Text-to-Image personalization is a task in deep learning for computer graphics that augments pre-trained text-to-image generative models. In this task, a generative model that was trained on large-scale data (usually a foundation model), is adapted such that it can generate images of novel, user-provided concepts. These concepts are typically unseen during training, and may represent specific objects (such as the user's pet) or more abstract categories (new artistic style or object relations). Text-to-Image personalization methods typically bind the novel (personal) concept to new words in the vocabulary of the model. These words can then be used in future prompts to invoke the concept for subject-driven generation, inpainting, style transfer and even to correct biases in the model. To do so, models either optimize word-embeddings, fine-tune the generative model itself, or employ a mixture of both approaches. == Technology == Text-to-Image personalization was first proposed during August 2022 by two concurrent works, Textual Inversion and DreamBooth. In both cases, a user provides a few images (typically 3–5) of a concept, like their own dog, together with a coarse descriptor of the concept class (like the word "dog"). The model then learns to represent the subject through a reconstruction based objective, where prompts referring to the subject are expected to reconstruct images from the training set. In Textual Inversion, the personalized concepts are introduced into the text-to-image model by adding new words to the vocabulary of the model. Typical text-to-image models represent words (and sometimes parts-of-words) as tokens, or indices in a predefined dictionary. During generation, an input prompt is converted into such tokens, each of which is converted into a ‘word-embedding’: a continuous vector representation which is learned for each token as part of the model's training. Textual Inversion proposes to optimize a new word-embedding vector for representing the novel concept. This new embedding vector can then be assigned to a user-chosen string, and invoked whenever the user's prompt contains this string. In DreamBooth, rather than optimizing a new word vector, the full generative model itself is fine-tuned. The user first selects an existing token, typically one which rarely appears in prompts. The subject itself is then represented by a string containing this token, followed by a coarse descriptor of the subject's class. A prompt describing the subject will then take the form: "A photo of
Cloud testing
Cloud testing is a form of software testing in which web applications use cloud computing environments (a "cloud") to simulate real-world user traffic. == Steps == Companies simulate real world Web users by using cloud testing services that are provided by cloud service vendors such as Advaltis, Compuware, HP, Keynote Systems, Neotys, RadView and SOASTA. Once user scenarios are developed and the test is designed, these service providers leverage cloud servers (provided by cloud platform vendors such as Amazon.com, Google, Rackspace, Microsoft, etc.) to generate web traffic that originates from around the world. Once the test is complete, the cloud service providers deliver results and analytics back to corporate IT professionals through real-time dashboards for a complete analysis of how their applications and the internet will perform during peak volumes. == Applications == Cloud testing is often seen as only performance or load tests, however, as discussed earlier it covers many other types of testing. Cloud computing itself is often referred to as the marriage of software as a service (SaaS) and utility computing. In regard to test execution, the software offered as a service may be a transaction generator and the cloud provider's infrastructure software, or may just be the latter. Distributed Systems and Parallel Systems mainly use this approach for testing, because of their inherent complex nature. D-Cloud is an example of such a software testing environment. == Tools == Leading cloud computing service providers include, among others, Amazon, Microsoft, Google, RadView, Skytap, HP and SOASTA. == Benefits == The ability and cost to simulate web traffic for software testing purposes has been an inhibitor to overall web reliability. The low cost and accessibility of the cloud's extremely large computing resources provides the ability to replicate real world usage of these systems by geographically distributed users, executing wide varieties of user scenarios, at scales previously unattainable in traditional testing environments. Minimal start-up time along with quality assurance can be achieved by cloud testing. Following are some of the key benefits: Reduction in capital expenditure Highly scalable
Visopsys
Visopsys (Visual Operating System), is an operating system, written by Andy McLaughlin. Development of the operating system began in 1997. The operating system is licensed under the GNU GPL, with the headers and libraries under the less restrictive LGPL license. It runs on the 32-bit IA-32 architecture. It features a multitasking kernel, supports asynchronous I/O and the FAT line of file systems. It requires a Pentium processor. == History == The development of Visopsys began in 1997, being written by Andy McLaughlin. The first public release of the Operating System was on 2 March 2001, with version 0.1. In this release, Visopsys was a 32 bit operating system, supporting preemptive multitasking and virtual memory. == System overview == Visopsys uses a monolithic kernel, written in the C programming language, with elements of assembly language for certain interactions with the hardware. The operating system supports a graphical user interface, with a small C library.
Full30
Full30 was an American online video-sharing platform primarily dedicated to firearms and shooting sports-related content. The service was established in 2014 by Tim Harmsen and Mark Hammonds as a result of YouTube's increasing restrictions on gun-related videos. == History == After the 2018 Parkland high school shooting, many companies attempted to distance themselves from any association with the firearms industry. As a result, YouTube began demonetizing and sometimes outright deleting firearms-related videos, and in one case, popular YouTube poster Hickok45's channel was completely deleted but later restored. In response, Harmsen, who operates the Military Arms Channel on YouTube, decided to create his own video-hosting website to allow himself and other firearms content creators a platform free from such restrictions; he named the website Full30 — a reference to the popular 30-round STANAG magazine. In July 2020, site representatives announced the site had new ownership. By the end of 2022, the site began to be redirected to a series of other websites. By 2025, it was largely deactivated with the front page replaced by a form to be filled out to receive "updates", with no other explanation. == Contributors == Hickok45 Military Arms Channel Forgotten Weapons Bavarian Shooter Liberty Doll CloverTac
Boba liberal
Boba liberal is a term mostly used within the Asian diaspora communities in the West, especially in the United States. It describes someone of East or Southeast Asian descent living in the West who has a shallow, surface-level liberal outlook. It is also occasionally used to describe conservatives who weaponize their East or Southeast Asian identity. The neologism emerged among the Asian American leftist community on Twitter who accused "boba liberals" of only holding their liberal beliefs to appear more white-adjacent by engaging in progressive social movements or viewpoints, while at the same time disregarding and trivializing issues concerning Asians. Mary Chao, writing for The North Jersey Record, said that "Asians call peers boba liberals when they aspire to liberal whiteness." An article in The Yale Herald described it as a term "used to describe the ethnocentric politics of Asian Americans, usually of East Asian descent, who exclusively advocate for issues that benefit themselves, without acknowledging problematic dimensions of their own history and working to support other people of color." The feminist magazine Fem said that "the faces of boba liberalism are Asian Americans that are part of the middle and upper economic class. As a result, boba liberals disregard the negative effects of capitalism because they profit from it. For instance, boba liberals tend to focus on advocating for Asian representation in white spaces, or discussing whether or not wearing chopsticks in one's hair is culture appropriation. These topics are popular within boba liberal circles, all while dialogue regarding inequality, globalization, and racial injustice are purposely neglected." UnHerd notes that conservative Asian Americans have used the term not to critique capitalism, but to "aim at a small but influential group of progressive Asian-American activists who are supposedly selling out other Asians, especially working-class Asians, in order to win brownie points from elite, generally white liberals." MRAsians have similarly used the term to attack Asian American feminists who supported the Black Lives Matter movement. The Asian identity of boba liberals has often been accused of being shallow and superficial. Boba liberals are accused of using surface-level stereotypical Asian traits such as liking boba tea to bolster their Asian credentials. Plan A Magazine, an Asian diaspora magazine, described the film Crazy Rich Asians and the sitcom Fresh Off the Boat as "boba liberal media", calling them the result of "a specific kind of atomized identity politics". Other media outlets have connected the Crazy Rich Asians film to boba liberalism. == Controversy == The term "boba liberal" was coined in 2019 by Vietnamese American Twitter user Redmond (@diaspora_is_red) to analyze a form of Asian American liberalism through a Marxist lens. Redmond has criticized the misappropriation of their neologism by stripping away the Marxist framework by failing to discuss "socialism, communism, the capitalist system, imperialism, and the diaspora bourgeoisie" and conflating "boba liberalism" with the flawed concept of "East Asian privilege". In 2024, Redmond criticized misuse of the term by conservatives and liberals, and said "The term boba liberalism can go away for all I care. It's corny and stale". === United States === One commentator described boba liberals as supporting policies that primarily benefit upper-income Asian-Americans, and not necessarily the Asian-American community as a whole. Therefore, while the word "liberal" is used in the term, it is not mutually exclusive to one specific ideology, as it may also extend to conservative-aligned Asians in some areas, as they would often take advantage of the "model minority" label by defending such measures.
Stability (learning theory)
Stability, also known as algorithmic stability, is a notion in computational learning theory of how a machine learning algorithm output is changed with small perturbations to its inputs. A stable learning algorithm is one for which the prediction does not change much when the training data is modified slightly. For instance, consider a machine learning algorithm that is being trained to recognize handwritten letters of the alphabet, using 1000 examples of handwritten letters and their labels ("A" to "Z") as a training set. One way to modify this training set is to leave out an example, so that only 999 examples of handwritten letters and their labels are available. A stable learning algorithm would produce a similar classifier with both the 1000-element and 999-element training sets. Stability can be studied for many types of learning problems, from language learning to inverse problems in physics and engineering, as it is a property of the learning process rather than the type of information being learned. The study of stability gained importance in computational learning theory in the 2000s when it was shown to have a connection with generalization. It was shown that for large classes of learning algorithms, notably empirical risk minimization algorithms, certain types of stability ensure good generalization. == History == A central goal in designing a machine learning system is to guarantee that the learning algorithm will generalize, or perform accurately on new examples after being trained on a finite number of them. In the 1990s, milestones were reached in obtaining generalization bounds for supervised learning algorithms. The technique historically used to prove generalization was to show that an algorithm was consistent, using the uniform convergence properties of empirical quantities to their means. This technique was used to obtain generalization bounds for the large class of empirical risk minimization (ERM) algorithms. An ERM algorithm is one that selects a solution from a hypothesis space H {\displaystyle H} in such a way to minimize the empirical error on a training set S {\displaystyle S} . A general result, proved by Vladimir Vapnik for an ERM binary classification algorithms, is that for any target function and input distribution, any hypothesis space H {\displaystyle H} with VC-dimension d {\displaystyle d} , and n {\displaystyle n} training examples, the algorithm is consistent and will produce a training error that is at most O ( d n ) {\displaystyle O\left({\sqrt {\frac {d}{n}}}\right)} (plus logarithmic factors) from the true error. The result was later extended to almost-ERM algorithms with function classes that do not have unique minimizers. Vapnik's work, using what became known as VC theory, established a relationship between generalization of a learning algorithm and properties of the hypothesis space H {\displaystyle H} of functions being learned. However, these results could not be applied to algorithms with hypothesis spaces of unbounded VC-dimension. Put another way, these results could not be applied when the information being learned had a complexity that was too large to measure. Some of the simplest machine learning algorithms—for instance, for regression—have hypothesis spaces with unbounded VC-dimension. Another example is language learning algorithms that can produce sentences of arbitrary length. Stability analysis was developed in the 2000s for computational learning theory and is an alternative method for obtaining generalization bounds. The stability of an algorithm is a property of the learning process, rather than a direct property of the hypothesis space H {\displaystyle H} , and it can be assessed in algorithms that have hypothesis spaces with unbounded or undefined VC-dimension such as nearest neighbor. A stable learning algorithm is one for which the learned function does not change much when the training set is slightly modified, for instance by leaving out an example. A measure of Leave one out error is used in a Cross Validation Leave One Out (CVloo) algorithm to evaluate a learning algorithm's stability with respect to the loss function. As such, stability analysis is the application of sensitivity analysis to machine learning. == Summary of classic results == Early 1900s - Stability in learning theory was earliest described in terms of continuity of the learning map L {\displaystyle L} , traced to Andrey Nikolayevich Tikhonov. 1979 - Devroye and Wagner observed that the leave-one-out behavior of an algorithm is related to its sensitivity to small changes in the sample. 1999 - Kearns and Ron discovered a connection between finite VC-dimension and stability. 2002 - In a landmark paper, Bousquet and Elisseeff proposed the notion of uniform hypothesis stability of a learning algorithm and showed that it implies low generalization error. Uniform hypothesis stability, however, is a strong condition that does not apply to large classes of algorithms, including ERM algorithms with a hypothesis space of only two functions. 2002 - Kutin and Niyogi extended Bousquet and Elisseeff's results by providing generalization bounds for several weaker forms of stability which they called almost-everywhere stability. Furthermore, they took an initial step in establishing the relationship between stability and consistency in ERM algorithms in the Probably Approximately Correct (PAC) setting. 2004 - Poggio et al. proved a general relationship between stability and ERM consistency. They proposed a statistical form of leave-one-out-stability which they called CVEEEloo stability, and showed that it is a) sufficient for generalization in bounded loss classes, and b) necessary and sufficient for consistency (and thus generalization) of ERM algorithms for certain loss functions such as the square loss, the absolute value and the binary classification loss. 2010 - Shalev Shwartz et al. noticed problems with the original results of Vapnik due to the complex relations between hypothesis space and loss class. They discuss stability notions that capture different loss classes and different types of learning, supervised and unsupervised. 2016 - Moritz Hardt et al. proved stability of gradient descent given certain assumption on the hypothesis and number of times each instance is used to update the model. == Preliminary definitions == We define several terms related to learning algorithms training sets, so that we can then define stability in multiple ways and present theorems from the field. A machine learning algorithm, also known as a learning map L {\displaystyle L} , maps a training data set, which is a set of labeled examples ( x , y ) {\displaystyle (x,y)} , onto a function f {\displaystyle f} from X {\displaystyle X} to Y {\displaystyle Y} , where X {\displaystyle X} and Y {\displaystyle Y} are in the same space of the training examples. The functions f {\displaystyle f} are selected from a hypothesis space of functions called H {\displaystyle H} . The training set from which an algorithm learns is defined as S = { z 1 = ( x 1 , y 1 ) , . . , z m = ( x m , y m ) } {\displaystyle S=\{z_{1}=(x_{1},\ y_{1})\ ,..,\ z_{m}=(x_{m},\ y_{m})\}} and is of size m {\displaystyle m} in Z = X × Y {\displaystyle Z=X\times Y} drawn i.i.d. from an unknown distribution D. Thus, the learning map L {\displaystyle L} is defined as a mapping from Z m {\displaystyle Z_{m}} into H {\displaystyle H} , mapping a training set S {\displaystyle S} onto a function f S {\displaystyle f_{S}} from X {\displaystyle X} to Y {\displaystyle Y} . Here, we consider only deterministic algorithms where L {\displaystyle L} is symmetric with respect to S {\displaystyle S} , i.e. it does not depend on the order of the elements in the training set. Furthermore, we assume that all functions are measurable and all sets are countable. The loss V {\displaystyle V} of a hypothesis f {\displaystyle f} with respect to an example z = ( x , y ) {\displaystyle z=(x,y)} is then defined as V ( f , z ) = V ( f ( x ) , y ) {\displaystyle V(f,z)=V(f(x),y)} . The empirical error of f {\displaystyle f} is I S [ f ] = 1 n ∑ V ( f , z i ) {\displaystyle I_{S}[f]={\frac {1}{n}}\sum V(f,z_{i})} . The true error of f {\displaystyle f} is I [ f ] = E z V ( f , z ) {\displaystyle I[f]=\mathbb {E} _{z}V(f,z)} Given a training set S of size m, we will build, for all i = 1....,m, modified training sets as follows: By removing the i-th element S | i = { z 1 , . . . , z i − 1 , z i + 1 , . . . , z m } {\displaystyle S^{|i}=\{z_{1},...,\ z_{i-1},\ z_{i+1},...,\ z_{m}\}} By replacing the i-th element S i = { z 1 , . . . , z i − 1 , z i ′ , z i + 1 , . . . , z m } {\displaystyle S^{i}=\{z_{1},...,\ z_{i-1},\ z_{i}',\ z_{i+1},...,\ z_{m}\}} == Definitions of stability == === Hypothesis Stability === An algorithm L {\displaystyle L} has hypothesis stability β with respect to the loss function V if the following holds: ∀ i ∈ { 1 , . . . , m } , E S , z [ | V ( f S , z ) − V ( f S |
Digital artifactual value
Digital artifactual value, a preservation term, is the intrinsic value of a digital object, rather than the informational content of the object. Though standards are lacking, born-digital objects and digital representations of physical objects may have a value attributed to them as artifacts. == Intrinsic value in analog materials == With respect to analog or non-digital materials, artifacts are determined to have singular research or archival value if they possess qualities and characteristics that make them the only acceptable form for long-term preservation. These qualities and characteristics are commonly referred to as the item's intrinsic value and form the basis upon which digital artifactual value is currently evaluated. Artifactual value based on this idea is predicated upon the artifact's originality, faithfulness, fixity, and stability. The intrinsic value of a particular object, as interpreted by archival professionals, largely determines the selection process for archives. The National Archives and Records Administration Committee on Intrinsic Value in "Intrinsic Value in Archival Material" classified an analog object as having intrinsic value if it possessed one or more of the follow qualities: Physical form that may be the subject for study if the records provide meaningful documentation or significant examples of the form. Aesthetic or artistic quality. Unique or curious physical features. Age that provides a quality of uniqueness. Value for use in exhibits. Questionable authenticity, date, author, or other characteristic that is significant and ascertainable by physical examination. General and substantial public interest because of direct association with famous or historically significant people, places, things, issues or events. Significance as documentation of the establishment or continuing legal basis of an agency or institution. Significance as documentation of the formulation of policy at the highest executive levels when the policy has significance and broad effect throughout or beyond the agency or institution. Other archival professionals such as Lynn Westney have written that the characteristics of materials exhibiting intrinsic value include age, content, usage, particularities of creation, signatures, and attached seals. Westney and others have stated that paper-based artifacts can be thought to have evidentiary value, or significant contextual markings, insofar that the original manifestation of the artifact can attest to the originality, faithfulness or authenticity, fixity, and stability of the content. For other analog materials, properly articulating intrinsic value remains essential for determining artifactual value. Similar to paper-based objects in many respects, artifactual value for images typically takes into account artistic value, age, authorial prestige, significant provenance, and institutional priorities. Analog audio preservation is based upon similar factors, including the cultural value of the item, its historical uniqueness, the estimated longevity of the medium, the current condition of the item, and the state of playback equipment, among other things. == Analog conventions in a digital realm == The standard definition of artifactual value, as it has applied to analog or non-digital materials in the twentieth century, is based upon a set of conventions which do not ordinarily apply to digital objects in toto. The Council on Library and Information Resources (CLIR) has stated that printed texts and other paper-based manuscripts, when considered as objects, are imbued with meaning distilled from a general set of understandings inherent to these conventions: The object is of a fixed and stable composition/form. Authorship and intellectual property are a recognizable concept. Duplication is possible. Fungibility of informational content (or, in other words, the ability to be replaced by another identical object). These conventions are important to consider because they help to describe the physical and even metaphysical relationship between a document's content and its physical manifestation. The underpinnings of this relationship are not identical and do not apply with the same degree of clarity to an immaterial digital realm. The idea of fixity with regard to printed materials, for example, is largely predicated on the notion that an object has been recorded on a relatively stable medium. The physical presence of a print text serves as proof of its authenticity as an object or artifact, as well as its scarcity and uniqueness in relation to other print materials. Variations in the chemical properties and storage conditions of print-based materials, as well as other cultural variables, certainly impact the fixity or stability of print materials, but there is little controversy about determining its fundamental existence or originality. However, uniqueness in the physical, paper-based sense does not translate to a digital realm in which immaterial objects are subject to theoretically infinite levels of reproduction and dissemination. Born-digital and digital surrogates may or may not look any different from each other on a server, and alterations can be made without explicit notice to the user. These alterations are normally called migration events, or actions taken on the digital object that change the original object's composition. They can enact subtle but fundamental alterations to the original document, thereby compromising its existence as an original object. Furthermore, because the tools used to generate and access digital objects have historically evolved quite rapidly, issues of playback obsolescence, incapability, data loss, and broken pathways to information have changed traditional ideas of fixity and stability. Therefore, artifactual value in a digital realm requires a modified set of generalized standards for determining artifactual originality. Michael J. Giarlo and Ronald Jantz, only two of many, have posited a list of methods for establishing digital intrinsic value by way of careful metadata generation and records maintenance. In their report, a digital original possesses three key characteristics that distinguishes it from identical copies. These include continuous verification and re-verification of the document's digital signature starting from the date of creation; retaining versions and recordings of all changes to the object in an audit trail; and having the archival master contain the creation date of the digital object. They also reported that originality in digital sources could be verified or produced by the following techniques: Digital object is given a date-time stamp that's automatically inserted into the METS-XML header upon creation. Date-time is inserted into archival metadata. Encapsulation. Digital signatures. == The role of digital surrogates == Digital surrogates are considered a utility for aiding in the preservation and increased access of certain artifacts. However, digital surrogates can have different utilities for objects depending on the nature of the original artifact and the condition the artifact is in. In 2001 the Council on Library and Information Resources (CLIR) published a report on the artifact in library collections. The CLIR states that the utility of the digital surrogate can be determined by dividing the original material (artifact) into two different categories, artifacts that are rare and those that are not. These two categories can be further divided by two categories, artifacts that are frequently used and those that are not. === Materials that are frequently used and not rare === According to the CLIR "it is not obvious that digital surrogates provide all the functionality, all the information, or all the aesthetic value of originals. Therefore, while it may be sensible to recommend that digital surrogates be used to reduce the cost and increase the availability of library holdings that circulate frequently, the decision to deaccession a physical object in library collections and replace it with a digital surrogate should be based on a careful assessment of the way in which library patrons use the original object or objects of its kind." === Materials that are infrequently used and not rare === Keeping the original is always the best solution for libraries and especially archives but in the case of libraries where an artifact is not rare or used infrequently there must be a barometer that is developed to help "balance functionality with actual use in order to help decide when digital surrogates that provide most of the functionality of originals are acceptable." === Materials that are rare and frequently used === A professional in the field of Library and Information Science (LIS) would almost certainly not argue that a digital surrogate could replace a rare object. However, in the case of a rare object that is falling into poor shape due to heavy use a digital surrogate could be extremely useful in reducing the wear a