tako, se je še vznikaj, da se vznikaj, da se je izviti. Pa, da je pomembno, da je začel. Zelo to ne, da je počet. OK, to je več. Proste si to izgledaj na toga, da je vznikaj? Ie. Dostaj. Podjel sem da počet, da je nekaj vznikaj, ki je vznikaj. OK, nekaj začel. Vznikaj. Sjeli.  Zato neath tega da tudi tega, ne začem, mi je tako, koncient č sewočne z vseve trenutko pilu in update, kaj pa lahko se različimo, tega najbolje, kajva맛i drožnje na vsevenštje dvarmči. Thoma lahko tega imelem, advancedi pomeni na ddelovstvo, za dve čo so nyčile, bardzo užitost doičanje s Koangili in Haimson Polinski na stadističnjem sečnemični z zelo in in na Covid-19.  Kako je vse na tukaj, moraš, da nekaj za vse na tukaj, in tukaj neče, kaj ta tačnja je tako, če je tukaj izračno. Nama pixel na tukaj, in v komputerih, zrpravljena, kaj zrpravljena, v tem operations we work in the so-called over parametrazi regime, where the number of trainable parameters, which in a fully connected architecture is proportional to the size of the hidden layer squared times the depth of the network, is much larger than the size of the trainings that we are using to train the network. Let's say that for the Amnist dataset, you have ten to the four training patterns, for instance, Danes, pravda se je z Resnet 50, ko je dobro 10 milijonov parametrov. So, in na vsezavstaj za magnitud nekaj treba. Tudi, da ne zelo, da ne bomo se vsezat, ali da se vsezat, da je bilo vsezat, da tez vsezavstaj, ki je to vsezavstaj, ne bila vsezavstav vsezavstav. Na performancji, vsezavstaj, na generalizacij. Vsezavstaj, ki je sradi v statistける, in teori, ki so mene matematičnih in odličnih, ne, ali je biti bolj in informačni argument in tvoj bias variance trade-off, ki je vse vse vse vse našlično, da imaš več našlično vse parametrično. To je vse našlično, ali je vse našlično. To je vse vse našlično vse našlično. Prvno vse našlično vse parametrično vse vse parametrično. When we think to statistical physics approach of course we cannot acknowledge two important pioneers in the field. One is Elizabeth Gardner who unfortunately passed away at the end of the 80s, the other guy of course is Majesty Heimson Polinsky.  S enkalizacjem vsečko mekanik, všim vidim, ne biti roadsima, sve zva bilo, da bi veččva nemonitivne in pričeljno kar načinam, vsečkih, vsečkih, načinan. Način, kaj načinamo, to stadi simple models, shallow models, linear perceptrons, random feature models, kernel learning, which is very related to random feature models, or shallow architectures, such as parity or committee machines where you know we have exact results. And the typical approach in statistical physics, but also in statistical learning theory is to have a data set, a training data set, I'm thinking to supervise the learning problems, and this data set is drawn from a certain input-output probability distribution. This is not the most general setting you can think of, because of course you cannot think of out of distribution problems and things like that, but let's say it's a reasonable setting. And what are statistics, well, the maximalist program of a statistical physics would be to say something about the partition function of the system in an equilibrium ensemble in a Gibbs ensemble, where beta is the inverse of the temperature, l is the loss, and you would like to say something about this partition function, because from the partition function you can compute observables. And the typical way we do this in statistical physics is by choosing some input-output probability distribution and then averaging over the logarithm of the partition function. And the reason why this average must be quenched, well, I cannot explain right now, but of course this is a complication. And the complication is that we do not know how to average over many probability distributions. We can average only over simple probability distributions. So of course one escape strategy to this is to introduce more complex models of data such as Sebastian did with his colleagues, of course, hidden manifold model, or another strategy would be to consider the perceptual manifold introduced by Sumpolinski, but you know, you are very limited in the kind of data you can average with. But there is, let me say, a class of results in deep neural networks, which are, we do not need to average over data. The first important class of results in this period is the so-called infinite-width limit, where you basically ask that the size of the hidden layers is much larger than the size of your training data, of your training data, OK? Well, in this limit basically you empirically observe that weights move very little with respect to their initialization. And these end up with beautiful formal proofs by mathematicians that basically infinite-width deep neural network are equivalent to Gaussian processes. And so all you need to know is a nonlinear kernel, which is the place where you find the details of the architecture, OK? In particular, the feed forward structure of the architecture is given in this recurrence relation that relates the kernel at layer l to the kernel at layer l minus one. And the activation function is given by, you see, is in this integral that defines the kernel, OK? And here perhaps it is important to stress that there is a difference in the infinite-width limit where when you consider Bayesian learning of gradient descent, and in particular you find two different kernels. One is the NNGP kernel, the other is the neural tangent kernel. I can argue on this later, if you like, but that's the story more or less. Once you have this result, of course, you can come back to statistical physics and saying, OK, I can now perform, I can now study data average partition functions. Well, this is very well done in a paper by Gengiz Pelevan at Harvard. And this paper is basically a generalization of a very old paper by D Tricoper and Sumpolinsky, where these guys were studying support vector machines with polynomial kernels. OK, so you can do that. Now, let's move to the second class of results. This is a very recent work by Leah and Sumpolinsky, where they consider deep linear networks. So we remove all the activation functions from the network. In this way, the problem from the viewpoint of expressivity is trivial, but still it retains some non-convex features, and we can try to investigate it. And this is exactly what these authors did at the level of the understanding of the partition function. The basic idea is that you can integrate weights backwards through the network, starting from the output layer. And this is what they call back propagating kernel renormalization. But now, if you go into the details, you can show that when you do this, there is a price to pay. And the price to pay is to insert new variables, OK, that they call UL. And you can show that in this thermodynamic limit, where you send P and the size of the layers at infinity, keeping the ratio fixed, you can determine this U self-consistently. Now, if you consider the isotropic limit, where all the aspect ratio of the network is the same for each layer, then what you find is that you can easily compute the generalization error that gives you the standard bias variance, the composition. And you have some self-consistent equations for this parameter U0 that gives you the test error on a new example. As you can see, everything is not averaged over data. This is a fixed data result, OK? But now, OK, they do the numerics. The numerics looks very good. But since they are very smart, they do something crazier. The idea is that, OK, as you can see, basically, this expression depends on the linear kernel, OK? So the idea of liesson-poliski is the following, OK? Since under scalar multiplication, the real kernel behaves exactly as the linear kernel, in a way, I'm endujving, of course, like they do. And you can just replace in those formulas the kernel, the relu and then gp kernel, OK? And you can see whether this theory is predictive or not at the level of the generalization error. And what you observe is that if you consider one in the layer architecture, you can plot the test error as a function of the size of the layer. And the theory is very predictive. Whereas if you move to other layers, well, you still see a good agreement as long as the number of layers is not too large, but then the theory starts to fail. Well, it's not surprising. It's a very heuristic theory derived with end-waving arguments. The end-waving arguments are nice, but we are missing something, of course. So let's say, I think these results are very fascinating. And we would like, for the rest of the talk, I would like to understand a bit more how to rationalize these results, let's say. So the two ingredients I will ask is this proportional or thermodynamic limit, where I send the number, the size of the trainings at p to infinity, with the size of the layers, and at the same time, I keep the ratio constant. And the second ingredient is that I will not average over data, but I will consider fix the data, fix data, OK, fix the instance of the training set. I think the only thing you need, well, no, in a sense, no, and I will come back to this later, maybe when I show the numerical results. OK, but of course, you can assume that you are considering your test set, which is drawn from the same probability distribution of the training set, but actually, I don't need to assume that, OK. I will show you, I will show you an example later. I will come back on this point. Well, of course, we start with some terminology. You define the deep neural networks in a fully connected deep neural networks in a recursive way, defining the preactivation at layer L as a function of the preactivation at layer L minus 1. We add one readout layer, which is this guy here, which is just one neuron in the last layer. And then we consider regression problems with quadratic loss function and a regularization, which is not really a regularization, but since I am dividing this regularization by beta, it should be thought more as a Gaussian prior. OK, and then I define my partition function in terms of this learning problem. Of course, the partition function is linked to the posterior distribution of the weights after training. And as I told, your regularization should be thought as a Gaussian prior on the weights. And of course, if again in the maximalist program you would like to compute this partition function and evaluate observable. And let's say, the main observable we are interested in, but not the only one, of course, is the generalization error on a new example, x0, y0. That you have never seen during training. Now I am very ambitious because I would like to tell you something about the calculation. The partition function is a complex object. Is this, let's say, almost horrible formula. And the crucial idea, which is not, of course, mine, but it's something that is also employed in the proofs of the infinite width limit, is that you should not think this guy in the parameter space, but you should work in the function space. So what I want to do is to integrate over the weights of the network, but I cannot do it for free. So what I do, I add the deltas. Basically, this is a change of variables where I identify the output function and the pre-activation functions. Of course, I have one output function for each neuron, and I have one output function for each pattern. So in the last layer, this variable, s mu, rp, and in the idel layer, you have one activation for each neuron in each activation form, in each pattern. OK, I very easily realized that if the prior of the weights is Gaussian, then I can perform these Gaussian integrals over the weights. And I come up with an alternative representation of the partition functions in terms of these output functions and pre-activation functions. OK, now I can massage a bit my partition function and realize that the only tricky point is to identify this probability distribution in green, which looks very, I mean, it looks very tempting to think this as a sort of probability distribution where I can apply a central limit theorem. But unfortunately, this probability p1 is not independent over the input. OK, and so you cannot really do that. But it turns out that there's a nice mathematical result by Breuer Major, which tells you that actually it exists a generalized central limit theorem in distributions for this kind of observable. OK, of course, you know, this is not what I'm going to say. By all means, it shouldn't be thought as a mathematical proof. My reasoning here is that I want to invoke a Gaussian equivalence based on this Breuer Major theorem. But of course, I leave to mathematicians to understand if this is correct or not. What I can tell you is that when I do that, I can end up with something that I can actually work out analytically. OK, so I invoke these Gaussian equivalents and I find that this is a Gaussian then. And then I find that the only thing I have to determine is its variance. And I find that this is a quadratic function of the dual variables of the output variables. And the kernel, it appears, is exactly the NMGP kernel. OK. And now when you do this, it's really a matter of algebra to show that if the lost function is quadratic, you end up with an approximation that shows you that the partition function in this limit satisfies a large deviation principle. OK. And in particular, this is an integral over two variables that we call q bar, bar q. And we have a sort of effective action, free energy, variational free energy. I mean, you can call this in many different ways. And of course, if you can say that this effective action is intensive, I can employ the saddle point method to solve the system. OK. At this point, and probably many in the audience are interested in this, I didn't tell you anything about the relation between the size of the data set and the size of the input. And I know that Jean Francesco, of course, are interested in this. But let's say we can talk about this later, if you like. But you know, it's very easy, at least at the physics level, to show that the last two terms of this equation, which is where the data set is, are intensive. I leave this to you as an exercise. And once you have this kind of result, you can also compute the generalization error. And you find a formula for the generalization error. Now I will, I think I will move very soon to tell you something about numerical experiment. But first, let me say something just for the mathematicians in the audience.  In the sense that you show that this variable s bar, that basically define the characteristic function of the prior over the output, they are Gaussian, OK? But now if you cannot, if you send p to infinity, you cannot anymore think of, let's say, Taylor expanding that. And what you find is that this distribution is well known in the literature. It is a multivariate t-student distribution. And so what we are basically claiming is that there are a link between finite with one in the layer neural networks and student in stochastic processes, which are something which are quite well studied in the literature on Bayesian inference, OK? But now we can do something more than that because we can realize that also what Sompolinski is doing is essentially to find a relation with the student processes, OK, in the deep linear case. This will be useful when I will say something about the L-layer case. But now let me move to numerical experiments. Of course, now we can say, OK, is our theory correct? Is it capturing something in the learning curves of one in the layer architectures? And what we find? Well, these are on the y-axis. There is the test loss. And on the x-axis, there is the size of the layers. We train our neural networks with a discrete range of dynamics. These, let's say, up to the discretization of the learning rate, it's almost sampling from the Boltzmann distribution if, as a result, you wait for long enough to claim thermalization. And as you can see, let's say, there is quite a nice agreement between the theory and the experiments. These are, let me stress this, these are real data, OK? I am taking images from the MNIST data set and images from the CIFAR10 data set. And I'm making a prediction about the generalization error of the one in the layer architectures, OK? But now, as you can see, at least in the regime we investigate, the smallest 10-1 we investigate is n equal 50. We observe monotonically increasing or decreasing learning curves. And, well, if you go to the analytical calculation, you realize that there exists an analytical criterion that tells you where these curves will be monotonically increasing or decreasing. And this is given by this criterion here, which is just a function of the data set and on the infinite width kernel. And this is actually another connection with student t, because you can realize that this criterion also appears in Bayesian inference with student t process in a reference where this is shown in this recent paper by Tracy and Wohlpert on Bayesian inference with student t processes. Now I can say something more general, OK? Let's say that I don't just want to consider a, you know, I can make prediction about the generalization error, but I would like to say something general, OK? It turns out that when you restrict yourself to zero temperature, you can find from the formula of the generalization error that the bias is a constant as a function of n1, the size of the layer, but also a constant as a function of the Gaussian prior of the last layer. The second point is that the variance instead depends on the size of the layers, and it goes to zero. You can check it in some cases as 1 over the square root of lambda 1, the Gaussian prior of the last layer. Of course, this is just two physical consequences in numerical experiments. The first one is that you should observe that increasing the magnitude of the last layer, Gaussian prior should lead to better generalization at any n1. And the second one, of course, in this thermodynamic limit that I'm studying, and the second one is that for large value of lambda 1, the dependence on the size of the layer in the learning curve should disappear. And this is the plot where we try to show this. These are, let's say, the test loss performance at three different values of lambda 1. And as you can see, larger lambda 1 helps generalization, helps the generalization performance, and it also seems that up to reasonable value, small value of n, the generalization performance seems to be almost independent on n. Inverse variance of the last layer, Gaussian prior. And these are numerical experiments where we waited for thermalization of the discrete launch of n dynamics. So now we can somewhat exploit this idea that when in the layer neural networks and deep linear networks are related to studenti process, to somewhat bootstrapping a result for the layer case, OK? And the way we do this is by, let's say, guessing in a more or less educated way what the probability distribution at layer L will be of the preactivations, will be in terms of the preactivation at layer L minus 1. I mean, I noticed that this approach is different from what Sompolinsky does, OK? Because Sompolinsky integrates backward. But this is not really consistent if you think to the proofs made in the infinite with case. Those proofs are made by an induction principle that starts from the first layer. I'm thinking to the paper, for instance, by the Google brain guys where they prove the infinite with limit. And when we do this, we realize that we find, again, a recurrence relation, but for a renormalized kernel, where new parameters q bar L appears inside the recurrence relation. And you should think that this q bar L should be self-consistency determined by the minimization of this effective action for the L layer system. OK, this is more or less the idea. I'm going very quick through this, but of course, I'm available to speak to discuss offline this. Well, now maybe I can skip this, but you can easily realize doing this calculation. Well, the results I showed are results for, let's say, odd activation function. ReLU is not an odd activation function. And Sumpolinsky is a heuristic theory by replacing the linear function, which is an odd activation function, with a non-odd. Well, you can see that the theory is slightly different. If you consider a non-odd activation function, and you can compute explicitly corrections to this theory. This is not true in the infinite with limit. It's only a marker of the finite with. In the infinite with limit, the two theories are odd and non-odd does not make any difference. And, well, this is how one gets this more general result. Let me show you one last slide with the preliminary verification of the theory at L layers. What you can basically derive in the L layer case is the very same criterion that could establish whether the finite with network outperformed or not the infinite with limit, at least in the case of ReLU out activation function. And here, there are numerical experiments close to the infinite with limit, where we can see that up to two layers, where this guy is less than p, where the criterion is satisfied, finite with networks outperform the infinite with limit. Otherwise, it's the opposite. And, well, I'm skipping this. But I want to tell you something more about the reason for this criterion. Well, if you give a look to the infinite with kernel for ReLU activation, you realize that this kernel, as L grows, develops almost singular eigenvalues. And this already happens for L equal 4, almost OK. So this is the reason. And since in the criterion, you see K to the minus 1 well, this seems somewhat an unpleasant result, OK. But of course, it is not really an unpleasant result because I'm focusing only on ReLU networks. And we know that, well, here I should say more. But basically, the recurrence relation for the infinite with limit described by discrete dynamical system, OK. And what I'm saying basically is that this discrete dynamical system for the ReLU system is converging to a fixed point. But this is not the only possibility for dynamical systems. So you know, we can have limit cycles. We can have chaos. This is something that was very well investigated in a series of influential paper by Ganguli and collaborators. And there is this edge of chaos conjecture on why you should stay very close to the edge of chaos for information theoretical reasons. But I'm not going to say anything else on this. And so in conclusion, we proposed with my collaborators an approach to investigate in an effective way the statistical physics of neural network with helpfully connected in the layers and to move beyond the infinite with limit. I think, at least to me, if I were a mathematician, this connection between finite with deep neural network and student processes seems something very worth investigating. And I discussed this already once with a very good mathematician, Boris Sannin, who also actually, I forgot, he wrote a paper on deep linear networks where he establishes some finite, let's say, he can compute the same partition function of some Polinsky in the not in the thermodynamic limit. It's a crazy paper where all the results are given in terms of major g function. Very nice paper, by the way. And, well, of course, there are many open questions. One is to understand the similarity and differences with a very recent work of Sirusina and Ringel. And this is something we are working out with the authors of this paper. Another thing that, at least to me, would be interesting is to understand if I can write such an effective theory for gradient descent dynamics. But probably the most interesting part would be to understand convolutions, feature learning. And even related to what I was telling you in the last slide, maybe to understand more precisely the relationship between generalization performance at finite width and this edge of chaos conjecture. And also, there's a paper on the side of the edge of chaos where Young and a colleague investigate the role of skip connections. And I think this could be a nice first step for this. And then it's the last slide, the most pleasant, where I thank my collaborators, Sebastiano, Rosalba and Riccardo, who are in the audience. And then Mauro, who is at ENS, Francesco, Marco, and Alessandro Raffaella, who are part of my in your group in Parma, where I moved recently. Thank you all for listening. Is there a layer in the network? No, I couldn't understand precisely the work of Yoshino, and I probably read it long ago, but I forgot what it does. I know a bit more precisely the work by Hugo and Lenka and Florana, where they are in the same very bias-optimal setting, and I'm studying that paper, but also it's quite recent. So let's say, I cannot really tell you much about comparisons between the fixed data and the data average case. Of course, well, maybe something is something that I can tell you very quickly. When you have this partition function, it is effective action. Of course, since you have this 1 over p trace log, you expect some self-averaging. And if you consider here Gaussian data, you are also allowed to start Taylor expanding this kernel. And what you basically realize is that you find some effective action for a linear kernel, at least in the regime where p is scaling with n0, n0 being the dimension of the input. And in this case, this term is very easy, because it's basically converging. Well, I can write you very quickly. There is a choke, no? Ah, here. Well, you expect that this 1 over p trace log of some matrix k that I'm approximating with some alpha identity plus c. OK, this is OK for c. It basically converges in this p n0 limit at p over n0 fixed to the integral over the eigenvalue distribution of the Markchenko-Pastur distribution of the eigenvalues log of, I don't know if this is the identity over beta plus k, something like gamma identity plus lambda, something like that. So now, if you choose a teacher here, OK, you can exploit some random matrix theory, at least in the linear case. And so, this is a way to avoid replicas, if you like. You exchange replicas with random matrix theory, but you have to know random matrix theory. So, good luck. Maybe I didn't reply to Francesco. I didn't clarify the point of you asked me about if I have to make some assumptions on, OK, well. Yeah, especially also, on the number of points that you have, like on the amount of data that you have. The amount? On the amount of data that you have. OK, let's say, as you can see in the, I don't know, wait a second. We moved up to p equal 1,000. It was very difficult to do this, at least to obtain quantitative agreement. And the reason is that the larger is p, the more time takes your bastard, OK, so, already here it takes 5 million epochs, self correlation times are huge. OK, so you cannot take one, to average, one data, every.  But I can tell you something, even when the theory, where you are not able to thermalize, you, at least, this is what we observed in many cases, the qualitative form of the curve is the same, and maybe our theory is a bit below or above. It depends, it really depends on cases. If you want to see this precise agreement between theory and the experiments, well, you have to wait for very long times. This is something also that the other group working on this, Zvar Ringel in Jerusalem is experiencing, and we laugh a lot about that, about how difficult are these. Yeah, also the Taliban rules, not the OS. Exactly. So, but to reply to Francesco, you know, of course, here we are using as the test examples, other examples from current planes, OK. But now imagine you use a picture of my grandmother, what you would get, you can still compute the theory, and you know, maybe the only thing, of course, the generalization performance would be very bad, except in the case, your mother is like, my grandmother is like this, so it resembles more a plane, but, so we can do more or less whatever we like, also studying, investigating out of distribution problems. Yeah, and I'm concerned with how to do this. We didn't do that, but for the same reason, I told you, well, I made this little heuristic argument to derive this, I expect self-averaging in a sense. You know, but we, no, no, of course, of course, it's everything but trivial, I think. Yes, we have, sorry for not mentioning this, I know that, of course, for you, and not only for you is an important point. I think that, well, the reasoning we employ in the paper is that we invoke this Gaussian equivalence, and then we look at the hypothesis of the Breuer Major theorem. Then you realize that basically the proofs of this Breuer Major theorem, the proof requires to introduce this concept over mitrank, and what we can, at least looking at the hypothesis of the Breuer Major theorem, we can say that if the activation function ever mitrank m, r, then p should scale at most as n0 to the r over 2, r over 2, at most. Of course, you know, we have one explicit, of course, this is telling you that for many activation functions with a mitrank one, which is, for instance, re lu, erf, which are the one I showed you in the experiment, these should scale at p, as p over at the one-half. This is not particularly nice, okay? But then there is at least one case, which is the case of an activation function with a linear and a quadratic, linear plus quadratic, where we can prove this, prove. We can provide an argument for this result without using the Breuer Major theorem. In this case, at least in the case of Gaussian data, we see that the derivation works as long in this proportional regime. At least for quadratic activation function. Of course, we made also some numerical experiment reducing the size and not, but let's say, I think this would more or less work, but since for many of you, and I understand now this is very important, I think we will conduct more detailed numerical experiments to understand this point in detail. Thank you. Actually, this is a entirely different topic. You showed a picture with the kernel singular values, different depths. So yeah, you're using fully connected. Yes, of course. So, that's the infinity. I don't know if I wanna speak about convolutionals. I already speak with you about convolutionals. No, no, I don't wanna speak about convolutionals. I will just puzzle because the infinity with kernel of fully connected should not depend on the, well, I don't know if you should not depend on the width. I know that it has basically the same reproducing kernel in the space independently of the depth. So, do you understand why you see all these differences while changing the depth, or do you think it's just that it's a matter of initialization or scaling like this? When we do these analysis, we are just using the well-known expressions, analytical expressions for the relu kernel, for instance. I don't remember it precisely, but of course it's a composition of function. I remember an arc sine square root of one minus something. I mean, yes, we are using the NNGP because we are considering the Bayesian case. Okay, so it doesn't make too much sense to use to employ the NTK. That's the point. Of course, in this kernel, there is a dependence from L, and actually you can see this dependence, for instance, in the eigenvalues. The eigenvalues of the kernel, as long as L is larger than five, they are very much close in this case to zero for relu. Of course, yes, I mean, this is not my discovery, this is something very... The scaling of the eigenvalue with, let's say, the wave vector, I know it's not a wave vector in the end because maybe we choose a different distribution or whatnot, but with an arc. Well, maybe the scaling is okay, but then the magnitude of this, you're reducing at each step the magnitude. You see my point? Yeah, yeah, it's probably just for the magnitude. And so, if you go to the infinite width limit, you can clearly see that you can also, actually improving generalization with increasing L in the infinite width limit, but once you have this finite width effect that depends on the inverse kernel, you are a bit screwed up because sooner or later, at least for relu kernel, you will find these almost zero eigenvalues. So, Pietro, you have a student T distribution on the conjugate variables. Of course. I didn't mention it, and this is why I say a link, but I cannot compute the Fourier transport. I mean, the characteristic, if you like, the characteristic function of the prior of the output is a student T distribution. Okay, so. To be a bit more precise. But did you look at, just visually, at the distribution of the moments? It's not really possible, because if you think for a while, the only reason why this distribution is not Gaussian is that I'm considering an extensive number of input, of training input, okay? So, it's very difficult to find unobservable. Actually, there is one, the generalization error, okay? But if you want a probe of this student T distribution, I think the only thing you can do if you want to compute only observables, which are evaluated over the training set is considering an extensive correlation function with an extensive number of variables. This is my feeling at least. Maybe you can do something smarter, but I don't know how to do it. Okay, we have time maybe for one last question. Otherwise, no, I think we're gonna move these questions now to the coffee session, okay, to the coffee break. So, thanks again very much, Pietro, Francesco for these nice talks.


It is Christmas afternoon and I'm just looking at some of the gifts I got, some fun little devices that I will definitely be using on my second channel, doing some tutorials on them and stuff. Stuff like, you know, for example, wireless light bulb sockets, I'm going to be hopefully hooking these up to an ESP chip so I can control them remotely through my cell phone or computer, a little credit card reader, I'm going to play around with that. So for those of you who don't know, my second channel is on hardware so I've got a few things in there, you know, game controller, I'll have a look at, you know, cheap little wireless game controller. And then this is one I'm excited for. I haven't had a decent webcam ever. And so I just got this 11080p, you know, not the fanciest thing in the world but it should work pretty decent. I haven't tried it out yet. So I'll do a quick review on it, you know, I'm pretty sure like most things are going to be plug and play on the Linux system. And so yeah, but hopefully also with a lot of my upcoming videos that are, you know, especially on this, my main channel are just screencasts. Maybe you'll see my face up on the screen a little more often and also maybe start doing some more live videos now that I have a webcam. So yeah, I mean, there's really no reason for you to see my face but a lot of people do and I guess viewers like to be able to see who's talking. Maybe it makes the channel a little more personable. So maybe you'll see this more often and that way people will recognize me. Maybe you'll bump into me and probably be like, Hey, you're that guy that does the boring videos on YouTube. So anyway, Merry Christmas and I mean, you're probably watching this like the day after Christmas. Probably want to post it. But I thank you for watching as always. I hope that you have a great day. And also, you know, check out my second channel again, that's going to be mainly on hardware stuff. There's a lot of videos already up there. Videos coming up, especially on stuff like the next few months, I'm going to be going over different displays that you can control with microcontrollers like Arduino or the ESP8226 chip, you know, and you can control them remotely with your phone and have different things display up on those different texts and whatnot. So anyway, I thank you for watching as always. I hope that you have a great day.


SIGFOX is unique because we are the only one having a network up and running in any continent targeting low-end connectivity for devices that will require a very low consumption and low cost connectivity. We have a lot about the network already in 19 countries and we plan to have the network up and running in 90% of GDP three years from now. The most innovative attribute that SIGFOX is providing for that awesome connectivity is that the fact that we don't use any SIM so as soon as the device is up and running it is connected to our network so there is no skill to connect them to the cloud. At the end of the day SIGFOX is targeting to connect the real world to the virtual world because we are convinced that in the future everything will be connected. Every device surrounding us will have a connection to the cloud because we will use technologies that are not based on battery anymore but on energy harvesting so we will get energy from the temperature, from the electromagnetism radiation, from everything so it means that any bulb in it shares, any donut, anything will put dead status into the cloud so with the big data or the algorithm we will be able to improve services to prevent disease and maybe to improve our daily life.


So this video might be slightly crooked, but we're going to have to go with it. First to my channel, where today's video I'm going to talk about the different books I'll be bringing to college with me for the first semester-ish. My plan is to bring a few books to college and then when I go back home for Thanksgiving I'm going to switch them out, the ones I've read for some new ones or ones I didn't end up liking or reading and just switching them out for some other ones. So a lot of the books I'm bringing with me are going to be a little bit more in preparation for Halloween and they're going to be like mystery slash I also just need to read them and I think they'd be really cool to read during the fall time. So that's what I'm going to do for you today. Let's get started. So the first book that I'm going to bring to college with me is a classic and it is a mystery and it is Murder on the Orient Express and Other Works by Agatha Christie. I'm bringing this with me like I said because it's a mystery, a murder mystery which is great for October. It's also a classic that I haven't read which in my opinion needs to change because I do love reading classics. I am hoping to start a classics club or at least join some sort of on-campus book club on campus and so I'm really hoping that I can read this and talk to some people about it. The next one that I will be bringing along is Stalking Jack the Ripper by Kari Manistakalco. Again, it's October, Stalking Jack the Ripper has a very like spooky vibe to it. It's also recently released and I've been really excited to read it. I've heard a lot of good things about it both on booktube and just from other people so I kind of figured that I really wanted to read this and it kind of gets me going into historical fiction a little bit more which I've been wanting to read a lot more lately. So Stalking Jack the Ripper I think is just great for the fall, the horror of Halloween and yeah. The third book that I will be bringing to college with me is As Old As Time which is A Twisted Tale by Liz Braswell. I recently read the Sleeping Beauty version Once Upon a Dream and I actually have started reading this one but I haven't quite finished it yet. I am bringing this to college because I want to finish it and I want to slowly work through these twisted tales. I'm hoping that it'll be a comfort in college if I'm ever like super stressed out but it's still something new to read so it's not like I'm doing anything repetitive. I also plan on taking Frankenstein by Mary Shelley. A lot of these books were in my recent haul I do recognize that but that's because I want to actually read the books that I buy and so I haven't actually read Frankenstein and I definitely need to and again it's a perfect classic for Halloween. You really can't get more perfect than this other than Dracula which I also recently bought but will not be taking to college with me just because I don't know how much time I'm actually gonna have to read all these books. I do have five or six or seven books that I hope to bring with me to college and I just don't know if I'm actually gonna be able to read them within the first semester but I'm gonna do my best luckily Frankenstein is short so I will for sure read this during the month of October and still get my homework done. Yeah so this is for sure one that I'm gonna bring to college and make sure I read even if there are others that I'm bringing that I don't read because it's a classic it's perfect for Halloween and it looks pretty. I will also be bringing The Candle and the Flame by Nathisa Azad to college with me because this is a recent purchase that I've been really really excited about reading. I've just gotten really into it just even without reading it just being excited because it's a beautiful cover but also the description of it sounds really good and I've also been trying to read more about like Middle East mythology and just history about them because I don't really read a lot of it and I think I really need to read more of it especially being like an Arabic major like I should know a few things and so even though it is a fantasy book it does have those bases of the fact that it's in Iran and it's all about the Jinn and so I'm really excited to read it because I love stories about the Jinn and I actually hope to write one one day I have a whole plot and everything set out for it I just need to actually sit down and finish writing it but but for now I will read this and take inspiration and hopefully learn a lot. I am also really excited to bring Scythe by Neil Schusterman probably said that wrong because I do have the first and second book of this and I'm really excited for the third that is coming out it's again it's a book that I recently got for my birthday and so I really want to read it and I've heard so many good things about it people have been really excited after reading this and really enjoying the series so far and so I want to hop on that bandwagon and see what I think of this book and it's it's a really interesting idea it's also good for October and that has to do with the Scythe who's obviously like the bringers of death because humans have found a way around death but you know humans really shouldn't live forever and so yeah that's what this book kind of handles where it's a lighter October read so if I don't get to it then it's okay if I save it for like the winter or spring time I won't be super disappointed um but yeah hopefully I can read this one and then when I come home over like Thanksgiving or Christmas or something then I can get the second one and bring it to college and read it but again like I said we'll see moving on to the last book that I'm gonna bring with me to college it is called everything everything by mccola yoon I'm bringing this because as you might sit there and be like whoa yes it is a very different genre it's completely different from all the other books I'm bringing with me to college I was so focused on bringing like horror mystery books for not really horror but bringing mystery and spooky books for Halloween in October but after October is done there's still the month of November and I just thought I should have something other than like mystery and spooky books to read in college if I just need a break or I need a breather and I don't go to library which I hope I do go to library for sure I'm gonna study in the library but if I don't check out other books then at least I'll have everything everything to lighten the mood and I don't know that's the right expression to use but to just be something different than all these murder mysteries and monsters and etc and so forth so I am excited to read this I don't really read a lot of contemporary but I'm trying to um so hopefully I can read this and I can enjoy it so those are the books I'm gonna bring to college and university with me this fall I leave in just a couple weeks and so I've been very busy in practically and frantically packing but I already know those were one of the first things I packed is that I packed those books that I'm going to bring with me to college and hopefully read while I'm there in the rest of August September October and the first majority of November um I'll let you know how that goes I do plan on continuing filming in college and hopefully uploading every Thursday or Friday-ish um but yeah thank you for sticking through it please comment down below if you are in college or if you are going to go to college or if you were to go to college what books you would bring or what books you brought or if you're out of college what books did you bring or what did you enjoy reading in college if you ever got the spare time or if you really just focused on books that your English classes required you I'm really interested to know what all you read and what all you brought to college with you um so yeah comment down below let me know if you like any of the books in here let me know if you have read them otherwise you can just like the video and then I'll know if you've read them or if you liked them and feel free to share this video however you want with other people I would love it if you know someone who's going to college and is thinking about what books they want to bring then share this video hopefully it'll help them out although mine was very October themed and lastly please don't forget to subscribe for more videos like this I will be doing a lot of filming before I go to college and I will be doing some more filming there but it won't be too much longer with these bookshelves but I will definitely do my best so please click that subscribe button and hit the bell so that you know when I am uploading again hopefully every Thursday but just in case you have a busy life or you don't really keep track of those things then just hit the bell and you'll get the notification and then you can decide if you want to continue watching or not I hope you all have a great start to the school year as you're probably very busy getting ready for it so yeah happy reading


tions that you're learning Capricorn here so um past lives okay this is other lives card um so are there a lot of past life energies around you people that you have known in the past you know are you doing a lot of reruns are you being drawn back to somebody who you know is toxic um you know that person is no good for you and yet you keep being drawn back you keep saying oh maybe they've changed I'll make another excuse for them or another excuse or another excuse um and slowly slowly you are wearing yourself down um and also the universe is saying well they're putting up with it so which means that that's what they deserve that's what they say they deserve so therefore I'm going to keep giving them what they deserve um if you deserve more or better then they'll give you that um so yeah there's a lot of other live stuff you know um this is all about your karmic footprint what has been going on in the past um listening within is going to be the challenge okay so understanding that sometimes yes there are people from our past that are entering our lives and we were meant to be with them we were meant to to experience this or that um to experience the ups and downs um but no when it's time to say okay I've done I'm done now that contract has finished um I feel it's finished I'm done I don't have to keep learning the same thing over and over and over again I get it so it's going to be the lesson um that you're going to be going through capricorns so we're going to finish off with Melissa okay oh one oh very good so um with Capricorn I feel the focus is how we see things and also your clear vision your clear sight because your chakra is the third eye for the week so I feel a lot of focus um but for me I sat here and my I was like okay the first card that came out for you was the sun capricorn and I was like okay we can just go home now it's going to be a great week so but this card says um it's what the message is um plans that work out perfectly amazing ideas that lead to reward and um accommodations and gratitude for the blessings in life so I feel like it's full steam ahead and you'll be riding some kind of a high and I feel like um there was a lot of spiritual energy um with this card with the Capricorn energy as well for me coming through and it's saying that you know you faced a lot of challenges and the sun is coming out and things are really good but I'm hearing that if you have any um trepidation or perhaps you've been holding back now is not the time to continue to do that it's time to let loose it's time to really um ride the energy ride that wave and just go with the flow and go with it because things really do look better then possibly you might think so if you're if you're feeling um like you're missing some key components this card is saying no it's just the way you see them so it kind of happens a lot of what Allie's message was too um with her card so I feel like it's really good though you just have to see it clearly and that's where that third eye in that vision came in and so as we went on as I continue to pull the cards I couldn't believe it um we had another major arcana card come in for the middle of your week and this is balanced so you start out really really strong but then there will be some challenges and it's about dropping into the heart finding that space where you rest on the rock that you are, your energy um what you bring to the table and the balance of everything that you bring to your situation I'm feeling especially at work so if there's a colleague or perhaps a supervisor that's challenging just step back and look at it with different eyes maybe put yourself in their situation and you might be able to see more clearly why things are like they are and kind of stepping out of the ego self and just going with it and knowing that everything is okay and um the message on the card is take everyone's input into consideration merge viewpoints to create a better solution take your time there's no need to rush and also I feel with this you put a lot of time and energy into things and this card is saying um you know you've worked really hard just stand back and look at that and just watch others do the work as well and really acknowledge teamwork and in love and relationship this is also saying find that balance you know sometimes you lead, sometimes you follow sometimes you're right, sometimes you're wrong it really doesn't matter it's just finding the peace and the balance wherever you are in any now moment and that's what the middle of the week looks like and then you finish it off again with another major arcana so that's three so you've got a lot of spiritual energy going on here there's a lot of like lessons unfolding for you all at once so if you're feeling overwhelmed the end of the week we have the unity card coming in so that's really really good so you really took things into consideration you really did find the balance you really did allow other people to do their part and the unity card is saying to you do the right thing for the right reasons compare traditional versus new approaches to see which works for you a spiritually binded community and I feel like really taking your focus away from work and really within and maybe making new connections new places, new people, new ideas will bring in a lot more of that unity that you seek capital calling that you really need right now and I feel like the end of the week you find that sweet spot and I feel like the weekend will be about possibly making new connections going new places and that's how I see the week for Capricorn a lot of strong strong energy a lot of major spiritual lessons unfolding for you this week so stay focused stay grounded and remember that the third eye chakra is the focus on seeing things clearly as you ride that very very strong wave of energy so that's my spin on Capricorn for this week coming up we did it I don't know those cars I was like are you wow totally blending all the way through really haven't they so yeah thank you thank you for your help thank you for coming in I didn't know how I was going to get through that with the pain that was going on so I really appreciate it I always love reading with you the water energy just comes out between the both of us so we're just kind of like going with the flow yep it is it does work so don't forget to check out guys our website mystics portal if you want anything you know spiritual there's 6000 items on there okay there's anything you want from tapestries wall tapestries to tarot cards to smudging things to candles gifts spiritual gifts there is so much on there there really is so go over to the mystics portal and have a look Greg's still got his t-shirt sound on it's $24.99 $24.99 for a t-shirt apart from the customized ones but that's on offer if you are a Patreon of Greg's it goes down even further to $19.99 okay so don't forget that and if you want to support Greg and I go to Patreon become a Patreon family member with us there's lots of offers it's like a membership okay I give away readings and mystery gifts sent to your home and all sorts of things so if you wanted to have a look go to patreon.com forward slash Psyche Alley or you could also support Greg patreon.com forward slash if you want to read in with our lovely Melissa there's been a banner running underneath all the time so yeah you can check out and there's also in the chat there's also the website it's whispering so whispering wins cowgirl yeah if Reverend Melissa your volume's cutting out completely there if they put in Reverend Melissa Kennedy it will also come up my website will come up so but it's whisperingwinscycocowgirl.com okay that's brilliant so well worth the reading with Melissa you've seen what she's capable of here here we go there's a Melissa's detail there and she like she said she's also Reverend so if you want to get married she can marry you too so there you go Melissa it's been amazing doing this with you again thank you for joining and I just want to say that the t-shirt like I have a whole bunch of them I gotta get a couple new ones so if somebody is on the fence about getting a t-shirt no they're awesome so I wear mine all the time I don't have one on tonight but get a t-shirt and the website is awesome that mystics portal so yeah it's awesome you guys deserve the support because you give so much thank you yeah we do try and we wanna support you and it's just lovely to have that bit of support back as well so yeah no make the most of the t-shirt so it's not gonna be on forever yeah make the most of it so anyway have a wonderful week Melissa and love to my gorgeous sexy husband who's in the chat there putting all these links out there we appreciate you darling and um Donna hello Donna and Jersey Jojo say I wonder if I'm Capricorn what I've said and I forgot to say it today but um with all of the readings there's always snippets of information that are for specifically for you even if you're not that sign I deliberately ask my guides and the same would happen with Melissa that there are the information if you feel it is for you then it is for you okay so it is important to know that okay we're gonna sign off now we love you and take care everybody everyone thank you see you soon


Welcome to Access to Trader, the number one community for those who are committed to taking control of their trading in order to achieve success, profitability, and longevity. Thank you for joining us. Here's Dan Shapiro to help you find your edge, master your process, and own your future. Hey guys, good evening everybody. Welcome to another edition of theaccentrator.com. Nightly wrap up show. Hope everybody is doing well. Obviously, the big story today was the Fed. If you are brand new to the channel, guys, welcome aboard. Thank you very much for spending a few minutes with us.  We want to be a part of your daily routine. And if you like unbiased market commentary, this is the place for you. So let's talk about it. Obviously, as you can imagine, the first part of the day was very, very slow today. The Fed was on deck followed by Jerome Powell's Q&A. The consensus was that we were going to hold a rate study. Now, they came out with the news. Rates were steady and on the surface, right? On the surface, it's not a bad thing because, again, what's basically telling us is that the fact that they didn't raise rates this time around basically meant, well, they were comfortable. Maybe inflation is starting to get under control. And then it started speaking. And that's the whole point why there's the initial headline and then there is the Q&A. And Powell basically left it on the table that there is potential additional hikes down the road. Obviously, the market did not like that news. As you can see here by the reaction after Powell stopped speaking, the market slid 1.5% down. And if you watched the video last night, we talked about the bottom of the range here, the 6680 level. That was now the line in the sand for yesterday. Obviously, that broke down. And now we are really, really aggressively below the 50-day moving average that couldn't reclaim back on even good news this morning. The market had at one point a nice little spike into those comments, into that 371-70s level. But it caught shy of that 372 level we discussed last night, which is the 50-day moving average. Confirmed yesterday's level was 366-80s and went all the way down here to this linear regression line into the 364 level. The key is for tomorrow. Again, because you can see all this little airspace down below. The key for tomorrow for the NASDAQ 100 is stay above 364. Okay, that's the key. If the NASDAQ went on here again, it won't mean it's a bullish thing. It just means that's a necessary area. You can see here by this linear regression line here, every single time it stopped at this channel here, it bounced. So we're at this channel here again. The question is, can the bulls hold? So it's a very, very open question. It's not really a yes or no. It's a rhetorical question. But if you look at today's setups, I'll give you, I'll share some setups with you guys for tomorrow. You'll notice there's a lot of stocks that cracked. And if they didn't crack, they're about to crack. And if they didn't about to crack, they're about to take out the previous week's lows, which is no good. So it's a very, very important day tomorrow for the bears. Again, like I said, even two, three, four videos in the last couple of days, it's not that I'm sell bias. We're getting there. Well, it's not that I'm sell bias. But again, as we said in the last three, four videos, how can you be bullish with the Nasdaq 100 continuously building underneath the 50-day moving average and stocks continuously sitting on the bottom of the range? Again, we'll get to some pivots in a second. But it's very, very, very, very interesting to kind of see kind of the big disconnect we've been seeing now all week. We've been talking about that all week. Some tech stocks are strong, some tech stocks are weak. And then when you look at the final tally today, again, an ugly close, one and a half percent down for the Nasdaq 100. You look at the SPX, we'll use SPY here as a proxy. Same levels. We talked about all these levels here in the last couple of days. Same levels here. Spies look like kind of a moonshot here. Well, not a moonshot. I got kind of a swan dive. Portraits of words. Kind of a swan dive here. Potential magnet all the way down to the 431 level. And when you look at a lot of the setups here, there's some really aggressive, good-looking setups that I want to share with you guys for tomorrow. Let me talk about the pivots first and then we'll get to some pivots for tomorrow. As you can imagine, the beginning of the day, the market did nothing. The market did absolutely nothing. At one point, I believe the squat box said the Nasdaq 100 volume was running 22 percent lower today than it did even though the last couple of weeks were lower. It was 30 days, I believe. So nothing was going on. Slowly, but surely, meta woke up this morning. We'll get to meta in a second. But the violence came towards the end of the day. As you'll see here, several really aggressive moves to the downside. And again, that will segue into some pivots that I really like for tomorrow. So here was Tesla. So in the middle of the day, Tesla came out with some news where actually Elon Musk started talking about Starlink that really set the stock going higher. What was odd about the move going higher on Tesla for that 271.50 break, right? You'll notice there was a lot of call buying coming in. 280s, 280 weeklys, 285 weeklys, 290s, 300s, 325s for October. So you started seeing yourself, wow, Tesla's going to really explode. And Tesla had a nice move, right? It took out this 271.50 level. It went to 274. It was a nice move. There was nothing wrong with the move. And then the market completely fell out of bed. Everything back gave back the 5D moving average. And now we're looking at the bottom of the range, right? Which is so amazing. I was telling the webinar, any close today above 271.50. I want to be low overnight. But now we are at the bottom of the range here, guys. This is how quickly things turn into market, especially on event days. And now we're facing this bottom range on Tesla for tomorrow. Again, remember, we're trading the ranges. We're not falling in love with the stock. We're falling in love with the ranges. It went really good. The rug got pulled in the bulls. And now I'm definitely, definitely watching ranges here to the bottom. But if you did catch Tesla, let's say, great job. 271.50s went all the way up to 274 before Jay Powell just started to pull everything. WWE, not a big move at all. 1220 needs to build. Went to like 1240. And obviously everything pulled in. Roblox didn't confirm. I like Roblox for tomorrow. Look at the setup for Roblox for tomorrow. If it can get below this bottom bolinger band, this thing can swan dive as well. Meta. Nice little pop here this morning. We talked about Meta last night on the video. 306.20 needs to build. Here was Meta. You can see this 306.20. I'll show you on the 60-minute chart. It took out this 306.20. Nice move. Went up a couple of bucks. And then obviously everything got rolled over towards the end of the day. AMAT, I still like this. Did not confirm. Guys, look at the setup on AMAT. Check out this. Look how tight this thing is. Look at the setup on AMAT. If this thing starts losing the bottom channel tomorrow, boy, this thing could get hit. NVIDIA. NVIDIA was definitely the big one in the afternoon. 430, if it builds below, can flush. So, NVIDIA got destroyed towards the end, right? It got absolutely destroyed. You can see it on the 60-minute view here. So, here is the 430.429 level finally kicked in here, guys. This thing is one day away from testing Monday's lows, right? You see that? If NVIDIA starts confirming Monday's lows, guys, we will go down all the way down to the August 14 lows potential, which is 403, and ultimately, this thing could get really, really violent if the market continues to pull a really, really big aggressive move on NVIDIA into the close. And in Carvada. When we talk about Carvada on the last night's video, well, Carvada finally confirmed today. It's held twice if it builds below, can flush, and look at Carvada. Carvada got absolutely smoked here. It lost a 46 area, which we talked about last night. We talked about the 9-8 lows, and yesterday's lows stopped exactly the same area. Today, it lost 46. It's trading pretty much traded close at the lows of the day, 43, and change. This thing does look lower as well. And NVCR, I forgot to short it on the close. NVCR, $18 earnings low if it closes below, can start its next leg down. I forgot to short it on the close, which sucks, but whatever. We'll watch it tomorrow. So this close, this is the lowest close of the earnings lows. It broke below 18 bucks. Not a big move yet, but I'm definitely watching it for tomorrow. A couple of swings that I've been holding for several days. Peloton continues to kind of, just drift. Again, I still think there's a shot at 442 next up, potentially 4. And CarGurus I shorted a couple of days ago. It's back to my entry. So it's literally back to my entry. Hopefully tomorrow loses the low from several days ago and finally starts the swan dive. But let me give you guys a couple of ideas for tomorrow that I really like. Nvidia definitely should be prime watch tomorrow. Absolutely prime watch tomorrow. If it loses, Monday's low. Again, there's a shot this thing goes all the way down to the August lows. Could be really, really good. Tesla we just talked about as well. Guys, look at AMD. AMD is very, very close to the bottom of the range here. I definitely want to keep an eye on this thing as well. Look at Google. Big, big reversal today. Google took down one, two, three, four, five, six, seven, eight, nine days, ten days worth of buying. That's not good. Just close below the 20-day moving average. This thing starts confirming today's chattels. This thing could get hit as well. And Tesla. Again, like I said a little earlier ago, Tesla, I am definitely watching now on the bottom of the range here because it stopped there twice in the last weeks. So again, guys, remember, it's not the stock that we're watching. It's not the stock that we care about. It's the ranges both long and short that's ready to confirm. So very aggressive sell-off here towards the end of the day. The Nasdaq 2100 continues to build below the 50-day moving average. Now it's day five. Now every level we talked about so far has confirmed. And now we're looking if we can start confirming today's channel, then we're looking at 62, 60, and 58, which would be the last straw for the bulls that potentially could break the camel's backs. That's it, guys. We are set up for tomorrow. I'm hoping we get a gap up open. The only reason why is because I'd like to have a chance to trade all these stocks at the pivots that we're watching for tomorrow. The last thing I want to see tomorrow is the video Gap Down 10. I don't have any. So I'd love to see a little bit of a gap up open tomorrow. The bulls to get potentially squashed into supply start taking down today's channels and hopefully we can get a big measure potential session for tomorrow. Just a reminder, guys, tomorrow is Thursday.  So if you are joining us tomorrow in the live webinar, welcome aboard. Welcome aboard. Look forward to working with you. Other than that, guys, the video continues starts up again over the weekend. Have an awesome, awesome Thursday. Have an awesome, awesome Friday. And with God's help, I'll see you all there. Take care.


Very important piece of bills that we're signing we get to go in the back Dealing with maternal health bill package. This is a public hearing today and These bills are crucial. I want to thank Speaker Adrienne Adams for her support on these bills as well as others that I'm going to acknowledge We're signing seven bills So New York City will continue to be a national leader and maternal health It's a critical moment for women in this city and across the country in New York City wants to continue to be the place Where maternal health is protected from the overturning of Roe vs. Wade To state efforts to criminalize those who seek abortions Reproductive health care is in danger The health choices should be made by women and their bodies They should make the decisions on these important medical moments for those who choose it motherhood could be tough physically medically and Psychologically, I've seen firsthand the resiliency of New York City mothers and my mother gave birth to six children And the numbers are clear black women are nine times More likely to die of pregnancy related causes than white women in New York City That is an alarming unacceptable number. It is not acceptable for mothers to be dying at the moment We should be welcoming a new life We must do right by every mother and every baby New York City has taken action to support women and ensure their safety We're making sure women have access to maternal health care when they need it the most No matter the color of their skin their income bracket or What state they come from? This bill and these bills I should say will promote education increased transparency and expand access to maternal health care Intro 86 that's a establishes a public education program Regarding the city standards for respectful care at birth as well as the right to be free from pregnancy related discrimination intro intro 409 a dash a Improves access to data and maternal mortality and morbidity intro 472 dash a codifies a department of health program to train doulas and Provide doulas services to residents in all five boroughs intro 478 dash a launches an educational campaign on the benefits and services provided by doulas and midwives Intro 482 dash a requires the Department of Health to provide information on polycystic ovary syndrome in other health related items Interim 490 dash a codifies the Department of Health Efforts to provide sexual and reproductive health services and conduct research on sexual and reproductive health Disparities within the city and finally intro four five zero nine dash a codifies a public education program on maternal mortality and morbidity and requires the Department of Health to issue a report regarding the incidence of Cessarian sections and recommendation to reduce maternal mortality and severe morbidity This will continue to empower women with information and that's the goal here that we are putting forward Takes a city to raise a child That starts with supporting our mothers and these bills would accomplish that task Again, I want to thank Speaker Adrienne Adams Vanessa Gibson The president of the borough of Bronx public advocate Jomani Williams For intro 86 dash a council member Farah Lewis for intro 49 dash a and 48 482 dash a council member crystal Hudson for set 478 dash a Council woman Julie Menon for 490 dash a council member of Thea Stevens For intro 509 dash a and so now at this time as we always do we want to invite the public to comment on these bills and Open the floor for those comment on these bills The comments okay no comments, so I thank you and we'll be signing these bills at a later day Did you kind of coordinate To move this forward or was this kind of council doing its own thing and you now support it combination We put the items of doula something that I thought is very important something that we need to invest in more We roll out a program And in Brooklyn and we will continue to do so and a council woman Council persons that's just a share of my desire about the powers of doulas and how doulas Help not only with caesareans, but also Leave a the use of pain-related medicine Some people who work in this kind of who work in this industry were concerned about I guess Government involvement which could sometimes be a very personal type of thing I don't know if you have any information on How this will work will be kind of bullet be a smaller scale to start and will there be People who've been doing it for years coming in to talk about how they can expand this to a city-wide You know much larger than it usually is Well, our goal is to look at some of the best practices that are taking place across the globe and bring people to the table Who have participated in doula services for years as you stated there are many who have been providing this service and Providing adequate doula care and having government involved is not going to interrupt that is going to give the resources and support That's needed. I haven't heard that at all and I've been engaged in assisting and expanding doula services for my days of All-President and that's something that has not become that has not come across our desk at all Okay, what was some off-topics The family of Darius Lee the basketball star killed in Harlem two months ago Just in general and about the recent violence we continue to see they flag two things for me I wanted to get your comment on it if you could they're not really hearing much from police The unsolved murder still on going and victim services. They're not seeing more police in their neighborhood and cops They are seeing you flag this a couple weeks ago standing around not doing as much as they would like What do you say to families that continue to experience this gun violence and not see change that they would like in their Well, I think it's a combination and you know, we were extremely supportive of Darius.  We sent too many young men and in general was Specifically and in general too many young people are the victims of this violence I want them to The families are not only these this particular families, but all of us to add our voices to the catch release repeat system oftentimes the the those who are perpetrating this violence are Repeated offenders and we have been clear on that and the police officers doing their job of Almost close to 5,000 guns removed up the street 27 year high increase in felonious arrests In this city for serious violent crimes and those officers those small number of officers That are congregating the police commissioner was clear. Not only was the police commissioners clear. I'm clear We need omnipresence in our subway system and on our streets, but let's zero in on what we are doing What has not been done before? Paperplace ghost vehicles those vehicles were used to commit crimes. We were ignoring them We're not under this administration Motorcycles ATVs Creating havoc in our system in our city We saw how they use and criminal behavior with zero in on those types of vehicles and those who are using these Tools so our holistic approach. Yes, it was successful in decreasing shooters decrease in homicides But we have so much more to do around those serious Predatory crimes like burglary and robbery we could do a far better job if we stop allowing Dangerous people to go through the criminal justice system Is there anything you see implementing in the next couple of months short of continuing to push for the Changes that your administration can do within your control. You have specific plans. You're planning to launch. Yes, listen I've made it clear. We're going to call on Albany. We're going to call on our prosecutors We're going to call on our judges, but I'm very clear on this the safety of this city is the responsibility of the mayor and In spite of what others are doing or not doing we have an obligation to do our job and more and we're going to continue To evolve you're going to see a greater Display of the omnipresent that's needed. I don't know if many people recognize that parade You don't see those large number of people that peaceful parades just standing around doing that anymore police officers I should say we are using our manpower better I've said this on the campaign trail and it was reinforced when I became the mayor our Failure to deploy our police personnel correctly. That is what we're zero zeroing in on if we deploy our police better It will continue to assist us in the job that we that we need to do and then we're going to Target and use probation to assist us. We're going to use the sheriff's department to assist us Our goal is to use every arm of law enforcement in a more coordinated way to go after violent people in the city Also I First on the asylum seekers We did a soft opening We want to make sure we get it right because as you know, the numbers are coming in at an increasing pace pace. We have close to 7600 Assignment seekers that have gone through our shelter currently we have about 5700 that are currently in our cell shelter This is in addition to the men and women of the city and families children and families that need shelter And so when you look at the large volumes that we brought in and managed In addition to those who are here already I have to take my hat off to the team for what they're doing We want to make sure when we open the site That we're able to the continuous success of managing large number of those who are seeking Asylum or seeking shelter in in this city some who have come are going to live with family members or moving on to other Municipalities in in the process, but we're looking to get it up in operating. We did a soft opening We want to make sure it's right. We don't want to just do something expeditiously. That's not going to be a successful Second question as well It's been a win week one I mean many of you I see in this room you were there week one when we rolled out of removing encampments giving people with mental health of Assistance people were there. There were a lot of naysayers first week 2,200 people Took us up on our offer people joked about when we said we're gonna create a brochure to show what the safe havens were like We did that we partnered with police and mental health professionals and volunteers and we were able to successfully Take off a substantial number of people that went into a safe havens or went into the shelter system Once they saw what we were representing and I was on the subway system I was speaking with people you ride to trains now You don't have those encampments everywhere that you saw before You don't have a large number of people who are homeless like you saw before Now winter time is going to going to bring new challenges. We're clear on that But we're going to be prepared when winter time comes so we could continue to do is right for our fellow New Yorkers So I think it was excess. I think the numbers the last I checked is close to 2000 but we're looking to the exact number on how many Took us up on our office to go into the shelter system or safe haven We give you the exact number the last I check I believe I saw somewhere in the area of 2000 Colleagues in the shaft did a story on the cover of the post today about the redeployment What it looks like of plant managers and transit and control bureaus wondering that's also part of With redeployment and also the direction that you see the NYPD going and also do you know how many officers that involves? You know it's it's police officers are supposed to be going after bad guys. I said this on the campaign trail the joy of Eric Adams, I'm doing what I said I said that we were going to civilianize the department too many police officers with guns and shields We're doing desk duties. I talked about this over and all over on the campaign trail And so when you see officers now Complimenting patrol these are officers that have a gun a shell went through the same training So when people say this is the bottom of the barrel or whatever terminology we're using That's just untrue. These are law enforcement officers that know how to fight crime they were doing civilian jobs and We're going to continue to decrease the number of officers who are doing civilian jobs and put them the job that they were Hired for they were hired to go after dangerous bad people and so the numbers we give you the exact numbers But we civilianize a substantial number of police officers would continue to expand the last numbers that I looked at I said, that's not good enough We're going to go into every agency and if there's not a real legitimate reason for that officer to be there That officer is going to go on patrol and fight crime and complement what we're doing I'll give you the exact number of what it was it was a I think was an area of 200 and something 76 I believe but we're going to go further. We get the exact number of how many we We civilianize but we're going to see substantial more Too many police officers are doing clerical duties During this on all hands on deck moment I need every police officer to carry out his function and that is to protect the public Now they may mean a time that two days a week They need to be in because they're doing a specific task. I accept that but every moment that we could have them out doing patrol During the omnipresent going to those areas where we're dealing with really violent people. I need my police officers to be there Mr. Mayor the public advocate the Comptroller and Constable member Today and council member Rivera said that the next month is she's gonna hold a hearing on that bill In the city council to ban solitary confinement punitive segregation your reaction to the visit and to the bill that is in front of the council first of all I come in Commissioner Molina and what he's doing, you know, you use there's a part of that. I didn't hear you say that they're seeing some improvements Okay, okay, okay. We got get that in there also somehow if Chris can get that in the story. I'll be happy, you know So there's part here's the question that no one is asking everyone who's critiquing Molina What do you do we're repeated dangerous? people who are in jail So if the council woman wants to ban these things that she's talking about and I don't believe the solitary confinement But if she wants to ban these things the question I need everyone to ask what you know one seems to ask that's critique jail people who are repeatedly dangerous over 80 percent of the inmates who are impunitive segregation are from attacking other inmates other inmates and so if they're saying Don't take a person who commits a crime on another inmates or a staffer Don't take them at a general population Then we're saying why we arresting people in the city general population is our straight our streets And so if the mission is if someone commits a crime We don't confine them then they need to say that say they don't believe anyone should ever go to jail for committing a predatory crime If you are in jail and you commit a predatory crime on a staffer a civilian or another inmate I want them to tell us what we should do with them because I don't know what they want us to do with them I think people who commit violent crimes should be removed from society and they commit violent crimes While they are removed from society they need to go somewhere so they don't hurt people again So she may have an answer that I'm not I'm aware of and just ask her for me What do we do with people who commit crimes inside jails violent crimes inside jails slashes stabbings assaults rapes What do we do with them? She said keep them a general population No one has made has answered that question yet Was saying that he does not believe the city is on track to shut down Rikers Island by 2027 due to a Lack of urgency about finding new places for the satellite jails. Just curious what your reaction is today Coach you accept Chris. Let you accept Chris. First of all, we're going to follow the law But can you ask the controller a question for me? I Believe with 2000 above the number of what we stated the Account was going to be I know we're higher in our and our ability when we go to the the Borough based jails The current numbers we have now is higher So I would like the controller to tell us. What do we do with those those? Inmates that's not going to fit in the Borough based jails. What do we do with them? See I need the folks that are idealistic to deal with the realism of this People are committing violent crimes and if the rule is from those who are saying of The idealism, let me tell them about the realism When you still have a higher number of people that can fit into the Borough based jails What do we do with those that continue to do shootings robberies assaults rapes and other crimes? What do we do with them? And if they don't have an answer for that Then they should ask themselves. How do we move forward? We're going to follow the law the law calls for the jails to be closed We're going to follow that law Actions that are being taken by the administration in terms of locating satellite jails We have to we have to we have to have a plan a plan B Because those who have created a plan a that I inherited Didn't obviously didn't think about a plan B did look at the incarceration numbers and One have to ask what was the plan B? What was the plan B that stated? If we don't drop down the prison population the way they thought we were what do we do? No one answered that question And that question should have been raised when we talked about the Borough based jail plan We have to look at everything from state facilities. We have to see we get help from the The governor we have to sit down with the Chair of Crime and Correction. We have to see what's available now Remember closing Rikers was dealing with a distance issue that we said it was unfair to have people travel great distances If we have to use state facilities We're gonna have to consider that every keeping the city safe Everything must be on the table and if it means using other facilities We have to we have to do that and that is what we are looking at because someone has to think through a Plan that I believe was put in place and we did not think through the numbers are higher Than what the Borough based jails would hold right now the numbers are higher So either we're saying once you reach a certain number Anyone who commits a crime a dangerous crime Once you reach a certain number then those who commit a dangerous crime no longer have to be arrested I mean, is that what people are saying someone has to start answering these questions before we make these policies Looking back last week at the the primaries you endorsed a couple candidates that lost one that got like blown out What do you think this says about the you know the moderate politics that you want to see in Albany and also did your Endorsement activities. Do you think it affected your standing in next year's session up in Albany? So, you know, you there's two parts to every statement You said that you endorsed candidates that lost now. What's the other part of that? Come on, Jeff One incumbent now you're putting attachments to it, you know Adabo my endorsement yes, I did right that that's why I'm here You know when you're doing analysis, I don't believe the congressional winner in the Park slope area was a DSA candidate. I find it to be more moderate than anything, you know, Kevin Parker I endorse You know, so if you're looking at the glass as being have empty Look at the fact that I see it as being half full and there's more support up in Albany than people think I get Real communications from my colleagues up there State that every we are in line with what you are saying And so I'm not worried about what's going to happen in Albany number go up there Just as that's maybe I started this year. Everybody said Eric is not gonna get any W's in Albany And we got everything from land trust in night share increase in the funding for our Childcare, you know They people thought we're gonna lose mayoral control. We got mayoral control and so, you know each session I worked in Albany each session, you know You have to stand up and get up there and convince people because what people learned when I went to Albany They said, you know what Eric agenda is the same as our gender. There was no one up there against Childcare increase. There was no one up there against The things that we were fighting for with land trust. So we'll be all right. You know, this is Albany Albany's Albany Hey, yeah, thanks. I wanted to ask you my colleague y'all go on and just publish your story about a police officer Michael sure Who he's evaded a discipline for an action he did during the 2020 protests He was caught on in his own body cam video taking the mask off of a protester and spraying them Remember that I remember that so given I know you've worked a lot in your career as a police officer on police reform and People are very upset about this that he only been docked ten vacations days. I want to know if you had a comment I know you haven't had a chance to read the story because it came out when you were up here But that's the gist of it. So don't have you any comment And if you think you should be another look at these this type of disciplinary action There were there were probably three incidents that I witnessed during the protests that were horrific That was one of them that was one of them to pull down the mask of a person and Mace them in that manner Really violated the trust and it really damaged the credibility of the number of officers That in spite of being yelled at being cursed at had things thrown at them. They showed the discipline There was no reason for For that officer to carry that out The committee commissioner, I'm sure she reviewed it the recommendations where it came from I will have a conversation with her because this is the first time I'm hearing about it but that was the poster child of what destroyed I believe of The good work that many of those officers carried out. I was in the street those days. I saw how challenging it was I saw how difficult it was and the overwhelming number of officers did not perform like that