing list at chaos.community.slash-participate. I'm curious if I spelled that right. Yeah, I did. So we have a number of regular meetings, mailing lists. Encourage you all to participate and thank you for participating in this session. Again, if you wanna chat outside the session and get started with chaos and you don't wanna do it publicly, there's my info and I appreciate all of your time. It was great to have a discussion on what I expected and talk with you all soon.


So I will present you some work we did in the use of deep learning tools in the context of the resolution of inverse problems. It's a work that was done in collaboration with my colleague in CVN, Jean-Christophe Pesquet, a former PhD student, Marie-Caroline Corbino, and also in the context of collaboration with a researcher from the University of Modena in Italy, Carla Bertocchi and Marco Prato. So the applicative motivation is indeed the resolution of inverse problems that can arise in image or signal processing. When we will observe some data, why is that all degraded version of an image of interest X-bars that we would like to retrieve? The model that is considered quite standard model is that we have a linear observation model that could be some blur operator or projection operator. And the model is noise perturbation. You can imagine some additive Gaussian noise, for example. Quite efficient and popular strategy to solve such kind of problem is to resort to a variational method where you would define the solution as the solution of an optimization problem. And you will minimize the sum of so-called data fidelity term that takes into account your model, HX, and that measures the discrepancy between this model and your observed data. And because in many applications, the problem, the inverse problem is said to be ill-posed. That is, we would have some, because of the presence of noise or the structure of matrix H, we need to add some regularization function in order to have better quality and more stable solution. And the idea of the regularization function, as you may know, is to incorporate some prior knowledge about the sort image. And here, lambda corresponds to a positive regularization factor. So the advantage of such method is that it's quite easy to incorporate this prior knowledge. Just playing with the regularization function, you can also enforce some constraints using specific regularization functions such as an indicator function. You solve your problem using theoretically-sundead optimization tools. So clearly, your method is grounded on clear mathematical concepts such as the convergence analysis of your optimization method. The difficulty is that for most interesting choices for F and the regularization function, you do not have a closed-form solution. And so for sure, you will need an iterative algorithm, which can be high in terms, for example, of complexity. And another kind of drawback is that at the end of the day, you build this objective function, but it may not reflect the final goal. For example, in image restoration, this function does not really contain any explicit information about the final quality of the image. Such as the signal-to-noise ratio on this image or maybe some visual perception metric on the final image. And the last point, which is a well-known drawback for the users of such kind of techniques, is the setting of this famous regularization parameter. Here, I consider the case when we have only one parameter to tune. But it can happen when you have more sophisticated regularization strategies, that you have more and more regularization terms and more and more factors to set, and it can be quite painful and time-consuming. So in contrast, we have seen recently the usage of deep learning methods, especially in the context of inverse problems that can lead to very promising results. So deep learning methods, as it was said in the previous talk, are generic methods actually for approximating complex nonlinear functions. In such methods, especially in what is called supervised deep learning methods, it's natural to incorporate some knowledge about the solution and it's done through what is called the training phase, because you will show to your network, to your method some existing results that can come from big database and it's true that in image processing, we have access to more and more and larger and larger database. So it can be very interesting to take advantage of the existence of this database. The difficulty is that in the context of inverse problems, it's quite difficult in deep learning methods to account for a physical model, especially a complex physical model. So as soon as we escape for the case when H is identity, that is a denoising problem, when you go to complex linear operator H, it's not so trivial to incorporate this into the deep learning framework. And the last point that is a typical critique of deep learning strategy is that it can be quite black box and sometimes a bit empirical in the way you construct the architecture. Basically you construct it to have at the end the best results in terms of a metric, but the architecture itself, it can be quite difficult to interpret why is it working or not and it can be difficult. It can be a problem in certain types of applications. So what we would like to do is to adopt this recent framework which is called the unfolding of optimization algorithm that combines the benefits of both variational methods and deep learning methods. A bit more precisely, what we will propose is to make the link between neural network architecture, so especially a fit world neural network, a specific but quite common architecture of neural network and a well-known iterative optimization method that relies on the proximity operator that was introduced by Mo. Let me give an example before starting the core of my presentation. Let's have a look on the first basic optimization problem, the least square problem. Imagine you want to solve your inverse problem, minimizing this least square function, quite basic formulation. You have your least square and you introduce some constraints that X should belong to a close convex subset of Rn that you can imagine based on some prior knowledge on your image. Typical strategy to solve such problem is to use the projected gradient algorithm where Xk plus 1 is defined as follows. So you have a gradient step on the least square and a projection onto C and here gamma k would be the step size in your projected gradient algorithm. So let's have a look more in detail in this iteration. What we can observe is that we have this projection and inside the projection what we have are actually linear operations. Then I choose or propose to introduce this operator Wk. What is very interesting is that actually one iteration of the projected gradient algorithm is actually very related to what can happen in one layer of a neural network. Why? Because we see that we will have this linear term, so linear weight Wk times Xk plus something that does not depend on Xk that is sometimes called the bias term of a neural network layer and then we have as the output of one iteration before the output we have the projection operation that would act as an activation function in a neural network and actually iterating this projected gradient algorithm is equivalent to pass through this neural network. Here I set the number of iteration constant equal to large k and this is just another way to reformulate the first large k iteration of the projected gradient algorithm. This is very interesting because we see that there is a clear mapping between a certain family of neural network architecture and optimization methods. Let's go one step further in this analysis. Actually what we will be interested in is this generic architecture that is called fit forward neural network. The idea would be to consider that each layer will be a consecutive linear operation with weight matrix Wk, a bias Bk and compose with an activation operator Rk. The output of your neural network would be the product of large k iterations of this specific operation. Just a remark. This is quite generic in the world of neural network architecture and of course here you see that you have some linear operation. Famous operation in a neural network is to use convolutive neural network. What I would like to emphasize is that here you can choose Wk so that Wkx actually models convolution. Just a specific choice for the linear operator Wk. Now we are interested in this Rk. Let's have a look in this Rk. In my simple example of projected gradient, Rk corresponds to the projection. In mathematics there has been a general notion generalizing the projection which is called the proximity operator. So it's defined as followed. It's defined for proper lower semi-continuous convex functions. The proximity operator of such function g at a given point x is defined in a unique manner as a minimizer of this function plus the quadratic distance to the point x where it is calculated. When g is the indicator function of a set C, that is a function that you would choose if you want to impose some of these constraints C, actually this proximity operator is equal to the standard projection onto the set C. So just another way to say it, the projected gradient algorithm is just a special case of what is called the proximal gradient algorithm that would alternate between gradient step and proximity step in the case when the proximity is done on the specific indicator function. Proximal gradient algorithm is also known as for backward algorithm and in special cases it has other names. So for example, we have the famous iterative of stress shielding algorithm, which is also a special case of forward-backward algorithm. So the question is that if I come back to my architecture. So before, remember, RK was my projection. Now I would like to replace this RK by the proximity operator. Can we make the link between proximity operator and the activation functions that are used in the literature of neural network? And actually what can be said is that most of the activation operators that are used in a standard neural network architecture are actually proximity operators of certain functions. So again, this is a very interesting link between these iterative optimization methods and the neural network architecture. So let me give you some examples. You may have heard about the ReLU activation function. The ReLU activation function is nothing else than the projection onto the set zero plus infinity. So as I said, projection is a particular case of proximity operator. You have some more sophisticated activation functions. So for example, the rectified linear unit, the parametric one, when you add a parameter alpha here, is also a proximity operator for a specific function. I listed here other examples. So the sigmoid activation function very often used. For example, in classification, binary classification, you have other examples like the aliot activation function. And it's very interesting to see that for many, many activation functions. As I said, you can find this equivalence like this activation is a process of a certain function. So here I give the example of the softmax. So this being said, what we would like to do is to take advantage of this very nice observation that there are link between iterative proximal methods and architecture neural network to try to develop some kind of hybrid method combining both words. And what we hope is that it benefits from both words and that we have a better result for the resolution of our inverse problem. So this is the main motivation for all this project. Another example. So it's a squashing function in CapsNet. So now let me go a bit more precisely on the problem. So I will focus on a specific class of variational formulation that I would like to use. Of course, I don't want to be restricted to project gradient. I would like to make something a bit more sophisticated. And so let me start with the optimization problem we consider in order to solve our inverse problem to retrieve x from the observation one. So this is a formulation we consider. We have our data-affiliated term f, hx, y. A regularization function r that is assumed in this particular work to be twice differentiable. And we also incorporate some constraints on our solution. The constraints are as follows. That is that they are defined as a list of inequalities that is x belongs to C if it satisfies a list of ci of x positive. Where all the minus ci or convex function in order to have that the set C is convex. We also assume that the interior of this set is non-empty so as to have a well-defined problem and to ensure the existence of a solution to our initial problem. You may wonder why do we have this strong assumption of twice differentiable because at the end of the day we want to construct a neural network architecture and we would like to benefit from the nice auto differentiation and stochastic gradient design tools for training our neural network and that is why we impose a twice differentiability on this particular problem. A way in optimization to account for constraints in optimization problems is to make use of an interior point strategy. So the idea of interior point strategy is to replace the difficult constraint optimization problem by a second of unconstrained optimization problem as the constraint are imposed in a more implicit manner through the introduction of what is called a barrier, logarithmic barrier in the objective function. So basically you would add this function b in your cost function and you make the constraint disappear. So what is the shape of this logarithmic barrier? It's minus the sum of the log of the ci of x. So more graphically what does it mean? So we start with our problem. So imagine a very simple example. In blue I have my function f plus r in the scalar phase. So sparse corresponds to my constraint. So in that example my constraint will be to say in this interval what I obtain when I construct my augmented problem with a barrier. So I add my barrier and by construction my cost function will look like this. So it means that we add because of the logarithmic terms a barrier that would avoid the minimizer of this augmented function to be outside the constraint set. Here mu corresponds, it's called the barrier parameter. It's strictly positive. And what is done in interior point method is that you will replace p0 by the seconds of sub-problems p mu g, mu j parameterized by mu j and you will solve approximately each sub-problem for a second of mu j that goes to zero. So there exist many analyses. Actually it's not a recent optimization method. It's well known in the world of constraint optimization. And you can show some convergence and even in some particular implementation the super linear convergence of such strategy depending on the, for example, on the speed you decrease mu j and on the properties of your function. What is very nice is that each iterates of this interior point algorithm are feasible. What I mean by feasible is that they will all belong to the, they will all satisfy strictly the list of constraints, CI, so which can be, again, very interesting in specific problems. The main difficulty is that the most famous interior point solvers are based on Newton-like techniques and typically they will need an inversion of an n-by-n matrix at each step. So here we will focus on a bit more, like improve the version of the standard logarithmic barrier algorithm that incorporates a proximal step. So the first strategy combining prox and interior point was proposed in these papers. Basically what the algorithm was doing is that at each iteration we needed to compute the proximity operator of all the functions, the augmented function f plus lambda r plus mu b and it defines some algorithm with a nice convergence property, but the problem is that at each iteration you need to solve some problems that can be quite difficult and even as difficult as solving the initial, the initial problem. So this is interesting for the theoretical analysis but not a very practical algorithm. So here what we propose instead is to take advantage of the fact that f plus r are assumed to be differentiable and what we will do is we will adopt this forward-backward, this proximal gradient algorithm and what it means is that at each iteration we will perform a gradient step on all that fidelity plus regularization term with a given step size for the gradient and we will compute the proximity operator for the barrier function. This is the generic scheme for our algorithm. Of course we need to have the second mu k that is evolving and decreasing to zero and some specific assumption on the step size gamma k. The nice advantage is that we see that now at each iteration we need just to compute the proximity operator for the barrier function. We showed in our work that actually for a large set of examples for the constraint we can actually express the proximity operator for the barrier associated to this constraint and also the Jacobian matrix for this proximity operator which will be a key ingredient when we will do auto-differentiation in our neural network. Here I give the result for the affine constraint for the hyper slab constraint that are defined as follows. Here again we have a closed form and for the bound constraint so I did not write the expression but this is in our paper and this is how the proximity operator looks like. So it's quite similar here to a thresholding operation with a different shape depending on the value for the step size gamma and the barrier parameter view. And the last example which is a bounded L2 norm constraint so we see that we can also encompass non-linear constraints. So now we have our algorithm we are happy with this algorithm it's quite efficient but we faced a difficulty is the setting for the step size and the barrier parameter and actually clearly it was quite hard to find a good compromise to make it to reach a good result and it was really problem dependent and again we had this famous problem of how to set the regularization parameter to solve our inverse problem in order to have the best image quality possible. So what we propose is to learn actually those parameters so mu, gamma and lambda along the iterations that we will fix large k iteration and we will see say that actually one iteration of our algorithm actually corresponds to one layer of a neural network the activation function and these different terms and we will use a training phase in order to learn the best choices for given image restoration tasks for gamma k, lambda k and mu k which could be actually different at each iterations so this is how it look like so we transform our iterative proximal algorithm into a neural network so this is really the idea of this unfolding of optimization algorithms that I tried to explain you a bit in the introduction with a projected gradient this is what is happening for our proximal interior point algorithm each layer corresponds to three different possibly neural network for now I put some boxes that will help us to learn gamma, lambda and mu and then once we have learned those gamma, lambda and mu we put them as an input of one iteration of our algorithm and then we can produce an output and then we iterate over those different steps let me be a bit more precise about the architecture we proposed so gamma k we call that it was a step size in our algorithm a step size should be positive we propose to use something quite simple the step size does not have much reason to depends on xk on the data we set something very simple just the output of a soft plus activation function and with input linear parameter ak that will be learned during the training this is for gamma this is what will be in the box here for the barrier parameter actually what we observe is that it is much better to make it depend on the current iterate xk which is quite intuitive in terms of optimization and we propose this specific network architecture which is made of different layers and finishing by fully connected layer and this soft plus again important to impose the positivity of the barrier parameter just want to satisfy the basic constraint in order to have some valid optimization method and for the last parameter the regularization parameter so with the differencing and what we propose actually is that this lambda it's good to make it depend on xk but actually also to incorporate our knowledge as people from the world of inverse problems and signal processing and actually to include some knowledge about the image statistics and the noise level as we will know that this may influence the setting of the regularization parameters so not to have something completely black box and as I said box A corresponds to one iteration of our algorithm when mu k gamma k and lambda k have been learned by the three previous steps at the end we add post-processing layers that is a very common in the literature of unfolding optimization methods in order to remove smaller artifacts and again this layer can be learned from end to end from the training database and it can be an application dependent actually. So thanks to the form of this algorithm and the fact that we managed to express the Jacobian for the proximity operator we managed to do the training co-initially with stochastic gradient descent and back propagation so as I said we can actually have an explicit form for the gradient of the box A thanks to what we obtained for proximity operators for the three other boxes we use the standard auto differentiation tools because those are quite standard architecture from neural network world. So last point before showing the numerical results something very important when you use neural network and quite often critics is the lack of robustness of some neural network architecture. There have been some very famous examples showing that when you modify the slightly some pixels in an image the neural network classifier could classify a cat into a dog or etc without any visible perturbation actually in the input. This is a major issue because if you want to use your architecture to do medical image processing automatic driving for sure you need theoretical guarantees about the robustness of your network. So what we use is that we use a recent work about the stability of fixed point structure that was made in this work of combat and pesquet and it allows us to study the stability of the network we proposed. So let me briefly present you the results of first something very important is that the stability of some iterations is highly related to this concept of alpha average that is defined mathematically as showed and basically what we will need is to have alpha average operators in our pipeline. So I don't really have time to go too much into the mathematical details but you have all the definitions here but what is very nice is that when you show that your architecture can be is alpha average. This is what we would like to show. You will obtain these very nice properties that actually you can bound the output variation of your network as soon as the input of the neural network is bounded and this is exactly what you would like. You would like that if you do not have too many variations in the input of your network, the output is not completely exploding but you can actually control very precisely what will happen. So the output will be a T of X and T of Y and the input is X minus Y. So we would like to show that our architecture satisfies this mathematical property of alpha averageness. So let's go back to our neural network. So what I said is that it's very important to rephrase it as a fit word architecture. So composition with activation function, linearities, etc. The proposed architecture actually shares this structure so I can show you very easily on a specific case when the regularization is quadratic you can rewrite directly the gradient and you will obtain something linear and you clearly see that you obtain activation function and linear operation. So actually what it means is that setting the barrier function is just a special kind of activation function. So that means a lot of work, theoretical work regarding the stability of such network and we will inherit from those nice results. So this is what we did. So this is a general serum under some mathematical conditions on the weights that are involved in your fit forward network then you can show that this is alpha average. So let's keep a bit the mass. To Sanu with a take home message, the stability of fit forward neural network depends on its weight operator. So the matrix is WK. So let's have a look more in detail in our particular architecture so I will consider a slightly simpler problem quadratic regularization I make some assumption on the linear on the linear operators. I introduce some specific notation and basically what we managed to obtain is that under some conditions about the eigenvalues of those matrices we can show that our network is alpha average can express the value of alpha so we can really control the stability of what is happening and we can see that it basically depends on what is in our problem and under some conditions that can be satisfied in practice we have the stability of the network which is very reassuring in the application view point. I will finish with some numerical experiments we apply our architecture in the problem of image deblurring so it means that H corresponds to a convolution with a known blur we are not in the blind case we have some additive white Gaussian noise we do not assume that we know the standard deviation of the noise it's important because actually we will learn it some way we consider color images so like three channels images this is a variational formulation so quite standard we have some constraint on the range of the pixel intensities and we use smooth version of the total variation regularization because I remember you that we need to have quite different stability for our particular algorithm and here DH and DV corresponds to horizontal and vertical gradient in the image so it will incorporate some smoothness about the image so when you do neural networks maybe you know that you need to make many settings what we set is the number of layers equal to 40 different things this is the final choice we arrived for the regularization parameter as I told you we use our knowledge on signal processing we propose a way to an estimator of the noise level which is based on the looking at the wavelet coefficient of the blur and noisy image and this is the sigma art is combined by some soft plus operation so basically here what we would learn are BK and and CK so we have only two parameters and and XK here which is an input of this of this function so it's very nice because contrary to many existing neural network methods for deblurring we do not need to know the noise level and for the post processing we use something quite standard which is as follows this is what we consider so we train on those images we validate that is we make the fine tuning of the parameters on 100 images and we finally test on 200 images so those test images were not never seen by the neural network we try with different gauchan with different kernels gauchan motion uniform and different noise level to see the validity of the noise estimator too and also information about the training and what is very interesting is that we will train our network to maximize the structural similarity measure which really denotes what we would like to have as a nice visual quality of the image so like the final goal of the resolution of our image restoration problem so just for information we implemented this in Pytorch and the training text 3-4 3-4 base we compared with some state of the art method so deep learning base method and also what is quite interesting is to compare with the standard optimization base method if we just solve it with an optimization method for which we fine tune manually the regularization parameter so it's quite a good benchmark for the optimization method so this is what we obtain in terms of SSIM so what we can see is that it's basically most of the deep learning base method are better than the optimization base method and in those 5 data sets we have that our unfolded optimization, our unfolded learning algorithm can be the standard deep learning strategy here in terms of SSIM so we just finish with some visual examples you can see what you would obtain with solving the TV TV problem and this is what we obtain so we have less artifacts and also the image are a bit less blurry than what you obtain with standard black box deep learning strategies so in conclusion what we show is that actually you can build neural network architectures that are explainable by mixing some ideas from iterative optimization algorithms especially proximal gradient algorithm and neural network techniques here we focus on a specific family of optimization methods with our interior point based method so we show that actually we can express the prox of some barrier functions and their gradients and what I would like to say is that it's not the end of the work for people in optimization I think it was also clear in the clear of the talk that for sure we need better especially non convex optimization methods for example for the training of those neural network very difficult optimization problems and also what is clear in the stability analysis is that optimization concepts are not only useful to train neural network but also to analyze them because the stability analysis really requires quite sophisticated tools from optimization so they still work for optimizers ok so thank you very much maybe I could answer your questions so maybe I could start with one question so from what I understood your starting point was classical optimization approaches, variational approaches for inverse problems and you try to take advantage of these algorithms to design new neural networks and to have some kind of understanding of these neural networks with these rules and I would say that in the inverse problems community so you go one way from optimization to learning and I think people also could do the reverse way because you can think of for instance prior distribution which would be data driven and that could be inspired by for instance GAN, the narrative and reverse networks and so I was wondering if you and so with this kind of approach you could learn the prior distribution and so is it something that you have you know and for instance could you improve classical approaches to solve inverse problems by taking advantage of neural network approaches yes so for sure so there have been another another track of works that exist for example what is called ISTA net there have been many other methods in order to learn the goal is to learn the regularization function like to replace the standard regularization by a neural network that will perform as a denoiser then of course there are many interesting theoretical questions about at the end the convergence analysis of such black box regular there are some results very promising results it's also a bit related to this alpha average and fixed point series basically like some kind of contraction properties of your denoiser and in the context of unfolding it's true that in the present work we didn't learn anything very precise about the regularization just the regularization constant but now we are working on combining this with the learning of for example the linear operator which is in the total variation you could have something more like dictionary and learn the dictionary from the database so yes for sure this is interesting what is important to denote is that the computational cost becomes I mean with those neural networks increases a lot rapidly here we learn only three scalar parameters we already have training of several days so you can imagine that you need to pay attention to what you are learning and what is important in the what is shown in the literature when you learn regularization is to impose a lot of structure like convolutive structure not to explode in memory not to give so so much freedom to the network but yes for sure I think this is this is promising and this is maybe the way to again improve the resolution of this complex inverse problems thank you other questions yeah so the question is is unrolling only adapted when your model based algorithm needs to tune some extra regularization parameters so I would say no I think it's a bit related to your question Charles actually you could learn it's much more flexible than this you could learn something about like inside the regularization function it does not need to be only a scalar parameters I don't know if it's if it answers the question so of course yeah maybe the question is do I need to enroll all my optimization algorithm it's true that the idea of unrolling is twofold so the first thing is that you can train so you can use training and learn from a database so of course you need to have something to learn can be parameters but it can be the regularization and the second thing I would like to emphasize maybe it was not so clear here but it's clear in the experiments we did is that putting this algorithm this optimization methods in neural network framework also means implementing them in PyTorch benefiting from the GPU acceleration and what we saw is that even from the optimization viewpoint it allows to accelerate with factors up to 50 standard optimization methods so I think this is some things that is maybe not so known and that is very interesting for people in inverse problems just making the effort of going to PyTorch and using the GPUs thank you very much see you next time


Welcome to This Sister 2 Sister. It is going to be a great show. We have a question that goes like this. The end of the world as we know it. There's a song about that and also money, money, money. How do I help my adult child with money? Do I, do I not? And what if I need to break up with a friend?  Hello and welcome to Sister 2 Sister. We are so glad that you've joined us. We are five opinionated women and we bring the questions and problems of the world to you with answers from our heart, mostly from the Bible. So you send us questions all the time and we really respect that and admire and need those questions. But this one is very interesting. Hear what we have to say.  This is, this is for you. What is the one thing that is destroying the world as we know it? You know, as soon as I read this, I thought of James 1. The anger of man does not produce the righteousness of God.  Cain did not repent. His anger, his jealousy rose up to murder and they were so close to God and His will, yet they allowed their emotions to get the best of them. So it starts with an individual. It starts in the heart and mind. It's not just nations. It's not just groups of people, but you, a person, we individually need to take responsibility for that emotion anger. So anger is destroying the world as we know it. That's how I feel. Individually and personally, and it begins there. Well, I didn't have anything about emotions. I had, this is what destroying the world, cell phones. But we all have them. We all use them. But I'm serious. Cell phones and social media destroying the world. So you're getting rid of yours? Do you see mine? No, no, no. I'm the only one here without a cell phone because I'm technologically inept in the group. What do you think? I was thinking about, you know, we just came off a week where, you know, the state of the union address, which was a political thing or the Grammys, which is media music thing in the arts industry. And I thought, boy, I could just pick out about anything and say that's destroying the things that we're seeing, the things that we're listening to, the divisions politically. But then I thought what I think is really destroying our country is the lack of value for life. Damn. Unborn lives, other people's lives, people that are different than you's lives, the sexual exploitation of girls and boys' lives, the, you know, puberty blockers for a third grader, that's a lack of a value of life. And I think that if we don't value life, where do we go from there? I like that. What do you have, wisdom of flow? I was just, I was really wanting to hear what my quarry had to say first. Okay, we'll all go to quarry first. Okay. Quarry. Well, I took it back further from Cain and Abel, back to Adam and Eve. Oh. Because I was just trying to think, I'm like, what is it? And I think it's the same thing from the beginning of time. What is destroying the world today is the same thing that has just been destroying the world since the perfection of Garden of Eden and it's sin. Sin is what is destroying the world today. Now, sin looks different than it looked back then, but ultimately it's the same, you know? It just takes on a different form. And sin is what continues to change the world. Now, Satan knows that, so he exploits that and he uses tools that are neutral like the cell phone. This cell phone is not sinful. This cell phone is neither good nor bad. It's how it's used. And so Satan takes that as a tool and says, how can that be used? And that's when it becomes sinful and how it's used, so. Okay. Well, do you have anything to piggyback your sister? Yeah, off of all of my sisters, you know? And I was taking notes as they were talking, you know? Cory, of course, talked about sin and Roxy so eloquently addressed us about anger and being mad, and of course, I'll admit your name and went on my head. I'm sorry, I'm on Benadryl Zertec, y'all. I can't fake it, okay? That's Amy. Amy? I'm sorry. Just wait, a zoop? I'm like, can't fake it. And Amy needs some. I'm back, I'm back. She's back, okay. So, the lack of value of life.  Versus the truth.  Cause there will be a time that we are not gonna be here and the world will come to an end, one way or another. Whether it's as you know it, like I knew it from back in whatever year, somebody knew it back in the 20s, the way somebody knew it growing up in the 50s, 60s, 70s, go on, that's chronologically. But there is an eternity piece to this. And I'd rather spend my time focusing on being prepared for that. And I have hope and having hope in the Lord. So this question, here's another question, this is gonna be really good. How do, I'm not crazy about this one, but how do I gracefully accept a rebuke when I don't believe it's true? So I Googled the dictionary for rebuke and it says stern, sharp, disapproval, reprimand. I have none of those in my vocab. Corey, what do you say? Gracefully accept it, I would never rebuke you. Oh gosh. How do I or how should I? How should you? Okay, well. Okay. Yeah. Do you get rebuke? Oh my gosh. I mean, I try to put off vibes so that I don't get rebuke. Yeah, come on. But I, how I want to is get the behind me Satan. Okay. If I'm being honest, this is a weakness in me. Okay, I am not good at taking rebuke or criticism or any, and especially when I don't think that it's true, this is really hard for me to be gracious about it. Like, have you ever seen the gift with the guy who just blinks and he just like is like that, that is my response. I'm just like, okay. But in Stephen Covey, some, the seven habits of a highly, yeah, highly, Successful. Effective people. Number five is seek first to understand and then to be understood. And so I think that's the best way to handle it when someone is rebuking you and you don't think it's true. It's like, okay, let me try to understand where they're coming from with this. Then try to be understood. Cause I think so often we jump to, no, no, no, you don't understand me and the defensiveness. Let's understand where they're coming from first. Cause maybe they're hurt, maybe they're offended, maybe something has happened. So let's understand where they're coming from first. Then come to the understanding. I think where we always need to go is the wisdom of Proverbs when it, you know, we're looking at these things. So Proverbs 18 to fools find no pleasure in understanding but delight in airing their own opinions. Okay. So that, I mean, that's me. Like I'm always like, I need to get my opinion out there. I need to, you know, I need to air this. Then the opposite Proverbs 16, 24, gracious words are a honeycomb, sweet to the soul and healing to the bones. So let me use my gracious words. These are two Proverbs I need to remind myself. So who else has rebuked? I'm in a different place where I'm almost constantly receiving corrections and I'm continually giving corrections. It's what we have to do as a growing church, growing leadership. If we don't correct and change, it could hurt a lot of people, including myself. If I don't correct the change. So I have people I'm always asking, I'm like an open book. Please tell me what I need to do to change. So what we, what we have taught our staff and it's really hard is that if we're all having a discussion like this, right? Kind of picture yourself with your hands, palms up under the table, kind of like I'm listening and I'm open and I'm hearing.  That's so easy to do. That's our flesh nature. I mean, we're just like, don't ever think about me like, don't ever correct. But what if reproofs and corrections were a way of life? Just like we love our kids. We correct and not to hurt them, but to help them. Maybe you foresee something or that's something that's really hindering their life. So anyway, that's my quick take. I'm blessed by both perspectives because they're needed. And you know, my key word is always balance. And I really believe that the whole aim of rebuke is not to shame or embarrass or to undermine. In 1st Corinthians 16 and 14, reminds us that everything should be done in love. And he talks about especially rebuke. And rebuke is a necessary part of our growth. He who the father loves, he chasens, right? And truth heals. So I think it is the way that, it's just like when we talk about criticism, we usually associate it with something negative, being condescending, being, but there's nothing, but we critique stuff every day. You critiqued your outfit before you came out. We critiqued our makeup. We critiqued, you know? So it doesn't have to be something negative. It can always be for the better, but it's how the person who is receiving it, hopefully it has been done in such love, the person giving it, that they are able to receive it.  I didn't, it's not like I did some cartwheels and oh, thank you, I'm running around. You know what I mean? There are times that I've heard it and went, I don't know about that or she got her own issues or he needs to deal with this. Only to go back into my quiet time with the Lord and have him put his finger on it. And sometimes it wasn't always right away, Roxy. It might have been months or even years later. It's like, I guess I do have an issue with that. Well, I just, I'm gonna move on to the next question, but I just wanna say one thing. Do not rebuke someone unless you're asked for your opinion. I'm just saying. Okay, this question is really good. I'm not about rebuking. Can you tell? Okay, this question is so good. I wanted to get to this for you. Someone wrote this, I may have told my adult son we could help him with his finances, but my husband is against it. Now, I'm really in trouble. Someone's gonna be mad. What should I do? This is called money, money, money. What should I do? I was thinking, I don't help my adult daughter with money, so I'm not sure I would help my adult son either. This totally depends on the situation. There are situations and times and seasons where an adult child will need your help. I mean, probably, I remember needing help at one point in my life at probably every adult child. Now, how long is that commitment? How much is that commitment? How dependent is that commitment upon me and am I hurting them or helping them? Because the goal is that you have independent children that contribute back to society and life. So, I made some hard decisions this year with Christmas and birthdays, and my kids are not happy about it, and I could care less. I'm thinking right now about retirement, I'm thinking about our future, and they're like, mom, our life is over. Jude is like, well, what's my budget for my birthday? I'm like, your budget for your birthday? Let me just tell you how much I've changed, and he was like, oh my gosh, mom. It's just, they have these huge expectations. And I said, you believe God for that, you on your own, develop your own faith for that. I want to go to you because you have five children. Yes, yes. How do you feel about this? You know, what hit me about this question is your husband is against it, and somebody's going to be mad at me.   You know, the scripture says, I think it's James, that wisdom is reasonable, easy to be entreated, full of mercy and good works. You three got to come together and learn how to resolve issues. Not just money, but issues as well. And I would say yes, as Amy said, everybody needs help at some time. That's why you're called a parent. But do it in unison, you know, try to do it in unison, make your arguments. I'm glad she didn't just count out to her husband's like, oh, you're against it, so forget it. You know, she has to make her plea and her reasons why, and not worry about who is going to be mad at her.  I mean, there are so many issues here. We could talk about this for an entire show. Okay, well, this needs to be a discussion with the husband, okay, and they need to have a unified front. And I am sorry, but financially, helping your child does not mean supporting them financially. Helping them means not enabling them in that way and saying, yeah, here's my wallet, open it up to you. If you need a place to live, you have a roof over your head, you have food on the table beyond that, you need to figure this out. Like that is helping your child by cutting them off and saying, no, the financial flow stops here. I'm sorry, but that is not helping your child by opening your wallet to them. Well, to you that wrote to us, we appreciate you writing and just remember this, it is, and I'm looking stuff up in my scripture too. It's Matthew 12, 25, a house divided will fall. So get together and you stay right there and we'll be together to bring you more sister to sister. The here we go. This is really crazy. Whoever wrote this, how do I break up with a friend? What? Cory, what does that even mean? Okay, I've actually heard this before.  Where something happens and there's this need where you feel like you have to like discuss like where there's this ending. I don't agree with that. I do not think you break up in a friendship. There are seasons and cycles to friendships. Friendships are not a covenant relationship like a marriage. And I think sometimes people put those friendships on a pedestal even higher than their marriages. And I think that's a problem. I really do. And so I don't, I don't agree with the whole breaking up and sitting down and saying, oh, we're breaking this off. There's seasons and cycles. And you know what? You never know if that's gonna come back around where you're gonna have a different season and you're gonna have a different relationship. And yeah, you might not be spending as much time together now, but maybe later you will. I just don't agree with the whole breaking up thing. Do I think you should communicate if there's an issue? Yes, breaking up, no. Well, do you have a scripture for it? Well, I kind of do in this way. Is the person a bad influence in a weak area? Cause the scripture says stay away because you will be drawn to that weak area also. So I think you need to speak the truth and love. If that friend is going down a path that's gonna be a bad influence, you need to speak the truth and love and tell them, look, you're not a good influence right now, but I will always love you. I will always be there, but I can't spend that time. Or is iron sharpening iron? Amen, amen. Is that person saying things to you, as you said before, that you don't wanna hear, but you need to hear because the word says, life giving reproof, rebuke, brings life, brings wisdom. Is that friend doing something that causes you alarm? But it's good for you, it's bringing life into you. But do you say that to them?  I just feel like, I don't know. To me, that's like, but if you love your friend, you're gonna rescue them from doing something wrong if they're truly your friend. I mean, I guess it depends on the relationship. We gotta have to keep in mind.  I'm a little more leaning towards Roxy because that's my personality. I'm gonna talk with you. We're gonna have that conversation because I take friendship very intimately. See, there's a difference between acquaintance and friendship and to the point of covenant, just humbly suggest the studying out of the scripture because there are covenant relationships outside of marriage, such as Jonathan and David to give you one. So it's identifying what this friendship or relationship is. Some are for a season, as we hear them say. Some are for a lifetime. One of the things one of my pastors taught me is healthy things grow, growing things change. What I've found in my true friends and think about it, those people that you have become, they might, it might be somebody from elementary school. They went their path, you went yours, but there's still that connection. I still, I have one friend that comes to mind right now and going to school together, we both got married, went off and had our children, kind of lost contact, but yet still any momentous occasion in my life, anything, she's there, you know. And so that to me is just, that's a true friend. Now I have some people who are just acquaintances and yeah, they can kind of fall more in that category. But I think any relationship that is toxic, you need to take a look at. Can I ask you something while I have your attention? Okay, because I wanna get to this question. Okay, good. I wanna get to this last question because. Oh, by the way, this is Kathy. Thank you. This is a really important question so I didn't want to gloss over it and I do want your wisdom on it. Does God wait for us to pray to make things happen and move in our lives? This is a really good question. Oh, but I am so grateful we're having this question because you know, the word of God says, what about his house? My house shall be called what? A house of prophecy. A house of worship. A house, he says, a house of prayer. But what is the thing that we do the least in church? Think about your Sunday service. How much time is really given to prayer? So why did I start with that? Because when you think about how prayer works and why God set it up, it is to legislate the heavens. What did he teach us in the Lord's Prayer? That what's in heaven would be manifested, we're here on earth. How does it get manifested? I must take dominion, all dominion in Genesis 21, I mean, Genesis 1, 26, 28. I have dominion. There's a way that God cannot, can he do it by way of his power and all of that? Absolutely. Will he do it by way of his character? He can't because he gave that to me. So what's in heaven? As a believer, I must legislate the heavens and bring it here on earth so that it can be manifested. You were talking about Daniel earlier. I can't wrap it up real fast. You were talking about Daniel earlier and I thought it was excellent because you talked about how Daniel prayed and the answer was released that day. So we're not talking about philosophies and what we think about stuff. What is the biblical answer to this question? Yes, I believe that when I work in conjunction with God, that brings to manifestation. Now timing is a whole another thing. That's right, that's right. Pastor, what do you tell your people? I do, to piggyback off of you, I heard a quote and it's from some famous evangelist that God can do nothing except man pray. So there's something about ask and it will be given to you. Seek and you're gonna find, knock and it shall be opened. It's like, whosoever shall say into this mountain be thou removed. So there is a level of, if God was going to do whatever he wanted to do, the whole world would be saved, the whole church would be tithers and givers. Everyone would be healed. There's an element of we're his kingdom, kids on earth saying thy kingdom come, thy will be done. And we're bringing it from heaven to earth through our prayers, through our request and through our asking. So I would say be relentless with your prayers but not like in fear, but in faith. Believe God for that kid, for your finances, for your health and you just hang on like a dog would hang on to a bone and don't let go. Right and I know that Roxy probably has a scripture for us on this but I do too. So I'm gonna give it to you right now cause it's really, really important. And it's in Luke 18, one day Jesus told his disciples a story to illustrate their need for constant prayer and to show them they must keep praying until the answer comes. And it's the story of the lady that goes to the judge. Help me, help me, help me. The judge said, ah, just do whatever she wants. So people say you don't need a laundry list of prayers but God needs to hear from your heart on a regular basis. And stay right there.  We can't stop talking. We all have answers on top of answers and opinions on top of opinions. I wish you were sitting at the table and we could hear your thoughts on some of these questions. We always like to end sister to sister with the scripture. Thank the Lord. Today it is from Hebrews 4.16. Let us then approach God's throne of grace with confidence so that we may receive mercy and find grace to help us in our time of need. Listen, whether it's dealing with money and your kids and finances, maybe it's with friendships and do I break up or do I need boundaries or it's in a marriage or life? We need mercy, we need grace and he is our very present help in time of need. So if you need help today, if you need prayer today will you give us a call at<PHONE_NUMBER>. We're here for you. We'd love to pray and really hook up our faith and believe God with you. Oh, that was so good, Amy. And we have another scripture we end with all the time and it goes like this, as iron sharpens iron. So does the countenance of a man or a woman or a sister sharpen the other. You see family, these girls make me a better Kathy. See you next time.


People can be jealous for a wide number of reasons, most of which are not rational. In the same way, everyone will not like someone. There must always be someone jealous of another person even if that person is the most cheerful and kind person you have met. According to the power of positivity, nothing feels worse than achieving a goal or becoming successful in an endeavor and discovering that the people around you don't feel proud or positive about it, but rather, they feel jealous. A person's jealous feelings can cost them to act uncandly towards us, especially in the face of our own achievements or success. In today's video, I'm going to be sharing with you 10 signs that someone is secretly jealous of you. If you're new here, subscribe to our channel for more awesome videos such as this one. 1. Unnecessarily competitive Have you ever told your friend of an opportunity that you applied for and they suddenly applied to, even though they generally have no interest in your career area? The first thing you should know is that friends usually don't compete with each other and secondly that the person may be jealous of you. If you have won some accolades before and seem reluctant to congratulate you, be careful. They might sabotage you just to feel like they are not complete failures. It is actually natural to feel a bit jealous if your friend wins a scholarship and you can't go to college without student loans. But if it is your friend, you don't take it beyond that. Competition is good. It makes for good sportsmanship, but unnecessary competition is unhealthy. 2. Makes negative comments about your progress There are people you shouldn't tell when you are doing things. They never have anything encouraging or good to say. You may probably have some friends like that. Don't say anything to them. If you're writing an important exam, don't tell them. They will ask you whether it's worth it and why you can't do other things. We must all come to terms with the fact that some of our friends or at least the people we think are our friends do not have our best interests at heart. Do you have a friend who always discourages you from doing things you probably do or used to have? They do it because they are jealous. They wish they had the courage to push for the things they want the way you do. You deserve friends who will encourage and support you, not the ones that are jealous of your progress. You can find these types of friends by rolling with people who share your ideas. 3.  If you are sick, even for one day, you may realize that a lot of your colleagues may not be willing to tell you what was thought, share notes or anything like that. According to the power of positivity, no matter what you achieve or how hard you worked to do so, jealous people will always try to make it seem like it was a fluke or that you didn't work so hard as you did to achieve your success. The reasons that they come up with may be rude or condescending. People who are jealous of you will never like you so if you try to please them, you will waste your time. As long as you continue to have what they want and can't have, they will continue to hate you. People who are not this way and who admire you may approach you to mentor them. They may even try to become your friend so that you can teach them how to achieve the type of result that you do. 4. They are insecure around you Some people will naturally be insecure around you. Those types of people typically don't have dreams and ambitions. They are like the wind looking for where to go. If they feel that you are good at something and that you enjoy it, they may start doing it too. Jealous and insecure people will have a lot of questions and seldom ask any. If a person is insecure around you, they may try to flaunt something they are good at too to feel better. According to the power of positivity, someone who is jealous of you may be more inclined to flaunt their success more than it's really worth flaunting. They may be more inclined to flaunt their own achievements while you are celebrating your own. These are the type of people to get engaged at a wedding. Why would you write a paper too just because your friend published a paper? If you don't like writing papers to start with, be yourself and the world will love you for it. 5. They don't offer support Jealous friends already want to see you fail so what type of support will they offer? They will typically say some vague words of advice but inwardly be hoping you don't make it. Anyone who don't want you to succeed is not your friend. Make friends who will stand by you. Anyone who consistently never tries to support you even though you guys are friends is jealous of you. Are they overly interested in what you are doing? And yet offer no help to help you accomplish them. People who are jealous of you want you to fall short even though it will give them no direct benefit beyond the satisfaction that they are not alone in their mediocrity. 6. They never acknowledge your achievement People who are jealous of you don't acknowledge your accomplishments. They try to belittle it so that you don't attach more importance to it. Their goal is to feel more comfortable at your expense. 7. While you may be too humble to voice or enjoy your achievement, jealous people often try to make themselves feel better by drowning out your achievement by overemphasizing their own. This will happen even if their achievements are way smaller in comparison to us. If your friends with this type of people confront them, the person may not even be doing it consciously. Your friends need to know that you support and care about them. They need to know you are there to support them to actualize their own dreams too. If your best friend is a billionaire and you are still trying to gain financial freedom, it is no reason to start to spite him for being so comfortable. A better approach will be trying to learn how to do things differently from him. In the end, everybody wins. 7. They never offer meaningful advice Since jealous people don't want to add more achievements to your kitty, they sabotage your attempt purposely by giving you bad advice or they may just simply discourage you from even trying. Do you have friends like this? If you have friends like this, cut them off or tell them that you don't like being discouraged from pursuing ideas. They may not be happy with your progress but only because they don't feel so successful themselves. People need to have a sense of fulfillment and personal goals. If my goal is to work and consult and get a job with McKinsey, why should I be jealous of my friend who got a scholarship to do it for the degree at Harvard? People who do not really know what they want are usually people who come into this category. If you have your priorities straight, you will be happy for everyone and cherish your achievements even if they are not what other people are doing. 8. The lack of interest in things that benefit you Jealous friends may even stop talking about things that benefit you in one way or the other. Maybe they feel that if they don't talk about you, you will have fewer accomplishments and make them feel less depressed.  According to I Hate Intelligence, when someone is jealous, they have a strong need to prove their superiority. As a result, people who are prone to experiencing regular feats of envy or jealousy also tend to be overcompetitive. They may also see you making a mistake and not correct you. If your friend is jealous of you, you can tell from their deliberate silence when you are doing something that will hurt you once your friend becomes this way. 9. They get irritated when you share good news with them Have you ever won a lottery and realized your friend is struggling to be as excited as you are? Don't worry, it won't change your future or anything about you. Good friends rejoice when their friends win whether or not it directly benefits them. Jealous people are already insecure. They do everything they can to get some attention including sabotage you. When you just keep excelling, this will only make them more jealous. It is okay to feel you deserve more but it isn't healthy to wish other people should have less for you to have more. That's an oppressive way to think. 10. They enjoy embarrassing and making fun of you publicly Do you have friends who constantly make fun of you? They always seem to have the perfect joke about something you are not proud of. Watch out for them and avoid them. They are jealous of you. Imagine a 30-year-old whose proudest moment was the day she made a joke that made everyone laugh at her billionaire friend. This should not be measured by the point of how many people a person has hot. Jealous people do with a lot of insecurity. If you are jealous of your rich friend, don't make jokes about their appearance because it suddenly won't make you rich. Hang it out with them and getting tips from them just might.