Geshe Lobsang Monlam (Tibetan: དགེ་བཤེས་བློ་བཟང་སྨོན་ལམ, Wylie: dge bshes blo bzang smon lam), born in 1976 in Ngawa eastern Tibet, is a Tibetan Buddhist scholar and programmer who uses digital technologies to preserve the Tibetan language and culture. He is best known for developing Tibetan typefaces and for the multi-volume Great Monlam Tibetan Dictionary. In 2025, he received the Snow Lion Award for Human Rights from the International Campaign for Tibet. He is also working on developing a "Dalai Lama AI," a specialized language model. == Biography == Lobsang Monlam was born in 1976 in Ngawa, eastern Tibet, anciently Tibetan Amdo, where he became a monk at the age of 12.. At the age of 17, in 1993, Lobsang Monlam fled Tibet by crossing the Himalayas to reach southern India and discovered computer science in a monastery. In 1993, he was ordained monk in the Sera Mey College in Bylakuppe, Karnataka, India, where he obtained a Geshe title in 2013.. By the early 2000s, Lobsang Monlam had already learned to paint thangkas and to compose plans and drawings. He used this knowledge to design a new assembly hall for Sera Mey, which the monks needed. Thanks to his work, Lobsang Monlam received donations from patrons of the monastery, which he was able to use to buy his first computer. He bought his first laptop in 2002 and largely taught himself how to use the hardware and software with the help of manuals. As a Buddhist scholar, he combines meditation practice with his digital work. In 2012, he founded and directs the Monlam Tibetan Information Technology Research Center in Dharamsala, which specializes in Tibetan language and software projects. Since then, he is its director, researching Tibetan language-related software. In 2019, advised by the 14th Dalai Lama, he founded Monlam IT and Research (OPC) Private Limited. Since the 2000s, Monlam has been developing Tibetan typefaces; the first Monlam Tibetan font was created in 2005. Under his direction, the Monlam Great Tibetan Dictionary was created, comprising 223 printed volumes and over 300,000 entries; approximately 150 people worked on this project for over nine years. On May 27, 2022, the Dalai Lama inaugurated the Monlam Tibetan Dictionary, produced by the Monlam Tibetan Information Technology Research Center, at Namgyal Monastery in McLeod Ganj. According to Penpa Tsering, this is the world's largest dictionary, created with guidance from the Dalai Lama, based on proposals from Lobsang Monlam and his team under the direction of Samdhong Rinpoche, and other lamas from all schools of Tibetan Buddhism and Yungdrung Bön. On December 5, 2024, Lobsang Monlam testified at a hearing of the US Congressional-Executive Commission on China in Washington, chaired by Christopher Smith, on the difficulties of preserving the Tibetan language and culture in Tibet and the Tibetan diaspora, and on the interest of the Monlam Tibetan Informatics Research Center in developing technologies for the preservation of the Tibetan language. On December 12, 2024, the work was presented to the Library of Congress in Washington, D.C., and launched at an event. The free Monlam Great Tibetan Dictionary app is available in several languages; the German version was created in collaboration with the Tibet Institute Rikon and has been downloaded millions of times. In total, Monlam has created over 37 apps related to the Tibetan language and translation; In 2023, its center launched the Monlam artificial intelligence platform, equipped with modules for machine translation, optical character recognition, speech transcription and speech synthesis.. For their efforts, he and Sophie Richardson received the Snow Lion Award in 2025, which was presented by Richard Gere and came with a prize of €3,000. In 2019, he started a PhD at Bangalore University on Library Science. He obtained his doctorate on November 30, 2023. Currently, he spearheads Monlam AI. Lobsang Monlam is developing "Dalai Lama AI" to digitally preserve the teachings of the 14th Dalai Lama, now 90 years old, for future generations. Lobsang Monlam states, "If we succeed in preserving the Dalai Lama, we also preserve the movement."
Scale space implementation
In the areas of computer vision, image analysis and signal processing, the notion of scale-space representation is used for processing measurement data at multiple scales, and specifically enhance or suppress image features over different ranges of scale (see the article on scale space). A special type of scale-space representation is provided by the Gaussian scale space, where the image data in N dimensions is subjected to smoothing by Gaussian convolution. Most of the theory for Gaussian scale space deals with continuous images, whereas one when implementing this theory will have to face the fact that most measurement data are discrete. Hence, the theoretical problem arises concerning how to discretize the continuous theory while either preserving or well approximating the desirable theoretical properties that lead to the choice of the Gaussian kernel (see the article on scale-space axioms). This article describes basic approaches for this that have been developed in the literature, see also for an in-depth treatment regarding the topic of approximating the Gaussian smoothing operation and the Gaussian derivative computations in scale-space theory, and for a complementary treatment regarding hybrid discretization methods. == Statement of the problem == The Gaussian scale-space representation of an N-dimensional continuous signal, f C ( x 1 , ⋯ , x N , t ) , {\displaystyle f_{C}\left(x_{1},\cdots ,x_{N},t\right),} is obtained by convolving fC with an N-dimensional Gaussian kernel: g N ( x 1 , ⋯ , x N , t ) . {\displaystyle g_{N}\left(x_{1},\cdots ,x_{N},t\right).} In other words: L ( x 1 , ⋯ , x N , t ) = ∫ u 1 = − ∞ ∞ ⋯ ∫ u N = − ∞ ∞ f C ( x 1 − u 1 , ⋯ , x N − u N , t ) ⋅ g N ( u 1 , ⋯ , u N , t ) d u 1 ⋯ d u N . {\displaystyle L\left(x_{1},\cdots ,x_{N},t\right)=\int _{u_{1}=-\infty }^{\infty }\cdots \int _{u_{N}=-\infty }^{\infty }f_{C}\left(x_{1}-u_{1},\cdots ,x_{N}-u_{N},t\right)\cdot g_{N}\left(u_{1},\cdots ,u_{N},t\right)\,du_{1}\cdots du_{N}.} However, for implementation, this definition is impractical, since it is continuous. When applying the scale space concept to a discrete signal fD, different approaches can be taken. This article is a brief summary of some of the most frequently used methods. == Separability == Using the separability property of the Gaussian kernel g N ( x 1 , … , x N , t ) = G ( x 1 , t ) ⋯ G ( x N , t ) {\displaystyle g_{N}\left(x_{1},\dots ,x_{N},t\right)=G\left(x_{1},t\right)\cdots G\left(x_{N},t\right)} the N-dimensional convolution operation can be decomposed into a set of separable smoothing steps with a one-dimensional Gaussian kernel G along each dimension L ( x 1 , ⋯ , x N , t ) = ∫ u 1 = − ∞ ∞ ⋯ ∫ u N = − ∞ ∞ f C ( x 1 − u 1 , ⋯ , x N − u N , t ) G ( u 1 , t ) d u 1 ⋯ G ( u N , t ) d u N , {\displaystyle L(x_{1},\cdots ,x_{N},t)=\int _{u_{1}=-\infty }^{\infty }\cdots \int _{u_{N}=-\infty }^{\infty }f_{C}(x_{1}-u_{1},\cdots ,x_{N}-u_{N},t)G(u_{1},t)\,du_{1}\cdots G(u_{N},t)\,du_{N},} where G ( x , t ) = 1 2 π t e − x 2 2 t {\displaystyle G(x,t)={\frac {1}{\sqrt {2\pi t}}}e^{-{\frac {x^{2}}{2t}}}} and the standard deviation of the Gaussian σ is related to the scale parameter t according to t = σ2. Separability will be assumed in all that follows, even when the kernel is not exactly Gaussian, since separation of the dimensions is the most practical way to implement multidimensional smoothing, especially at larger scales. Therefore, the rest of the article focuses on the one-dimensional case. == The sampled Gaussian kernel == When implementing the one-dimensional smoothing step in practice, the presumably simplest approach is to convolve the discrete signal fD with a sampled Gaussian kernel: L ( x , t ) = ∑ n = − ∞ ∞ f ( x − n ) G ( n , t ) {\displaystyle L(x,t)=\sum _{n=-\infty }^{\infty }f(x-n)\,G(n,t)} where G ( n , t ) = 1 2 π t e − n 2 2 t {\displaystyle G(n,t)={\frac {1}{\sqrt {2\pi t}}}e^{-{\frac {n^{2}}{2t}}}} (with t = σ2) which in turn is truncated at the ends to give a filter with finite impulse response L ( x , t ) = ∑ n = − M M f ( x − n ) G ( n , t ) {\displaystyle L(x,t)=\sum _{n=-M}^{M}f(x-n)\,G(n,t)} for M chosen sufficiently large (see error function) such that 2 ∫ M ∞ G ( u , t ) d u = 2 ∫ M t ∞ G ( v , 1 ) d v < ε . {\displaystyle 2\int _{M}^{\infty }G(u,t)\,du=2\int _{\frac {M}{\sqrt {t}}}^{\infty }G(v,1)\,dv<\varepsilon .} A common choice is to set M to a constant C times the standard deviation of the Gaussian kernel M = C σ + 1 = C t + 1 {\displaystyle M=C\sigma +1=C{\sqrt {t}}+1} where C is often chosen somewhere between 3 and 6. Using the sampled Gaussian kernel can, however, lead to implementation problems, in particular when computing higher-order derivatives at finer scales by applying sampled derivatives of Gaussian kernels. When accuracy and robustness are primary design criteria, alternative implementation approaches should therefore be considered. For small values of ε (10−6 to 10−8) the errors introduced by truncating the Gaussian are usually negligible. For larger values of ε, however, there are many better alternatives to a rectangular window function. For example, for a given number of points, a Hamming window, Blackman window, or Kaiser window will do less damage to the spectral and other properties of the Gaussian than a simple truncation will. Notwithstanding this, since the Gaussian kernel decreases rapidly at the tails, the main recommendation is still to use a sufficiently small value of ε such that the truncation effects are no longer important. == The discrete Gaussian kernel == A more refined approach is to convolve the original signal with the discrete Gaussian kernel T(n, t) L ( x , t ) = ∑ n = − ∞ ∞ f ( x − n ) T ( n , t ) {\displaystyle L(x,t)=\sum _{n=-\infty }^{\infty }f(x-n)\,T(n,t)} where T ( n , t ) = e − t I n ( t ) {\displaystyle T(n,t)=e^{-t}I_{n}(t)} and I n ( t ) {\displaystyle I_{n}(t)} denotes the modified Bessel functions of integer order, n. This is the discrete counterpart of the continuous Gaussian in that it is the solution to the discrete diffusion equation (discrete space, continuous time), just as the continuous Gaussian is the solution to the continuous diffusion equation. This filter can be truncated in the spatial domain as for the sampled Gaussian L ( x , t ) = ∑ n = − M M f ( x − n ) T ( n , t ) {\displaystyle L(x,t)=\sum _{n=-M}^{M}f(x-n)\,T(n,t)} or can be implemented in the Fourier domain using a closed-form expression for its discrete-time Fourier transform: T ^ ( θ , t ) = ∑ n = − ∞ ∞ T ( n , t ) e − i θ n = e t ( cos θ − 1 ) . {\displaystyle {\widehat {T}}(\theta ,t)=\sum _{n=-\infty }^{\infty }T(n,t)\,e^{-i\theta n}=e^{t(\cos \theta -1)}.} With this frequency-domain approach, the scale-space properties transfer exactly to the discrete domain, or with excellent approximation using periodic extension and a suitably long discrete Fourier transform to approximate the discrete-time Fourier transform of the signal being smoothed. Moreover, higher-order derivative approximations can be computed in a straightforward manner (and preserving scale-space properties) by applying small support central difference operators to the discrete scale space representation. As with the sampled Gaussian, a plain truncation of the infinite impulse response will in most cases be a sufficient approximation for small values of ε, while for larger values of ε it is better to use either a decomposition of the discrete Gaussian into a cascade of generalized binomial filters or alternatively to construct a finite approximate kernel by multiplying by a window function. If ε has been chosen too large such that effects of the truncation error begin to appear (for example as spurious extrema or spurious responses to higher-order derivative operators), then the options are to decrease the value of ε such that a larger finite kernel is used, with cutoff where the support is very small, or to use a tapered window. == Recursive filters == Since computational efficiency is often important, low-order recursive filters are often used for scale-space smoothing. For example, Young and van Vliet use a third-order recursive filter with one real pole and a pair of complex poles, applied forward and backward to make a sixth-order symmetric approximation to the Gaussian with low computational complexity for any smoothing scale. By relaxing a few of the axioms, Lindeberg concluded that good smoothing filters would be "normalized Pólya frequency sequences", a family of discrete kernels that includes all filters with real poles at 0 < Z < 1 and/or Z > 1, as well as with real zeros at Z < 0. For symmetry, which leads to approximate directional homogeneity, these filters must be further restricted to pairs of poles and zeros that lead to zero-phase filters. To match the transfer function curvature at zero frequency of the discrete Gaussian, which ensures an approximate semi-group property of additive t, two poles at Z = 1 + 2 t − ( 1 + 2 t ) 2 − 1 {\displaystyle
DBOS
DBOS (Formerly Database-Oriented Operating System, now just DBOS) is an open source durable workflow execution software library written for the Python, TypeScript, Java, and Go programming languages. DBOS arose from a joint open source project from MIT and Stanford, after a discussion between Michael Stonebraker and Matei Zaharia on how to scale and improve scheduling and performance of millions of Apache Spark tasks. Today it is a commercial company that offers an open source system to add durable computing to any software, built on concepts derived from the joint research project. == History == === 2020: Academic R&D Project === DBOS originated in 2020 as a joint open source project between MIT, Stanford, and Carnegie Mellon. The project explored the idea of operating system services built atop a distributed database - a database-oriented operating system meant to simplify and improve the scalability, security and resilience of large-scale distributed applications. The basic concept was to run a multi-node multi-core, transactional, highly-available distributed database, such as VoltDB, as the only application for a microkernel, and then to implement scheduling, messaging, file systems and other operating system services on top of the database. The architectural philosophy is described by this quote from the abstract of their initial preprint: All operating system state should be represented uniformly as database tables, and operations on this state should be made via queries from otherwise stateless tasks. This design makes it easy to scale and evolve the OS without whole-system refactoring, inspect and debug system state, upgrade components without downtime, manage decisions using machine learning, and implement sophisticated security features. A prototype was built with competitive performance to existing systems. ==
Hype (marketing)
Hype in marketing is a strategy of using extreme publicity. Hype as a modern marketing strategy is closely associated with social media. Marketing through hype often uses artificial scarcity to induce demand. Consumers of hyped products often participate as a form of conspicuous consumption to signify characteristics about themselves. Hype allows brands to promote their image above the actual quality of the product. Streetwear brands have collaborated with luxury fashion to justify charging premium prices for their goods. As an example, fashion label Vetements used social media channels to promote a limited-edition hoodie which sold 500 units in hours, recording sales of €445,000. When hype marketing is used to drive demand for limited-edition goods, consumers sometimes attempt resell those good on secondary markets for a profit (comparable to ticket scalping). The resale market is a $24 billion industry. == Method == Luxury brands may release products as a collaborate with ready-made garment brands as a way to build hype. Collaborations have been used by some luxury brands to circumvent fast fashion brands copying their designs. NYU Professor Adam Alter says that for an established brand to create a scarcity frenzy, they need to release a limited number of different products, frequently. Hype is often built via Pop-up retail. Comme des Garçons was one of the first to use this strategy, leasing a short-term vacant shop solved the storage problems of releasing product for quick sale. Hype campaigns also rely on influencer marketing, where brands enlist creators whose parasocial relationships with their followers help convert audience attention into demand for limited releases. == In popular culture == The term 'hypebeast' has been coined to define consumers vulnerable to hype marketing. The origins of the term come from the Hong Kong-based company Hypebeast. The behaviours of the hypebeast define hype marketing; the purchase of popular goods they can't afford to impress others. Hype also manifests itself in queues with brands often retailing hyped products through pop-up stores. Many luxury brands release hyped products via their online shop. This has led to the creation of companies that allow consumers to use bots to guarantee or improve their chances of purchasing a limited-edition product.
Personal media
Personal media are media of communication which are used by an individual rather than by a corporation or institution. They are generally contrasted with mass media which are produced by teams of people and broadcast to a general population. In other words, personal media allow individuals, as opposed to corporate entities, to contribute knowledge and opinion to the public. The term dates from the 1980s. New technologies such as social media and self-publishing are creating a variety of modes for modern media. Marika Lüders suggests a two-dimensional model for classifying such media with one dimension being the degree of interaction between the senders and receivers; and the other dimension being the level of institutionalisation and professionalism. Katherine Nashleanas links the concept of personal media to the notion of 'control' by an individual as opposed to a centralised authority. She argues that although personal media including the fax have been available to the general public since the 1960s, more recent technologies such as the smartphone confer greater control over content production and distribution to their users.
E-on Vue
Vue is a software tool for world generation by Bentley Systems, with support for many visual effects, animations, and various other features. The tool has been used in several feature-length films. In 2024, Bentley Systems announced that Vue would be discontinued, and be freely available to those that still wish to use it. == Versions == == Features == This is a list of features as of the 2023 release of Vue: === Terrains === Heightfield terrains Procedural terrains Infinite terrains Planetary terrains Real-world terrains 3D terrain sculpting Terrain export === EcoSystem Instancing Technology === Material-based EcoSystems Global EcoSystems Dynamic EcoSystems 360° EcoSystem Population Paint EcoSystem instances EcoParticles Export EcoSystem populations === Vegetation === Built-in Plant editor Compatible with PlantFactory Vegetation assets === Atmosphere, Skies and Clouds === Standard atmospheric model Spectral atmospheric model Photometric atmospheric model Atmosphere presets Procedural Volumetric 3D cloud layers Standalone 3D Metaclouds Convert meshes to Clouds Cloud morphing Import OpenVDB Export standalone and cloud layer zones to OpenVDB Export skies as HDRI === Modeling === Primitive and Feature modeling 3D Text edition tool Metablobbing Hyperblobs Export baked hyperblobs Splines Built in Road Construction toolkit Random rock generator Export rocks === Texturing and UVs === Material presets PBR Substance support Node-based procedural materials Volumetric materials and Hypertextures Stacked UVs Unwrapped UVs Ptex === Interoperability, Integration And Export === Export single assets to generic 3D formats Full scene export Integration plugins Import and Export Camera data as FBX and Nuke.chan Python API ZBrush GoZ bridge === Animation === Animate objects, materials, atmospheres, clouds, waves... Automatic wind and breeze Localized wind effects per plant / per EcoSystem population Omni and directional ventilators for local modifications of plants Time spline editor Automatic keyframe creation Automatic synchronization of cameras and lights Animation export as AfterEffects Import motion tracking information === Lighting === Global illumination, Global Radiosity, Ambient occlusion Subsurface Scattering HDRI image based lighting Point light, Quadratic point light, Spotlight, Quadratic spotlight, Directional light Use IES distribution profiles on photometric lights Area lights, light panels, light portals Physically accurate caustics computation === Rendering === Render with Ray Tracer Render with Path Tracer Stereoscopic rendering 360/180 VR Panorama Render Option Spherical panoramic rendering Tone mapping options Multipass & G-Buffer Network rendering with HyperVue / RenderCows Network rendering with RenderNodes == Users == Blue Sky Studios Digital Domain DreamWorks Animation: Kung Fu Panda Industrial Light & Magic: Indiana Jones and the Kingdom of the Crystal Skull, Pirates of the Caribbean: Dead Man's Chest Sony Pictures Imageworks Warner Bros. Interactive Entertainment Weta Digital
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