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<div class="elementToProof" style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
Dear Steve,</div>
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Thank you for your detailed response. </div>
<div class="elementToProof" style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
To focus on my three key questions, I respond to your comments below by clarifying the three questions.</div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);"> (1) You agreed that the grandmother cell does not explain how to learn invariances,
such as location invariance, scale invariance, and orientational invariance. I object to the
</span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);">grandmother cell idea
</span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">because it is a symbolic concept. </span></div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);"> (a) You wrote you glanced at our Cresceptron paper. It says: “the user manually
draws a polygon outlining the region of interest and types in the label of its class….” Contrary to what you wrote about Cresceptron, Cresceptron is a real-time and incremental learning algorithm. Cresceptron was the first neural network that learns incrementally
from natural and cluttered scenes, with my image annotation using a polygon. Its polygon idea was stolen by ImageNet (without citing) and ImageNet simplified my polygon to a rectangle. </span></div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);"> (b) Carpenter, Grossber, Reynolds, ARTMAP 1991 wrote, "on a trial and error basis
using only local operations". Does your ARTMAP algorithm step (A.1) give different accuracies from different initializations? </span></div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">
<a href="https://www.sciencedirect.com/science/article/abs/pii/089360809190012T" id="LPlnk" class="OWAAutoLink">
https://www.sciencedirect.com/science/article/abs/pii/089360809190012T</a></span></div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);"> (c) If I understand correctly, the above ARTMAP takes only monolithic inputs, where
"the vectors may encode visual representations of objects" not a vector of clutter science which contains irrelevant backgrounds like Cresceptron did. A latter paper of yours deals with partial views but it does not deal with natural images of cluttered scenes.</span></div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);"> (2) The
</span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);">SOVEREIGN</span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);"> model
discussed in your book does not start from a single cell like the human brain and does not learn incrementally. </span></div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);"> You wrote, "I am bewildered by your comment above". Probably you have not considered
brain-scale development. A zygote starts from a single cell. Thus, the brain should start from a single cell too. DN3 deals with brain-patterning from a single cell. </span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 16px; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);">Fig.
16.42 of SOVEREIGN in your book </span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">has a static block diagram and therefore, it does not deal with brain patterning.
It has an open skull to allow you to manually inject symbols as blocks.</span></div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);"> (3) Your models do not explain how to learn any Turing machines. <br>
You wrote, "no biological neural network model of how brains make minds learn Turing machines, except in the sense that our cognitive systems, that are parts of our brains, have learned to generate emergent properties that invented and can mathematically
analyze Turing machines. Is that what you mean?" <br>
No, please read the paper below, how a DN overall learns any Turing Machine, not just its cognitive subsystem. This is a necessary condition for any brain-modeling network because any such models must at least be complete in Turing machine logic.</span></div>
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<a href="https://www.scirp.org/reference/referencespapers?referenceid=1400949" id="LPlnk" class="OWAAutoLink">
https://www.scirp.org/reference/referencespapers?referenceid=1400949</a></span></div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);"> Best regards,</span></div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">-John</span></div>
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<a target="_blank" id="LPUrlAnchor301287" href="https://www.scirp.org/reference/referencespapers?referenceid=1400949" style="text-decoration: none; color: var(--themePrimary);">Weng, J. (2011) Three Theorems Brain-Like Networks Logically Reason and Optimally
Generalize. International Joint Conference on Neural Networks, San Jose, 31 July-5 August 2011, 2983-2990. - References - Scientific Research Publishing
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Weng, J. (2011) Three Theorems Brain-Like Networks Logically Reason and Optimally Generalize. International Joint Conference on Neural Networks, San Jose, 31 July-5 August 2011, 2983-2990.</div>
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<div class="elementToProof"><span style="font-family: Arial, Helvetica, sans-serif; font-size: 9.75pt; color: rgb(34, 34, 34);">On Sat, Feb 24, 2024 at 6:32 PM Grossberg, Stephen <steve@bu.edu> wrote:</span></div>
<blockquote style="background-color: rgb(255, 255, 255); margin: 0px 0px 0px 0.8ex; padding-left: 1ex; border-left: 1px solid rgb(204, 204, 204);">
<p style="text-align: left; text-indent: 0px; margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt; color: rgb(34, 34, 34);">Dear John,</span></p>
<p style="text-align: left; margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt; color: rgb(34, 34, 34);"> </span></p>
<p style="text-align: left; text-indent: 0px; margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt; color: rgb(34, 34, 34);">I reply below
<i>in italics</i>, among your questions:</span></p>
<p style="text-align: left; margin: 0px;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt; color: rgb(34, 34, 34);"> </span></p>
<div style="padding: 3pt 0in 0in; border-top: 1pt solid rgb(181, 196, 223);">
<p style="margin: 0px 0px 12pt;"><span style="color: black;"><b>From: </b>Weng, Juyang <</span><span style="color: rgb(17, 85, 204);"><a href="mailto:weng@msu.edu" target="_blank" id="OWAca904029-1ab9-1f3d-8aa4-abaaea2464ad" class="OWAAutoLink" style="color: rgb(17, 85, 204); margin-top: 0px; margin-bottom: 0px;">weng@msu.edu</a></span><span style="color: black;">><br>
<b>Date: </b>Saturday, February 24, 2024 at 4:44</span><span style="font-family: Arial, sans-serif; color: black;"> </span><span style="color: black;">PM<br>
<b>To: </b>Jeffrey Bowers <</span><span style="color: rgb(17, 85, 204);"><a href="mailto:J.Bowers@bristol.ac.uk" target="_blank" id="OWA91934823-e63a-d260-e9e3-c4b5a1c65380" class="OWAAutoLink" style="color: rgb(17, 85, 204); margin-top: 0px; margin-bottom: 0px;">J.Bowers@bristol.ac.uk</a></span><span style="color: black;">>,
Grossberg, Stephen <</span><span style="color: rgb(17, 85, 204);"><a href="mailto:steve@bu.edu" target="_blank" id="OWA1237c836-28f2-58f7-d09e-8e7d8c6db8f0" class="OWAAutoLink" style="color: rgb(17, 85, 204); margin-top: 0px; margin-bottom: 0px;">steve@bu.edu</a></span><span style="color: black;">>,
KENTRIDGE, ROBERT W. <</span><span style="color: rgb(17, 85, 204);"><a href="mailto:robert.kentridge@durham.ac.uk" target="_blank" id="OWAf263110d-6b91-4726-a32d-78e63b07cc73" class="OWAAutoLink" style="color: rgb(17, 85, 204); margin-top: 0px; margin-bottom: 0px;">robert.kentridge@durham.ac.uk</a></span><span style="color: black;"><wbr style="scroll-behavior:auto !important">>,
Gary Marcus <</span><span style="color: rgb(17, 85, 204);"><a href="mailto:gary.marcus@nyu.edu" target="_blank" id="OWAf9b65817-3944-9a6c-ea0f-8ee81af9370e" class="OWAAutoLink" style="color: rgb(17, 85, 204); margin-top: 0px; margin-bottom: 0px;">gary.marcus@nyu.edu</a></span><span style="color: black;">>,
Laurent Mertens <</span><span style="color: rgb(17, 85, 204);"><a href="mailto:laurent.mertens@kuleuven.be" target="_blank" id="OWAb6cc2129-e081-6dbd-abad-da9ca4a796f8" class="OWAAutoLink" style="color: rgb(17, 85, 204); margin-top: 0px; margin-bottom: 0px;">laurent.mertens@kuleuven.be</a></span><span style="color: black;">><br>
<b>Cc: </b></span><span style="color: rgb(17, 85, 204);"><a href="mailto:connectionists@mailman.srv.cs.cmu.edu" target="_blank" id="OWAa6eed9c0-78d4-c127-bc14-445cd98f3b88" class="OWAAutoLink" style="color: rgb(17, 85, 204); margin-top: 0px; margin-bottom: 0px;">connectionists@mailman.srv.cs.<wbr style="scroll-behavior:auto !important">cmu.edu</a></span><span style="color: black;"> <</span><span style="color: rgb(17, 85, 204);"><a href="mailto:connectionists@mailman.srv.cs.cmu.edu" target="_blank" id="OWA849d9d1c-60fa-99dd-1c3b-6d44f51a9c8d" class="OWAAutoLink" style="color: rgb(17, 85, 204); margin-top: 0px; margin-bottom: 0px;">connectionists@mailman.srv.<wbr style="scroll-behavior:auto !important">cs.cmu.edu</a></span><span style="color: black;">><br>
<b>Subject: </b>Re: Connectionists: Early history of symbolic and neural network approaches to AI</span></p>
</div>
<p style="margin: 0px;"><span style="font-family: Arial, sans-serif; font-size: 10pt; color: rgb(34, 34, 34);">Dear Steve,</span></p>
<p style="margin: 0px;"><span style="font-family: Arial, sans-serif; font-size: 10pt; color: rgb(34, 34, 34);"> I have had pleasure to listen and follow your various ART models. With your suggestions, I also bought your Book "Conscious Mind, Resonant Brain:
How Each Brain Makes a Mind" and browsed it. </span></p>
<p style="margin: 0px;"><span style="font-family: Arial, sans-serif; font-size: 10pt; color: rgb(34, 34, 34);"> Let me ask some questions that will be useful for many people on this list:<br>
(1) Do you agree that the grandmother cell does not explain how to learn invariances, such as location invariance, scale invariance, and orientational invariance? Of course, those invariances are not perfect as explained in my Cresceptron paper (IJCV 197),
arguably the first Deep Learning network for 3D?</span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"> </span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>The grandmother cell concept is nothing more than a verbal term. It is not a computational model, so does not learn anything.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>I glanced at your Cresceptron paper. It says:</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>“</i></span><span style="font-family: Roboto; font-size: 10.5pt; color: rgb(19, 19, 20); background-color: white;"><i> the user manually draws a
<wbr style="scroll-behavior:auto !important">polygon outlining the region <wbr style="scroll-behavior:auto !important">
of interest and types in the <wbr style="scroll-behavior:auto !important">label of its class….”</i></span></p>
<p style="margin: 0px;"><span style="font-family: Roboto; font-size: 10.5pt; color: rgb(19, 19, 20); background-color: white;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-family: Roboto; font-size: 10.5pt; color: rgb(19, 19, 20); background-color: white;"><i>It seems that this is not a self-organizing model that learns in real time through incremental learning. Our model is, and explains
challenging neurobiological data along the way.</i></span></p>
<p style="margin: 0px;"><span style="font-family: Roboto; font-size: 10.5pt; color: rgb(19, 19, 20); background-color: white;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-family: Roboto; font-size: 10.5pt; color: rgb(19, 19, 20); background-color: white;"><i>You also write that your model may be “the first Deep Learning network for 3D”. Our work does not use Deep Learning, which has
17 serious computational problems in addition to not being biologically plausible. None of these problems of back propagation and Deep Learning have been a problem for Adaptive Resonance Theory since I introduced it in 1976.</i></span></p>
<p style="margin: 0px;"><span style="font-family: Roboto; font-size: 10.5pt; color: rgb(19, 19, 20); background-color: white;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-family: Roboto; font-size: 10.5pt; color: rgb(19, 19, 20); background-color: white;"><i>In particular, Deep Learning is both untrustworthy (because it is not explainable) and unreliable (because it can experience catastrophic
forgetting).</i></span></p>
<p style="margin: 0px;"><span style="font-family: Roboto; font-size: 10.5pt; color: rgb(19, 19, 20); background-color: white;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-family: Roboto; font-size: 10.5pt; color: rgb(19, 19, 20); background-color: white;"><i>I review these 17 problems in my 2021 Magnum Opus. You can also find them discussed in Section 17 of the following 1988 article
that was published in the first issue of Neural Networks:</i></span></p>
<p style="margin: 0px;"><span style="font-family: Roboto; font-size: 10.5pt; color: rgb(19, 19, 20); background-color: white;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>Grossberg, S. (1988) Nonlinear neural networks: Principles, mechanisms, and architectures. </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>Neural Networks, </i><b><i>1
</i></b><i>, 17-61. </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt; color: rgb(17, 85, 204);"><i><a href="https://urldefense.com/v3/__https://sites.bu.edu/steveg/files/2016/06/Gro1988NN.pdf__;!!HXCxUKc!xN35yPj-P6e15c7znKJoQrBxAmtXY8YThFRNcdGPQeAqt2AeFfCCSk5RiXmHJEHJeJVKCv18BA$" target="_blank" id="OWA73f68098-ede4-69a1-9a51-21a7b181e109" class="OWAAutoLink" style="color: rgb(17, 85, 204); margin-top: 0px; margin-bottom: 0px;">https://sites.bu.edu/steveg/<wbr style="scroll-behavior:auto !important">files/2016/06/Gro1988NN.pdf</a></i></span></p>
<p style="margin: 0px;"><span style="font-family: Arial, sans-serif; font-size: 10pt; color: rgb(34, 34, 34);"><br>
(2) Your model discussed your book is not developmental, namely, does not start from a single cell like the human brain and does not learn incrementally. Could you point me to an incremental learning algorithm in your book if what I write is incorrect?</span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"> </span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>I am bewildered by your comment above, since it is obviously not true about ANY of my neural models of brain development and learning, all of which self-organize and work in an incremental learning setting.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>Such models are described in a self-contained and non-technical way in my book.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>Scores of my articles about self-organizing brain development and learning are described with all technical details on my web page
</i></span><span style="font-size: 11pt; color: rgb(17, 85, 204);"><i><a href="http://sites.bu.edu/steveg" target="_blank" id="OWA8121aae2-3616-1a2a-b0e2-c7ea8b1c9056" class="OWAAutoLink" style="color: rgb(17, 85, 204); margin-top: 0px; margin-bottom: 0px;">sites.bu.edu/steveg</a></i></span><span style="font-size: 11pt;"><i>.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>I am unclear what you mean by the phrase: “does not start from a single cell like the human brain” since you clearly do not mean that the human brain is composed of a single cell.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>On the other hand, Chapter 17 of my 2021 Magnum Opus clarifies that principles of complementarity, uncertainty, and resonance that are embodied in Adaptive Resonance Theory, as well as in various of
my other neural network models, also have precursors in cellular organisms that existed long before human brains did, including slime molds and Hydras.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>These design principles thus seem to have been conserved for a very long time during the evolutionary process.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>Principles of uncertainty, complementarity, and resonance also have analogs in the laws of physics with which our brains have ceaselessly interacted for eons during their evolution. Quantum mechanics
is one example of these principles in physics.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>Explaining in detail how our brains were shaped during evolution to also embody these physical principles is a long-term project worthy of a great deal of additional research.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"> </span></p>
<p style="margin: 0px;"><span style="font-family: Arial, sans-serif; font-size: 10pt; color: rgb(34, 34, 34);"> (3) Your model does not explain how to learn any Turing machines. </span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"> </span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>Human brains self-organize using analog signals and parallel computations, and do so in real time. Turing machines do not have these properties.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>So, yes, no biological neural network model of how brains make minds learn Turing machines, except in the sense that our cognitive systems, that are parts of our brains, have learned to generate emergent
properties that invented and can mathematically analyze Turing machines.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>Is that what you mean?</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>To explain how our brains can mathematically understand Turing machines, you first need to explain how our brains have learned to represent and use numerical representations in the first place.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>The question “where do numbers come from” is a fundamental one.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>Some progress has been made in modeling “where numbers come from” and how our brains can learn to use numerical representations and mathematical symbols. A LOT more work needs to be done on this fundamental
problem.</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>Perhaps the following article may be helpful:</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>Grossberg, S. and Repin, D. (2003) A neural model of how the brain represents and compares multi-digit numbers: Spatial and categorical processes. Neural Networks,</i><b><i> 16</i></b><i>, 1107-1140. </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt; color: rgb(17, 85, 204);"><i><a href="https://urldefense.com/v3/__https://sites.bu.edu/steveg/files/2016/06/GroRep2003NN.pdf__;!!HXCxUKc!xN35yPj-P6e15c7znKJoQrBxAmtXY8YThFRNcdGPQeAqt2AeFfCCSk5RiXmHJEHJeJXNTJH1kg$" target="_blank" id="OWA3a8cafc5-cfaa-33e2-f5e1-4d9b632fce80" class="OWAAutoLink" style="color: rgb(17, 85, 204); margin-top: 0px; margin-bottom: 0px;">https://sites.bu.edu/steveg/<wbr style="scroll-behavior:auto !important">files/2016/06/GroRep2003NN.pdf</a></i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>Best,</i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i> </i></span></p>
<p style="margin: 0px;"><span style="font-size: 11pt;"><i>Steve</i></span></p>
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