Two Harvard graduates presented an award for faculty who show “a strong commitment to social justice” to former Harvard President Claudine Gay during the University-wide celebration of Black graduates on Tuesday evening in Sanders Theatre.

‘Our Forever President’: Black Harvard Graduates Celebrate Claudine Gay at Affinity Ceremony | News | The Harvard Crimson

Canva, the popular online design platform, announced several new features and a new business-focused subscription on Thursday. The announcements were made at the first international Canva Create event in Los Angeles. The biggest highlight was the platform’s biggest redesign in more than a ...

Canva Revamps Platform and Editing Experience; New Affinity Apps, Canva Enterprise Launched | Technology News

arfinity

Taint and affinity in Kubernetes are two mechanisms that allow users to specify how pods should be scheduled on nodes in a cluster. Node selectors are another mechanism that allows users to specify which nodes a pod should be scheduled on. In this article, we will explain the difference between taint, affinity, and node selector in Kubernetes and how they can be used to control pod scheduling in a cluster. Taint Taint is a feature in Kubernetes that allows users to mark a node as "tainted" with a specific key and value. Pods that do not have toleration for the taint will not be scheduled on the tainted node. Taint can be used to mark nodes as unavailable for certain pods, for example, if the node is undergoing maintenance or if it has a specific hardware or software configuration that is not suitable for certain workloads. To taint a node, use the command. For example, to taint a node with key "disktype" and value "ssd", use the following command: kubectl taint kubectl taint nodes <node-name> disktype=ssd:NoSchedule To tolerate a taint, you can specify a toleration in the pod specification. For example, the following pod specification tolerates the taint "disktype=ssd" with a toleration period of 3600 seconds: apiVersion: v1
kind: Pod
metadata:
  name: my-pod
spec:
  tolerations:
  - key: "disktype"
    operator: "Equal"
    value: "ssd"
    effect: "NoSchedule"
    tolerationSeconds: 3600
  containers:
  - name: my-container
    image: nginx 📋Important Note: Taints and Tolerations do not guarantee that the pods only prefer these nodes. So the pods may be scheduled on the other untainted nodes. Affinity Affinity in Kubernetes is a mechanism that allows users to specify rules for pod scheduling based on node attributes. There are two types of affinity: node affinity and pod affinity. Node Affinity Node affinity allows users to specify which nodes a pod should or should not be scheduled on based on node labels. For example, you can use node affinity to specify that a pod should be scheduled on a node with a specific label, such as . disktype=ssd To specify node affinity in a pod specification, use the field and set the field to the desired node affinity rules. For example, the following pod specification specifies that the pod should be scheduled on a node with the label : affinity nodeAffinity disktype=ssd apiVersion: v1
kind: Pod
metadata:
  name: my-pod
spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: disktype
            operator: In
            values:
            - ssd
  containers:
  - name: my-container
    image: nginx 📋Important Note: ** Node Affinity by itself does not guarantee that other pods are not also scheduled on the same nodes as well. Pod Affinity Pod affinity allows users to specify which pods a pod should or should not be scheduled with based on labels. For example, you can use pod affinity to specify that a pod should be scheduled on the same node as other pods with a specific label, such as . app=database To specify pod affinity in a pod specification, use the field and set the `podAffinity affinity Node selector Node selector is a feature in Kubernetes that allows users to specify which nodes a pod should be scheduled on based on node labels. Node selector works by adding labels to nodes and then specifying the desired node labels in the pod specification. To add labels to a node, use the command. For example, to add a label to a node, use the following command: kubectl label disktype=ssd kubectl label nodes <node-name> disktype=ssd apiVersion: v1
kind: Pod
metadata:
  name: my-pod
spec:
  nodeSelector:
    disktype: ssd
  containers:
  - name: my-container
    image: nginx Node selector is similar to node affinity in that both allow users to specify which nodes a pod should be scheduled on based on node labels. However, node selector is a simpler and more primitive mechanism compared to node affinity, which provides more fine-grained control over pod scheduling. In summary, taint and affinity in Kubernetes allow users to specify rules for pod scheduling based on node attributes, while node selector allows users to specify which nodes a pod should be scheduled on based on node labels. All three mechanisms can be used to control pod scheduling in a cluster and ensure that pods are scheduled on the appropriate nodes based on the requirements of the workload. Guidelines and Best Practices Here are some guidelines for when to use taint, affinity, and node selector in Kubernetes: A combination of Taints & Tolerations and Node Affinity rules can be used together to completely dedicate nodes for specific pods. We use Taints & Tolerations to prevent other pods from being scheduled on the desired nodes and then we use Node Affinity to have our pods to be scheduled on the desired nodes. Taint should be used when you want to mark a node as unavailable for certain pods. For example, you can use taint to mark a node as "maintenance" and prevent pods from being scheduled on the node while it is undergoing maintenance. Node affinity should be used when you want to specify which nodes a pod should or should not be scheduled on based on node labels. Node affinity provides more fine-grained control over pod scheduling compared to node selector and allows you to specify complex rules for pod scheduling based on multiple node labels. Pod affinity should be used when you want to specify which pods a pod should or should not be scheduled with based on labels. Pod affinity can be used to ensure that certain pods are co-located on the same node or to ensure that certain pods are separated from each other. Node selector should be used when you want to specify which nodes a pod should be scheduled on based on node labels, but do not need the fine-grained control provided by node affinity. Node selector is a simpler and more primitive mechanism compared to node affinity and is sufficient for many use cases. In general, you should use taint and affinity when you need to specify complex rules for pod scheduling based on node attributes, and use node selector when you only need to specify simple rules based on node labels. Hope you enjoyed reading this article, follow for more DevOps related content 👾🦄 Additional Resources: Short video explaining Taint & Tolerations and Node Affinity Official Kubernetes Documentation for Assigning Pods to Nodes Also published . here

A Guide to Taints and Tolerations, Node Affinity and Node Selector

Abstract In the last couple of years, short videos have become the new darling of the digital mediascape. After the internet boom in India, many influencers are emerging daily. We all have our favorite creators and can spend hours watching their content. For a platform like ours, we needed a user-creator affinity recommendation model such that we recommend creator stories to users based on the affinity (likeability) factor where a consumer’s (user) likeability for a creator is defined by: Follow, Profile Visit, Like, Comment, Share, etc. Overview Affinity means a natural liking for and understanding of someone or something. Affinity is a factor that changes with time and interest niche. Our goal is to capture user-creator affinity strength, which also captures users’ i.e., what type of stories a consumer (user) prefers more. temporal interest niche Business Goals Improve stories recommendation algorithm such that user’s session time increases Improve user niche discovery for content Improve visibility of and content discovery based on consumer’s likeability factor. long-tail creators Expected Outcome Recommend a list of story ids of creators for whom user-creator affinity is high. : A creator is also a user on the platform. Hence, I will address as who watch a creator’s video. NOTE users consumers Interaction between Creator-Consumer on Roposo Out of these different interactions b/w consumer-creator, we decided to pick profile visit as a stronger signal to map out similarity between creators. Approach High-Level Approach We divided the problem into 2 parts: For each consumer (user) find the top K creators based on the for which the True Affinity score is high; where affinity is defined by like, follow, profile visit, comment, loop_count, perc_seen (percentage of video seen based on video duration), etc. Multi-Criteria Decision Making TOPSIS technique Post finding these True Top K creators for whom affinity is high based on MCDM. We take embedding of these creators (embedding computed using node2vec embedding methodology) and find out nearest neighbors and recommend similar creators. *: First, we find out true high-affinity creators for a consumer based on MCDM. Then we find similar creators with respect to high-affinity creators.* In Summary Implementation Details (TLDR) — Refer to the above Approach Figure with Steps Step 1: Finding True Top Affinity creator for a Consumer (user) from interactions. This is a as we wanted to rank all creators for a consumer (user) with whom the consumer interacted in the last 30 days. multi-criteria decision-making (MCDM) or multi-criteria decision analysis (MCDA) problem Consumer-Creator Interaction Data We use the an MCDM algorithm to rank creators in order of affinity. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the ideal solution and the longest geometric distance from the worst solution. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Scikit Criteria_:_ Link One can check out my blogs to get a detailed understanding of MCDM: Ranking of entities with Multi-Criteria Decision Making Methods (MCDM) — Part One | Ranking and Selection of the best with Multi-Criteria Decision Making (MCDM) — Part Two Post step1, for every consumer we have a ranked creator set based on affinity factors with whom the consumer has interacted in the last 30 days. Sum up: **Step2 & Step3: Creator Graph — Profile Visits to Embedding**We constructed a Creator-Creator graph based on the profile visits of a consumer. Connections between those creators were made for which profile visits by consumers co-occurred on a particular day. The graph weights were defined by co-occurrence strength (number of times profile visits by a consumer co-occurred). We computed the creator embeddings based on that uses word2vec skip-gram model. Paper Link Node2vec+ Node2vec Params: How to set p and q? The top and bottom panels correspond to the embedding generated using q = 0.5 and q = 2. One can see that in the top panel, nodes that fall into the same (i.e., ) are colored the same. On the other hand, in the bottom panel, nodes are colored the same. node2vec local network neighborhood homophily structurally equivalent Params q=0.5 and p=1 in this setting node2vec discover clusters/communities of characters that frequently interact with each other. Since the edge b/w nodes are based on co-appearances. Post Step2 & Step3, we now have creator embedding computed based on creator-creator graph build based on co-occurrence of profile visit. Sum up: **Step4: Recommending Top Creators**Now, we have true Consumer (User)-Creator Affinity Ranked based on MCDM and we have embeddings of all (active) creators on our platform. We pick the top 5 True Affinity Creators ranked from the MCDM technique and recommend Nearest Neighbours to get the top 100 high-affinity creators. Why top 5 True Affinity Creators were picked as query vectors? Why not pick the best top 1 or create a mean vector of top 5 creators and show similar creators to the query vectors in embedding space? Idea of picking top 5 creators is inspired from Pinterest Research Paper . PinnerSage It is true a user cannot be represented by one particular “interest” embedding.In general even in example of movies everyone shows interests in multiple genres likes honor, action, sci-fi, comedy, etc.To identify user interest we pick top 5 creators from the ranked set. For vector similarity search we used Approximate Nearest Neighbour Algorithm (ANN) over creator embeddings for fast vector similarity search. ScaNN Approach Summary (Recap) We use MCDM to rank creators for each consumer using the interactive features (affinity-defining features). This ranked set of creators is the True Affinity Creators for a consumer (user). Now, we create a creator-creator graph based on profile visits co-occurrence in a session of a consumer. On this graph, we apply the random-walk algorithm Node2Vec+ with a set breadth-first search and depth-first search parameters. This gets us a creator vector representation. At last, we pick the top 5 creators from the True Affinity set (ranked set based on MCDM) for a consumer and use the creator embeddings to find the top 100 most similar creators from the entire creator. For a fast vector similarity search, we use the ScaNN algorithm. Stories Recommendation Our expected outcome is a list of storyid. Hence, from the 100 top affinity creators for each consumer (from the above approach), we pick the latest not watched story of each creator and add it to the recommendation pool of the consumer, stories ranked based on creator similarity score wrt. user’s true creator affinity. Conclusion This approach of Topsis MCDM and Node2Vec+ not only ranks creators for a consumer but also helped us to find similarities between creators of the same niche using a profile-visit co-occurrence graph. Reference PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest Billion Scale Recommendation at Taobao DeepWalk: Online Learning of Social Representations Ranking of entities with Multi-Criteria Decision Making Methods (MCDM) — Part One Ranking and Selection of the best with Multi-Criteria Decision Making (MCDM) — Part Two I hope you learned something new from this post. If you liked it, hit ❤️, subscribe, and share this blog with others. Want to discuss it further? Connect with me . here This newsletter is now read by more than 4500+ subscribers. If you are building an AI or a data product or service, you are invited to become a sponsor of one of the future newsletter issues. Feel free to reach out to shauryauppal97@gmail.com for more details on sponsorships. I am nominated for the HackerNoon 2022 Noonies, Vote for me: https://www.noonies.tech/2022/programming/2022-hackernoon-contributor-of-the-year-data Connect 1:1 Meeting here: https://topmate.io/shaurya I am open to Consults you can reach out to me on LinkedIn: https://www.linkedin.com/in/shaurya-uppal/ Data Science Book Recommendations: [1] The Book of Why [2] Naked Statistics

How to Build a User-Creator Affinity Model for a Short Video Platform

The Essays of Adam Smith, by Adam Smith is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here . OF THE AFFINITY BETWEEN CERTAIN ENGLISH AND ITALIAN VERSES OF THE AFFINITY BETWEEN CERTAIN ENGLISH AND ITALIAN VERSES THE measure of the verses, of which the octave of the Italians, their terzetti, and the greater part of their sonnets, are composed, seems to be as nearly the same with that of the English Heroic Rhyme, as the different genius and pronunciation of the two languages will permit. The English Heroic Rhyme is supposed to consist sometimes of ten, and sometimes of eleven syllables: of ten, when the verse ends with a single, and of eleven, when it ends with a double rhyme. The correspondent Italian verse is supposed to consist sometimes of ten, sometimes of eleven, and sometimes of twelve syllables, according as it happens to end with a single, a double, or a triple rhyme. The rhyme ought naturally to fall upon the last syllable of the verse; it is proper likewise that it should fall upon an accented syllable, in order to render it more sensible. When, therefore, the accent happens to fall, not upon the last syllable, but upon that immediately before it, the rhyme must fall both upon the accented syllable and upon that which is not accented. It must be a double rhyme. In the Italian language, when the accent falls neither upon the last syllable, nor upon that immediately before it, but upon the third syllable from the end, the rhyme must fall upon all the three. It must be a triple rhyme, and the verse is supposed to consist of twelve syllables: Forsè era ver, non però credìbile, &c. Triple rhymes are not admitted into English Heroic Verse. In the Italian language the accent falls much more rarely, either upon the third syllable from the end of a word, or upon the last syllable, than it does upon the one immediately before the last. In reality, this second syllable from the end seems, in that language, to be its most common and natural place. The Italian Heroic Poetry, therefore, is composed principally of double rhymes, or of verses supposed to consist of eleven syllables. Triple rhymes occur but seldom, and single rhymes still more seldom. In the English language the accent falls frequently upon the last syllable of the word. Our language, besides, abounds in words of one syllable, the greater part of which do (for there are few which do not) admit of being accented. Words of one syllable are most frequently the concluding words of English rhymes. For both these reasons, English Heroic Rhyme is principally composed of single rhymes, or of verses supposed to consist of ten syllables. Double Rhymes occur almost as rarely in it, as either single or triple do in the Italian. The rarity of double rhymes in English Heroic Verse makes them appear odd, and awkward, and even ludicrous, when they occur. By the best writers, therefore, they are reserved for light and ludicrous occasions; when, in order to humour their subject, they stoop to a more familiar style than usual. When Mr. Pope says; Worth makes the man, and want of it the fellow; The rest is all but leather or prunello; he means, in compliance with his subject, to condescend a good deal below the stateliness of his diction on the Essay on Man. Double rhymes abound more in Dryden than in Pope, and in Butler’s Hudibras more than in Dryden. The rarity both of single and of triple rhyme in Italian Heroic Verse, gives them the same odd and ludicrous air which double rhymes have in English Verse. In Italian, triple rhymes occur more frequently than single rhymes. The slippery, or if I may be allowed to use a very low, but a very expressive word, the glib pronunciation of the triple rhyme (verso sotrucciolo) seems to depart less from the ordinary movement of the double rhyme, than the abrupt ending of the single rhyme (verso tronco e cadente), of the verse that appears to be cut off and to fall short of the usual measure. Single rhymes accordingly appear in Italian verse much more burlesque than triple rhymes. Single rhymes occur very rarely in Ariosto; but frequently in the more burlesque poem of Ricciardetto. Triple rhymes occur much oftener in all the best writers. It is thus, that what in English appears to be the verse of the greatest gravity and dignity, appears in Italian to be the most burlesque and ludicrous; for no other reason, I apprehend, but because in the one language it is the ordinary verse, whereas in the other it departs most from the movements of ordinary verse. The common Italian Heroic Poetry being composed of double rhymes, it can admit both of single and of triple rhymes; which seem to recede from the common movement on opposite sides to nearly equal distances. The common English Heroic Poetry, consisting of single rhymes, it can admit of double; but it cannot admit of triple rhymes, which would recede so far from the common movements as to appear perfectly burlesque and ridiculous. In English, when a word accented upon the third syllable from the end happens to make the last word of a verse, the rhyme falls upon the last syllable only. It is a single rhyme, and the verse consists of no more than ten syllables: but as the last syllable is not accented, it is an imperfect rhyme, which, however, when confined to the second verse of the couplet, and even there introduced but rarely, may have a very agreeable grace, and the line may even seem to run more easy and natural by means of it: Bùt of this fràme, the beàrings, and the tìes. The strìct connèctions, nìce depèndencies, &c. When by a well accented syllable in the end of the first line of a couplet, it has once been clearly ascertained what the rhyme is to be, a very slight allusion to it, such as can be made by a syllable of the same termination that is not accented, may often be sufficient to mark the coincidence in the second line; a word of this kind in the end of the first line seldom succeeds so well: Th’ inhabitants of old Jerusalem Were Jebusites; the town so called from them. A couplet in which both verses were terminated in this manner, would be extremely disagreeable and offensive. In counting the syllables, even of verses which to the ear appear sufficiently correct, a considerable indulgence must frequently be given, before they can, in either language, be reduced to the precise number of ten, eleven, or twelve, according to the nature of the rhyme. In the following couplet, for example, there are, strictly speaking, fourteen syllables in the first line, and twelve in the second. And many a hŭmoŭrous, many an amorous lay, Was sung by many a bard, on many a day. By the rapidity, however, or, if I may use a very low word a second time, by the glibness of the pronunciation, those fourteen syllables in the first line, and those twelve in the second, appear to take up the time but of ten ordinary syllables. The words many a, though they plainly consist of three distinct syllables, or sounds, which are all pronounced successively, or the one after the other, yet pass as but two syllables; as do likewise these words, hŭmoŭroŭs, and amorous. The words heaven and given, in the same manner, consist each of them of two syllables, which, how rapidly so ever they may be pronounced, cannot be pronounced but successively, or the one after the other. In verse, however, they are considered as consisting but of one syllable. In counting the syllables of the Italian Heroic Verse, still greater indulgences must be allowed: three vowels must there frequently be counted as making but one syllable, though they are all pronounced, rapidly indeed, but in succession, or the one after the other, and though no two of them are supposed to make a diphthong. In these licenses too, the Italians seem not to be very regular, and the same concourse of vowels which in one place makes but one syllable, will in another sometimes make two. There are even some words which in the end of a verse are constantly counted for two syllables, but which in any other part of it are never counted for more than one; such as the words suo, tuo, suoi, tuoi. Ruscelli observes, that in the Italian Heroic Verse the accent ought to fall upon the fourth, the sixth, the eighth, and the tenth syllables; and that if it falls upon the third, the fifth, the seventh, or the ninth syllables, it will spoil the verse. In English, if the accent falls upon any of the above-mentioned odd syllables, it equally spoils the verse. Bow’d their stiff necks, loaden with stormy blasts, though a line of Milton, has not the ordinary movement of an English Heroic Verse, the accent falls upon the third and sixth syllables. In Italian frequently, and in English sometimes, an accent is with great grace thrown upon the first syllable, in which case it seldom happens that any other syllable is accented before the fourth; Cánto l’armé pietóse e’l capitáno. Fírst in these fiélds I trý the sýlvan stráins. Both in English and in Italian the second syllable may be accented with great grace, and it generally is so when the first syllable is not accented: E in van l’ inferno a’ lui s’ oppose; e in vano S’ armó d’ Asia, e di Libia il popol misto, &c. Let us, since life can little more supply Than just to look about us, and to die, &c. Both in English and in Italian Verse, an accent, though it must never be misplaced, may sometimes be omitted with great grace. In the last of the above-quoted English Verses there is no accent upon the eighth syllable; the conjunction and not admitting of any. In the following Italian Verse there is no accent upon the sixth syllable: O Musa, tu, che di caduchi allori, &c. The preposition di will as little admit of an accent as the conjunction and. In this case, however, when the even syllable is not accented, neither of the odd syllables immediately before or behind it must be accented. Neither in English nor in Italian can two accents running be omitted. It must be observed, that in Italian there are two accents, the grave and the acute: the grave accent is always marked by a slight stroke over the syllable to which it belongs; the acute accent has no mark. The English language knows no distinction between the grave and the acute accents. The same author observes, that in the Italian Verse the Pause, or what the grammarians call the Cesura, may with propriety be introduced after either the third, the fourth, the fifth, the sixth, or the seventh syllables. The like observations have been made by several different writers upon the English Heroic Verse. Dobie admires particularly the verse in which there are two pauses; one after the fifth, and another after the ninth syllable. The example he gives is from Petrarch: Nel dolce tempo de la prima etade, &c. In this verse, the second pause, which he says comes after the ninth syllable, in reality comes in between the two vowels, which, in the Italian way of counting syllables, compose the ninth syllable. It may be doubtful, therefore, whether this pause may not be considered as coming after the eighth syllable. I do not recollect any good English Verse in which the pause comes in after the ninth syllable. We have many in which it comes in after the eighth: Yet oft, before his infant eyes, would run, &c. In which verse there are two pauses; one after the second, and the other after the eighth syllable. I have observed many Italian Verses in which the pause comes after the second syllable. Both the English and the Italian Heroic Verse, perhaps, are not so properly composed of a certain number of syllables, which vary according to the nature of the rhyme; as of a certain number of intervals, (of five invariably,) each of which is equal in length, or time, to two ordinary distinct syllables, though it may sometimes contain more, of which the extraordinary shortness compensates the extraordinary number. The close frequently of each of those intervals, but always of every second interval, is marked by a distinct accent. This accent may frequently, with great grace, fall upon the beginning of the first interval; after which, it cannot, without spoiling the verse, fall any where but upon the close of an interval. The syllable or syllables which come after the accent that closes the fifth interval are never accented. They make no distinct interval, but are considered as a sort of excrescence of the verse, and are in a manner counted for nothing. THE END. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Smith, Adam. 2018. The Essays of Adam Smith. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/58559/58559-h/58559-h.htm#page468 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

The Essays of Adam Smith: OF THE AFFINITY BETWEEN CERTAIN ENGLISH AND ITALIAN VERSES

The Essays of Adam Smith, by Adam Smith is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here . Of the Affinity between Music, Dancing, and Poetry. Of the Affinity between Music, Dancing, and Poetry. IN the second part of the preceding Essay I have mentioned the connection between the two arts of Music and Dancing, formed by the Rhythmus, as the ancients termed it, or, as we call it, the tune or measure that equally regulates both. It is not, however, every sort of step, gesture, or motion, of which the correspondence with the tune or measure of Music will constitute a Dance. It must be a step, gesture, or motion of a particular sort. In a good opera-actor, not only the modulations and pauses of his voice, but every motion and gesture, every variation, either in the air of his head or in the attitude of his body, correspond to the time and measure of Music. The best opera-actor, however, is not, according to the language of any country in Europe, understood to dance, yet in the performance of his part, he makes use of what is called the stage step; but even this step is not understood to be a dancing step. Though the eye of the most ordinary spectator readily distinguishes between what is called a dancing step and any other step, gesture, or motion, yet it may not perhaps be very easy to express what it is which constitutes this distinction. To ascertain exactly the precise limits at which the one species begins, and the other ends, or to give an accurate definition of this very frivolous matter, might perhaps require more thought and attention than the very small importance of the subject may seem to deserve. Were I, however, to attempt to do this, I should observe, that though in performing any ordinary action—in walking, for example—from the one end of the room to the other, a person may show both grace and agility, yet if he betrays the least intention of showing either, he is sure of offending more or less, and we never fail to accuse him of some degree of vanity and affectation. In the performance of any such ordinary action, every person wishes to appear to be solely occupied about the proper purpose of the action: if he means to show either grace or agility, he is careful to conceal that meaning, and he is very seldom successful in doing so: he offends, however, just in proportion as he betrays it, and he almost always betrays it. In Dancing, on the contrary, every person professes, and avows, as it were, the intention of displaying some degree either of grace or of agility, or of both. The display of one, or other, or both of these qualities, is in reality the proper purpose of the action; and there can never be any disagreeable vanity or affectation in following out the proper purpose of any action. When we say of any particular person, that he gives himself many affected airs and graces in Dancing, we mean either that he gives himself airs and graces which are unsuitable to the nature of the Dance, or that he executes awkwardly, perhaps exaggerates too much, (the most common fault in Dancing,) the airs and graces which are suitable to it. Every Dance is in reality a succession of airs and graces of some kind or other, and of airs and graces which, if I may say so, profess themselves to be such. The steps, gestures, and motions which, as it were, avow the intention of exhibiting a succession of such airs and graces, are the steps, the gestures, and the motions which are peculiar to Dancing, and when these are performed to the time and the measure of Music, they constitute what is properly called a Dance. But though every sort of step, gesture, or motion, even though performed to the time and measure of Music, will not alone make a Dance, yet almost any sort of sound, provided it is repeated with a distinct rhythmus, or according to a distinct time and measure, though without any variation as to gravity or acuteness, will make a sort of Music, no doubt indeed, an imperfect one. Drums, cymbals, and, so far as I have observed, all other instruments of percussion, have only one note; this note, however, when repeated with a certain rhythmus, or according to a certain time and measure, and sometimes, in order to mark more distinctly that time and measure, with some little variation as to loudness and lowness, though without any as to acuteness and gravity, does certainly make a sort of Music, which is frequently far from being disagreeable, and which even sometimes produces considerable effects. The simple note of such instruments, it is true, is generally a very clear, or what is called a melodious, sound. It does not however seem indispensably necessary that it should be so. The sound of the muffled drum, when it beats the dead march, is far from being either clear or melodious, and yet it certainly produces a species of Music which is sometimes affecting. Even in the performance of the most humble of all artists, of the man who drums upon the table with his fingers, we may sometimes distinguish the measure, and perhaps a little of the humour, of some favourite song; and we must allow that even he makes some sort of Music. Without a proper step and motion, the observation of tune alone will not make a Dance; time alone, without tune, will make some sort of Music. That exact observation of tune, or of the proper intervals of gravity and acuteness, which constitutes the great beauty of all perfect Music, constitutes likewise its great difficulty. The time, or measure of a song are simple matters, which even a coarse and unpractised ear is capable of distinguishing and comprehending: but to distinguish and comprehend all the variations of the tune, and to conceive with precision the exact proportion of every note, is what the finest and most cultivated ear is frequently no more than capable of performing. In the singing of the common people we may generally remark a distinct enough observation of time, but a very imperfect one of tune. To discover and to distinguish with precision the proper intervals of tune, must have been a work of long experience and much observation. In the theoretical treatises upon Music, what the authors have to say upon time is commonly discussed in a single chapter of no great length or difficulty. The theory of tune fills commonly all the rest of the volume, and has long ago become both an extensive and an abstruse science, which is often but imperfectly comprehended, even by intelligent artists. In the first rude efforts of uncivilized nations towards singing, the niceties of tune could be but little attended to: I have, upon this account, been frequently disposed to doubt of the great antiquity of those national songs, which it is pretended have been delivered down from age to age by a sort of oral tradition, without having been ever noted or distinctly recorded for many successive generations. The measure, the humour of the song, might perhaps have been delivered down in this manner, but it seems scarcely possible that the precise notes of the tune should have been so preserved. The method of singing some of what we reckon our old Scotch songs, has undergone great alterations within the compass of my memory, and it may have undergone still greater before. The distinction between the sounds or tones of singing and those of speaking seems to be of the same kind with that between the steps, gestures, and motions of Dancing, and those of any other ordinary action; though in speaking, a person may show a very agreeable tone of voice, yet if he seems to intend to show it, if he appears to listen to the sound of his own voice, and as it were to tune it into a pleasing modulation, he never fails to offend, as guilty of a most disagreeable affectation. In speaking, as in every other ordinary action, we expect and require that the speaker should attend only to the proper purpose of the action, the clear and distinct expression of what he has to say. In singing, on the contrary, every person professes the intention to please by the tone and cadence of his voice; and he not only appears to be guilty of no disagreeable affectation in doing so, but we expect and require that he should do so. To please by the choice and arrangement of agreeable sounds is the proper purpose of all Music, vocal as well as instrumental; and we always expect and require, that every person should attend to the proper purpose of whatever action he is performing. A person may appear to sing, as well as to dance, affectedly; he may endeavour to please by sounds and tones which are unsuitable to the nature of the song, or he may dwell too much on those which are suitable to it, or in some other way he may show an overweening conceit of his own abilities, beyond what seems to be warranted by his performance. The disagreeable affectation appears to consist always, not in attempting to please by a proper, but by some improper modulation of the voice. It was early discovered that the vibrations of chords or strings, which either in their lengths, or in their densities, or in their degrees of tension, bear a certain proportion to one another, produce sounds which correspond exactly, or, as the musicians say, are the unisons of those sounds or tones of the human voice which the ear approves of in singing. This discovery has enabled musicians to speak with distinctness and precision concerning the musical sounds or tones of the human voice; they can always precisely ascertain what are the particular sounds or tones which they mean, by ascertaining what are the proportions of the strings of which the vibrations produce the unisons of those sounds or tones. What are called the intervals; that is, the differences, in point of gravity and acuteness, between the sounds or tones of a singing voice, are much greater and more distinct than those of the speaking voice. Though the former, therefore, can be measured and appreciated by the proportions of chords or strings, the latter cannot. The nicest instruments cannot express the extreme minuteness of these intervals. The heptamerede of Mr. Sauveur could express an interval so small as the seventh part of what is called a comma, the smallest interval that is admitted in modern Music. Yet even this instrument, we are informed by Mr. Duclos, could not express the minuteness of the intervals in the pronunciation of the Chinese language; of all the languages in the world, that of which the pronunciation is said to approach the nearest to singing, or in which the intervals are said to be the greatest. As the sounds or tones of the singing voice, therefore, can be ascertained or appropriated, while those of the speaking voice cannot; the former are capable of being noted or recorded, while the latter are not. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Smith, Adam. 2018. The Essays of Adam Smith. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/58559/58559-h/58559-h.htm#G This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

The Essays of Adam Smith: Of the Affinity between Music, Dancing, and Poetry.

This paper proposes a real-time approach to detect the 2D pose of multiple people in an image. OpenPose is an open-source multi-person detection system supporting the body, hand, foot, and facial key points. Human pose estimation is focused on finding individuals and their pose from an image. This problem is quite challenging and is quite complex to determine the number of people that can appear in an image. Moreover, the interferences caused due to contact or proximity between people make the association of parts quite challenging. The current state-of-the-art techniques use a top-down strategy that estimates the pose of each person in each of the detected regions. This approach suffers from performance issues because the run time is directly proportional to the number of people in the image. The method used in the paper uses a nonparametric representation called Part Affinity Fields (PAFs). This approach uses the location and orientation of body parts to encode unstructured pairwise relationships between the body parts of the individuals in the image. In the earlier version of the same paper, they refined both the confidence maps and the part affinity fields (PAFs) at each stage using a branched multistage Convolutional neural network (CNN) and this resulted in much more computation and time at each stage. [1] This paper focused on refining the existing techniques and the architecture of the CNN [1]. They found that PAF refinement is crucial for maximising accuracy, while body part prediction refinement is not that important. In the paper, the author used the first 10 layers of the VGG-19 model as a baseline network to extract feature maps. These feature maps are input to the model to derive the PAFs that define the degree of association between the parts and the confidence maps, which are then refined using the greedy approach. The paper OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields is published by Zhe Cao, Student Member, IEEE, Gines Hidalgo, Student Member, IEEE, Tomas Simon, Shih-En Wei, and Yaser Sheikh under IEEE Transactions on Pattern Analysis and Machine Intelligence journal. Summary of the work, results, and findings In the first stage, the feature maps of the input image are processed by passing it through a baseline network. In this paper, the author uses the first 10 layers of the VGG-19 model for generating the feature maps. Then the feature maps are processed with multiple stages of CNN to generate a set of Part Confidence Maps and a set of Part Affinity Fields. The system uses a multi-stage CNN. It consists of two types of stages. The first set of stages predicts part affinity fields i.e., degree of associations between the body parts the last set of stages predicts the confidence maps. By replacing each 7x7 convolutional kernel with 3 consecutive 3x3 kernels, the current model was able to preserve the receptive field while computation expense is reduced. Using an approach like DenseNet [11] the output of the 3 convolution kernels is concatenated. This approach tripled the non-linearity layers and helped the model to keep both higher and lower-level features. A loss function is applied at the end of every step in both branches. Finally, the Confidence Maps and Part Affinity Fields are processed by a greedy algorithm where optimal partite graph matching is used to obtain the poses for each person in the image. The results and findings are presented, and the accuracy of the multi-person keypoint detection has been compared to the existing methods [2][3]. The real-time performance metric for the system has been specified by mentioning the number of frames it can process each second. All findings are visualized, and graphs are provided for better understanding. The author finds that the newly refined approach increased both the speed and accuracy by approximately 200% and 7% respectively. The paper contributed to the field of foot detection by presenting an annotated foot dataset with 15K human foot instances that have been publicly released for further study and development. The author open-sourced their work and the OpenPose system provides easy-to-use pipelines with command-line interfaces, python API, Unity plugin. It has also been included in the OpenCV library for ease of access. The system runs on CPUs and GPUs such as CUDA and OpenCL. They also provide portable executable binaries which can easily be downloaded and run with minimal setup. Pose-ShuffleNet is a lightweight multi-person pose estimation method developed by integrating the concepts of this paper [4]. This method is highly suited for resource-constrained scenarios. It is based on the building blocks of the ShuffleNet unit [5] for image analysis in place of VGG-19. An architecture of multi-stages with two branches, jointly learning parts detection and parts association has been used. Pose-ShuffleNet has a significantly lower resource (network size and FLOPs) consumption than some of the available leading methods. Using the concepts of OpenPose the research has been further extended to the 3D human pose estimation [6]. Further research on Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation has been carried out. Overall, the proposed pipeline provides promising results on the intention recognition of vulnerable road users. [7] Pros and Cons of the paper: Explanation with pictorial references clarifies the topic in a better way and was effective in clarifying relationships, simplifying complex topics, and was one of the main key factors that had me inclined toward the paper. All the edge cases have been acknowledged and future actions that can mitigate the failure cases have also been suggested. The accuracy and performance of the combined model with body and foot key points is also an important contribution to the paper. The generality of the method by applying it to the task of vehicle key point estimation and the use of the approach has contributed to the wide applications and competitive performance of the model. The paper also showcased three benchmarks performed on reputed and easily available datasets such as MPII [9], COCO [10], and a subset of COCO used as a foot dataset. And provide runtime analysis comparison against Mask R-CNN and Alpha-Pose to quantify the efficiency of the system and analyses the main failure cases. The model performs up to 3 times faster on GPUs when compared to CPUs and in the case of the body and foot model the performance difference is up to 30 times. Making the system more optimised on CPUs can help to make the model more accessible in smaller and less powerful systems and increase the use cases. [8] Potential Advancement and Future Work There have been many advancements in the field of pose estimation, and it is being used in various fields and applications, but at present, the practical applications are limited due to the challenges of crowded people and occluded body parts. A hybrid system of both top-down and bottom-up approaches along with Using anthropometric ratios for determining context can make the model more efficient [7]. Future developments can be made to generate a similar performance on CPUs using lightweight CNNs such as MobileNetV1, EfficientNet or a combination and can help the system be more efficient so that it is less reliant on GPUs and work on embedded systems. It can have various applications in security cameras, child monitors, automating warehouses, gesture control etc. Data plays a key role in improving the robustness of the network. Adding complex scenarios of human poses along with samples of human statues and paintings would boost the accuracy by avoiding the problem of occluded body parts. Improvement in the greedy approach can help to make the pose estimation more accurate. The greedy approach used in the current system suffers from limited structural context when subjects are occluded. Also, the Hungarian algorithm [8] employed is having a cubic complexity. By using a larger structural context of a subset of pre-assigned parts rather than using only the immediate predecessors as in [7], this time complexity can become linear in the number of candidates of any single part-class. Refining the algorithm to reject less likely or complex poses and adding an animal dataset to train the system can have wide applications in pest control, animal farming, analysis of the bees as shown in Honeybee Detection and Pose Estimation using Convolutional Neural Networks [10]. References [1] Cao, Zhe, et al. "Realtime multi-person 2d pose estimation using part affinity fields." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. [2] Fang, Hao-Shu, et al. "Rmpe: Regional multi-person pose estimation." Proceedings of the IEEE international conference on computer vision. 2017. [3] He, Kaiming, et al. "Mask r-cnn." Proceedings of the IEEE international conference on computer vision. 2017. [4] Guan, Chen-zhi. "Realtime multi-person 2d pose estimation using shufflenet." 2019 14th International Conference on Computer Science & Education (ICCSE). IEEE, 2019. [5] Zhang, Xiangyu, et al. "Shufflenet: An extremely efficient convolutional neural network for mobile devices." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. [6] Schwarcz, Steven, and Thomas Pollard. "3d human pose estimation from deep multi-view 2d pose." 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. [7] Fang, Zhijie, and Antonio M. López. "Intention recognition of pedestrians and cyclists by 2d pose estimation." IEEE Transactions on Intelligent Transportation Systems 21.11 (2019): 4773-4783. [8] Osokin, Daniil. "Real-time 2d multi-person pose estimation on cpu: Lightweight openpose." arXiv preprint arXiv:1811.12004 (2018). [9] Andriluka, Mykhaylo, et al. "2d human pose estimation: New benchmark and state of the art analysis." Proceedings of the IEEE Conference on computer Vision and Pattern Recognition. 2014. [10] Rodríguez, I. F., et al. "Honeybee detection and pose estimation using convolutional neural networks." Congres Reconnaissance des Formes, Image, Apprentissage et Perception (RFIAP). 2018. [11] Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. 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Reviewing “OpenPose - Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields”

We apply Affinity Propagation clustering to the Rad 30 Crypto Composite and find that some sectors of the crypto economy are systematically different from the others. Introduction Radicle’s Crypto research arm recently released the Rad 30 Crypto Composite, which tracks thirty assets across ten decentralized sectors of the crypto economy, much like how the Dow Jones Industrial Average tracks stocks across various sectors of the U.S. economy. It is a carefully designed tool that allows us to evaluate how individual segments of the crypto economy are developing outside of Bitcoin and Ethereum. Well, you can learn more about that in ’s , but in general, it’s largely unexplored territory, and that’s exactly our area of expertise at ––we research startups and technologies that are breaking into new ground. We surveyed the landscape of index style metrics in the crypto space and found them all to be solely focused on crypto assets with high market capitalizations––largely for investment optimization in crypto hedge funds. Those approaches are not ideal for understanding how the overall ecosystem is developing outside of Bitcoin and Ethereum, the current market leaders. And if you believe that the potential for crypto and decentralization is much larger than Bitcoin and Ethereum, then the RAD 30 is a good tool to see how that belief plays out IRL. More generally however, the RAD 30 expands our own scope of understanding and gives Radicle’s Crypto and Machine Learning teams a powerful new tool for analysis. Why? Harry release notes Radicle The Rad 30 Crypto Composite tracks leading protocols, platforms and networks around advertising, currency exchanges, distributed cloud computing, e-commerce, file storage, financial services, venture capital & crowdfunding, healthcare, social & digital media networks, and energy. If you look at the correlation matrix heatmap at the top of this article, you’ll find that the currency exchanges, distributed cloud computing, and financial services sectors are notable in having the darkest spots on the map. That tells us there’s some divergence between those sectors and most of the other sectors being tracked. Unfortunately, that’s as far as a correlation matrix will take us. We apply Affinity Propagation clustering to these economy level components of the composite to see if we can identify which sectors, if any, appear to be splitting from the rest in a systematic manner. Like most unsupervised clustering work, this is an exercise in knowledge discovery, with the overall objective of grouping sectors of the crypto economy into clusters by the similarity in their market movements across time. blockchain ML Background This paper is a follow up to , where we applied affinity propagation to individual crypto assets and discovered that a vast majority of them do not move according to Bitcoin’s volatile news cycle. More specifically, we found 3 distinct clusters of crypto assets that move in-tandem and can be generalized by the movements of Tether, Ripple, and DigixDAO––the cluster exemplars. Don’t worry, I won’t assume you know what that means. Clustering Cryptocurrencies with Affinity Propagation _To better understand coin correlations we deployed an Affinity Propagation algorithm and found three distinct clusters…_towardsdatascience.com Clustering Cryptocurrencies with Affinity Propagation Affinity Propagation, published in by Brendan Frey and Delbert Dueck, takes as input measures of similarity between data points and exchanges real-valued messages between matrices until high-quality clusters naturally emerge. This “message passing” occurs over multiple iterations until the cluster boundaries stabilize and the algorithm achieves convergence. Science While exchanging messages the algorithm identifies exemplars, which are observations that do a good job of describing a cluster. You can basically think of them as centroids, with the exception that they’re not the arithmetic mean of all objects in each clustered group, but rather a real observed data point that is representative of its cluster. As discussed in our first piece in this series, we considered a few other algorithms for the clustering task, however none fulfilled one or more of our desiderata. We knew that identifying groups of sectors that are very similar for only a few weeks out of the year, but completely dissimilar the other months measured, would be undesirable. Equally important, we wanted to isolate, as much as possible, the daily impact of the news cycle on market movements; ignoring any delayed effects. Therefore, similarity needed to be evaluated exclusively at each date index, but across the entire array. , which is a popular approach for measuring the similarity of time series data with varying lengths and with out-of-phase similarities would not work — exactly because it attempts to find out-of-phase similarities. We have the luxury of a problem where the data is intentionally in-phase and of the same length. and its many variants would also not work because we don’t want to specify how many clusters should be identified, as that would, in our minds, reduce the objectivity of the analysis. Dynamic Time Warping K-means clustering RAD 30 😎 + Affinity Propagation 🤖 = 👽 We deployed the implementation of Affinity Propagation for this project. For data processing, each data array was log transformed and normalized by subtracting its mean and dividing by its standard deviation. These steps control for the magnitude of volatility in crypto and puts all sectors on a comparable scale. As far as hyperparameters, we set the damping factor to 0.9 and defined the distance measure as negative euclidean distance. The beauty of Affinity Propagation is its power and simplicity. Sci-kit Learn Before discussing the results, let’s briefly consider the philosophy of the whole thing. Currently, it is widely believed that Bitcoin and Ethereum set the pace for the whole crypto economy. On a granular level, that assumption implies that new dApps are at the mercy of how Bitcoin and Ethereum perform, no matter: (1) the actual market of opportunity for any given decentralized application, (2) the network and experience of the startup’s executive team, and even (3) fundamental changes in the underlying technology. We don’t believe that’s actually true, and the data agrees. The algorithm achieved convergence after 67 iterations. It found 5 clusters, and therefore 5 exemplars: (1) RAD 30, (2) Currency Exchanges, (3) Distributed Cloud Computing, (4) Bitcoin, and (5) Ethereum. The side-by-side plots below correspond to each of the five identified clusters. On the left we present the box plot for each cluster. On the right we present the corresponding cluster in time series form, with the exemplar in bright green and the other constituents in the space in faint green. The first cluster is also the largest. It contains the RAD 30 and the decentralized markets for advertising, e-commerce, venture capital and crowdfunding, healthcare, social & digital media networks, and energy. We more or less expected to find at least one large cluster such as this one, considering the low level of maturity of this entire industry. Indeed, a portion of the assets we’re tracking did not exist just a year ago. The fact that the Rad 30 Crypto Composite is the exemplar for this cluster is a bit obvious, since it’s the aggregate sum of all crypto sectors. In trial runs where we didn’t include the aggregate composite, the energy sector becomes the exemplar. We expect some of these sectors to splinter off as they mature. but as of now, they seem to be moving in-tandem. The currency exchanges space is interesting in being the only decentralized sector of the crypto economy identified as having unique market movements across time. This result was unexpected but seems intuitive. Currency exchanges operate at a higher level of the crypto economy, allowing individuals to buy, sell, and trade crypto assets for others or for fiat currencies. Not to mention that they also interface more regularly with centralized institutions and regulatory authorities. They are the portal into the decentralized economy for most, so it makes sense that their market behavior is unique. Distributed cloud computing, file storage, and financial services are clustered above. The heatmap at the top of this article alluded to these sectors being somewhat different from the rest, but it wasn’t clear that they were . As to why, well, we have to humbly state that we don’t have a rigorous answer to this. Anecdotally, we know that the largest ICO in 2017 was , and also that the distributed cloud computing space has some early market entrants. So, it’s possible that this cluster is, in general, more mature. If you have another idea feel free to share in the comments. different together Filecoin’s Lastly, Bitcoin and Ethereum, plotted above and below, are each their own unique cluster. This detail is important to us. The Rad 30 Crypto Composite was designed to track how the crypto economy is developing outside of Bitcoin and Ethereum, and therefore it wouldn’t be ideal if it was systematically associated with either Bitcoin or Ethereum. From an empirical point of view, this result validates that we’re capturing some distinct signal in the crypto economy. Conclusion Overall, the results from this study suggest that the crypto economy is already starting to splinter as it matures. We’re interested to see how the RAD 30 helps us decipher the changes in the crypto economy in the coming months and years. If you enjoyed this article please 👏🏼 and share so others can find it. The Inference and Machine Learning Group is Radicle’s data science research arm. We work at the intersection of computational statistics, machine learning, and venture capital. Subscribe to our email list or follow on Twitter if you’d like to be notified when future research is released. Legal Disclaimer : Radicle is not an investment advisor, and Radicle makes no representation regarding the advisability of investing in any security, asset, token, fund or other investment vehicle. Radicle is not a tax advisor. Inclusion of a security, asset or token within a Radicle composite or any Radicle analysis is not a recommendation by Radicle to buy, sell, or hold such security, nor is it considered to be investment advice. Past performance of a composite is not an indication or guarantee of future results. All Radicle materials have been prepared solely for informational purposes based upon information generally available to the public and from sources believed to be reliable. The composite data and analysis shown do not represent the results of actual trading of investable assets/securities. Radicle maintains the composite(s) and calculates the composite levels and performance shown or discussed, but does not manage actual assets.

Clustering Cryptocurrencies with Affinity Propagation and the RAD 30 Crypto Composite

I wish I could write that the design tools don’t matter. Sometimes I think they don’t. Then I get on Sketch. Or Affinity Designer. I use and . Sketch Pixelmator When I heard about , I thought, “Just another , a heavier Pixelmator with more features at best.” I was not interested. Affinity Designer Acorn My approach to Affinity Designer changed a bit when I saw a novice designer learning Sketch. I recalled how I was learning Sketch myself before it was popular. I recalled why I started to learn Sketch. I recalled how I negotiated an agreement under which I could work a few months using Sketch, without touching any Adobe tools. Software requires time to evaluate. It takes time to appreciate. Especially if we’re talking about programs that we are going to use often, such as every week or even every day. I bought an Affinity Designer license months ago. I spent some time with the application. I haven’t replaced Sketch with it — that would require convincing the two designers I work with of Affinity in a situation where I didn’t see anything good enough in Affinity to mention or recommend. The thing that changed my relationship with Affinity Designer was the book “Affinity Designer Workbook”, released by the application developers themselves. Actually, I don’t know why I bought it. It was an impulsive purchase, but I’m glad that I did it. The book doesn’t allow you to just pass by Affinity Designer indifferently. Three elements of Affinity Designer that draw attention to the Sketch user: 1. Text tools If you feel like you’re lacking authority over text in Sketch, check out the text tools in Affinity Designer. Baseline, tracking, leading override, advanced underline and strikethrough settings, shear, horizontal and vertical scale, standard ligatures, contextual alternatives, ordinals, fractions, superscript, subscript, all caps, small caps, indenting the first line of paragraphs, tab stops — you can manage all this from the panels in Affinity. 2. Unprecedented support for Adobe Photoshop and Illustrator files If you want to use the .psd and .ai file formats without touching software from Adobe, try Affinity. I hadn’t ever seen such good support for the aforementioned file types outside of Adobe. 3. Resizing Affinity Designer can resize objects using AutoLayout–style constraints with a panel resembling the one I remember in Apple Xcode from when I used to program apps for iOS. I understand that this feature may not speak to you, but it spoke to me so strongly that I had to write about it. Take 25 minutes to get to know Affinity Designer. If it is not bad, give it another 25 minutes. Who knows, you may want to invest 20 hours of learning in it to make it your primary tool of work. Did I mention that Affinity Designer is also available on Windows? It needs your attention. It’s a perfect addition to Sketch and Pixelmator. & click 👏 . Download free trial now _For designers who can or want to code_blog.prototypr.io Design podcasts: Where to start? 2019 Edition _The fact that Microsoft is abandoning the engine of its web browser for Chromium and that we are threatened with a…_uxdesign.cc We are one Firefox away from a monopoly _A testimonial_uxdesign.cc The best holiday gift for a designer _615‑star repository and 50 survey responses in 20 days_uxdesign.cc User research in open-source design hackernoon.com How hackers buy design services uxdesign.cc Questions to ask yourself about design side projects uxdesign.cc What a short pro bono civic tech design project for a non–governmental organization looks like _Why it’s worth it for UX/UI/product/… designers to get interested in GitHub_medium.com Dribbble? GitHub! _You don’t want to miss this one._blog.prototypr.io Design podcasts: Where to start? 2018 Edition _No clickbaity descriptions here. :)_medium.com What won’t change in prototyping in 2018 _What to expect after writing on Medium for 12 months as a designer. See my numbers & what writing can look like after a…_blog.prototypr.io On writing as a designer _Offline states, notification channels, the icon framework, settings, guidance on the color system, the color tool, text…_medium.com What has changed in Material Design in 2017 _Five hundred open source projects in need of design help_medium.freecodecamp.org Open Source Design 500 _Conversation starters for UX designers_uxdesign.cc Small talk + UX Designers = ? _The tools I use to be and stay professional as a designer._medium.com Backup for every UX Designer (yes, you!) _Three reasons: Constraints, goals, and actions_medium.com Why you should talk to a designer earlier than you think _Money, prestige, the deciding vote. “Write something honest, uncomfortable, and unpopular.”_medium.com The Second Designer _…but what if Material Design does not suit some of your needs?_medium.com Don’t risk making a crappy UI, use Material Design _More than a year ago, I realized that “empathy” is one of those words that everyone seems to define in their own way …_blog.prototypr.io Empathy in design, practically _You may have heard that great design is transparent. But that’s not what I’m writing about here._medium.com Transparent Designers _Let’s talk about design. You & 2 other designers, online, audio + video, every 2 weeks, 1.5 hours._medium.com Design mastermind group _What does that mean to you in practice?_medium.com Dear designer, everything is a remix _Don’t look at this if you already know all podcasts about design._blog.prototypr.io Design Podcasts: Where to start?

Sketch? I use it every week, but can’t pass indifferently by Affinity Designer

ADA: A Powerful Data Augmentation Technique for Improved Regression Robustness

Systems, by their nature, exist to bring order, consistency, and logic to the chaos of complexity. They promise to solve problems at scale and allow for outcomes that are reliable and repeatable. On-chain governance was supposed to represent the supposedly sacrosanct cornerstone of decentralized technologies - to solve humanity's governance challenges. And yet, after over a decade of experimentation, it has consistently failed to live up to the promise. It remains the unsolved puzzle in a decentralized world, a paradox of sorts. But why is that the case? In abstract terms, governance is defined as the system through which decisions are made and enforced. It serves as a method of power distribution, ways of allocating resources, and resolving conflicts. Governance represents the final leap in the decentralized space. It's when a system can run itself - a collective intelligence wherein no central authority pulls the strings. The sad thing is that the failure of on-chain governance is not theoretical in its promise but rather in its implementation. The very thing it tries to solve, which is coordinating decentralized decision-making, appears to fall apart when put into practice. The Tyranny of Incentives The first fracture in the system derives from a player that usually remains hidden: incentives. In decentralized governance, players act out of their self-interest, driven by profit, social status, or ideological affinity. The notion of distributed power crumbles when the incentives become misaligned - or are hijacked by short-term thinking. Why would participants vote for long-term stability when short-term gains give them far more lucrative rewards? Worse yet, how do you prevent those in the most powerful positions (I mean whales and early adopters) from concentrating control and making a plutocratic nightmare out of the decentralized dream? This is a human failure, not a technological one, as decentralized governance relies on human actors behaving in ways that value collective outcomes. However, it doesn't provide systems resilient enough to allow for greed, ignorance, or apathy. The absence of a central authority does not eradicate power. It merely changes where power resides in unpredictable ways. As the saying goes, "If everyone is in charge, then no one is." UX: The Interface of Governance The complexity of blockchain governance is alluring to technologists, while the average user recoils at the opacity of the mechanics involved. That's a deeper problem than whether or not the interfaces are user-friendly. The problem is really that most users lack either cognitive bandwidth or interest to participate deeply in governance. Governance of blockchain is not something that comes to the top of every average person's life. Voting on protocols, changing parameters, and debating minor details of network upgrades are activities quite removed from everyday concerns. Governance as an experience becomes an operation, not an empowerment means. Just as under traditional systems of political government, where voter turnout is minimal, the same is true with decentralized governance - the few loud voices drowned out in the ambient noise of general apathy and disinterest. A system developed for mass participation turns out, almost paradoxically, to be a domain of specialists that undermines decentralization. The Eternal Dance of Complexity and Simplicity It thus makes a robust but adaptive system under decentralized governance. The tension between the other degree of complexity-simplicity may be irreconcilable: governance needs to reflect the intricacies of the system it governs, yet when it becomes complex, fewer people can understand and engage with it. It creates a governance model that either ends up: Too shallow (dumbed down to the point of complete insignificance)
Too obtuse (most people get completely excluded from meaningful involvement) Nobody has walked that tightrope. Too simple and you lose nuance and control. Too complex, and you lose accessibility and trust. The Ghost in the System: Humanity Perhaps most profoundly, though, the problem is that decentralized governance systems are (at their core) systems. They can codify rules and enforce decisions. They can distribute tokens and apportion rights. But they can't reckon with the raw, messy unpredictability of human nature. Governance isn't just a question of voting and consensus. It's a question of ethics, of conflict resolution, of trust. Of the way people negotiate individual needs with the collective good. There is, perhaps, a reason why traditional forms of governance—flawed as they are—have endured. They are not systems of pure logic but rather systems that accommodate the irrational, emotional, and often contradictory nature of human beings. Decentralized governance faces an upward struggle of adaptation in a world resistant to codifying human behavior. On-chain governance promises the ability to operate without trust, without the fallibility of human leaders whereas It is precisely trust that keeps traditional governance systems working. It's the unseen glue binding participants to the system even when the latter fails. In all these processes, on-chain governance has forgotten this most basic of human ingredients in its drive towards decentralization. While automating trust, it seems to have forgotten the simple fact that trust is not a technological problem; rather, it is a relational issue. Conclusion The failure of decentralized governance does not come from technological limitations. The system itself is the only problem: a paradox in decentralization conflicted with the very basic human need for structure, leadership, and ironically, governance. On-chain governance's future might not be in more complex algorithms or wiser contracts but in embracing the idea that decentralization is not a perfect solution. Governance, because of its very nature, requires compromise, trust, and human intervention. Systems, by their nature, exist to bring order, consistency, and logic to the chaos of complexity. They promise to solve problems at scale and allow for outcomes that are reliable and repeatable. On-chain governance was supposed to represent the supposedly sacrosanct cornerstone of decentralized technologies - to solve humanity's governance challenges. And yet, after over a decade of experimentation, it has consistently failed to live up to the promise. It remains the unsolved puzzle in a decentralized world, a paradox of sorts. But why is that the case? But why is that the case? In abstract terms, governance is defined as the system through which decisions are made and enforced. It serves as a method of power distribution, ways of allocating resources, and resolving conflicts. Governance represents the final leap in the decentralized space. It's when a system can run itself - a collective intelligence wherein no central authority pulls the strings. The sad thing is that the failure of on-chain governance is not theoretical in its promise but rather in its implementation. The very thing it tries to solve, which is coordinating decentralized decision-making, appears to fall apart when put into practice. The Tyranny of Incentives The first fracture in the system derives from a player that usually remains hidden: incentives. In decentralized governance, players act out of their self-interest, driven by profit, social status, or ideological affinity. The notion of distributed power crumbles when the incentives become misaligned - or are hijacked by short-term thinking. Why would participants vote for long-term stability when short-term gains give them far more lucrative rewards? Worse yet, how do you prevent those in the most powerful positions (I mean whales and early adopters) from concentrating control and making a plutocratic nightmare out of the decentralized dream? This is a human failure, not a technological one, as decentralized governance relies on human actors behaving in ways that value collective outcomes. However, it doesn't provide systems resilient enough to allow for greed, ignorance, or apathy. The absence of a central authority does not eradicate power. It merely changes where power resides in unpredictable ways. As the saying goes, "If everyone is in charge, then no one is." UX: The Interface of Governance The complexity of blockchain governance is alluring to technologists, while the average user recoils at the opacity of the mechanics involved. That's a deeper problem than whether or not the interfaces are user-friendly. The problem is really that most users lack either cognitive bandwidth or interest to participate deeply in governance. Governance of blockchain is not something that comes to the top of every average person's life. Voting on protocols, changing parameters, and debating minor details of network upgrades are activities quite removed from everyday concerns. Governance as an experience becomes an operation, not an empowerment means. Just as under traditional systems of political government, where voter turnout is minimal, the same is true with decentralized governance - the few loud voices drowned out in the ambient noise of general apathy and disinterest. A system developed for mass participation turns out, almost paradoxically, to be a domain of specialists that undermines decentralization. The Eternal Dance of Complexity and Simplicity It thus makes a robust but adaptive system under decentralized governance. The tension between the other degree of complexity-simplicity may be irreconcilable: governance needs to reflect the intricacies of the system it governs, yet when it becomes complex, fewer people can understand and engage with it. It creates a governance model that either ends up: Too shallow (dumbed down to the point of complete insignificance) Too obtuse (most people get completely excluded from meaningful involvement) Too shallow (dumbed down to the point of complete insignificance) Too obtuse (most people get completely excluded from meaningful involvement) Nobody has walked that tightrope. Too simple and you lose nuance and control. Too complex, and you lose accessibility and trust. Nobody has walked that tightrope. Too simple and you lose nuance and control. Too complex, and you lose accessibility and trust. The Ghost in the System: Humanity Perhaps most profoundly, though, the problem is that decentralized governance systems are (at their core) systems. They can codify rules and enforce decisions. They can distribute tokens and apportion rights. But they can't reckon with the raw, messy unpredictability of human nature. Governance isn't just a question of voting and consensus. It's a question of ethics, of conflict resolution, of trust. Of the way people negotiate individual needs with the collective good. There is, perhaps, a reason why traditional forms of governance—flawed as they are—have endured. They are not systems of pure logic but rather systems that accommodate the irrational, emotional, and often contradictory nature of human beings. Decentralized governance faces an upward struggle of adaptation in a world resistant to codifying human behavior. On-chain governance promises the ability to operate without trust, without the fallibility of human leaders whereas It is precisely trust that keeps traditional governance systems working. It's the unseen glue binding participants to the system even when the latter fails. In all these processes, on-chain governance has forgotten this most basic of human ingredients in its drive towards decentralization. While automating trust, it seems to have forgotten the simple fact that trust is not a technological problem; rather, it is a relational issue. Conclusion The failure of decentralized governance does not come from technological limitations. The system itself is the only problem: a paradox in decentralization conflicted with the very basic human need for structure, leadership, and ironically, governance. On-chain governance's future might not be in more complex algorithms or wiser contracts but in embracing the idea that decentralization is not a perfect solution. Governance, because of its very nature, requires compromise, trust, and human intervention.

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