January 13th, Gallimard published Grothendieck’s text Recoltes et Semailles in a fancy box containing two books.
Here’s a G-translation of Gallimard’s blurb:
“Considered the mathematical genius of the second half of the 20th century, Alexandre Grothendieck is the author of Récoltes et semailles, a kind of “monster” of more than a thousand pages, according to his own words. The mythical typescript, which opens with a sharp criticism of the ethics of mathematicians, will take the reader into the intimate territories of a spiritual experience after having initiated him into radical ecology.
In this literary braid, several stories intertwine, “a journey to discover a past; a meditation on existence; a picture of the mores of a milieu and an era (or the picture of the insidious and implacable shift from one era to another…); an investigation (almost police at times, and at others bordering on the swashbuckling novel in the depths of the mathematical megapolis…); a vast mathematical digression (which will sow more than one…); […] a diary ; a psychology of discovery and creation; an indictment (ruthless, as it should be…), even a settling of accounts in “the beautiful mathematical world” (and without giving gifts…)”.”
All literary events, great or small, are cause for the French to fill a radio show.
The embedded YouTube above starts at 12:06, when Bourguignon describes Grothendieck’s main achievements.
Clearly, he starts off with the notion of schemes which, he says, proved to be decisive in the further development of algebraic geometry. Five years ago, I guess he would have continued mentioning FLT and other striking results, impossible to prove without scheme theory.
Now, he goes on saying that Grothendieck laid the basis of topos theory (“to define it, I would need not one minute and a half but a year and a half”), which is only now showing its first applications.
Grothendieck, Bourguignon goes on, was the first to envision the true potential of this theory, which we should take very seriously according to people like Lafforgue and Connes, and which will have applications in fields far from algebraic geometry.
Topos20 is spreading rapidly among French mathematicians. We’ll have to await further results before Topos20 will become a pandemic.
Another interesting fragment starts at 16:19 and concerns Grothendieck’s gribouillis, the 50.000 pages of scribblings found in Lasserre after his death.
Bourguignon had the opportunity to see them some time ago, and when asked to describe them he tells they are in ‘caisses’ stacked in a ‘libraire’.
Here’s a picture of these crates taken by Leila Schneps in Lasserre around the time of Grothendieck’s funeral.
If you want to know what’s in these notes, and how they ended up at that place in Paris, you might want to read this and that post.
If Bourguignon had to consult these notes at the Librairie Alain Brieux, it seems that there is no progress in the negotiations with Grothendieck’s children to make them public, or at least accessible.
Here’s a nice introduction to neural networks for category theorists by Bruno Gavranovic. At 1.49m he tries to explain supervised learning with neural networks in one slide. Learners show up later in the talk.
$\mathbf{Poly}$ is the category of all polynomial functors, that is, things of the form
\[
p = \sum_{i \in p(1)} y^{p[i]}~:~\mathbf{Sets} \rightarrow \mathbf{Sets} \qquad S \mapsto \bigsqcup_{i \in p(1)} Maps(p[i],S) \]
with $p(1)$ and all $p[i]$ sets.
Last time I gave Spivak’s ‘corolla’ picture to think about such functors.
I prefer to view $p \in \mathbf{Poly}$ as an horribly discrete ‘sheaf’ $\mathcal{P}$ over the ‘space’ $p(1)$ with stalk $p[i]=\mathcal{P}_i$ at point $i \in p(1)$.
A morphism $p \rightarrow q$ in $\mathbf{Poly}$ is a map $\varphi_1 : p(1) \rightarrow q(1)$, together with for all $i \in p(1)$ a map $\varphi^{\#}_i : q[\varphi_1(i)] \rightarrow p[i]$.
In the sheaf picture, this gives a map of sheaves over the space $p(1)$ from the inverse image sheaf $\varphi_1^* \mathcal{Q}$ to $\mathcal{P}$.
But, unless you dream of sheaves in the night, by all means stick to Spivak’s corolla picture.
A learner $A \rightarrow B$ between two sets $A$ and $B$ is a complicated tuple of things $(P,I,U,R)$:
$P$ is a set, a parameter space of some maps from $A$ to $B$.
$I$ is the interpretation map $I : P \times A \rightarrow B$ describing the maps in $P$.
$U$ is the update map $U : P \times A \times B \rightarrow P$, the learning procedure. The idea is that $U(p,a,b)$ is a map which sends $a$ closer to $b$ than the map $p$ did.
$R$ is the request map $R : P \times A \times B \rightarrow A$.
Here’s a nice application of $\mathbf{Poly}$’s set-up:
Morphisms $\mathbf{P y^P \rightarrow Maps(A,B) \times Maps(A \times B,A) y^{A \times B}}$ in $\mathbf{Poly}$ coincide with learners $\mathbf{A \rightarrow B}$ with parameter space $\mathbf{P}$.
This follows from unpacking the definition of morphism in $\mathbf{Poly}$ and the process CT-ers prefer to call Currying.
The space-map $\varphi_1 : P \rightarrow Maps(A,B) \times Maps(A \times B,A)$ gives us the interpretation and request-map, whereas the sheaf-map $\varphi^{\#}$ gives us the more mysterious update-map $P \times A \times B \rightarrow P$.
$\mathbf{Learn(A,B)}$ is the category with objects all the learners $A \rightarrow B$ (for all paramater-sets $P$), and with morphisms defined naturally, that is, maps between the parameter-sets, compatible with the structural maps.
$\mathbf{Learn(A,B)}$ is a topos. In fact, it is the topos of all set-valued representations of a (huge) directed graph $\mathbf{G_{AB}}$.
This will take some time.
Let’s bring some dynamics in. Take any polynmial functor $p \in \mathbf{Poly}$ and fix a morphism in $\mathbf{Poly}$
\[
\varphi = (\varphi_1,\varphi[-])~:~p(1) y^{p(1)} \rightarrow p \]
with space-map $\varphi_1$ the identity map.
We form a directed graph:
the vertices are the elements of $p(1)$,
vertex $i \in p(1)$ is the source vertex of exactly one arrow for every $a \in p[i]$,
the target vertex of that arrow is the vertex $\phi[i](a) \in p(1)$.
Here’s one possibility from Spivak’s paper for $p = 2y^2 + 1$, with the coefficient $2$-set $\{ \text{green dot, yellow dot} \}$, and with $1$ the singleton $\{ \text{red dot} \}$.
Start at one vertex and move after a minute along a directed edge to the next (possibly the same) vertex. The potential evolutions in time will then form a tree, with each node given a label in $p(1)$.
If we start at the green dot, we get this tree of potential time-evolutions
There are exactly $\# p[i]$ branches leaving a node labeled $i \in p(1)$, and all subtrees emanating from equal labelled nodes are isomorphic.
If we had started at the yellow dot we had obtained a labelled tree isomorphic to the subtree emanating here from any yellow dot.
We can do the same things for any morphism in $\mathbf{Poly}$ of the form
\[
\varphi = (\varphi_1,\varphi[-])~:~Sy^S \rightarrow p \]
Now, we have a directed graph with vertices the elements $s \in S$, with as many edges leaving vertex $s$ as there are elements $a \in p[\varphi_1(s)]$, and with the target vertex of the edge labeled $a$ starting in $s$ the vertex $\varphi[\varphi_1(s)](A)$.
Once we have this directed graph on $\# S$ vertices we can label vertex $s$ with the label $\varphi_1(s)$ from $p(1)$.
In this way, the time evolutions starting at a vertex $s \in S$ will give us a $p(1)$-labelled rooted tree.
But now, it is possibly that two distinct vertices can have the same $p(1)$-labeled tree of evolutions. But also, trees corresponding to equal labeled vertices can be different.
Right, I guess we’re ready to define the graph $G_{AB}$ and prove that $\mathbf{Learn(A,B)}$ is a topos.
In the case of learners, we have the target polynomial functor $p=C y^{A \times B}$ with $C = Maps(A,B) \times Maps(A \times B,A)$, that is
\[
p(1) = C \quad \text{and all} \quad p[i]=A \times B \]
Start with the free rooted tree $T$ having exactly $\# A \times B$ branches growing from each node.
Here’s the directed graph $G_{AB}$:
vertices $v_{\chi}$ correspond to the different $C$-labelings of $T$, one $C$-labeled rooted tree $T_{\chi}$ for every map $\chi : vtx(T) \rightarrow C$,
arrows $v_{\chi} \rightarrow v_{\omega}$ if and only if $T_{\omega}$ is the rooted $C$-labelled tree isomorphic to the subtree of $T_{\chi}$ rooted at one step from the root.
A learner $\mathbf{A \rightarrow B}$ gives a set-valued representation of $\mathbf{G_{AB}}$.
We saw that a learner $A \rightarrow B$ is the same thing as a morphism in $\mathbf{Poly}$
\[
\varphi = (\varphi_1,\varphi[-])~:~P y^P \rightarrow C y^{A \times B} \]
with $P$ the parameter set of maps.
Here’s what we have to do:
1. Draw the directed graph on vertices $p \in P$ giving the dynamics of the morphism $\varphi$. This graph describes how the learner can cycle through the parameter-set.
2. Use the map $\varphi_1$ to label the vertices with elements from $C$.
3. For each vertex draw the rooted $C$-labeled tree of potential time-evolutions starting in that vertex.
In this example the time-evolutions of the two green vertices are the same, but in general they can be different.
4. Find the vertices in $G_{AB}$ determined by these $C$-labeled trees and note that they span a full subgraph of $G_{AB}$.
5. The vertex-set $P_v$ consists of all elements from $p$ whose ($C$-labeled) vertex has evolution-tree $T_v$. If $v \rightarrow w$ is a directed edge in $G_{AB}$ corresponding to an element $(a,b) \in A \times B$, then the map on the vertex-sets corresponding to this edge is
\[
f_{v,(a,b)}~:~P_v \rightarrow P_w \qquad p \mapsto \varphi[\varphi_1(p)](a,b) \]
A set-valued representation of $\mathbf{G_{AB}}$ gives a learner $\mathbf{A \rightarrow B}$.
1. Take a set-valued representation of $G_{AB}$, that is, the finite or infinite collection of vertices $V$ in $G_{AB}$ where the vertex-set $P_v$ is non-empty. Note that these vertices span a full subgraph of $G_{AB}$.
And, for each directed arrow $v \rightarrow w$ in this subgraph, labeled by an element $(a,b) \in A \times B$ we have a map
\[
f_{v,(a,b)}~:~P_v \rightarrow P_w \]
2. The parameter set of our learner will be $P = \sqcup_v P_v$, the disjoint union of the non-empty vertex-sets.
3. The space-map $\varphi_1 : P \rightarrow C$ will send an element in $P_v$ to the $C$-label of the root of the tree $T_v$. This gives us already the interpretation and request maps
\[
I : P \times A \rightarrow B \quad \text{and} \quad R : P \times A \times B \rightarrow A \]
4. The update map $U : P \times A \times B \rightarrow P$ follows from the sheaf-map we can define stalk-wise
\[
\varphi[\varphi_1(p)](a,b) = f_{v,(a,b)}(p) \]
if $p \in P_v$.
That’s all folks!
$\mathbf{Learn(A,B)}$ is equivalent to the (covariant) functors $\mathbf{G_{AB} \rightarrow Sets}$.
Changing the directions of all arrows in $G_{AB}$ any covariant functor $\mathbf{G_{AB} \rightarrow Sets}$ becomes a contravariant functor $\mathbf{G_{AB}^o \rightarrow Sets}$, making $\mathbf{Learn(A,B)}$ an honest to Groth topos!
Every topos comes with its own logic, so we have a ‘learners’ logic’. (to be continued)
Following up on the deep learning and toposes-post, I was planning to do something on the logic of neural networks.
Prepping for this I saw David Spivak’s paper Learner’s Languages doing exactly that, but in the more general setting of ‘learners’ (see also the deep learning post).
And then … I fell under the spell of $\mathbf{Poly}$.
Spivak is a story-telling talent. A long time ago I copied his short story (actually his abstract for a talk) “Presheaf, the cobbler” in the Children have always loved colimits-post.
Last week, he did post Poly makes me happy and smart on the blog of the Topos Institute, which is another great read.
If you are more the reading-type, the 273 pages of the Poly-book will also kill a good number of your living hours.
Personally, I have no great appetite for category theory, I prefer to digest it in homeopathic doses. And, I’m allergic to co-terminology.
So then, how to define $\mathbf{Poly}$ for the likes of me?
$\mathbf{Poly}$, you might have surmised, is a category. So, we need ‘objects’ and ‘morphisms’ between them.
Any set $A$ has a corresponding ‘representable functor’ sending a given set $S$ to the set of all maps from $A$ to $S$
\[
y^A~:~\mathbf{Sets} \rightarrow \mathbf{Sets} \qquad S \mapsto S^A=Maps(A,S) \]
This looks like a monomial in a variable $y$ ($y$ for Yoneda, of course), but does it work?
What is $y^1$, where $1$ stands for the one-element set $\{ \ast \}$? $Maps(1,S)=S$, so $y^1$ is the identity functor sending $S$ to $S$.
What is $y^0$, where $0$ is the empty set $\emptyset$? Well, for any set $S$ there is just one map $\emptyset \rightarrow S$, so $y^0$ is the constant functor sending any set $S$ to $1$. That is, $y^0=1$.
Going from monomials to polynomials we need an addition. We add such representable functors by taking disjoint unions (finite or infinite), that is
\[
\sum_{i \in I} y^{A_i}~:~\mathbf{Sets} \rightarrow \mathbf{Sets} \qquad S \mapsto \bigsqcup_{i \in I} Maps(A_i,S) \]
If all $A_i$ are equal (meaning, they have the same cardinality) we use the shorthand $Iy^A$ for this sum.
The objects in $\mathbf{Poly}$ are exactly these ‘polynomial functors’
\[
p = \sum_{i \in I} y^{p[i]} \]
with all $p[i] \in \mathbf{Sets}$. Remark that $p(1)=I$ as for any set $A$ there is just one map to $1$, that is $y^A(1) = Maps(A,1) = 1$, and we can write
\[
p = \sum_{i \in p(1)} y^{p[i]} \]
An object $p \in \mathbf{Poly}$ is thus described by the couple $(p(1),p[-])$ with $p(1)$ a set, and a functor $p[-] : p(1) \rightarrow \mathbf{Sets}$ where $p(1)$ is now a category with objects the elements of $p(1)$ and no morphisms apart from the identities.
We can depict $p$ by a trimmed down forest, Spivak calls it the corolla of $p$, where the tree roots are the elements of $p(1)$ and the tree with root $i \in p(1)$ has one branch from the root for any element in $p[i]$. The corolla of $p=y^2+2y+1$ looks like
If $M$ is an $m$-dimensional manifold, then you might view its tangent bundle $TM$ set-theoretically as the ‘corolla’ of the polynomial functor $M y^{\mathbb{R}^m}$, the tree-roots corresponding to the points of the manifold, and the branches to the different tangent vectors in these points.
Morphisms in $\mathbf{Poly}$ are a bit strange. For two polynomial functors $p=(p(1),p[-])$ and $q=(q(1),q[-])$ a map $p \rightarrow q$ in $\mathbf{Poly}$ consists of
a map $\phi_1 : p(1) \rightarrow q(1)$ on the tree-roots in the right direction, and
for any $i \in p(1)$ a map $q[\phi_1(i)] \rightarrow p[i]$ on the branches in the opposite direction
In our manifold/tangentbundle example, a morphism $My^{\mathbb{R}^m} \rightarrow y^1$ sends every point $p \in M$ to the unique root of $y^1$ and the unique branch in $y^1$ picks out a unique tangent-vector for every point of $M$. That is, vectorfields on $M$ are very special (smooth) morphisms $Mu^{\mathbb{R}^m} \rightarrow y^1$ in $\mathbf{Poly}$.
A smooth map between manifolds $M \rightarrow N$, does not determine a morphism $My^{\mathbb{R}^m} \rightarrow N y^{\mathbb{R}^n}$ in $\mathbf{Poly}$ because tangent vectors are pushed forward, not pulled back.
If instead we view the cotangent bundle $T^*M$ as the corolla of the polynomial functor $My^{\mathbb{R}^m}$, then everything works well.
But then, I promised not to use co-terminology…
Another time I hope to tell you how $\mathbf{Poly}$ helps us to understand the logic of learners.
Judging from this and that paper, deep learning is the string theory of the 2020s for geometers and representation theorists.
String theory is the 90s answer to the tears of algebraic geometers worldwide trying to write the "Applications" part of their grant proposals. https://t.co/AboZ5WkPtc
If you want to know quickly what neural networks really are, I can recommend the post demystifying deep learning.
The typical layout of a deep neural network has an input layer $L_0$ allowing you to feed $N_0$ numbers to the system (a vector $\vec{v_0} \in \mathbb{R}^{N_0}$), an output layer $L_p$ spitting $N_p$ numbers back (a vector $\vec{v_p} \in \mathbb{R}^{N_p}$), and $p-1$ hidden layers $L_1,\dots,L_{p-1}$ where all the magic happens. The hidden layer $L_i$ has $N_i$ virtual neurons, their states giving a vector $\vec{v_i} \in \mathbb{R}^{N_i}$.
For simplicity let’s assume all neurons in layer $L_i$ are wired to every neuron in layer $L_{i+1}$, the relevance of these connections given by a matrix of weights $W_i \in M_{N_{i+1} \times N_i}(\mathbb{R})$.
If at any given moment the ‘state’ of the neural network is described by the state-vectors $\vec{v_1},\dots,\vec{v_{p-1}}$ and the weight-matrices $W_0,\dots,W_p$, then an input $\vec{v_0}$ will typically result in new states of the neurons in layer $L_1$ given by
\[
\vec{v_1}’ = c_0(W_0.\vec{v_0}+\vec{v_1}) \]
which will then give new states in layer $L_2$
\[
\vec{v_2}’ = c_1(W_1.\vec{v_1}’+\vec{v_2}) \]
and so on, rippling through the network, until we get as the output
\[
\vec{v_p} = c_{p-1}(W_{p-1}.\vec{v_{p-1}}’) \]
where all the $c_i$ are fixed smooth activation functions $c_i : \mathbb{R}^{N_{i+1}} \rightarrow \mathbb{R}^{N_{i+1}}$.
This is just the dynamic, or forward working of the network.
The learning happens by comparing the computed output with the expected output, and working backwards through the network to alter slightly the state-vectors in all layers, and the weight-matrices between them. This process is called back-propagation, and involves the gradient descent procedure.
For any two sets $A$ and $B$, a learner $A \rightarrow B$ is a tuple $(P,I,U,R)$ where
$P$ is a set, a parameter space of some functions from $A$ to $B$.
$I$ is the interpretation map $I : P \times A \rightarrow B$ describing the functions in $P$.
$U$ is the update map $U : P \times A \times B \rightarrow P$, part of the learning procedure. The idea is that $U(p,a,b)$ is a map which sends $a$ closer to $b$ than the map $p$ did.
$R$ is the request map $R : P \times A \times B \rightarrow A$, the other part of the learning procedure. The idea is that the new element $R(p,a,b)=a’$ in $A$ is such that $p(a’)$ will be closer to $b$ than $p(a)$ was.
The request map is also crucial is defining the composition of two learners $A \rightarrow B$ and $B \rightarrow C$. $\mathbf{Learn}$ is the (symmetric, monoidal) category with objects all sets and morphisms equivalence classes of learners (defined in the natural way).
In this way we can view a deep neural network with $p$ layers as before to be the composition of $p$ learners
\[
\mathbb{R}^{N_0} \rightarrow \mathbb{R}^{N_1} \rightarrow \mathbb{R}^{N_2} \rightarrow \dots \rightarrow \mathbb{R}^{N_p} \]
where the learner describing the transition from the $i$-th to the $i+1$-th layer is given by the equivalence class of data $(A_i,B_i,P_i,I_i,U_i,R_i)$ with
\[
A_i = \mathbb{R}^{N_i},~B_i = \mathbb{R}^{N_{i+1}},~P_i = M_{N_{i+1} \times N_i}(\mathbb{R}) \times \mathbb{R}^{N_{i+1}} \]
and interpretation map for $p = (W_i,\vec{v}_{i+1}) \in P_i$
\[
I_i(p,\vec{v_i}) = c_i(W_i.\vec{v_i}+\vec{v}_{i+1}) \]
The update and request maps (encoding back-propagation and gradient-descent in this case) are explicitly given in theorem III.2 of the paper, and they behave functorial (whence the title of the paper).
More generally, we will now associate objects of a topos (actually just sheaves over a simple topological space) to a network op $p$ learners
\[
A_0 \rightarrow A_1 \rightarrow A_2 \rightarrow \dots \rightarrow A_p \]
inspired by section I.2 of Topos and Stacks of Deep Neural Networks.
The underlying category will be the poset-category (the opposite of the ordering of the layers)
\[
0 \leftarrow 1 \leftarrow 2 \leftarrow \dots \leftarrow p \]
The presheaf on a poset is a locale and in this case even the topos of sheaves on the topological space with $p+1$ nested open sets.
\[
X = U_0 \supseteq U_1 \supseteq U_2 \supseteq \dots \supseteq U_p = \emptyset \]
If the learner $A_i \rightarrow A_{i+1}$ is (the equivalence class) of the tuple $(A_i,A_{i+1},P_i,I_i,U_i,R_i)$ we will now describe two sheaves $\mathcal{W}$ and $\mathcal{X}$ on the topological space $X$.
$\mathcal{W}$ has as sections $\Gamma(\mathcal{W},U_i) = \prod_{j=i}^{p-1} P_i$ and the obvious projection maps as the restriction maps.
$\mathcal{X}$ has as sections $\Gamma(\mathcal{X},U_i) = A_i \times \Gamma(\mathcal{W},U_i)$ and restriction map to the next smaller open
\[
\rho^i_{i+1}~:~\Gamma(\mathcal{X},U_i) \rightarrow \Gamma(\mathcal{X},U_{i+1}) \qquad (a_i,(p_i,p’)) \mapsto (p_i(a_i),p’) \]
and other retriction maps by composition.
A major result in Topos and Stacks of Deep Neural Networks is that back-propagation is a natural transformation, that is, a sheaf-morphism $\mathcal{X} \rightarrow \mathcal{X}$.
In this general setting of layered learners we can always define a map on the sections of $\mathcal{X}$ (for every open $U_i$), $\Gamma(\mathcal{X},U_i) \rightarrow \Gamma(\mathcal{X},U_i)$
\[
(a_,(p_i,p’)) \mapsto (R(p_i,a_i,p_i(a_i)),(U(p_i,a_i,p_i(a_i)),p’) \]
But, in order for this to define a sheaf-morphism, compatible with the restrictions, we will have to impose restrictions on the update and restriction maps of the learners, in general.
Olivia Caramello, who also contributed to the seminar, posts on her blog Around Toposes that the proceedings of this lectures series is now available from the SMF.
Olivia’s blogpost links also to the YouTube channel of the seminar. Several of these talks are well worth your time watching.
In 1973, Grothendieck gave three lectures series at the Department of Mathematics of SUNY at Buffalo, the first on ‘Algebraic Geometry’, the second on ‘The Theory of Algebraic Groups’ and the third one on ‘Topos Theory’.
This MathOverflow (soft) question links to this page stating:
“The copyright of all these recordings is that of the Department of Mathematics of SUNY at Buffalo to whose representatives, in particular Professors Emeritus Jack DUSKIN and Bill LAWVERE exceptional thanks are due both for the preservation and transmission of this historic archive, the only substantial archive of recordings of courses given by one of the greatest mathematicians of all time, whose work and ideas exercised arguably the most profound influence of any individual figure in shaping the mathematics of the second half od the 20th Century. The material which it is proposed to make available here, with their agreement, will form a mirror site to the principal site entitled “Grothendieck at Buffalo” (url: ).”
Sadly, the URL is still missing.
Fortunately, another answer links to the Grothendieck project Thèmes pour une Harmonie by Mateo Carmona. If you scroll down to the 1973-section, you’ll find there all of the recordings of these three Grothendieck series of talks!
To whet your appetite, here’s the first part of his talk on topos theory on April 4th, 1973:
For all subsequent recordings of his talks in the Topos Theory series on May 11th, May 18th, May 25th, May 30th, June 4th, June 6th, June 20th, June 27th, July 2nd, July 10th, July 11th and July 12th, please consult Mateo’s website (under section 1973).
“While the origins of the “$\mathbb{F}_1$-story” go back to attempts to transfer Weil’s proof of the Riemann Hypothesis from the function field case to that of number fields on one hand, and Tits’s Dream of realizing Weyl groups as the $\mathbb{F}_1$ points of algebraic groups on the other, the “$\mathbb{F}_1$” moniker has come to encompass a wide variety of phenomena and analogies spanning algebraic geometry, algebraic topology, arithmetic, combinatorics, representation theory, non-commutative geometry etc. It is therefore impossible to compile an exhaustive list of topics that might be discussed. The following is but a small sample of topics that may be covered:
Algebraic geometry in non-additive contexts – monoid schemes, lambda-schemes, blue schemes, semiring and hyperfield schemes, etc.
Arithmetic – connections with motives, non-archimedean and analytic geometry
Tropical geometry and geometric matroid theory
Algebraic topology – K-theory of monoid and other “non-additive” schemes/categories, higher Segal spaces
Representation theory – Hall algebras, degenerations of quantum groups, quivers
Combinatorics – finite field and incidence geometry, and various generalizations”
The seminar takes place on alternating Wednesdays from 15:00 PM – 16:00 PM European Standard Time (=GMT+1). There will be room for mathematical discussion after each lecture.
The first meeting takes place Wednesday, January 19th 2022. If you want to receive abstracts of the talks and their Zoom-links, you should sign up for the mailing list.
“Every year, the Huawei Young Talents Program will fund on average 7 postdoctoral fellowships that will be awarded by the Institute’s Scientific Council, only on the basis of scientific excellence. The fellows will collaborate with the Institute’s permanent professors and work on topics of their interest.”
Over the next ten years, Huawei will invest 5 million euros in this program, and an additional 1 million euros goes into the creation of the ‘Huawei Chair in Algebraic Geometry’. It comes as no particular surprise that the first chairholder is Laurent Lafforgue.
The latter part of the talk (starting at 47:50) clarifies somewhat Huawei’s interest in topos theory, and what Lafforgue (and others) hope to get out of their collaboration with the telecom company.
Clearly, Huawei is interested in deep neural networks, and if you can convince them your expertise is useful in that area, perhaps they’ll trow some money at you.
Jean-Claude Belfiore, another mathematician turned Huaweian, is convinced topos theory is the correct tool to study DNNs. Here’s his Huawei-clip from which it is clear he was originally hired to improve Huawei’s polar code.
Towards the end of his talk, Lafforgue suggests the idea of creating an institute devoted to toposes and their applications, endorsed by IHES and supported by Huawei. Surely he knows that the Topos Institute already exists.
And, if you wonder why Huawei trows money at IHES rather than your university, I leave you with Lafforgue’s parting words:
“IHES professors are able to think and evaluate for themselves, whereas most mathematicians just follow ‘group thinking'”
Huawei, the Chinese telecom giant, appears to support (and divide) the French mathematical community.
I was surprised to see that Laurent Lafforgue’s affiliation recently changed from ‘IHES’ to ‘Huawei’, for example here as one of the organisers of the Lake Como conference on ‘Unifying themes in geometry’.
Judging from this short Huawei-clip (in French) he thoroughly enjoys his new position.
Huawei claims that ‘Three more winners of the highest mathematics award have now joined Huawei’:
These news-stories seem to have been G-translated from the Chinese, resulting in misspellings and perhaps other inaccuracies. Maxim’s research field is described as ‘kink theory’ (LoL).
Apart from luring away Fields medallist, Huawei set up last year the brand new Huawei Lagrange Research Center in the posh 7th arrondissement de Paris. (This ‘Lagrange Center’ is different from the Lagrange Institute in Paris devoted to astronomy and physics.)
It aims to host about 30 researchers in mathematics and computer science, giving them the opportunity to live in the ‘unique eco-system of Paris, having the largest group of mathematicians in the world, as well as the best universities’.
“In the context of a very insufficient research and development effort in France, and bleak prospects for our young researchers, it is tempting to welcome the creation of the Lagrange center. We welcome the rise of Chinese mathematics to the highest level, and we are obviously in favour of scientific cooperation with our Chinese colleagues.
But in view of the role played by Huawei in the repression in Xinjiang and potentially everywhere in China, we call on mathematicians and computer scientists already engaged to withdraw from this project. We ask all researchers not to participate in the activities of this center, as we ourselves are committed to doing.”
In recent months, my primary focus was on teaching and family matters, so I make advantage of this Christmas break to catch up with some of the things I’ve missed.
In Corona times, virtual conferences seem to sprout up out of nowhere, everywhere (zero costs), giving us an inflation of YouTubeD talks. I’m always grateful to the organisers of such events to provide the slides of the talks separately, as the generic YouTubeD-talk consists merely in reading off the slides.
Allow me to point you to one of the rare exceptions to this rule.
When I downloaded the slides of Alain Connes’ talk at the conference From noncommutative geometry to the tropical geometry of the scaling site I just saw a collage of graphics from his endless stream of papers with Katia Consani, and slides I’d seen before watching several of his YouTubeD-talks in recent years.
Boy, am I glad I gave Alain 5 minutes to convince me this talk was different.
For the better part of his talk, Alain didn’t just read off the slides, but rather tried to explain the thought processes that led him and Katia to move on from the results on this slide to those on the next one.
If you’re pressed for time, perhaps you might join in at 49.34 into the talk, when he acknowledges the previous (tropical) approach ran out of steam as they were unable to define any $H^1$ properly, and how this led them to ‘absolute’ algebraic geometry, meaning over the sphere spectrum $\mathbb{S}$.
Sadly, for some reason Alain didn’t manage to get his final two slides on screen. So, in this case, the slides actually add value to the talk…
A fantastic collection of handouts for a two week summer workshop entitled ’Geometry and the Imagination’, led by John Conway, Peter Doyle, Jane Gilman and Bill Thurston at the Geometry Center in Minneapolis, June 1991, based on a course ‘Geometry and the Imagination’ they taught twice before at Princeton.
Among the goodies a long list of exercises in imagining (always useful to budding architects) and how to compute curvature by peeling potatoes and other vegetables…
If you think this will be too complicated, have a look at the proof that the orbifold Euler characteristic of any symmetry pattern in the plane with bounded fundamental domain is zero :
Take a large region in the plane that is topologically a disk (i.e. without holes). Its Euler characteristic is $1$. This is approximately equal to $N$ times the orbifold Euler characteristic for some large $N$, so the orbifold Euler characteristic must be $0$.
This then leads to the Orbifold Shop where they sell orbifold parts:
a handle for 2 Euros,
a mirror for 1 Euro,
a cross-cap for 1 Euro,
an order $n$ cone point for $(n-1)/n$ Euro,
an order $n$ corner reflector for $(n-1)/2n$ Euro, if you have the required mirrors to install this piece.
Here’s a standard brick wall, with its fundamental domain and corresponding orbifold made from a mirror piece (1 Euro), two order $2$ corner reflectors (each worth $.25$ Euro), and one order $2$ cone point (worth $.5$ Euro). That is, this orbifold will cost you exactly $2$ Euros.
If you spend exactly $2$ Euros at the Orbifold Shop (and there are $17$ different ways to do this), you will have an orbifold coming from a symmetry pattern in the plane with bounded fundamental domain, that is, one of the $17$ wallpaper patterns.
The aspiring architect should be warned that some constructions are simply not possible in 3D, even when they look convincing on paper, such as Escher’s Waterfall.
In his paper, Penrose gives a unified approach to debunk such drawings by using cohomology groups.
Clearly I have no desire to introduce cohomology, but it may still be possible to get the underlying idea across. Let’s take the Penrose triangle (all pictures below taken from Penrose’s paper)
The idea is to break up such a picture in several parts, each of which we do know to construct in 3D (that is, we take a particular cover of our figure). We can slice up the Penrose triangle in three parts, and if you ever played with Lego you’ll know how to construct each one of them.
Next, position the constructed pieces in space as in the picture and decide which of the two ends is closer to you. In $Q_1$ it is clear that point $A_{12}$ is closer to you than $A_{13}$, so we write $A_{12} < A_{13}$.
Similarly, looking at $Q_2$ and $Q_3$ we see that $A_{23} < A_{21}$ and that $A_{31} < A_{32}$.
Next, if we try to reassemble our figure we must glue $A_{12}$ to $A_{21}$, that is $A_{12}=A_{21}$, and similarly $A_{23}=A_{32}$ and $A_{31}=A_{13}$. But, then we get
\[
A_{13}=A_{31} < A_{32}=A_{23} < A_{21}=A_{12} < A_{13} \]
which is clearly absurd.
Once again, if you have suggestions for more material to be included, please let me know.
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