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Table of Links Abstract and I. Introduction II. Preliminaries III. Proposed Design: IBis IV. Detailed Construction V. Implementation on DAML VI. Evaluation VII. Conclusion and References V. IMPLEMENTATION ON DAML A. Blockchain Platform We implemented the proof of concept framework using Daml (Digital Asset Modeling Language) [27] atop the Canton blockchain ledger protocol [28]. We used Daml version 2.8.3 with the corresponding Daml ledger model and Canton protocol. The rationale for utilizing Daml in implementing our framework is twofold. First, in Daml, a smart contract codifies the terms of the agreement between parties, including the rights and obligations of each party. A Daml contract template describes the data schema of the contract and rules for manipulating the data. In our implementation, these contract templates align closely with the data models outlined in Sec.IV-A. Second, the Daml ledger model uses smart contracts as privacy-enabled data containers, enforcing data access controls as specified in the contract. The access control permissions of a party/user are explicitly stated in contract templates by assigning the party one of the following predefined roles: • signatory: A party whose authority is required to create the contract or archive it. Every contract must have at least one signatory. Signatories are guaranteed to see actions on that contract. • observer: A party that is guaranteed to see actions that create and archive the contract. • controller: A party that can exercise a particular choice (i.e., invoke a function) on the contract. • choice observer: A party that is guaranteed to see a particular choice being exercised on the contract. While similar approaches can be implemented with other smart contract languages, the contract-level access control typically does not extend to the ledger itself. For example, although chaincodes in Hyperledger Fabric can enforce access control, all channel members still synchronize the entire ledger [50]. In contrast, the Canton blockchain ledger protocol extends the Daml ledger model to the ledger level, where a blockchain node synchronizes only the contract data relevant to its party permissions. Therefore, by adopting Daml and Canton, we facilitate the integration of many AOs and COs onto the same IBIS platform, while ensuring that commercially sensitive license agreements between an AO and COs, dataset metadata, and model metadata remain concealed from other parties, even at the ledger level. Table III lists the resources in IBIS along with their authorizations. B. Smart Contract Implementation Listing 1 illustrates the license smart contract template, constructed based on the data schema in Table I. The copyrightOwner and modelOwner are designated as signatories of the template (Line 10). This reflects the bilateral nature of the license agreement. Consequently, authorization from both parties is required to create a license contract. Subsequently, once the contract is created, both parties can access it, while no other party has access to this contract either onchain or on-ledger. Hence, this implementation closely aligns with the access control requirements specified in Table III. In addition, the identifier attribute of each license serves as the primary key (specified by Line 12-13), which can facilitate efficient queries during the graph traversal. Similarly, dataset and model metadata are represented in smart contract templates according to their corresponding data models, except that only the modelOwner is designated as the signatory of the DatasetMeta and ModelMeta contracts. This setup enforces two access control rules: • Only modelOwner has the authority to create and access these contracts. • modelOwner is restricted to accessing DatasetMeta or ModelMeta contracts created by themselves. Full implementation details are open sourced[3]. Fig.7 illustrates the class diagram of IBIS design. C. Multi-party Signing The generation of a bilaterally signed license contract adheres to Daml’s Propose and Accept Pattern[4]. In this pattern, one party initiates a proposal contract, which the counterparty can either accept or reject. The acceptance (or rejection) is implemented as a contract choice[5], allowing the counterparty to exercise their decision. Upon exercising the accept choice, a result contract is generated, symbolizing the agreement between the two parties. In our implementation, the proposal contract takes the form of the draft license agreement, and it is depicted in Listing 2. LicenseAgreement shares the same data schema as License (see Listing 1), with the copyrightOwner designated as the signatory. This ensure only the copyrightOwner has the authority to create a contract. Alternatively, modelOwner, as the controller of the Accept choice in Line 16, has the authority to exercise the choice, resulting in the creation of a License contract. This setup ensures that only the modelOwner specified in a LicenseAgreement contract has the authority to exercise the Accept choice of that contract. Authors:
(1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(3) Guangsheng Yu, CSIRO Data61;
(4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. This paper is available on arxiv under CC BY 4.0 DEED license. [3] Open released at https://github.com/yilin-sai/ai-copyright-framework. [4] https://docs.daml.com/daml/patterns/initaccept.html# the-propose-and-accept-pattern. [5] https://docs.daml.com/daml/intro/4 Transformations.html# choices-as-methods Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Preliminaries II. Preliminaries III. Proposed Design: IBis III. Proposed Design: IBis IV. Detailed Construction IV. Detailed Construction V. Implementation on DAML V. Implementation on DAML VI. Evaluation VI. Evaluation VII. Conclusion and References VII. Conclusion and References V. IMPLEMENTATION ON DAML A. Blockchain Platform We implemented the proof of concept framework using Daml (Digital Asset Modeling Language) [27] atop the Canton blockchain ledger protocol [28]. We used Daml version 2.8.3 with the corresponding Daml ledger model and Canton protocol. The rationale for utilizing Daml in implementing our framework is twofold. First, in Daml, a smart contract codifies the terms of the agreement between parties, including the rights and obligations of each party. A Daml contract template describes the data schema of the contract and rules for manipulating the data. In our implementation, these contract templates align closely with the data models outlined in Sec.IV-A. contract template Second, the Daml ledger model uses smart contracts as privacy-enabled data containers, enforcing data access controls as specified in the contract. The access control permissions of a party/user are explicitly stated in contract templates by assigning the party one of the following predefined roles: • signatory : A party whose authority is required to create the contract or archive it. Every contract must have at least one signatory. Signatories are guaranteed to see actions on that contract. signatory • observer : A party that is guaranteed to see actions that create and archive the contract. observer • controller : A party that can exercise a particular choice (i.e., invoke a function) on the contract. controller • choice observer: A party that is guaranteed to see a particular choice being exercised on the contract. • choice observer: While similar approaches can be implemented with other smart contract languages, the contract-level access control typically does not extend to the ledger itself. For example, although chaincodes in Hyperledger Fabric can enforce access control, all channel members still synchronize the entire ledger [50]. In contrast, the Canton blockchain ledger protocol extends the Daml ledger model to the ledger level, where a blockchain node synchronizes only the contract data relevant to its party permissions. Therefore, by adopting Daml and Canton, we facilitate the integration of many AOs and COs onto the same IBIS platform, while ensuring that commercially sensitive license agreements between an AO and COs, dataset metadata, and model metadata remain concealed from other parties, even at the ledger level. Table III lists the resources in IBIS along with their authorizations. B. Smart Contract Implementation Listing 1 illustrates the license smart contract template, constructed based on the data schema in Table I. The copyrightOwner and modelOwner are designated as signatories of the template (Line 10). This reflects the bilateral nature of the license agreement. Consequently, authorization from both parties is required to create a license contract. Subsequently, once the contract is created, both parties can access it, while no other party has access to this contract either onchain or on-ledger. Hence, this implementation closely aligns with the access control requirements specified in Table III. In addition, the identifier attribute of each license serves as the primary key (specified by Line 12-13), which can facilitate efficient queries during the graph traversal. Similarly, dataset and model metadata are represented in smart contract templates according to their corresponding data models, except that only the modelOwner is designated as the signatory of the DatasetMeta and ModelMeta contracts. This setup enforces two access control rules: • Only modelOwner has the authority to create and access these contracts. • modelOwner is restricted to accessing DatasetMeta or ModelMeta contracts created by themselves. Full implementation details are open sourced[3]. Fig.7 illustrates the class diagram of IBIS design. C. Multi-party Signing The generation of a bilaterally signed license contract adheres to Daml’s Propose and Accept Pattern [4]. In this pattern, one party initiates a proposal contract, which the counterparty can either accept or reject. The acceptance (or rejection) is implemented as a contract choice[5], allowing the counterparty to exercise their decision. Upon exercising the accept choice, a result contract is generated, symbolizing the agreement between the two parties. Propose and Accept Pattern In our implementation, the proposal contract takes the form of the draft license agreement, and it is depicted in Listing 2. LicenseAgreement shares the same data schema as License (see Listing 1), with the copyrightOwner designated as the signatory. This ensure only the copyrightOwner has the authority to create a contract. Alternatively, modelOwner, as the controller of the Accept choice in Line 16, has the authority to exercise the choice, resulting in the creation of a License contract. This setup ensures that only the modelOwner specified in a LicenseAgreement contract has the authority to exercise the Accept choice of that contract. Authors: (1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (3) Guangsheng Yu, CSIRO Data61; (4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. Authors: Authors: (1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (3) Guangsheng Yu, CSIRO Data61; (4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv [3] Open released at https://github.com/yilin-sai/ai-copyright-framework. [4] https://docs.daml.com/daml/patterns/initaccept.html# the-propose-and-accept-pattern. [5] https://docs.daml.com/daml/intro/4 Transformations.html# choices-as-methods

Implementing Secure and Private Smart Contracts with Daml on the Canton Blockchain

ERIK ADOLPH vs. Uber Court Filing, retrieved on July 17, 2023, is part of . You can jump to any part in this filing . This is part 11 of 15. HackerNoon’s Legal PDF Series here D. Next, Uber contends that a PAGA plaintiff must have a “financial stake in the outcome of the case” and that if an arbitrator grants an award to the plaintiff based on his or her personally sustained violations, the plaintiff loses standing to litigate non-individual claims because he or she has no financial stake in those claims. For purposes of standing, however, the statute does not require a PAGA plaintiff who has alleged one or more personally sustained violations to seek civil penalties for those violations in the same forum as the litigation of non-individual claims. As the Attorney General observes in his amicus curiae brief, “it is not the promise of economic recovery — in court or elsewhere — that gives an aggrieved employee standing to pursue PAGA claims based on violations committed against other workers.” We agree with Adolph that “it is plaintiff’s status as an aggrieved employee, not the redressability of any injury the plaintiff may have suffered, that determines the availability of PAGA standing.” The Legislature clearly delineated PAGA’s standing requirements, and “ ‘ “ ‘[w]here the words of the statute are clear, we may not add to or alter them to accomplish a purpose that does not appear on the face of the statute or from its legislative history.’ ” ’ ” (Kim, supra, 9 Cal.5th at p. 85.) We also note that a PAGA plaintiff compelled to arbitrate individual claims may have a personal stake in the litigation of non-individual claims. For instance, PAGA has a provision for recovery of attorney’s fees and costs. (§ 2699, subd. (g)(1).) This provision may help plaintiffs secure representation by enticing attorneys to take cases they might not have if limited to recovering fees and costs for individual claims alone. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case S274671 retrieved on September 22, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. courts.ca.gov

Uber Is Doing Everything They Can to Get Out of This Lawsuit, But It's Not Working

On War by Carl Von Clausewitz, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . Attack on the Enemy’s Army in its Cantonments Book VII: Chapter XIX. Attack on the Enemy’s Army in its Cantonments We have not treated of this subject in the defence, because a line of cantonments is not to be regarded as a defensive means, but as a mere existence of the army in a state which implies little readiness for battle. In respect to this readiness for battle, we therefore did not go beyond what we required to say in connection with this condition of an army in the 13th chapter of the 5th book. But here, in considering the attack, we have to think of an enemy’s army in cantonments in all respects as a special object; for, in the first place, such an attack is of a very peculiar kind in itself; and, in the next place, it may be considered as a strategic means of particular efficacy. Here we have before us, therefore, not the question of an onslaught on a single cantonment or a small corps dispersed amongst a few villages, as the arrangements for that are entirely of a tactical nature, but of the attack of a large army, distributed in cantonments more or less extensive; an attack in which the object is not the mere surprise of a single cantonment, but to prevent the assembly of the army. The attack on an enemy’s army in cantonments is therefore the surprise of an army not assembled. If this surprise succeeds fully, then the enemy’s army is prevented from reaching its appointed place of assembly, and, therefore, compelled to choose another more to the rear; as this change of the point of assembly to the rear in a state of such emergency can seldom be effected in less than a day’s march, but generally will require several days, the loss of ground which this occasions is by no means an insignificant loss; and this is the first advantage gained by the assailant. But now, this surprise which is in connection with the general relations, may certainly at the same time, in its commencement, be an onslaught on some of the enemy’s single cantonments, not certainly upon all, or upon a great many, because that would suppose a scattering of the attacking army to an extent which could never be advisable. Therefore, only the most advanced quarters, only those which lie in the direction of the attacking columns, can be surprised, and even this will seldom happen to many of them, as large forces cannot easily approach unobserved. However, this element of the attack is by no means to be disregarded; and we reckon the advantages which may be thus obtained, as the second advantage of the surprise. A third advantage consists in the minor combats forced upon the enemy in which his losses will be considerable. A great body of troops does not assemble itself at once by single battalions at the spot appointed for the general concentration of the army, but usually forms itself by brigades, divisions, or corps, in the first place, and these masses cannot then hasten at full speed to the rendezvous; in case of meeting with an enemy’s column in their course, they are obliged to engage in a combat; now, they may certainly come off victorious in the same, particularly if the enemy’s attacking column is not of sufficient strength, but in conquering, they lose time, and, in most cases, as may be easily conceived, a corps, under such circumstances, and in the general tendency to gain a point which lies to the rear, will not make any beneficial use of its victory. On the other hand, they may be beaten, and that is the most probable issue in itself, because they have not time to organise a good resistance. We may, therefore, very well suppose that in an attack well planned and executed, the assailant through these partial combats will gather up a considerable number of trophies, which become a principal point in the general result. Lastly, the fourth advantage, and the keystone of the whole, is a certain momentary disorganisation and discouragement on the side of the enemy, which, when the force is at last assembled, seldom allows of its being immediately brought into action, and generally obliges the party attacked to abandon still more ground to his assailant, and to make a change generally in his plan of operations. Such are the proper results of a successful surprise of the enemy in cantonments, that is, of one in which the enemy is prevented from assembling his army without loss at the point fixed in his plan. But by the nature of the case, success has many degrees; and, therefore, the results may be very great in one case, and hardly worth mentioning in another. But even when, through the complete success of the enterprise, these results are considerable, they will seldom bear comparison with the gain of a great battle, partly because, in the first place, the trophies are seldom as great, and in the next, the moral impression never strikes so deep. This general result must always be kept in view, that we may not promise ourselves more from an enterprise of this kind than it can give. Many hold it to be the non plus ultra of offensive activity; but it is not so by any means, as we may see from this analysis, as well as from military history. One of the most brilliant surprises in history, is that made by the Duke of Lorraine in 1643, on the cantonments of the French, under General Ranzan, at Duttlingen. The corps was 16,000 men, and they lost the General commanding, and 7,000 men; it was a complete defeat. The want of outposts was the cause of the disaster. The surprise of Turenne at Mergentheim (Mariendal, as the French call it,) in 1644, is in like manner to be regarded as equal to a defeat in its effects, for he lost 3,000 men out of 8,000, which was principally owing to his having been led into making an untimely stand after he got his men assembled. Such results we cannot, therefore, often reckon upon; it was rather the result of an ill-judged action than of the surprise, properly speaking, for Turenne might easily have avoided the action, and have rallied his troops upon those in more distant quarters. A third noted surprise is that which Turenne made on the Allies under the great Elector, the Imperial General Bournonville and the Duke of Lorraine, in Alsace, in the year 1674. The trophies were very small, the loss of the Allies did not exceed 2,000 or 3,000 men, which could not decide the fate of a force of 50,000; but the Allies considered that they could not venture to make any further resistance in Alsace, and retired across the Rhine again. This strategic result was all that Turenne wanted, but we must not look for the causes of it entirely in the surprise. Turenne surprised the plans of his opponents more than the troops themselves; the want of unanimity amongst the allied generals and the proximity of the Rhine did the rest. This event altogether deserves a closer examination, as it is generally viewed in a wrong light. In 1741, Neipperg surprised Frederick the Great in his quarters; the whole of the result was that the king was obliged to fight the battle of Mollwitz before he had collected all his forces, and with a change of front. In 1745, Frederick the Great surprised the Duke of Lorraine in his cantonments in Lusatia; the chief success was through the real surprise of one of the most important quarters, that of Hennersdorf, by which the Austrians suffered a loss of 2,000 men; the general result was that the Duke of Lorraine retreated to Bohemia by Upper Lusatia, but that did not at all prevent his returning into Saxony by the left bank of the Elbe, so that without the battle of Kesselsdorf, there would have been no important result. 1758. The Duke Ferdinand surprised the French quarters; the immediate result was that the French lost some thousands of men, and were obliged to take up a position behind the Aller. The moral effect may have been of more importance, and may have had some influence on the subsequent evacuation of Westphalia. If from these different examples we seek for a conclusion as to the efficacy of this kind of attack, then only the two first can be put in comparison with a battle gained. But the corps were only small, and the want of outposts in the system of war in those days was a circumstance greatly in favour of these enterprises. Although the four other cases must be reckoned completely successful enterprises, it is plain that not one of them is to be compared with a battle gained as respects its result. The general result could not have taken place in any of them except with an adversary weak in will and character, and therefore it did not take place at all in the case of 1741. In 1806 the Prussian army contemplated surprising the French in this manner in Franconia. The case promised well for a satisfactory result. Buonaparte was not present, the French corps were in widely extended cantonments; under these circumstances, the Prussian army, acting with great resolution and activity, might very well reckon on driving the French back across the Rhine, with more or less loss. But this was also all; if they reckoned upon more, for instance, on following up their advantages beyond the Rhine, or on gaining such a moral ascendancy, that the French would not again venture to appear on the right bank of the river in the same campaign, such an expectation had no sufficient grounds whatever. In the beginning of August, 1812, the Russians from Smolensk meditated falling upon the cantonments of the French when Napoleon halted his army in the neighbourhood of Witepsk. But they wanted courage to carry out the enterprise; and it was fortunate for them they did; for as the French commander with his centre was not only more than twice the strength of their centre, but also in himself the most resolute commander that ever lived, as further, the loss of a few miles of ground would have decided nothing, and there was no natural obstacle in any feature of the country near enough up to which they might pursue their success, and by that means, in some measure make it certain, and lastly, as the war of the year 1812 was not in any way a campaign of that kind, which draws itself in a languid way to a conclusion, but the serious plan of an assailant who had made up his mind to conquer his opponent completely,—therefore the trifling results to be expected from a surprise of the enemy in his quarters, appear nothing else than utterly disproportionate to the solution of the problem, they could not justify a hope of making good by their means the great inequality of forces and other relations. But this scheme serves to show how a confused idea of the effect of this means may lead to an entirely false application of the same. What has been hitherto said, places the subject in the light of a strategic means. But it lies in its nature that its execution also is not purely tactical, but in part belongs again to strategy so far, particularly that such an attack is generally made on a front of considerable width, and the army which carries it out can, and generally will, come to blows before it is concentrated, so that the whole is an agglomeration of partial combats. We must now add a few words on the most natural organisation of such an attack. The first condition is:— (1.) To attack the front of the enemy’s quarters in a certain width of front, for that is the only means by which we can really surprise several cantonments, cut off others, and create generally that disorganisation in the enemy’s army which is intended.—The number of, and the intervals between, the columns must depend on circumstances. (2.) The direction of the different columns must converge upon a point where it is intended they should unite; for the enemy ends more or less with a concentration of his force, and therefore we must do the same. This point of concentration should, if possible, be the enemy’s point of assembly, or lie on his line of retreat, it will naturally be best where that line crosses an important obstacle in the country. (3.) The separate columns when they come in contact with the enemy’s forces must attack them with great determination, with dash and boldness, as they have general relations in their favour, and daring is always there in its right place. From this it follows that the commanders of the separate columns must be allowed freedom of action and full power in this respect. (4.) The tactical plan of attack against those of the enemy’s corps that are the first to place themselves in position, must always be directed to turn a flank, for the greatest result is always to be expected by separating the corps, and cutting them off. (5.) Each of the columns must be composed of portions of the three arms, and must not be stinted in cavalry, it may even sometimes be well to divide amongst them the whole of the reserve cavalry; for it would be a great mistake to suppose that this body of cavalry could play any great part in a mass in an enterprise of this sort. The first village, the smallest bridge, the most insignificant thicket would bring it to a halt. (6.) Although it lies in the nature of a surprise that the assailant should not send his advanced guard very far in front, that principle only applies to the first approach to the enemy’s quarters. When the fight has commenced in the enemy’s quarters, and therefore all that was to be expected from actual surprise has been gained, then the columns of the advanced guard of all arms should push on as far as possible, for they may greatly increase the confusion on the side of the enemy by more rapid movement. It is only by this means that it becomes possible to carry off here and there the mass of baggage, artillery, non-effectives, and camp-followers, which have to be dragged after a cantonment suddenly broken up, and these advanced guards must also be the chief instruments in turning and cutting off the enemy. (7.) Finally, the retreat in case of ill-success must be thought of, and a rallying point be fixed upon beforehand. 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. Carl von Clausewitz (2006). On War. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/1946/pg1946-images.html 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 .

Attack on the Enemy’s Army in its Cantonments

On War by Carl Von Clausewitz, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . Cantonments Book V: Chapter XIII. Cantonments In the modern system of war cantonments have become again indispensable, because neither tents nor a complete military train make an army independent of them. Huts and open-air camps (bivouacs as they are called), however far such arrangements may be carried, can still never become the usual way of locating troops without sickness gaining the upper hand, and prematurely exhausting their strength, sooner or later, according to the state of the weather or climate. The campaign in Russia in 1812 is one of the few in which, in a very severe climate, the troops, during the six months that it lasted hardly ever lay in cantonments. But what was the consequence of this extreme effort, which should be called an extravagance, if that term was not much more applicable to the political conception of the enterprise! Two things interfere with the occupation of cantonments the proximity of the enemy, and the rapidity of movement. For these reasons they are quitted as soon as the decision approaches, and cannot be again taken up until the decision is over. In modern wars, that’s, in all campaigns during the last twenty-five years which occur to us at this moment, the military element has acted with full energy. Nearly all that was possible has generally been done in them, as far as regards activity and the utmost effort of force; but all these campaigns have been of short duration, they have seldom exceeded half a year; in most of them a few months sufficed to bring matters to a crisis, that is, to a point where the vanquished enemy saw himself compelled to sue for an armistice or at once for peace, or to a point where, on the conqueror’s part, the impetus of victory had exhausted itself. During this period of extreme effort there could be little question of cantonments, for even in the victorious march of the pursuer, if there was no longer any danger, the rapidity of movement made that kind of relief impossible. But when from any cause the course of events is less impetuous, when a more even oscillation and balancing of forces takes place, then the housing of troops must again become a foremost subject for attention. This want has some influence even on the conduct of war itself, partly in this way, that we seek to gain more time and security by a stronger system of outposts, by a more considerable advanced guard thrown further forward; and partly in this way, that our measures are governed more by the richness and fertility of the country than by the tactical advantages which the ground affords in the geometrical relations of lines and points. A commercial town of twenty or thirty thousand inhabitants, a road thickly studded with large villages or flourishing towns give such facilities for the assembling in one position large bodies of troops, and this concentration gives such a freedom and such a latitude for movement as fully compensate for the advantages which the better situation of some point may otherwise present. On the form to be followed in arranging cantonments we have only a few observations to make, as this subject belongs for the most part to tactics. The housing of troops comes under two heads, inasmuch as it can either be the main point or only a secondary consideration. If the disposition of the troops in the course of a campaign is regulated by grounds purely tactical and strategical, and if, as is done more especially with cavalry, they are directed for their comfort to occupy the quarters available in the vicinity of the point of concentration of the army, then the quarters are subordinate considerations and substitutes for camps; they must, therefore, be chosen within such a radius that the troops can reach the point of assembly in good time. But if an army takes up quarters to rest and refresh, then the housing of the troops is the main point, and other measures, consequently also the selection of the particular point of assembly, will be influenced by that object. The first question for examination here is as to the general form of the cantonments as a whole. The usual form is that of a very long oval, a mere widening as it were of the tactical order of battle. The point of assembly for the army is in front, the head-quarters in rear. Now these three arrangements are, in point of fact, adverse, indeed almost opposed, to the safe assembly of the army on the approach of the enemy. The more the cantonments form a square, or rather a circle, the quicker the troops can concentrate at one point, that is the centre. The further the place of assembly is placed in rear, the longer the enemy will be in reaching it, and, therefore, the more time is left us to assemble. A point of assembly in rear of the cantonments can never be in danger. And, on the other hand, the farther the head-quarters are in advance, so much the sooner reports arrive, therefore so much the better is the commander informed of everything. At the same time, the first named arrangements are not devoid of points which deserve some attention. By the extension of cantonments in width, we have in view the protection of the country which would otherwise be laid under contributions by the enemy. But this motive is neither thoroughly sound, nor is it very important. It is only sound as far as regards the country on the extremity of the wings, but does not apply at all to intermediate spaces existing between separate divisions of the army, if the quarters of those divisions are drawn closer round their point of assembly, for no enemy will then venture into those intervals of space. And it is not very important, because there are simpler means of shielding the districts in our vicinity from the enemy’s requisitions than scattering the army itself. The placing of the point of assembly in front is with a view to covering the quarters, for the following reasons: In the first place, a body of troops, suddenly called to arms, always leaves behind it in cantonments a tail of stragglers sick, baggage, provisions, etc., etc. which may easily fall into the enemy’s hands if the point of assembly is placed in rear. In the second place, we have to apprehend that if the enemy with some bodies of cavalry passes by the advanced guard, or if it is defeated in any way, he may fall upon scattered regiments or battalions. If he encounters a force drawn up in good order, although it is weak, and in the end must be overpowered, still he is brought to a stop, and in that way time is gained. As respects the position of the head-quarters, it is generally supposed that it cannot be made too secure. According to these different considerations, we may conclude that the best arrangement for districts of cantonments is where they take an oblong form, approaching the square or circle, have the point of assembly in the centre, and the head-quarters placed on the front line, well protected by considerable masses of troops. What we have said as to covering of the wings in treating of the disposition of the army in general, applies here also; therefore corps detached from the main body, right and left, although intended to fight in conjunction with the rest, will have particular points of assembly of their own in the same line with the main body. Now, if we reflect that the nature of a country, on the one hand, by favourable features in the ground determines the most natural point of assembly, and on the other hand, by the positions of towns and villages determines the most suitable situation for cantonments, then we must perceive how very rarely any geometrical form can be decisive in our present subject. But yet it was necessary to direct attention to it, because, like all general laws, it affects the generality of cases in a greater or less degree. What now remains to be said as to an advantageous position for cantonments is that they should be taken up behind some natural obstacle of ground affording cover, whilst the sides next the enemy can be watched by small but numerous detached parties; or they may be taken up behind fortresses, which, when circumstances prevent any estimate being formed of the strength of their garrisons, impose upon the enemy a greater feeling of respect and and caution. We reserve the subject of winter quarters, covered by defensive works for a separate article. The quarters taken up by troops on a march differ from those called standing cantonments in this way, that, in order to save the troops from unnecessary marching, cantonments on a march are taken up as much as possible along the lines of march, and are not at any considerable distance on either side of these roads; if their extension in this sense does not exceed a short day’s march, the arrangement is not one at all unfavourable to the quick concentration of the army. In all cases in presence of the enemy, according to the technical phrase in use, that is in all cases where there is no considerable interval between the advance guards of the two armies respectively, the extent of the cantonments and the time required to assemble the army determine the strength and position of the advanced guard and outposts; but when these must be suited to the enemy and circumstances, then, on the contrary, the extent of the cantonments must depend on the time which we can count upon by the resistance of the advance guard. In the third(*) chapter of this book, we have stated how this resistance, in the case of an advanced corps, may be estimated. From the time of that resistance we must deduct the time required for transmission of reports and getting the men under arms, and the remainder only is the time available for assembling at the point of concentration. (*) 8th Chapter.—Tr. We shall conclude here also by establishing our ideas in the form of a result, such as is usual under ordinary circumstances. If the distance at which the advanced guard is detached is the same as the radius of the cantonments, and the point of assembly is fixed in the centre of the cantonments, the time which is gained by checking the enemy’s advance would be available for the transmission of intelligence and getting under arms, and would in most cases be sufficient, even although the communication is not made by means of signals, cannon-shots, etc., but simply by relays of orderlies, the only really sure method. With an advanced guard pushed forward three miles in front, our cantonments might therefore cover a space of thirty square miles. In a moderately-peopled country there would be 10,000 houses in this space, which for an army of 50,000, after deducting the advanced guard, would be four men to a billet, therefore very comfortable quarters; and for an army of twice the strength nine men to a billet, therefore still not very close quarters. On the other hand, if the advanced guard is only one mile in front, we could only occupy a space of four square miles; for although the time gained does not diminish exactly in proportion as the distance of the advanced guard diminishes, and even with a distance of one mile we may still calculate on a gain of six hours, yet the necessity for caution increases when the enemy is so close. But in such a space an army of 50,000 men could only find partial accommodation, even in a very thickly populated country. From all this we see what an important part is played here by great or at least considerable towns, which afford convenience for sheltering 10,000 or even 20,000 men almost at one point. From this result it follows that, if we are not very close to the enemy, and have a suitable advanced guard we might remain in cantonments, even if the enemy is concentrated, as Frederick the Great did at Breslau in the beginning of the year 1762, and Buonaparte at Witebsk in 1812. But although by preserving a right distance and by suitable arrangements we have no reason to fear not being able to assemble in time, even opposite an enemy who is concentrated, yet we must not forget that an army engaged in assembling itself in all haste can do nothing else in that time; that it is therefore, for a time at least, not in a condition to avail itself in an instant of fortuitous opportunities, which deprives it of the greater part of its really efficient power. The consequence of this is, that an army should only break itself up completely in cantonments under some one or other of the three following cases: 1. If the enemy does the same. 2. If the condition of the troops makes it unavoidable. 3. If the more immediate object with the army is completely limited to the maintenance of a strong position, and therefore the only point of importance is concentrating the troops at that point in good time. The campaign of 1815 gives a very remarkable example of the assembly of an army from cantonments. General Ziethen, with Blücher’s advanced guard, 30,000 men, was posted at Charleroi, only two miles from Sombreff, the place appointed for the assembly of the army. The farthest cantonments of the army were about eight miles from Sombreff, that is, on the one side beyond Ciney, and on the other near Liége. Notwithstanding this, the troops cantoned about Ciney were assembled at Ligny several hours before the battle began, and those near Liége (Bulow’s Corps) would have been also, had it not been for accident and faulty arrangements in the communication of orders and intelligence. Unquestionably, proper care for the security of the Prussian army was not taken; but in explanation we must say that the arrangements were made at a time when the French army was still dispersed over widely extended cantonments, and that the real fault consisted in not altering them the moment the first news was received that the enemy’s troops were in movement, and that Buonaparte had joined the army. Still it remains noteworthy that the Prussian army was able in any way to concentrate at Sombreff before the attack of the enemy. Certainly, on the night of the 14th, that is, twelve hours before Ziethen was actually attacked, Blücher received information of the advance of the enemy, and began to assemble his army; but on the 15th at nine in the morning, Ziethen was already hotly engaged, and it was not until the same moment that General Thielman at Ciney first received orders to march to Namur. He had therefore then to assemble his divisions, and to march six and a half miles to Sombreff, which he did in 24 hours. General Bulow would also have been able to arrive about the same time, if the order had reached him as it should have done. But Buonaparte did not resolve to make his attack on Ligny until two in the afternoon of the 16th. The apprehension of having Wellington on the one side of him, and Blücher on the other, in other words, the disproportion in the relative forces, contributed to this slowness; still we see how the most resolute commander may be detained by the cautious feeling of the way which is always unavoidable in cases which are to a certain degree complicated. Some of the considerations here raised are plainly more tactical than strategic in their nature; but we have preferred rather to encroach a little than to run the risk of not being sufficiently explicit. 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. Carl von Clausewitz (2006). On War. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/1946/pg1946-images.html 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 .

Modern system of war cantonments

12/01/2019 Sasha Baron Cohen’s Op-ed I don’t necessarily agree with Sasha’s politics, but he does make one good point in his op-ed related to costs. The social media companies could do a much better job of content compliance, but this would increase their expenses. Heretofore, they’ve been able to avoid this increased cost due to the regulation Cohen mentions in the op-ed, which is part of the reason these social media businesses have been so profitable. Virgil Griffith Arrested for Presenting in North Korea I’m staying out of the politics of this. Mostly because I’m not in a position to do anything about it, so my opinion largely doesn’t matter. Anyway, Virgil Griffith was arrested for teaching North Koreans how to avoid sanctions using cryptocurrency. For reference, here is the Department of Justice post about it. It also looks like there might be a petition started to “Free Virgil” soon. Lebanese Bitcoiners The only thing I’m seeing the public use Bitcoin for right now is as an alternative to traditional financial institutions when they begin to fail. While this provides an obvious benefit to society, does this niche use case justify the current Bitcoin price? Probably not. California DMV Makes $50 Million a Year Selling Personal Data In case you needed another reason to hate the DMV. Trump Signs S. 1838 Last week Congress passed the bill, this week Trump signed it. Viral Manga This manga went viral with 2.5 million views. I’m not interested in discussing the manga itself, but rather something else. When discussing how the Uighurs are being treated I’ve sometimes heard the argument that we in the U.S. are getting an American centric view of the situation. However, the fact that this manga went viral in Japan might discredit this argument. Uber Denied London License Things are not looking good for Uber right now. LVMH Seeks to Acquire Tiffany for $16 Billion Bernard Arnault continues to kill it. Pretty soon he’s going to have a monopoly on luxury. This week’s newsletter not your cup of tea? Check out some of the more popular weeks below! The $1 Billion Bitcoin Whale, A $243,000 Deepfake, Pre-Mature Optimization, and The Yandhi Leak GitHub Acquires Semmle, Vitalik’s Time Machine, and Condolences to the Facebook Engineer Shapeshift’s FOX Token, H.R.3289, and Of Course…The Cybertruck This content is for informational purposes only, you should not construe any such information or other material as legal, tax, investment, financial, or other advice. Nothing contained in this post constitutes a solicitation, recommendation, endorsement, or offer by myself to buy or sell any securities or other financial instruments in this or in any other jurisdiction in which such solicitation or offer would be unlawful under the securities laws of such jurisdiction. The opinions expressed in this publication are those of the author. They do not purport to reflect the opinions or views of any of the author’s employers. Don’t forget to follow me on twitter, add me on linkedin, add me on facebook, or follow me on medium for more content like this. I post a review of topics I find interesting every week. I’m always looking for new topics to cover, so if you have anything you find interesting and would like to discuss please reach out! 12/01/2019 Sasha Baron Cohen’s Op-ed Sasha Baron Cohen’s Op-ed I don’t necessarily agree with Sasha’s politics, but he does make one good point in his op-ed related to costs. The social media companies could do a much better job of content compliance, but this would increase their expenses. Heretofore, they’ve been able to avoid this increased cost due to the regulation Cohen mentions in the op-ed, which is part of the reason these social media businesses have been so profitable. could Virgil Griffith Arrested for Presenting in North Korea Virgil Griffith Arrested for Presenting in North Korea I’m staying out of the politics of this. Mostly because I’m not in a position to do anything about it, so my opinion largely doesn’t matter. Anyway, Virgil Griffith was arrested for teaching North Koreans how to avoid sanctions using cryptocurrency. Virgil Griffith For reference, here is the Department of Justice post about it . For reference, here is the Department of Justice post about it It also looks like there might be a petition started to “Free Virgil” soon. It also looks like there might be a petition started to “Free Virgil” soon. Lebanese Bitcoiners Lebanese Bitcoiners The only thing I’m seeing the public use Bitcoin for right now is as an alternative to traditional financial institutions when they begin to fail. While this provides an obvious benefit to society, does this niche use case justify the current Bitcoin price? Probably not. California DMV Makes $50 Million a Year Selling Personal Data California DMV Makes $50 Million a Year Selling Personal Data In case you needed another reason to hate the DMV. Trump Signs S. 1838 Trump Signs S. 1838 Last week Congress passed the bill , this week Trump signed it. Last week Congress passed the bill Viral Manga Viral Manga This manga went viral with 2.5 million views. I’m not interested in discussing the manga itself, but rather something else. When discussing how the Uighurs are being treated I’ve sometimes heard the argument that we in the U.S. are getting an American centric view of the situation. However, the fact that this manga went viral in Japan might discredit this argument. Uber Denied London License Uber Denied London License Things are not looking good for Uber right now. LVMH Seeks to Acquire Tiffany for $16 Billion LVMH Seeks to Acquire Tiffany for $16 Billion Bernard Arnault continues to kill it. Pretty soon he’s going to have a monopoly on luxury. This week’s newsletter not your cup of tea? Check out some of the more popular weeks below! The $1 Billion Bitcoin Whale, A $243,000 Deepfake, Pre-Mature Optimization, and The Yandhi Leak The $1 Billion Bitcoin Whale, A $243,000 Deepfake, Pre-Mature Optimization, and The Yandhi Leak GitHub Acquires Semmle, Vitalik’s Time Machine, and Condolences to the Facebook Engineer GitHub Acquires Semmle, Vitalik’s Time Machine, and Condolences to the Facebook Engineer Shapeshift’s FOX Token, H.R.3289, and Of Course…The Cybertruck Shapeshift’s FOX Token, H.R.3289, and Of Course…The Cybertruck This content is for informational purposes only, you should not construe any such information or other material as legal, tax, investment, financial, or other advice. Nothing contained in this post constitutes a solicitation, recommendation, endorsement, or offer by myself to buy or sell any securities or other financial instruments in this or in any other jurisdiction in which such solicitation or offer would be unlawful under the securities laws of such jurisdiction. This content is for informational purposes only, you should not construe any such information or other material as legal, tax, investment, financial, or other advice. Nothing contained in this post constitutes a solicitation, recommendation, endorsement, or offer by myself to buy or sell any securities or other financial instruments in this or in any other jurisdiction in which such solicitation or offer would be unlawful under the securities laws of such jurisdiction. The opinions expressed in this publication are those of the author. They do not purport to reflect the opinions or views of any of the author’s employers. The opinions expressed in this publication are those of the author. They do not purport to reflect the opinions or views of any of the author’s employers. Don’t forget to follow me on twitter , add me on linkedin , add me on facebook , or follow me on medium for more content like this. I post a review of topics I find interesting every week. I’m always looking for new topics to cover, so if you have anything you find interesting and would like to discuss please reach out! Don’t forget to follow me on twitter twitter , add me on linkedin linkedin , add me on facebook facebook , or follow me on medium medium for more content like this. I post a review of topics I find interesting every week. I’m always looking for new topics to cover, so if you have anything you find interesting and would like to discuss please reach out!

Roundup #11: "How I Evaded Sanctions and How You Can To"

Building Scalable E-commerce Infrastructure on Magento

Authors:
(1) Yuwei Guo, The Chinese University of Hong Kong;
(2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author;
(3) Anyi Rao, Stanford University;
(4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory;
(5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory;
(6) Yu Qiao, Shanghai Artificial Intelligence Laboratory;
(7) Maneesh Agrawala, Stanford University;
(8) Dahua Lin, Shanghai Artificial Intelligence Laboratory;
(9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Table of Links Abstract and 1 Introduction 2 Work Related 3 Preliminary AnimateDiff 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.2 Learn Motion Priors with Motion Module 4.3 Adapt to New Motion Patterns with MotionLora 4.4 AnimateDiff in Practice 5 Experiments and 5.1 Qualitative Results 5.2 Qualitative Comparison 5.3 Ablative Study 5.4 Controllable Generation 6 Conclusion 7 Ethics Statement 8 Reproducibility Statement, Acknowledgement and References ABSTRACT With the advance of text-to-image (T2I) diffusion models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. However, adding motion dynamics to existing high-quality personalized T2Is and enabling them to generate animations remains an open challenge. In this paper, we present AnimateDiff, a practical framework for animating personalized T2I models without requiring model-specific tuning. At the core of our framework is a plug-and-play motion module that can be trained once and seamlessly integrated into any personalized T2Is originating from the same base T2I. Through our proposed training strategy, the motion module effectively learns transferable motion priors from real-world videos. Once trained, the motion module can be inserted into a personalized T2I model to form a personalized animation generator. We further propose MotionLoRA, a lightweight fine-tuning technique for AnimateDiff that enables a pre-trained motion module to adapt to new motion patterns, such as different shot types, at a low training and data collection cost. We evaluate AnimateDiff and MotionLoRA on several public representative personalized T2I models collected from the community. The results demonstrate that our approaches help these models generate temporally smooth animation clips while preserving the visual quality and motion diversity. Codes and pre-trained weights are available at https://github.com/guoyww/AnimateDiff. 1 INTRODUCTION Text-to-image (T2I) diffusion models (Nichol et al., 2021; Ramesh et al., 2022; Saharia et al., 2022; Rombach et al., 2022) have greatly empowered artists and amateurs to create visual content using text prompts. To further stimulate the creativity of existing T2I models, lightweight personalization methods, such as DreamBooth (Ruiz et al., 2023) and LoRA (Hu et al., 2021) have been proposed. These methods enable customized fine-tuning on small datasets using consumer-grade hardware such as a laptop with an RTX3080, thereby allowing users to adapt a base T2I model to new domains and improve visual quality at a relatively low cost. Consequently, a large community of AI artists and amateurs has contributed numerous personalized models on model-sharing platforms such as Civitai (2022) and Hugging Face (2022). While these personalized T2I models can generate remarkable visual quality, their outputs are limited to static images. On the other hand, the ability to generate animations is more desirable in real-world production, such as in the movie and cartoon industries. In this work, we aim to directly transform existing high-quality personalized T2I models into animation generators without requiring model-specific fine-tuning, which is often impractical in terms of computation and data collection costs for amateur users. We present AnimateDiff, an effective pipeline for addressing the problem of animating personalized T2Is while preserving their visual quality and domain knowledge. The core of AnimateDiff is an approach for training a plug-and-play motion module that learns reasonable motion priors from video datasets, such as WebVid-10M (Bain et al., 2021). At inference time, the trained motion module can be directly integrated into personalized T2Is and produce smooth and visually appealing animations without requiring specific tuning. The training of the motion module in AnimateDiff consists of three stages. Firstly, we fine-tune a domain adapter on the base T2I to align with the visual distribution of the target video dataset. This preliminary step guarantees the motion module concentrates on learning the motion priors rather than pixel-level details from the training videos. Secondly, we inflate the base T2I together with the domain adapter and introduce a newly initialized motion module for motion modeling. We then optimize this module on videos while keeping the domain adapter and base T2I weights fixed. By doing so, the motion module learns generalized motion priors and can, via module insertion, enable other personalized T2Is to generate smooth and appealing animations aligned with their personalized domains. The third stage of AnimateDiff, also dubbed as MotionLoRA, aims to adapt the pre-trained motion module to specific motion patterns with a small number of reference videos and training iterations. We achieve this by fine-tuning the motion module with the aid of Low-Rank Adaptation (LoRA) (Hu et al., 2021). Remarkably, adapting to a new motion pattern can be achieved with as few as 50 reference videos. Moreover, a MotionLoRA model requires only approximately 30M of additional storage space, further enhancing the efficiency of model sharing. This efficiency is particularly valuable for users who are unable to bear the expensive costs of pre-training but desire to fine-tune the motion module for specific effects. We evaluate the performance of AnimateDiff and MotionLoRA on a diverse set of personalized T2I models collected from model-sharing platforms (Civitai, 2022; Hugging Face, 2022). These models encompass a wide spectrum of domains, ranging from 2D cartoons to realistic photographs, thereby forming a comprehensive benchmark for our evaluation. The results of our experiments demonstrate promising outcomes. In practice, we also found that a Transformer (Vaswani et al., 2017) architecture along the temporal axis is adequate for capturing appropriate motion priors. We also demonstrate that our motion module can be seamlessly integrated with existing content-controlling approaches (Zhang et al., 2023; Mou et al., 2023) such as ControlNet without requiring additional training, enabling AnimateDiff for controllable animation generation. In summary, (1) we present AnimateDiff, a practical pipeline that enables the animation generation ability of any personalized T2Is without specific fine-tuning; (2) we verify that a Transformer architecture is adequate for modeling motion priors, which provides valuable insights for video generation; (3) we propose MotionLoRA, a lightweight fine-tuning technique to adapt pre-trained motion modules to new motion patterns; (4) we comprehensively evaluate our approach with representative community models and compare it with both academic baselines and commercial tools such as Gen2 (2023) and Pika Labs (2023). Furthermore, we showcase its compatibility with existing works for controllable generation. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Yuwei Guo, The Chinese University of Hong Kong; (2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author; (3) Anyi Rao, Stanford University; (4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory; (5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory; (6) Yu Qiao, Shanghai Artificial Intelligence Laboratory; (7) Maneesh Agrawala, Stanford University; (8) Dahua Lin, Shanghai Artificial Intelligence Laboratory; (9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Authors: Authors: (1) Yuwei Guo, The Chinese University of Hong Kong; (2) Ceyuan Yang, Shanghai Artificial Intelligence Laboratory with Corresponding Author; (3) Anyi Rao, Stanford University; (4) Zhengyang Liang, Shanghai Artificial Intelligence Laboratory; (5) Yaohui Wang, Shanghai Artificial Intelligence Laboratory; (6) Yu Qiao, Shanghai Artificial Intelligence Laboratory; (7) Maneesh Agrawala, Stanford University; (8) Dahua Lin, Shanghai Artificial Intelligence Laboratory; (9) Bo Dai, The Chinese University of Hong Kong and The Chinese University of Hong Kong. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Work Related 2 Work Related 3 Preliminary 3 Preliminary AnimateDiff AnimateDiff 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.1 Alleviate Negative Effects from Training Data with Domain Adapter 4.2 Learn Motion Priors with Motion Module 4.2 Learn Motion Priors with Motion Module 4.3 Adapt to New Motion Patterns with MotionLora 4.3 Adapt to New Motion Patterns with MotionLora 4.4 AnimateDiff in Practice 4.4 AnimateDiff in Practice 5 Experiments and 5.1 Qualitative Results 5 Experiments and 5.1 Qualitative Results 5.2 Qualitative Comparison 5.2 Qualitative Comparison 5.3 Ablative Study 5.3 Ablative Study 5.4 Controllable Generation 5.4 Controllable Generation 6 Conclusion 6 Conclusion 7 Ethics Statement 7 Ethics Statement 8 Reproducibility Statement, Acknowledgement and References 8 Reproducibility Statement, Acknowledgement and References ABSTRACT With the advance of text-to-image (T2I) diffusion models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. However, adding motion dynamics to existing high-quality personalized T2Is and enabling them to generate animations remains an open challenge. In this paper, we present AnimateDiff, a practical framework for animating personalized T2I models without requiring model-specific tuning. At the core of our framework is a plug-and-play motion module that can be trained once and seamlessly integrated into any personalized T2Is originating from the same base T2I. Through our proposed training strategy, the motion module effectively learns transferable motion priors from real-world videos. Once trained, the motion module can be inserted into a personalized T2I model to form a personalized animation generator. We further propose MotionLoRA, a lightweight fine-tuning technique for AnimateDiff that enables a pre-trained motion module to adapt to new motion patterns, such as different shot types, at a low training and data collection cost. We evaluate AnimateDiff and MotionLoRA on several public representative personalized T2I models collected from the community. The results demonstrate that our approaches help these models generate temporally smooth animation clips while preserving the visual quality and motion diversity. Codes and pre-trained weights are available at https://github.com/guoyww/AnimateDiff. 1 INTRODUCTION Text-to-image (T2I) diffusion models (Nichol et al., 2021; Ramesh et al., 2022; Saharia et al., 2022; Rombach et al., 2022) have greatly empowered artists and amateurs to create visual content using text prompts. To further stimulate the creativity of existing T2I models, lightweight personalization methods, such as DreamBooth (Ruiz et al., 2023) and LoRA (Hu et al., 2021) have been proposed. These methods enable customized fine-tuning on small datasets using consumer-grade hardware such as a laptop with an RTX3080, thereby allowing users to adapt a base T2I model to new domains and improve visual quality at a relatively low cost. Consequently, a large community of AI artists and amateurs has contributed numerous personalized models on model-sharing platforms such as Civitai (2022) and Hugging Face (2022). While these personalized T2I models can generate remarkable visual quality, their outputs are limited to static images. On the other hand, the ability to generate animations is more desirable in real-world production, such as in the movie and cartoon industries. In this work, we aim to directly transform existing high-quality personalized T2I models into animation generators without requiring model-specific fine-tuning, which is often impractical in terms of computation and data collection costs for amateur users. We present AnimateDiff, an effective pipeline for addressing the problem of animating personalized T2Is while preserving their visual quality and domain knowledge. The core of AnimateDiff is an approach for training a plug-and-play motion module that learns reasonable motion priors from video datasets, such as WebVid-10M (Bain et al., 2021). At inference time, the trained motion module can be directly integrated into personalized T2Is and produce smooth and visually appealing animations without requiring specific tuning. The training of the motion module in AnimateDiff consists of three stages. Firstly, we fine-tune a domain adapter on the base T2I to align with the visual distribution of the target video dataset. This preliminary step guarantees the motion module concentrates on learning the motion priors rather than pixel-level details from the training videos. Secondly, we inflate the base T2I together with the domain adapter and introduce a newly initialized motion module for motion modeling. We then optimize this module on videos while keeping the domain adapter and base T2I weights fixed. By doing so, the motion module learns generalized motion priors and can, via module insertion, enable other personalized T2Is to generate smooth and appealing animations aligned with their personalized domains. The third stage of AnimateDiff, also dubbed as MotionLoRA, aims to adapt the pre-trained motion module to specific motion patterns with a small number of reference videos and training iterations. We achieve this by fine-tuning the motion module with the aid of Low-Rank Adaptation (LoRA) (Hu et al., 2021). Remarkably, adapting to a new motion pattern can be achieved with as few as 50 reference videos. Moreover, a MotionLoRA model requires only approximately 30M of additional storage space, further enhancing the efficiency of model sharing. This efficiency is particularly valuable for users who are unable to bear the expensive costs of pre-training but desire to fine-tune the motion module for specific effects. We evaluate the performance of AnimateDiff and MotionLoRA on a diverse set of personalized T2I models collected from model-sharing platforms (Civitai, 2022; Hugging Face, 2022). These models encompass a wide spectrum of domains, ranging from 2D cartoons to realistic photographs, thereby forming a comprehensive benchmark for our evaluation. The results of our experiments demonstrate promising outcomes. In practice, we also found that a Transformer (Vaswani et al., 2017) architecture along the temporal axis is adequate for capturing appropriate motion priors. We also demonstrate that our motion module can be seamlessly integrated with existing content-controlling approaches (Zhang et al., 2023; Mou et al., 2023) such as ControlNet without requiring additional training, enabling AnimateDiff for controllable animation generation. In summary, (1) we present AnimateDiff, a practical pipeline that enables the animation generation ability of any personalized T2Is without specific fine-tuning; (2) we verify that a Transformer architecture is adequate for modeling motion priors, which provides valuable insights for video generation; (3) we propose MotionLoRA, a lightweight fine-tuning technique to adapt pre-trained motion modules to new motion patterns; (4) we comprehensively evaluate our approach with representative community models and compare it with both academic baselines and commercial tools such as Gen2 (2023) and Pika Labs (2023). Furthermore, we showcase its compatibility with existing works for controllable generation. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

How to Transform Static Text-to-Image Models into Dynamic Animation Generators

So wandeln Sie statische Text-zu-Bild-Modelle in dynamische Animationsgeneratoren um

Cómo transformar modelos estáticos de texto a imagen en generadores de animación dinámicos

Nola eraldatu testu-irudi eredu estatikoak animazio-sorgailu dinamikoetan

静的なテキストから画像へのモデルを動的なアニメーション ジェネレータに変換する方法

ວິທີການປ່ຽນຕົວແບບຂໍ້ຄວາມເປັນຮູບພາບຄົງທີ່ໄປສູ່ເຄື່ອງສ້າງພາບເຄື່ອນໄຫວແບບໄດນາມິກ

गतिशील एनिमेसन जेनरेटरहरूमा स्थिर पाठ-देखि-छवि मोडेलहरू कसरी रूपान्तरण गर्ने

Как преобразовать статические модели «текст-изображение» в динамические генераторы анимации

Sida Loo Beddelo Qaabka Qoraal-Ka-Sawirka Jooga Ah Loona Bedeli Karaa Animation Generators

Jinsi ya Kubadilisha Miundo Tuli ya Maandishi-hadi-Picha kuwa Jenereta za Uhuishaji Zinazobadilika

Statiki tekstden-şekil modellerini dinamiki animasiýa generatorlaryna nädip öwürmeli

Statik Metin-Görüntü Modellerini Dinamik Animasyon Oluşturucularına Nasıl Dönüştürebilirsiniz

جامد متن سے تصویری ماڈلز کو متحرک اینیمیشن جنریٹرز میں کیسے تبدیل کیا جائے

Don't spend your time doing work a well-trained monkey could do | From automate the boring stuff with python book Automation is not a replacement for our workforce; it presents an opportunity to embrace agility in a constantly evolving digital society. Considering network engineers' numerous tedious tasks each week, automation becomes a crucial ingredient for success. Beyond the hype and rankings, Python has emerged as the de facto language for automation. Its elegant syntax and versatility have led to the creation of amazing tools. What is Cisco PyATS pyATS, which stands for Python Automated Test System, was developed by Cisco Systems to automate testing and validate networks. It is often called a "Swiss Army knife" due to its ability to perform a wide range of tasks beyond conventional expectations. In addition to automated testing, pyATS supports network configuration testing and updates,... Language: Python, it is oriented for DevOps and NetDevOps


Typical usage: Network configuration tests, anomaly detection, connectivity validation…


Objective: Automating network tests and validation by providing a modular and extensible environment.
Main modules


PyATS: the main framework for automated tests.


Genie: Collection of automation libraries for configuring, analyzing, and verifying network data. PyATS is a big ecosystem of innovations where we can do amazing and it goes beyond Cisco environments! How to install pyATS quickly on Windows Subsystem for Linux ( Ubuntu 24.04 LTS) The best way to install pyATS is to create a Python virtual environment and this is the quickest way to do it by observing the capture below installing pyATS To install Genie the process is the same! Checking the installation PyATS and Genie are installed with their packages. Verifying PyATS versions pyats version check After running the command we see the version,pyATS is properly installed and we can achieve amazing things we want. Key concepts to know about pyATS What makes pyATS unique is its abstraction, and to understand it better, you need to engage with these abstractions! Testbed: This is the representation of network environments. A YAML file describes devices, their roles, and IP addresses, and here is how it looks when the file is written.
# Step 0: list of devices
devices:
  iosxr1:
    # Step 1: OS and Type
    type: iosxr-Prod
    os: iosxr
    # Step 2: credentials
    credentials:
      default:
        username: admin
        password: Hacker@204k
    # Step 3: connection parameters
    connections:
      vty:
        protocol: ssh
        ip: test-mgmt.talesoftechnology.com
        port: 8080 Test scripts: python scripts that contain all instructions for every test.
Parsers: with an impressive library and over 1500 parsers, Genie expertly converts raw text into structured text.
Triggers: they enable specific automated actions, in response to events, in other words, they are similar to webhooks. Main features and use cases States comparison: network comparison state before and after a change or update.
Report and journaling: it generates test reports and detailed logs for every
Multi-environment execution: it is compatible with physical and virtual environments. Practical use cases State validation: Compare the configuration status of a device before and after a change to ensure there is no configuration drift.
Anomaly detection: Automate the identification of anomalies in device configurations or states.
Connectivity Test: Use ping or traceroute commands to check connectivity between devices Conclusion Now you know how to install pyATS using the Windows Subsystem for Linux, and you don’t need a lengthy guide! You understand the basic concepts; even though pyATS can seem abstract, the best way to grasp it is through interaction with various abstractions. Automation is essential for agility today, you don’t have to reinvent the wheel with tedious tasks. With pyATS, you can save time and   reduce stress compared to handling  everything manually. In the next article, you will learn how to create a testbed, and we will demystify pyATS, it’s not as hard as you think! Don't spend your time doing work a well-trained monkey could do | From automate the boring stuff with python book Don't spend your time doing work a well-trained monkey could do | From automate the boring stuff with python book Automation is not a replacement for our workforce; it presents an opportunity to embrace agility in a constantly evolving digital society. Considering network engineers' numerous tedious tasks each week, automation becomes a crucial ingredient for success. Beyond the hype and rankings, Python has emerged as the de facto language for automation. Its elegant syntax and versatility have led to the creation of amazing tools. Python Python What is Cisco PyATS pyATS , which stands for Python Automated Test System, was developed by Cisco Systems to automate testing and validate networks. It is often called a "Swiss Army knife" due to its ability to perform a wide range of tasks beyond conventional expectations. In addition to automated testing, pyATS supports network configuration testing and updates,... pyATS pyATS Language: Python, it is oriented for DevOps and NetDevOps Typical usage: Network configuration tests, anomaly detection, connectivity validation… Objective: Automating network tests and validation by providing a modular and extensible environment.
Main modules PyATS: the main framework for automated tests. Genie: Collection of automation libraries for configuring, analyzing, and verifying network data. Language: Python, it is oriented for DevOps and NetDevOps Language : Python, it is oriented for DevOps and NetDevOps Language Typical usage: Network configuration tests, anomaly detection, connectivity validation… Typical usage : Network configuration tests, anomaly detection, connectivity validation… Typical usage Objective: Automating network tests and validation by providing a modular and extensible environment. Main modules Objective : Automating network tests and validation by providing a modular and extensible environment. Objective Main modules PyATS: the main framework for automated tests. PyATS : the main framework for automated tests. PyATS Genie: Collection of automation libraries for configuring, analyzing, and verifying network data. Genie : Collection of automation libraries for configuring, analyzing, and verifying network data. Genie PyATS is a big ecosystem of innovations where we can do amazing and it goes beyond Cisco environments! PyATS is a big ecosystem of innovations where we can do amazing and it goes beyond Cisco environments! PyATS is a big ecosystem of innovations where we can do amazing and it goes beyond Cisco environments! How to install pyATS quickly on Windows Subsystem for Linux ( Ubuntu 24.04 LTS) The best way to install pyATS is to create a Python virtual environment and this is the quickest way to do it by observing the capture below installing pyATS To install Genie the process is the same! Genie Genie Checking the installation PyATS and Genie are installed with their packages. Verifying PyATS versions pyats version check pyats version check After running the command we see the version,pyATS is properly installed and we can achieve amazing things we want. Key concepts to know about pyATS What makes pyATS unique is its abstraction, and to understand it better, you need to engage with these abstractions! Testbed: This is the representation of network environments. A YAML file describes devices, their roles, and IP addresses, and here is how it looks when the file is written.
# Step 0: list of devices
devices:
  iosxr1:
    # Step 1: OS and Type
    type: iosxr-Prod
    os: iosxr
    # Step 2: credentials
    credentials:
      default:
        username: admin
        password: Hacker@204k
    # Step 3: connection parameters
    connections:
      vty:
        protocol: ssh
        ip: test-mgmt.talesoftechnology.com
        port: 8080 Testbed: This is the representation of network environments. A YAML file describes devices, their roles, and IP addresses, and here is how it looks when the file is written. # Step 0: list of devices
devices:
  iosxr1:
    # Step 1: OS and Type
    type: iosxr-Prod
    os: iosxr
    # Step 2: credentials
    credentials:
      default:
        username: admin
        password: Hacker@204k
    # Step 3: connection parameters
    connections:
      vty:
        protocol: ssh
        ip: test-mgmt.talesoftechnology.com
        port: 8080 Testbed : This is the representation of network environments. A YAML file describes devices, their roles, and IP addresses, and here is how it looks when the file is written. Testbed # Step 0: list of devices
devices:
  iosxr1:
    # Step 1: OS and Type
    type: iosxr-Prod
    os: iosxr
    # Step 2: credentials
    credentials:
      default:
        username: admin
        password: Hacker@204k
    # Step 3: connection parameters
    connections:
      vty:
        protocol: ssh
        ip: test-mgmt.talesoftechnology.com
        port: 8080 # Step 0: list of devices
devices:
  iosxr1:
    # Step 1: OS and Type
    type: iosxr-Prod
    os: iosxr
    # Step 2: credentials
    credentials:
      default:
        username: admin
        password: Hacker@204k
    # Step 3: connection parameters
    connections:
      vty:
        protocol: ssh
        ip: test-mgmt.talesoftechnology.com
        port: 8080 Test scripts: python scripts that contain all instructions for every test. Parsers: with an impressive library and over 1500 parsers, Genie expertly converts raw text into structured text. Triggers: they enable specific automated actions, in response to events, in other words, they are similar to webhooks. Test scripts : python scripts that contain all instructions for every test. Test scripts Parsers: with an impressive library and over 1500 parsers, Genie expertly converts raw text into structured text. Parsers: Triggers : they enable specific automated actions, in response to events, in other words, they are similar to webhooks. Triggers Main features and use cases States comparison: network comparison state before and after a change or update. Report and journaling: it generates test reports and detailed logs for every Multi-environment execution: it is compatible with physical and virtual environments. States comparison : network comparison state before and after a change or update. States comparison Report and journaling : it generates test reports and detailed logs for every Report and journaling Multi-environment execution : it is compatible with physical and virtual environments. Multi-environment execution Practical use cases State validation: Compare the configuration status of a device before and after a change to ensure there is no configuration drift. Anomaly detection: Automate the identification of anomalies in device configurations or states. Connectivity Test: Use ping or traceroute commands to check connectivity between devices State validation : Compare the configuration status of a device before and after a change to ensure there is no configuration drift. State validation Anomaly detection : Automate the identification of anomalies in device configurations or states. Anomaly detection Connectivity Test : Use ping or traceroute commands to check connectivity between devices Connectivity Test Conclusion Now you know how to install pyATS using the Windows Subsystem for Linux, and you don’t need a lengthy guide! You understand the basic concepts; even though pyATS can seem abstract, the best way to grasp it is through interaction with various abstractions. Automation is essential for agility today, you don’t have to reinvent the wheel with tedious tasks. With pyATS, you can save time and   reduce stress compared to handling  everything manually. In the next article, you will learn how to create a testbed, and we will demystify pyATS, it’s not as hard as you think!

Network Test and Automation: PyATS 101 in 5 Minutes

How to Learn Web Development: HTML, CSS, MERN, and Python - 22 GitHub Repositories to Help You

Authors:
(1) Zhaoqing Wang, The University of Sydney and AI2Robotics;
(2) Xiaobo Xia, The University of Sydney;
(3) Ziye Chen, The University of Melbourne;
(4) Xiao He, AI2Robotics;
(5) Yandong Guo, AI2Robotics;
(6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence;
(7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction 2. Related works 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.2. Main results 4.3. Ablation study 5. Conclusion 6. Broader impacts and References A. Framework details B. Promptable segmentation C. Visualisation 3.2. Baseline We introduce a straightforward baseline using the knowledge of image-text and image-mask pairs. Specifically, we employ a CLIP model as the visual and text encoder, which is trained on a large amount of image-text pairs. Afterward, we use the image-mask pairs to obtain a branch of mask generation, predicting a set of binary masks. To perform open-vocabulary segmentation, we crop and pool the CLIP image features based on these predicted masks, which are further classified by the CLIP text embeddings. Although this straightforward baseline enables open-vocabulary segmentation, it exhibits an obvious knowledge gap between the image-level and pixel-level tasks. 3.3. Uni-OVSeg framework Overview. An overview of our framework Uni-OVSeg, for weakly-supervised open-vocabulary segmentation is illustrated in Fig. 2. On a macro level, Uni-OVSeg contains a CLIP model to extract features of both images and text descriptions. With the image-mask pairs, a branch of mask generation, including a visual prompt encoder, a pixel decoder and a mask decoder, is employed to predict a set of binary masks of an input image. With the image-text pairs, mask-text bipartite matching is used to exploit confident pairs between predicted masks and entities in text descriptions. Afterward, we adopt a multi-scale feature adapter to enhance the mask-wise visual embeddings, which are further aligned with associated entity embeddings based on the confident pairs. Finally, we perform open-vocabulary segmentation with the above-mentioned parts. More details can be found in Appendix A. Mask-text alignment. To enable the model categorising the predicted masks from an open set of vocabulary, given the image-text pairs, we build the correspondence between objects in the image and entities in the text description. Once a set of binary masks m are generated from the input image, we obtain the region embeddings ri by employing a mask pooling layer P and the CLIP visual projector Fv, Open-vocabulary inference. During inference, given the test categories Ctest, we conduct prompt engineering [21] and use the CLIP text encoder to extract text embeddings 1https://openai.com/chatgpt for open-vocabulary segmentation. For each input image, we input an uniform point grid as the visual prompt to predict a set of binary masks, and compute the cosine similarity with the text embeddings to predict the category with the maximum similarity as the label of the corresponding mask. This paper is available on arxiv under CC BY 4.0 DEED license. [1] https://openai.com/chatgpt Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Authors: Authors: (1) Zhaoqing Wang, The University of Sydney and AI2Robotics; (2) Xiaobo Xia, The University of Sydney; (3) Ziye Chen, The University of Melbourne; (4) Xiao He, AI2Robotics; (5) Yandong Guo, AI2Robotics; (6) Mingming Gong, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence; (7) Tongliang Liu, The University of Sydney. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Related works 2. Related works 3. Method and 3.1. Problem definition 3. Method and 3.1. Problem definition 3.2. Baseline and 3.3. Uni-OVSeg framework 3.2. Baseline and 3.3. Uni-OVSeg framework 4. Experiments 4.1. Implementation details 4.1. Implementation details 4.2. Main results 4.2. Main results 4.3. Ablation study 4.3. Ablation study 5. Conclusion 5. Conclusion 6. Broader impacts and References 6. Broader impacts and References A. Framework details A. Framework details B. Promptable segmentation B. Promptable segmentation C. Visualisation C. Visualisation 3.2. Baseline We introduce a straightforward baseline using the knowledge of image-text and image-mask pairs. Specifically, we employ a CLIP model as the visual and text encoder, which is trained on a large amount of image-text pairs. Afterward, we use the image-mask pairs to obtain a branch of mask generation, predicting a set of binary masks. To perform open-vocabulary segmentation, we crop and pool the CLIP image features based on these predicted masks, which are further classified by the CLIP text embeddings. Although this straightforward baseline enables open-vocabulary segmentation, it exhibits an obvious knowledge gap between the image-level and pixel-level tasks. 3.3. Uni-OVSeg framework Overview . An overview of our framework Uni-OVSeg, for weakly-supervised open-vocabulary segmentation is illustrated in Fig. 2. On a macro level, Uni-OVSeg contains a CLIP model to extract features of both images and text descriptions. With the image-mask pairs, a branch of mask generation, including a visual prompt encoder, a pixel decoder and a mask decoder, is employed to predict a set of binary masks of an input image. With the image-text pairs, mask-text bipartite matching is used to exploit confident pairs between predicted masks and entities in text descriptions. Afterward, we adopt a multi-scale feature adapter to enhance the mask-wise visual embeddings, which are further aligned with associated entity embeddings based on the confident pairs. Finally, we perform open-vocabulary segmentation with the above-mentioned parts. More details can be found in Appendix A. Overview Mask-text alignment. To enable the model categorising the predicted masks from an open set of vocabulary, given the image-text pairs, we build the correspondence between objects in the image and entities in the text description. Once a set of binary masks m are generated from the input image, we obtain the region embeddings ri by employing a mask pooling layer P and the CLIP visual projector Fv, Mask-text alignment. Open-vocabulary inference. During inference, given the test categories Ctest, we conduct prompt engineering [21] and use the CLIP text encoder to extract text embeddings 1https://openai.com/chatgpt for open-vocabulary segmentation. For each input image, we input an uniform point grid as the visual prompt to predict a set of binary masks, and compute the cosine similarity with the text embeddings to predict the category with the maximum similarity as the label of the corresponding mask. Open-vocabulary inference. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [1] https://openai.com/chatgpt

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