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Petite teen ladyboy jerking and sucking a white cock
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Tits Out: Reacting and Cringing To My Old Chaturbate Screen Captures From Live-streams!
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#ponderousness#southland#gnomesque#preinvolvement#overseated#outthrowing#cnn#self-dialogue#Darryn#exteriorly#subfigures#tranquillo#coffeeweed#vaxildan#fossilism#inquietudes#subcancellate#cypselomorphic#farrier#marinheiro
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I'm struggling a little bit to find the right 'data type' for a scissors morphism (of which a scissors congruence would be an isomorphism) in an arbitrary category C. Because the arrows of C aren't necessarily functions, you can't have a scissors morphism just be a more lax notion of function. The heavy handed way to do it would be to have a scissors morphism be an indexed family of C-morphisms, defined on a covering family of its domain object, satisfying certain coherence properties (this is how Zakharevich does it, or something like it). But that feels bad! It's too extrinsic, and I think you run the risk of having multiple morphisms for what 'should' be the same action.
One thing I tried is to have it be a specific 'sieve correspondence'. A sieve is kind of like a subobject, except there's way more of them. Say your morphisms are inclusions of squares into squares in the plane. There's no subobject in this category corresponding to a disk inside the square, but there is the sieve of all squares contained within that disk. Specifically, you can identify sieves on an object with the subfunctors of the representable presheaf of that object. So you can say that the data type of a scissors morphism is a (possibly union-preserving, to make it function-like) action from the sieves on the domain to the sieves on the codomain!
There's an issue here, though. Including the C-morphisms as trivial scissors morphisms (for the covering family consisting of just the identity arrow) in this way is not generally faithful. If you have the category of polygons and isometric inclusions then it is faithful, because an isometry on a figure with interior is uniquely determined by its action on the subfigures (so certainly by the action on the sieves!). But for the cyclic group of order 2 considered as a one-object category you run into issues. The presheaves on this category are the C₂-sets and the unique representable presheaf is the two point set with the obvious action. This object has two automorphisms, but both have the same action on the subobjects, because neither of the points are invariant subsets on their own. So reducing to sieve actions is not faithful!
So famously one of the consequences of the Yoneda lemma is that the action on representable presheaves (i.e. homset functors with fixed codomain) of postcomposition by some arrow is a faithful representation of the category; if postcomposing by f, f': A -> B gives the same function Hom(X,A) -> Hom(X,B) for all objects X, then f = f'. The problem is that it is also full; all such natural transformations are given by some postcomposition, specifically postcomposition by the image of id ∈ Hom(A,A) (and that's the proof of the Yoneda lemma, by the way, that's why people say it's so simple).
So maybe just straight up transformations (sans naturality) between representable presheaves? That is, just a set function Hom(X,A) -> Hom(X,B) for every object X? The problem is that you then need to decide where id_A gets sent to. And the 'image' of a scissors morphism that you do want to exist might not be a subobject. Like, if your category only contains connected polygons, you still want the scissors morphisms to contain actions that disconnect the object. That's what scissors do! So again mapping it onto a sieve is perfect, since it contains exactly those phantom subobjects that you need to exist. You just need enough sieves to differentiate the C-arrows. But how to get them?
#math#this is not an effortpost to explain anything btw this is literally just me rambling about my research
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A guide to matplotlib subfigures for creating complex multi-panel figures
#Technology #DataAnalytics #DataDriven https://towardsdatascience.com/a-guide-to-matplotlib-subfigures-for-creating-complex-multi-panel-figures-70fa8f6c38a4?utm_source=dlvr.it&utm_medium=tumblr
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kɒɪðəpɪkkəʒədɪntʃnraɪ
Pronounced: kouithuhpikkuhzuhdintshnrai.
Pantheon of: amount, water, originality, cytoplasm, mathematical space, figure, aclinic line, note, ground state, worthlessness.
Entities
Eɑʊɛhzrʌzbəfæuɛəræsz
Pronounced: eahooayhzruzbuhfauayuhrasz
Mathematical Space: null space.
Amount: critical mass.
Cytoplasm: sarcoplasm.
Originality: freshness.
Worthlessness: valuelessness.
Figure: equilateral.
Legends: impairment, bombing, escape.
Prophecies: passive air defense.
Relations: əðʌɛɑədiŋɪfnɛsəuɑɛɛm (deoxythymidine monophosphate), ðzzuydrzdrfliæiʌtltɪ (sponge), ripuwɑɪlrrzstptfznɪr (butyric acid).
Kɪitwɑwrzoskʌgnltled
Pronounced: kiitwahwrzoskugnltled
Mathematical Space: subspace.
Amount: quantity.
Cytoplasm: centrosome.
Originality: unorthodoxy.
Worthlessness: groundlessness.
Figure: equilateral.
Legends: direct discourse, conservation.
Prophecies: scrunch, sixteen personality factor questionnaire, shortening, win, economic strangulation.
Məypspəgəsaɪɪɒksʃiərt
Pronounced: muhypspuhguhsaiiouksshiuhrt
Mathematical Space: subspace.
Amount: number.
Cytoplasm: sarcoplasm.
Originality: freshness.
Worthlessness: damn.
Figure: plane figure.
Prophecies: peekaboo, bhakti, lavage.
Relations: əðʌɛɑədiŋɪfnɛsəuɑɛɛm (fulminic acid), kɪitwɑwrzoskʌgnltled (tare), rəntəəɑəmðɛənɪfɒwdmu (chlorous acid).
Nndsnəərɪəətrgʌəmθpr
Pronounced: nndsnuhuhriuhuhtrguuhmthpr
Mathematical Space: subspace.
Amount: decrease.
Cytoplasm: hyaloplasm.
Originality: unorthodoxy.
Worthlessness: groundlessness.
Figure: parallel.
Legends: graduation, grant-in-aid.
Prophecies: division, schooling, no-trump, apheresis.
Relations: ðzzuydrzdrfliæiʌtltɪ (sebum).
Pprddntɪpɛrrmurwfnrk
Pronounced: pprddntipayrrmurwfnrk
Mathematical Space: subspace.
Amount: smallness.
Cytoplasm: cytoplast.
Originality: unorthodoxy.
Worthlessness: fecklessness.
Figure: subfigure.
Prophecies: invocation, secretary of transportation, jerry-building.
Relations: rnðtɑəɒnksɪəʌɪttnəsɑ (methacrylic acid), ərewnlziwraɪptɪəɑtæɛo (inwardness).
Ripuwɑɪlrrzstptfznɪr
Pronounced: ripuwahilrrzstptfznir
Mathematical Space: manifold.
Amount: insufficiency.
Cytoplasm: centrosome.
Originality: freshness.
Worthlessness: damn.
Figure: equilateral.
Legends: triple play, doubling.
Prophecies: bat mitzvah, child's game, ball game.
Relations: pprddntɪpɛrrmurwfnrk (lepidomelane), ðzzuydrzdrfliæiʌtltɪ (asparagine), rəntəəɑəmðɛənɪfɒwdmu (absorber).
Rnðtɑəɒnksɪəʌɪttnəsɑ
Pronounced: rnthtahuhounksiuhuittnuhsah
Mathematical Space: manifold.
Amount: critical mass.
Cytoplasm: plasmodium.
Originality: unorthodoxy.
Worthlessness: fecklessness.
Figure: solid figure.
Legends: writing, sonography.
Prophecies: free kick, counterblast, bong.
Relations: məypspəgəsaɪɪɒksʃiərt (provitamin), ərewnlziwraɪptɪəɑtæɛo (candelilla wax).
Rəntəəɑəmðɛənɪfɒwdmu
Pronounced: ruhntuhuhahuhmthayuhnifouwdmu
Mathematical Space: manifold.
Amount: increase.
Cytoplasm: ectoplasm.
Originality: freshness.
Worthlessness: groundlessness.
Figure: plane figure.
Legends: visitation.
Relations: rnðtɑəɒnksɪəʌɪttnəsɑ (benzoic acid), ðzzuydrzdrfliæiʌtltɪ (surgical spirit), kɪitwɑwrzoskʌgnltled (sycamore).
Ðzzuydrzdrfliæiʌtltɪ
Pronounced: thzzuydrzdrfliaiutlti
Mathematical Space: null space.
Amount: positivity.
Cytoplasm: centrosome.
Originality: freshness.
Worthlessness: fecklessness.
Figure: subfigure.
Legends: first base, shtik, traffic, benefit of clergy.
Prophecies: burn, civil war, samsara, electronic communication, rush.
Relations: rəntəəɑəmðɛənɪfɒwdmu (charge), əðʌɛɑədiŋɪfnɛsəuɑɛɛm (ferrite), nndsnəərɪəətrgʌəmθpr (wood), kɪitwɑwrzoskʌgnltled (sea room).
Ərewnlziwraɪptɪəɑtæɛo
Pronounced: uhrewnlziwraiptiuhahtaayo
Mathematical Space: null space.
Amount: margin.
Cytoplasm: sarcoplasm.
Originality: unorthodoxy.
Worthlessness: shoddiness.
Figure: subfigure.
Prophecies: radiation, printing, crash, bookbinding.
Relations: əðʌɛɑədiŋɪfnɛsəuɑɛɛm (nucleic acid), ripuwɑɪlrrzstptfznɪr (rappee).
Əðʌɛɑədiŋɪfnɛsəuɑɛɛm
Pronounced: uhthuayahuhdingifnaysuhuahayaym
Mathematical Space: subspace.
Amount: quantity.
Cytoplasm: plasmodium.
Originality: unorthodoxy.
Worthlessness: groundlessness.
Figure: pencil.
Legends: calibration, symbolizing, decrepitation, proconsulship, genocide.
Prophecies: liveliness, judgment in rem.
Relations: ripuwɑɪlrrzstptfznɪr (homogenized milk).
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\documentclass{beamer}
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\date{}
\title{04.01.2017}
% A subtitle is optional and this may be deleted
\subtitle{Vector LBP with aggregation via DD: results}
\author{Gorodnitskii Oleg: [email protected]}
\AtBeginSubsection[GaBP preconditioned conjugate gradient method]
{
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\tableofcontents[currentsection,currentsubsection]
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}
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\begin{document}
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\titlepage
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\begin{frame}{Short description:}
Consider \textbf{Vector LBP}. It is known that the \textbf{Vector} version of LBP has significantly stronger convergence properties under correct choice of separation onto blocks, compared to the \textbf{Scalar} version. \cite{mal08}\\
Optimal and correct (in sense that it will lead to the convergence of Vector LBP) choice of separation onto blocks is unknown for arbitrary case.\\
Here we propose separation via DD properties of each node.\\
We examine each individual node $\pmb{i}$ and set of its neighbors $\mathcal{N}(\pmb{i})$:
\begin{enumerate}
\item If $\mathcal{N}(\pmb{i})$ is such that $J_{ii} \leq \sum_{j \in \mathcal{N}(\pmb{i})}|J_{ij}|$ (DD is violated in i-th row) then we replace $\pmb{i}$ and $\mathcal{N}(\pmb{i})$ with supernode $\mathcal{S}_i = \{\pmb{i}\} \cup \mathcal{N}(\pmb{i})$
\item If $\pmb{j} \in \mathcal{N}(\pmb{i})$ and $J_{jj} \leq \sum_{k \in \mathcal{N}(\pmb{j})}|J_{jk}|$ (DD is violated both in i-th and j-th row) we put $\pmb{j}$ in $\mathcal{S}_i$
\item If $\pmb{j} \notin \mathcal{N}(\pmb{i})$ and $J_{jj} \leq \sum_{k \in \mathcal{N}(\pmb{j})}|J_{jk}|$, and $\mathcal{N}(\pmb{i}) \cap \mathcal{N}(\pmb{j}) \neq \emptyset$ we put $\pmb{k} \in \mathcal{N}(\pmb{i}) \cap \mathcal{N}(\pmb{j}) $ randomly into $\mathcal{S}_i$ or $\mathcal{S}_j$
\end{enumerate}\\
Thus we eliminate all nodes and it's neighbors which violate DD, replacing them with supernodes.
\end{frame}
%-----------------------------------------------------------------------------------------------------
\begin{frame}{Results: Case 1}
\begin{center}
\includegraphics[scale = 0.35]{graph_1}
\end{center} (28 nodes, p = 0.35, Non-Walksummable)\\
\end{frame}
%-----------------------------------------------------------------------------------------------------
\begin{frame}{Results: Case 1, convergence}
\begin{center}
\includegraphics[scale = 0.42]{Means_err_vec_1}
\includegraphics[scale = 0.42]{Means_err_scalar_1}
\end{center} (Vector LBP and Scalar LBP convergence - x-axis - number of iteration, y-axis - $\text{Norm}_2$ error for means)\\
\end{frame}
%-----------------------------------------------------------------------------------------------------
\begin{frame}{Results: Case 2}
\begin{center}
\includegraphics[scale = 0.33]{graph_2}
\end{center} (80 nodes, p = 0.32, Non-Walksummable)\\
\end{frame}
%-----------------------------------------------------------------------------------------------------
\begin{frame}{Results: Case 2, convergence}
\begin{center}
\includegraphics[scale = 0.42]{Means_err_vec_2}
\includegraphics[scale = 0.42]{Means_err_scalar_2}
\end{center} (x-axis - number of iteration, y-axis - $\text{Norm}_2$ error for means)\\
\end{frame}
%-----------------------------------------------------------------------------------------------------
\begin{frame}{Results: Case 3}
\begin{center}
\includegraphics[scale = 0.35]{graph_3}
\end{center} (90 nodes, p = 0.29, Non-Walksummable)\\
\end{frame}
%-----------------------------------------------------------------------------------------------------
\begin{frame}{Results: Case 3, convergence}
\begin{center}
\includegraphics[scale = 0.42]{Means_err_vec_3}
\includegraphics[scale = 0.42]{Means_err_scalar_3}
\end{center} (x-axis - number of iteration, y-axis - $\text{Norm}_2$ error for means)\\
\end{frame}
\printbibliography
\end{document}
[ @taylorswift ]* 💜💜♾️♾️
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7 Closely-Guarded Politics Secrets Explained In Explicit Detail
This is predicted and is because of the difficulty of the fantastic-grained job. The diversity of actions will increase as a result of proliferation of latest ideas, after which decreases as brokers hone in on the fittest actions. From then on, the ConvNetPart can be high quality-tuned using as floor-truth the final tracker predictions on extremely assured frames (HCFs). However, considering begin time and meeting size simultaneously, then the maximal approval occurs within the areas the place two voters agree. At the identical time nonetheless, a minority of users look like spread out all through all the ideology house, presumably also invading a region populated by members of another social gathering. Figure 8: Comparison between the distribution of floor-truth customers and of our predictions, inside the latent ideology space. Top-left corner of Figure 6(a). To a lower extent, the same additionally happens in Figure 6(b). These regions of the confusion matrices allow to visualize the errors that we mentioned earlier - that is, wrong occasion classifications that develop into correct predictions when techniques are evaluated pole-clever.
youtube
E additionally appear to be unfold-out across the highest-left quadrant of the ideology space, which makes it troublesome to cluster them all together. Specifically in each subfigure, the scatter plot distribution reveals the place ground-truth customers of a given occasion are positioned within the shared ideology area. It reveals the high quality-grained confusion matrices of the 2 methods, along with the marginal distributions of both floor-fact and predicted labels. Such customers will be erroneously predicted as supporters of FdI by our method. Overlaid, the contour lines show the distribution of the take a look at-set customers predicted by our technique for that social gathering. Under this favorable laboratory situation, supervised classifiers are in a position to maximise their studying phase on knowledge situations in the training-set, and to successfully carry over what they learnt to the take a look at-set. An interesting consequence that clearly emerges from Tables 5 and 6 is the superiority of all of the approaches primarily based on SVC classifiers for the prediction step.
Independently on the methodology used for obtaining political ideologies and on the general approach to the task (e.g., semi-supervised or supervised), the three strategies leveraging an SVC consistently obtained the 3 best general leads to each the high quality- and coarse-grained tasks. In this paper, we present an method to supporting early-stage suggestions on societal and ethical dimensions of AI analysis. To answer certainly one of the primary analysis questions on this paper, we examine whether there exists a correlation between retweets rely and sentiment. Instead, unsupervised approaches, such as the one proposed in our work, are ready to better adapt to potential drifts. It is possible solely to formulate relatively common necessities for this process. Politicians of the same parties seem shut, that means that their posts are commented by the identical communities. Furthermore, it explains why the identical techniques obtain strikingly higher outcomes when evaluated for the prediction of poles as a substitute of events. Moreover, both strategies exhibit a bias in the direction of overestimating right-leaning events. For the long run, it will be interesting to guage and diagnose novel strategies for studying latent political ideologies and for predicting political leaning, via this visualization technique.
This represents a limitation of our technique for studying ideologies or an intrinsic limitation of working with noisy textual data, which inevitably results in flawed predictions at clustering time. Figure 7: Comparison of the confusion matrices, with marginal distributions, for superb-grained (occasion) predictions between our proposed method and the unsupervised technique by ? Overall, results introduced in Tables three and four and in Figure 7 exhibit that it is rather challenging to tell apart between the totally different parties that lay on the same facet of the political spectrum. Contrarily, now we have more difficulties in predicting far-proper and much-left events. Organizational fixes usually come from laws, and the type this regulation takes can have substantial results on organizational outcomes (bamberger2015privacy, ). This simplicity does not come by chance, somewhat, it is important and desired by the researchers: the issues to be approached are themselves so advanced that whichever components of complexity will be diminished (or no less than postponed), the reduction is always welcome. POSTSUBSCRIPT |. Thus, the objective of our dimension reduction is to infer these latent entities and their related textual content realizations. POSTSUBSCRIPT is the revenue described above. important news of this comparison for the positive-grained prediction task, while Table 6 presents results for the coarse-grained job.
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(Jason Fernandes)
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The simple classification of the geometric 2D figures using angles
You might not know that at the very high end of navigation like systems all the complexity you experience is built on top of abstracted layers of concepts that are a gained two-dimensional geometric figures knowledge.
In this article, I will help you build a mental paradigm that capable of recognizing different two-dimensional shapes and a basic perception of an angle classification system.
By two-dimensional figures, I anticipate all the closed shapes that can fit into a plane surface or a planar. There are too many legitimate ways to classify 2D geometric figures but in this article, I will only describe the angles classification system.
Now, Let's jump into it...
Our first shapes family is called the zero-angles family: It is very limited in members count, It has no sides either corners therefore it owns zero angles. The first member of this family is called the "Circle". And it's a very quite known member. the "Circle" has 360 degrees and the mathematicians don't know why only 360 degrees?!! No one knows.
What are the Circle's main characteristics? Simply, All of the drawn lines that pass the shape's center point must be all equal in length within its circumference boundary.
The zero degrees family has another member called the "Oval". The main rule to differentiate the second member is by negating the Circle's judgment. This means you can find unequal drawn lines crossing the shape's center point within its circumference boundary.
#1: The first group of the 2D figures has no degrees and no sides. We can register the Circle and the Oval as members of this group.
The second family in our classification system is the "Triangles" family I know that the word "Triangle" fires up some neural activities in your brain cells, Yes you are right it's the family that owns the Triangle. But let me explain it to you: We coined the term "TriAngle" Because it has three angles, three sides, and three corners. I believe that the TriAngle is a very common geometric figure, And to me, it's a very dear figure maybe because it's interconnected with my first memories of stepping in mathematics, Maybe it's because it's heavily used to calculate the length between any given points on a planar.
#2: The second group is the TriAngles family that owns three sides and three degrees.
The third family is called the "Quadrilateral" family, And from the descriptive term, you might now resolve its content. It's the popular four-sided family. In the "Quadrilaterals" we can find all the shapes that own four sides and four inner corners. But based on the type of corners' angles we segment this family into two groups.
The first group is the "RectAngles": are all the figures that consist of four sides and you can certainly place a square in each corner. Or in another tone, it forms a right 90-degree angle within each of its corners. This group has a very dear member is called the "Square", And the "Square" is when you have four sides that exactly equal in length. So we can say that the "Square" is a specific figure of a "Rectangle".
The second group in the "Quadrilaterals" family consists of two subfigures, The first is called the "Trapezoid" and simply it's the four sides shape where you cannot place a square in each of its corners. So the inner degrees are cute or obtuse angles. To judge a "Trapezoid" figure you must find two opposite parallel lines and the other two opposite sides can have one future intersection.
This group also has a very dear member is called the "Rhombus", It's simply a four-sided shape where all the sides are exactly equaled in length. Fun fact the "Square" can be counted as a "Rhombus" but the opposite is false.
#3: The "Quadrilaterals" have two groups. The "Rectangles" are the first main group. The "Square" is a rectangle and also is a "Rhombus". The "Trapezoid" must have two opposite parallel lines and one future intersection for the other two opposite sides.
The fourth family is called the "Pentagon" and from the name, you can guess it's the five sided-figures. The members of this family must have five sides, five corners, and five angles.
The fifth is the "Hexagon" where all of its members must have six sides, six corners, and six angles.
Thank you so much for your time, I hope I added to your knowledge.
#mathematics#geometry#basic math#basic geometry#triangle#quadrilateral#square#rectangle#rhombus#trapezoid#circle#oval
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Plant Milk Project Plan
Project Statement
Making plant-based milk is a messy, time-consuming process. The fruit of that labor, however, is well worth it, with homemade plant milk tasting much better than store-bought versions. Our project aims to simplify and improve the process of making milk in your own kitchen.
The process of making plant milk involves soaking the nuts in water (for some types of milk), blending them with fresh water, salt, and other flavorings/sweeteners, and squeezing the blended mixture through a nut milk bag to remove the pulp. Currently available solutions either don’t incorporate all of these steps, are prohibitively expensive, or don’t have the versatility to produce a wide variety of milks. We propose a plant milk maker that performs all three stages of the process seamlessly, with no mess, and with many different types of nuts. Our goal is to create detailed 3D models, a looks-like prototype, a works-like prototype, and a final product that includes the machine, packaging, and instructions.
Precedent Work
Vitamix:
The Vitamix is a high speed blender which comes in many variations with different functionalities, ranging in price from $290 to $600. One of the most popular blenders is the Vitamix 5200, which retails at $499.95. It consists of a motor base and a 64 ounce pitcher, has dimensions of 20.5 x 8.75 x 7.25 in, and weighs 10.5 lb. It has laser-cut, stainless-steel blades measuring 3 inches in diameter, and the motor has 10 speed modes, peaking at 2 hp. When using a Vitamix to make plant-based milk, the blended nut and water mixture has to be strained through a strainer bag.
Almond Cow:
The Almond Cow costs $200 and measures 12 x 9.5 x 6.5 in, and weighs 4.6 lb. It consists of a blender base, the head, which includes a built-in immersion blender and metal strainer, a plastic draining cup, and a removable power cord. The blender operates in cycles; each cycle is 1 minute long and alternates between blending and resting, which is repeated twice. It makes 5-6 cups of milk based on a predefined ratio of nuts to water, and is dishwasher safe.
NutraMilk Nut Processor:
The NutraMilk Nut Processor costs $499.95, measures 20.7 x 17.2 x 11.9 in, and weighs 27 lb. It consists of a motor base, mixing basin, inner filter, wiper blades, and stainless steel cutting blades. It has a 3/4 hp motor and the motorized wiper blades push down the nuts in the basin for easier blending. It first makes nut butter, and when water is added, the photo-etched stainless steel filter emulsifies the nut butter in the water, filtering out fine particles and resulting in a smooth nut milk with little leftover pulp. All the components except the motor base are dishwasher safe.
ModernMilk:
The ModernMilk milkpress is a filter and bottle system for making plant-based milk. The body of the press is made of heat-resistant borosilicate glass, which is protected by a silicone collar. It also includes a stainless steel filter and stainless steel stick to press the milk. To make the milk, the nuts and water are first blended using a blender, and then poured through the filter and pressed using the stick. It can store up to 4 cups of milk, and costs $149.95.
Initial Sketches
Existing Solution:
Brainstorming Sketches:
The following sketches for potential versions of our plant-based milk maker all feature two main components: the filter to separate the nuts from their milk (A) and the blade to blend the nuts (B).
Our first idea is shown in Figure 1, through three subfigures showing its process. Figure 1a shows the empty machine and its three components: the blade, motor, and collector. The nuts are poured into the top in Figure 1b, and then water is poured over to incorporate and filter with the pulp in Figure 1c.
Our second idea is shown in Figure 2. In this design, the inner strainer compartment holds the nuts and collects the pulp, and is easily removable along with its retractable blade. The control buttons for various settings can be found on the charging base.
Our third and fourth ideas feature a clever flipping mechanism, in which the blade side can be inverted to complete the filtration process. In Figure 4, the filter also doubles as a press to fully squeeze the milk from the nuts. These figures are further labeled with the magnetic lock (C) and dispenser holes (D).
Plan for remote collaboration
As a sleek kitchen appliance, our project is relatively small-scale, but its compact design necessitates careful construction and well-planned collaboration. The design phase of the project can be completed fully remotely, with the utilization of Zoom, Google Drive, and Facebook messenger to work together both synchronously and asynchronously. Detailed sketches and some CAD work will be required to thoroughly prepare the concept for our product. Once prototyping begins, we anticipate the majority of the hands-on work to be completed asynchronously, with group members completing different tasks at different times to limit exposure to one another. Our product’s size does not require excessive labor, and is conducive to a balance more in favor of planning than building. Hence, in the event that Brown University cancels hybrid courses in favor of wholly remote instruction, the vast majority of the product design would be completed, with the only remaining task being the physical assembly.
Provisional Schedule
Provisional Budget
The motor, blade, and blending vessel of our product are the main budgetary items. We have not decided on a battery-powered or plug-in design, nor have we decided between glass, plastic, or metal for the main receptacle. A initial list of parts needed for the full plant-based milk production process as advertised by our product can be found below:
Motor: ~$50
Blade (perhaps multiple): ~$7 each
Cylinder or other vessel for blending: TBD, expected under $50
Compressive piece: TBD, expected under $20
Cheesecloth or mesh: ~$10
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https://soundcloud.com/jasonfernandes/subfigure-021-easter-awakenings-special
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(Jason Fernandes)
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So the collection of scissors congruences between figures forms a groupoid. In particular, it forms a preordered groupoid if you say that a scissors congruence is larger than a parallel one if its partition of the domain is finer than the other's partition. Composition of scissors congruences is monotone wrt this ordering, so you get a preordered groupoid.
A preordered groupoid is in particular a specific kind of bicategory, so we can ask what a monad is in this category. An object is a figure P, and an endomorphism is a scissors congruence of that figure with itself. The unit 2-cell is simply the statement that the domain partition of this scissors congruence is finer than the trivial partition {P}, which always holds. The multiplication 2-cell states that the domain partition of the scissors congruence composed with itself is coarser than the original domain partition. It will always be finer, so because partitions are partially ordered by refinement the partitions must be equal. This means that the scissors congruence is in fact a permutation of the subfigures that make up the partition, because every subfigure must be mapped onto another one of the subfigures of the domain partition. The naturality conditions of the monad follow from the fact that the morphism categories are preordered sets, so this is all the data we need for a monad I believe. A figure and a scissors congruence of that figure with itself that simply permutes some of its subfigures by way of congruences.
#math#if this is correct it's not super interesting sadly#so i won't devote text to it in my thesis#but it's a fun way of combining two things i'm studying :> hehe
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A guide to matplotlib subfigures for creating complex multi-panel figures
#AI #ML #Tech https://towardsdatascience.com/a-guide-to-matplotlib-subfigures-for-creating-complex-multi-panel-figures-70fa8f6c38a4?utm_source=dlvr.it&utm_medium=tumblr
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sɒtpkrisʌzʌlioʃiɒrɪə
Pronounced: soutpkrisuzulioshiouriuh.
Pantheon of: figure, lowness, dissonance, lunar latitude, futility.
Entities
Bprvtʃlwəɪneəgwmvnəlɛ
Pronounced: bprvtshlwuhineuhgwmvnuhlay
Figure: plane figure.
Dissonance: discordance.
Legends: skirl, logistic support, foremanship.
Relations: eðdæwæetrsidgðŋaʊnʒɛn (coin silver), ðtʃənrθssðuəpɛpdʃæʌt (pay dirt), wlrəpllgfeənðdlʌaɪrnm (carnotite), ɪdrrbtfəɑnɪpɑdtbəɪrb (antepenult).
Eðdæwæetrsidgðŋaʊnʒɛn
Pronounced: ethdawaetrsidgthngownzayn
Figure: solid figure.
Dissonance: disharmony.
Legends: jolly.
Prophecies: lottery, sanction, pruning.
Relations: ðtʃənrθssðuəpɛpdʃæʌt (butadiene), irirdttunərwəəhtsrkə (pseudonym), həɪpriɪwseʌəddʒzaɪibtp (hydrochlorofluorocarbon).
Həɪpriɪwseʌəddʒzaɪibtp
Pronounced: huhipriiwseuuhdjzaiibtp
Figure: subfigure.
Dissonance: discordance.
Legends: counterclaim, logic programming, card game, intrusion, ageism.
Prophecies: terrorism.
Relations: wlrəpllgfeənðdlʌaɪrnm (polish).
Irirdttunərwəəhtsrkə
Pronounced: irirdttunuhrwuhuhhtsrkuh
Figure: equilateral.
Dissonance: discordance.
Legends: proctorship, rut.
Prophecies: perambulation.
Relations: ɪdrrbtfəɑnɪpɑdtbəɪrb (northeast), ðtʃənrθssðuəpɛpdʃæʌt (alum), bprvtʃlwəɪneəgwmvnəlɛ (isometry), əmɪɛvəviiodtnrwæɪʌnk (overcompensation).
Irəɪəbɪɒðtəæmzʃətsyɪ
Pronounced: iruhiuhbiouthtuhamzshuhtsyi
Figure: equilateral.
Dissonance: disharmony.
Relations: eðdæwæetrsidgðŋaʊnʒɛn (lycopene), əmɪɛvəviiodtnrwæɪʌnk (placement), irirdttunərwəəhtsrkə (verbal noun).
Wlrəpllgfeənðdlʌaɪrnm
Pronounced: wlruhpllgfeuhnthdluairnm
Figure: solid figure.
Dissonance: cacophony.
Legends: quarrel, duty, puppet show, plague.
Prophecies: formation, spritz, authorship.
Relations: irirdttunərwəəhtsrkə (plutonium 239), ɪdrrbtfəɑnɪpɑdtbəɪrb (rock crystal), irəɪəbɪɒðtəæmzʃətsyɪ (fruit punch), eðdæwæetrsidgðŋaʊnʒɛn (cyanide).
Ðtʃənrθssðuəpɛpdʃæʌt
Pronounced: thtshuhnrthssthuuhpaypdshaut
Figure: plane figure.
Dissonance: disharmony.
Legends: pay cut, demonstration, olympian games.
Relations: bprvtʃlwəɪneəgwmvnəlɛ (asynchronism), irəɪəbɪɒðtəæmzʃətsyɪ (chlorophyll).
Əmɪɛvəviiodtnrwæɪʌnk
Pronounced: uhmiayvuhviiodtnrwaiunk
Figure: pencil.
Dissonance: disharmony.
Prophecies: gunrunning, default, round, affusion, affray.
Relations: ðtʃənrθssðuəpɛpdʃæʌt (frontage).
Ɪdrrbtfəɑnɪpɑdtbəɪrb
Pronounced: idrrbtfuhahnipahdtbuhirb
Figure: solid figure.
Dissonance: cacophony.
Legends: dismemberment, purge, spade casino, sparring match, arrival.
Prophecies: prowl, flying mare.
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\documentclass{beamer}
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\date{}
\title{02.24.2017}
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\begin{document}
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\begin{frame}{Convergence of GaBP beliefs to delta functions for $\beta \rightarrow \infty$:}
For Gaussian probability density function: $p(x) \sim \mathcal{N}(\mu, A^{-1}), \ \mu = A^{-1}b$ marginal densities are also Gaussian \cite{bic} (page 9) $$p_i(x_i) \sim \mathcal{N}(\mu_i = \{A^{-1}b\}_i,\{A^{-1}\}_{ii})$$
In case of GaBP beliefs converge to true marginals for walk-summable model.\\
Consider inverse temperature $\beta = T^{-1}$ and substitution $A \rightarrow \beta A, b \rightarrow \beta b$
After substitution we get:
$$p_i(x_i) \sim \mathcal{N}(\mu_i = \{A^{-1}b\}_i,\{\frac{A^{-1}}{\beta}\}_{ii})$$\\
Letting $\beta \rightarrow \infty$ we get:\\
$$p_i(x_i) \sim \mathcal{N}(\mu_i = \{A^{-1}b\}_i,\{\frac{A^{-1}}{\beta}\}_{ii} \rightarrow 0) \rightarrow \mu_i \times \delta(x_i - \mu_i) \text{ - Dirac delta function}$$\\
\end{frame}
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\begin{frame}{}
\textbf{Conclusion}: Gaussian BP is derived from \textbf{integral-product} rule, via direct integration. If we apply $A \rightarrow \beta A, b \rightarrow \beta b, \beta \rightarrow \infty$ substitution we get max-product algorithm from integral-product algorithm.\\
In this case, as showed above, beliefs always converge to delta functions in case of walk-summable models.\\
\bibliographystyle{plain}
\bibliography{references}
\end{frame}
%-----------------------------------------------------------------------------------------------------
[ @taylorswift ]* 💜💜♾️♾️
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Home Alone Keep The Change You Filthy Animal Shirt
Home Alone Keep The Change You Filthy Animal Shirt
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