Differentiable imaging is a method within computational imaging that incorporates differentiable programming to design imaging systems. It treats the entire imaging process - from light passing through optical components to the numerical reconstruction—as a differentiable programming problem. This approach links optical hardware with numerical reconstruction, enabling joint optimization of both parts through differentiable programming. Differentiable imaging additionally extends the scope of computational imaging beyond image reconstruction, such as by aiding in characterization of optical components. == Background == Computational imaging combines optical hardware and computational algorithms to capture and reconstruct information that conventional imaging system cannot. This is achieved from a combination of the imaging system and the software used in the image reconstruction. Since the captured information may not directly show the image of the target, these systems often rely on numerical models that describe how light encodes the target. In practice, such models may deviate from the physical systems due to uncertainties such as noise, misalignments, manufacturing imperfections, environmental variations, etc. These uncertainties can cause a mismatch between the physical system and its numerical model, which may degrade reconstruction quality and limit the effectiveness of the hardware–software co-design. Uncertainty quantification is also studied in other hybrid physical–numerical systems, such as digital twin. While numerical modeling imaging systems date back to the several decades, such as the multislice method in electron microscopy or X-Ray nanotomography, differentiable imaging emphasizes jointly modeling uncertainties and solving inverse problems with image reconstruction simultaneously. Differentiable imaging transforms the traditional encoding model y = f ( x ) {\textstyle y=f(x)} into a more comprehensive formulation y = f ( x , θ ) {\textstyle y=f(x,\theta )} , where θ {\displaystyle \theta } represents a parameter set of mismatches between physical systems and numerical models. The forward model captures the entire imaging pipeline through a series of interconnected component functions: y = f ( x , θ ) , f = f n o i s e ∘ f c ∘ f o c ∘ f x ∘ f o i ∘ f i , {\displaystyle y=f(x,\theta ),\qquad f=f_{noise}\circ f_{c}\circ f_{oc}\circ f_{x}\circ f_{oi}\circ f_{i},} where the function composition operator ∘ {\displaystyle \circ } connects each system component, and θ = { θ c , θ o c , … } {\displaystyle \theta =\{\theta _{c},\theta _{oc},\ldots \}} encompasses uncertainty system parameters. Each component corresponds to specific physical processes within the imaging system, from illumination through object interactions to sensor behavior and noises. This forward model enables the formulation of an inverse problem that simultaneously optimizes system parameters while reconstructing images: x ∗ , θ ∗ = argmin x , θ L ( f ( x , θ ) , y ) + ∑ n = 1 N β n R n ( x ) {\displaystyle x^{},\theta ^{}={\text{argmin}}_{x,\theta }{\mathcal {L}}(f(x,\theta ),y)+\sum _{n=1}^{N}\beta _{n}{\mathcal {R}}_{n}(x)} s . t . x ∈ Ω x , θ ∈ Ω θ {\displaystyle s.t.\quad x\in \Omega _{x},\theta \in \Omega _{\theta }} Here, L ( f ( x , θ ) , y ) {\displaystyle {\mathcal {L}}(f(x,\theta ),y)} represents the fidelity term that quantifies the discrepancy between the model predictions and measured data. The whole process of the y = f ( x , θ ) {\displaystyle y=f(x,\theta )} is constructed as a computer graph based on differentiable programming, and the inverse problem is solved with gradient based algorithm, while the gradient is calculated with automatic differentiation. == Applications == One application of differentiable imaging is uncertainty management, which seeks to quantify and mitigate the impact of factors induce reality-numerical mismatch. Explicitly accounting for uncertainties can improve reconstruction accuracy and system robustness. Examples include: Model-related uncertainties: unknown or unmeasurable variables—for instance, optical system quantities that differ from the design specifications Data and system uncertainties: artifacts introduced during image acquisition, such as low-quality data, noise, or hardware imperfections Manufacturing uncertainties: variability in the production of imaging hardware—such as slight deviations in lens curvature or sensor alignment—that alters the physical system's behavior
Salience (neuroscience)
Salience (also called saliency, from Latin saliō meaning "leap, spring") is the property by which some thing stands out. Salient events are an attentional mechanism by which organisms learn and survive; those organisms can focus their limited perceptual and cognitive resources on the pertinent (that is, salient) subset of the sensory data available to them. Saliency typically arises from contrasts between items and their neighborhood. They might be represented, for example, by a red dot surrounded by white dots, or by a flickering message indicator of an answering machine, or a loud noise in an otherwise quiet environment. Saliency detection is often studied in the context of the visual system, but similar mechanisms operate in other sensory systems. Just what is salient can be influenced by training: for example, for human subjects particular letters can become salient by training. There can be a sequence of necessary events, each of which has to be salient, in turn, in order for successful training in the sequence; the alternative is a failure, as in an illustrated sequence when tying a bowline; in the list of illustrations, even the first illustration is a salient: the rope in the list must cross over, and not under the bitter end of the rope (which can remain fixed, and not free to move); failure to notice that the first salient has not been satisfied means the knot will fail to hold, even when the remaining salient events have been satisfied. When attention deployment is driven by salient stimuli, it is considered to be bottom-up, memory-free, and reactive. Conversely, attention can also be guided by top-down, memory-dependent, or anticipatory mechanisms, such as when looking ahead of moving objects or sideways before crossing streets. Humans and other animals have difficulty paying attention to more than one item simultaneously, so they are faced with the challenge of continuously integrating and prioritizing different bottom-up and top-down influences. == Neuroanatomy == The brain component named the hippocampus helps with the assessment of salience and context by using past memories to filter new incoming stimuli, and placing those that are most important into long term memory. The entorhinal cortex is the pathway into and out of the hippocampus, and is an important part of the brain's memory network; research shows that it is a brain region that suffers damage early on in Alzheimer's disease, one of the effects of which is altered (diminished) salience. The pulvinar nuclei (in the thalamus) modulate physical/perceptual salience in attentional selection. One group of neurons (i.e., D1-type medium spiny neurons) within the nucleus accumbens shell (NAcc shell) assigns appetitive motivational salience ("want" and "desire", which includes a motivational component), aka incentive salience, to rewarding stimuli, while another group of neurons (i.e., D2-type medium spiny neurons) within the NAcc shell assigns aversive motivational salience to aversive stimuli. The primary visual cortex (V1) generates a bottom-up saliency map from visual inputs to guide reflexive attentional shifts or gaze shifts. According to V1 Saliency Hypothesis, the saliency of a location is higher when V1 neurons give higher responses to that location relative to V1 neurons' responses to other visual locations. For example, a unique red item among green items, or a unique vertical bar among horizontal bars, is salient since it evokes higher V1 responses and attracts attention or gaze. The V1 neural responses are sent to the superior colliculus to guide gaze shifts to the salient locations. A fingerprint of the saliency map in V1 is that attention or gaze can be captured by the location of an eye-of-origin singleton in visual inputs, e.g., a bar uniquely shown to the left eye in a background of many other bars shown to the right eye, even when observers cannot tell the difference between the singleton and the background bars. == In psychology == The term is widely used in the study of perception and cognition to refer to any aspect of a stimulus that, for any of many reasons, stands out from the rest. Salience may be the result of emotional, motivational or cognitive factors and is not necessarily associated with physical factors such as intensity, clarity or size. Although salience is thought to determine attentional selection, salience associated with physical factors does not necessarily influence selection of a stimulus. === Salience bias === Salience bias (also referred to as perceptual salience) is a cognitive bias that predisposes individuals to focus on or attend to items, information, or stimuli that are more prominent, visible, or emotionally striking. This is as opposed to stimuli that are unremarkable, or less salient, even though this difference is often irrelevant by objective standards. The American Psychological Association (APA) defines the salience hypothesis as a theory regarding perception where "motivationally significant" information is more readily perceived than information with little or less significant motivational importance. Perceptual salience (salience bias) is linked to the vividness effect, whereby a more pronounced response is produced by a more vivid perception of a stimulus than the mere knowledge of the stimulus. Salience bias assumes that more dynamic, conspicuous, or distinctive stimuli engage attention more than less prominent stimuli, disproportionately impacting decision making, it is a bias which favors more salient information. ==== Application ==== ===== Cognitive Psychology ===== Salience bias, like all other cognitive biases, is an applicable concept to various disciplines. For example, cognitive psychology investigates cognitive functions and processes, such as perception, attention, memory, problem solving, and decision making, all of which could be influenced by salience bias. Salience bias acts to combat cognitive overload by focusing attention on prominent stimuli, which affects how individuals perceive the world as other, less vivid stimuli that could add to or change this perception, are ignored. Human attention gravitates towards novel and relevant stimuli and unconsciously filters out less prominent information, demonstrating salience bias, which influences behavior as human behavior is affected by what is attended to. Behavioral economists Tversky and Kahneman also suggest that the retrieval of instances is influenced by their salience, such as how witnessing or experiencing an event first-hand has a greater impact than when it is less salient, like if it were read about, implying that memory is affected by salience. ===== Language ===== It is also relevant in language understanding and acquisition. Focusing on more salient phenomena allows people to detect language patterns and dialect variations more easily, making dialect categorization more efficient. ===== Social Behavior ===== Furthermore, social behaviors and interactions can also be influenced by perceptual salience. Changes in the perceptual salience of an individual heavily influences their social behavior and subjective experience of their social interactions, confirming a "social salience effect". Social salience relates to how individuals perceive and respond to other people. ===== Behavioral Science ===== The connection between salience bias and other heuristics, like availability and representativeness, links it to the fields of behavioral science and behavioral economics. Salience bias is closely related to the availability heuristic in behavioral economics, based on the influence of information vividness and visibility, such as recency or frequency, on judgements, for example:Accessibility and salience are closely related to availability, and they are important as well. If you have personally experienced a serious earthquake, you're more likely to believe that an earthquake is likely than if you read about it in a weekly magazine. Thus, vivid and easily imagined causes of death (for example, tornadoes) often receive inflated estimates of probability, and less-vivid causes (for example, asthma attacks) receive low estimates, even if they occur with a far greater frequency (here, by a factor of twenty). Timing counts too: more recent events have a greater impact on our behavior, and on our fears, than earlier ones.Humans have bounded rationality, which refers to their limited ability to be rational in decision making, due to a limited capacity to process information and cognitive ability. Heuristics, such as availability, are employed to reduce the complexity of cognitive and social tasks or judgements, in order to decrease the cognitive load that result from bounded rationality. Despite the effectiveness of heuristics in doing so, they are limited by systematic errors that occur, often the result of influencing biases, such as salience. This can lead to misdirected or misinformed judgements, based on an overemphasis or overweighting of
GasBuddy
GasBuddy is a technology company headquartered in Dallas, United States, that offers mobile applications and websites for tracking crowd-sourced locations and prices of gas stations and convenience stores in the United States and Canada. Their platforms offer information sourced from users, gas station operators, and partner companies. They also provide business-to-business services to gas stations and convenience store owners. == History == GasBuddy was founded in Minneapolis in 2000 by Dustin Coupal, Jason Toews as a community website for sharing gas prices. In 2004, they filed as a for-profit corporation in Minnesota under the name GasBuddy Organization Inc. In 2009, GasBuddy launched OpenStore, a platform that allows convenience stores to build and manage their own mobile apps. In 2010, the company launched its own mobile apps that allowed users to input gas prices from their smartphones. In 2013, Oil Price Information Service (OPIS), a subsidiary of UCG, acquired GasBuddy. OPIS is a provider of petroleum pricing and news for businesses. In 2016, IHS acquired OPIS, separating from GasBuddy, which remained with UCG as a subsidiary company. Initially only available in the United States and Canada, GasBuddy launched in Australia in March 2016. Also in that year, GasBuddy released a completely redesigned app, its first major redesign since its release in 2010. GasBuddy also unveiled a new logo and launched GasBuddy Business Pages. GasBuddy shut down the Australian version of their app in 2022. In 2017, GasBuddy launched a gas savings program titled "Pay with GasBuddy" intended to let consumers save at gas stations in the United States. In the same year, GasBuddy was involved in a lawsuit with Reveal Mobile, a location-based marketing company, over the sale of user location data. It was revealed that GasBuddy sold information on more than 4.5 million users to Reveal each month for $9.50 per 1000 users. According to CNET, that information included "users' latitude, longitude, IP address, and time stamps on the data collected," which sparked concern in the media and between its users. In 2021, the GasBuddy app rose to the most popular app on both Android and iPhone platforms in the wake of the Colonial Pipeline ransomware attack PDI acquired GasBuddy in 2021.
Referring expression generation
Referring expression generation (REG) is the subtask of natural language generation (NLG) that received most scholarly attention. While NLG is concerned with the conversion of non-linguistic information into natural language, REG focuses only on the creation of referring expressions (noun phrases) that identify specific entities called targets. This task can be split into two sections. The content selection part determines which set of properties distinguish the intended target and the linguistic realization part defines how these properties are translated into natural language. A variety of algorithms have been developed in the NLG community to generate different types of referring expressions. == Types of referring expressions == A referring expression (RE), in linguistics, is any noun phrase, or surrogate for a noun phrase, whose function in discourse is to identify some individual object (thing, being, event...) The technical terminology for identify differs a great deal from one school of linguistics to another. The most widespread term is probably refer, and a thing identified is a referent, as for example in the work of John Lyons. In linguistics, the study of reference relations belongs to pragmatics, the study of language use, though it is also a matter of great interest to philosophers, especially those wishing to understand the nature of knowledge, perception and cognition more generally. Various devices can be used for reference: determiners, pronouns, proper names... Reference relations can be of different kinds; referents can be in a "real" or imaginary world, in discourse itself, and they may be singular, plural, or collective. === Pronouns === The simplest type of referring expressions are pronoun such as he and it. The linguistics and natural language processing communities have developed various models for predicting anaphor referents, such as centering theory, and ideally referring-expression generation would be based on such models. However most NLG systems use much simpler algorithms, for example using a pronoun if the referent was mentioned in the previous sentence (or sentential clause), and no other entity of the same gender was mentioned in this sentence. === Definite noun phrases === There has been a considerable amount of research on generating definite noun phrases, such as the big red book. Much of this builds on the model proposed by Dale and Reiter. This has been extended in various ways, for example Krahmer et al. present a graph-theoretic model of definite NP generation with many nice properties. In recent years a shared-task event has compared different algorithms for definite NP generation, using the TUNA corpus. === Spatial and temporal reference === Recently there has been more research on generating referring expressions for time and space. Such references tend to be imprecise (what is the exact meaning of tonight?), and also to be interpreted in different ways by different people. Hence it may be necessary to explicitly reason about false positive vs false negative tradeoffs, and even calculate the utility of different possible referring expressions in a particular task context. === Criteria for good expressions === Ideally, a good referring expression should satisfy a number of criteria: Referential success: It should unambiguously identify the referent to the reader. Ease of comprehension: The reader should be able to quickly read and understand it. Computational complexity: The generation algorithm should be fast No false inferences: The expression should not confuse or mislead the reader by suggesting false implicatures or other pragmatic inferences. For example, a reader may be confused if he is told Sit by the brown wooden table in a context where there is only one table. == History == === Pre-2000 era === REG goes back to the early days of NLG. One of the first approaches was done by Winograd in 1972 who developed an "incremental" REG algorithm for his SHRDLU program. Afterwards researchers started to model the human abilities to create referring expressions in the 1980s. This new approach to the topic was influenced by the researchers Appelt and Kronfeld who created the programs KAMP and BERTRAND and considered referring expressions as parts of bigger speech acts. Some of their most interesting findings were the fact that referring expressions can be used to add information beyond the identification of the referent as well as the influence of communicative context and the Gricean maxims on referring expressions. Furthermore, its skepticism concerning the naturalness of minimal descriptions made Appelt and Kronfeld's research a foundation of later work on REG. The search for simple, well-defined problems changed the direction of research in the early 1990s. This new approach was led by Dale and Reiter who stressed the identification of the referent as the central goal. Like Appelt they discuss the connection between the Gricean maxims and referring expressions in their culminant paper in which they also propose a formal problem definition. Furthermore, Reiter and Dale discuss the Full Brevity and Greedy Heuristics algorithms as well as their Incremental Algorithm(IA) which became one of the most important algorithms in REG. === Later developments === After 2000 the research began to lift some of the simplifying assumptions, that had been made in early REG research in order to create more simple algorithms. Different research groups concentrated on different limitations creating several expanded algorithms. Often these extend the IA in a single perspective for example in relation to: Reference to Sets like "the t-shirt wearers" or "the green apples and the banana on the left" Relational Descriptions like "the cup on the table" or "the woman who has three children" Context Dependency, Vagueness and Gradeability include statements like "the older man" or "the car on the left" which are often unclear without a context Salience and Generation of Pronouns are highly discourse dependent making for example "she" a reference to "the (most salient) female person" Many simplifying assumptions are still in place or have just begun to be worked on. Also a combination of the different extensions has yet to be done and is called a "non-trivial enterprise" by Krahmer and van Deemter. Another important change after 2000 was the increasing use of empirical studies in order to evaluate algorithms. This development took place due to the emergence of transparent corpora. Although there are still discussions about what the best evaluation metrics are, the use of experimental evaluation has already led to a better comparability of algorithms, a discussion about the goals of REG and more task-oriented research. Furthermore, research has extended its range to related topics such as the choice of Knowledge Representation(KR) Frameworks. In this area the main question, which KR framework is most suitable for the use in REG remains open. The answer to this question depends on how well descriptions can be expressed or found. A lot of the potential of KR frameworks has been left unused so far. Some of the different approaches are the usage of: Graph search which treats relations between targets in the same way as properties. Constraint Satisfaction which allows for a separation between problem specification and the implementation. Modern Knowledge Representation which offers logical inference in for example Description Logic or Conceptual Graphs. == Problem definition == Dale and Reiter (1995) think about referring expressions as distinguishing descriptions. They define: The referent as the entity that should be described The context set as set of salient entities The contrast set or potential distractors as all elements of the context set except the referent A property as a reference to a single attribute–value pair Each entity in the domain can be characterised as a set of attribute–value pairs for example ⟨ {\displaystyle \langle } type, dog ⟩ {\displaystyle \rangle } , ⟨ {\displaystyle \langle } gender, female ⟩ {\displaystyle \rangle } or ⟨ {\displaystyle \langle } age, 10 years ⟩ {\displaystyle \rangle } . The problem then is defined as follows: Let r {\displaystyle r} be the intended referent, and C {\displaystyle C} be the contrast set. Then, a set L {\displaystyle L} of attribute–value pairs will represent a distinguishing description if the following two conditions hold: Every attribute–value pair in L {\displaystyle L} applies to r {\displaystyle r} : that is, every element of L {\displaystyle L} specifies an attribute–value that r {\displaystyle r} possesses. For every member c {\displaystyle c} of C {\displaystyle C} , there is at least one element l {\displaystyle l} of L {\displaystyle L} that does not apply to c {\displaystyle c} : that is, there is an l {\displaystyle l} in L {\displaystyle L} that specifies an attribute–value that c {\displaystyle c} does not possess. l {\displaystyle l} is said
Recursive transition network
A recursive transition network ("RTN") is a graph theoretical schematic used to represent the rules of a context-free grammar. RTNs have application to programming languages, natural language and lexical analysis. Any sentence that is constructed according to the rules of an RTN is said to be "well-formed". The structural elements of a well-formed sentence may also be well-formed sentences by themselves, or they may be simpler structures. This is why RTNs are described as recursive. == Notes and references ==
Fyuse
Fyuse is a spatial photography app which lets users capture and share interactive 3D images. By tilting or swiping one's smartphone, one can view such "fyuses" from various angles — as if one were walking around an object or subject. The app blends photography and video to create an interactive medium and was first published for iOS in April 2014. The Android version was released at the end of 2014. == The app == Fyuse lets users capture panoramas, selfies, and full 360° views of objects and allows one to view captured moments from different angles. It has its own personal gallery, social network and standalone web integration. With the app, Fyusion also created a social networking platform similar to Instagram. Fyuses can be shared, commented on, liked and re-shared to one's followers (called Echoes). One can build a network of followers and with engagement tracking, one can see how many times an image has been interacted with The images can also be saved for private, offline view, or shared to other social networks, like Facebook or Twitter, or embedded on a website where the images can be interacted with by desktop users via dragging the mouse. Furthermore, in the compass tab other fyuses can be discovered using the app's system of tags and categories. One's Fyuse feed is prepopulated with top users, and one can follow people to see when they post a new fyuse. The app will also find one's friends if one signs up with Facebook or connects it with one's Twitter account. To create a fyuse one moves around a person or object with one's phone's camera in one direction or moving/tilting one's phone around while holding one's finger on the screen. By combining photography and video the app allows one to capture moments that one may not have otherwise been able to capture by recording not one moment in time but stitched together little moments. According to Fyusion CEO Radu Rusu, a photo freezes a moment in time, while a video captures moments in a linear timeline — both still flat, when viewed. A fyuse image captures a moment in space, where one can not only see one side of something, but also around it. When it is done rendering, fyuses can also be edited – one can trim the fyuse for length and edit the brightness, contrast, exposure, saturation and sharpness. One can also add a vignette and apply a filters, with options to adjust their intensity. After editing, one can write a description, add hashtags, and tag parts of the fyuse before one can (voluntarily) publish and share it. Version 1.0 has been described as "alpha prototype" and version 2.0 was released on 17 December 2014. Version 3.0 introduced 3D tagging by which users can layer 3D graphic that animate accordingly with each interaction to add some context to the content. Version 4.0 was released on December 21, 2016 for iOS. Since January 2016 (v3.2) the app allows the export of fyuses as Live Photos. The app has also been described as a more sophisticated version of 3D stickers and flip images. == Applications == The app has many applications for e-commerce such as for fashion designers who want to showcase a garment from every angle, or real estate listings and Airbnb-type sites that want to make their rental properties seem as enticing as possible. The app can also be used for interactive art, 360° panoramas and selfies. == History == San Francisco-based Fyusion Inc.'s three founders — Radu B. Rusu, CTO Stefan Holzer, and VP of Engineering Stephen Miller — worked together at Willow Garage, the robotics research lab started by early Google employee Scott Hassan in the area of "personal robotics" — Hassan decided to turn the lab into more of an incubator, suggesting that the members spin off their technologies into consumer-facing enterprises. Rusu first set out with an open-source 3D perception software startup called Open Perception. Fyusion was officially founded in 2013, and soon after Rusu and his cofounders patented the technology for spatial photography. The company closed a seed funding round at the end of May, raising $3.35 million from investors, including an angel investment from Sun Microsystems cofounder Andreas Bechtolsheim. In 2014 the Fyuse team consisted of 13 employees, mostly engineers and designers, recruited from around the globe. In March 2015 the team displayed their app at Katy Perry's premiere for the movie "Prismatic World Tour on Epix" where Perry also took Fyuse for a test run. == Augmented reality == In September 2016 Fyusion unveiled its platform for creating augmented reality content using ones smartphone. It takes the images from ones smartphone and converts them into 3D holographic images, which one can then view on an AR headset. According to Rusu "by making it easy for people to capture their surroundings on any mobile device, [Fyusion is] revolutionizing the way that people view the world around them" and also states that for "AR to be successful, anyone should be able to create content for it" opposed to the current "small number of content creators and an even smaller number of hardware players". According to him "the applications of [Fyusion's] technology for consumers and businesses are incredibly limitless". The platform uses the company's patented 3D spatio-temporal platform that uses advanced sensor fusion, machine learning and computer vision algorithms and part of the platform is built into the Fyuse app. Before committing to releasing a separate consumer product the company intends to wait until the HoloLens device becomes available to the public. Until then any Fyuse representation created using Fyuse is AR ready and will be able to be shown in HoloLens in the future. == Fyuse - Point of No Return == Fyuse - Point of No Return is a science fiction short advert for Fyuse 3.0 in which Fyuse's digital medium is extrapolated into the future. In the film a woman uses a mini scanning-drone to 3D scan a tree with Fyuse and later recreate it as an augmented reality object at another place.
Mobile cloud storage
Mobile cloud storage is a form of cloud storage that is accessible on mobile devices such as laptops, tablets, and smartphones. Mobile cloud storage providers offer services that allow the user to create and organize files, folders, music, and photos, similar to other cloud computing models. Services are used by both individuals and companies. Most cloud file storage providers offer limited free use but charge for additional storage once the free limit is exceeded. These costs are usually charged as a monthly subscription rate and have different rates depending on the amount of storage desired. In 2018, cloud services revenue was about $182.4 billion and in 2022 it is projected to grow to $331.2 billion. The cloud storage industry was projected to grow 17.2 percent in 2019 (Costello, 2019). == History == The concept of cloud computing trace back to 1960s, when the groundwork for modern internet and network technologies was being laid (Human for humans, 2024). One of the pivotal figures in this early period was J.C.R. Licklider, a visionary computer scientist who worked on ARPANET, the precursor to the internet. Licklider's ideas set the stage for the development of distributed computing systems, which are fundamental to cloud computing. Moving into the 1990s, AT&T introduced PersonaLink Services, a more advanced online platform offering electronic mail and online storage. Major turning point in 2006 The launch of Amazon Web Services (AWS) in 2006 marked a major turning point. AWS introduced Amazon S3 (Simple Storage Service), which allowed businesses and developers to store and retrieve any amount of data, at any time, from anywhere on the web. This development was revolutionary, providing scalable, reliable, and low-cost data storage infrastructure that transformed how organizations managed their data. == Applications == Some mobile device manufacturers include mobile cloud storage apps with their product. These apps facilitate synchronization of user files across multiple platforms. Part of the process for setting up new mobile devices frequently includes configuring a cloud storage service to Backup the device's files and information. Apple iOS devices come pre-loaded and configured to use Apple's mobile cloud storage service iCloud. Google offers a similar feature with the Android operating system by backing up the device using a Google Drive account. The Samsung Galaxy smartphone has partnered with Dropbox, while Microsoft similarly offers Microsoft OneDrive. Some mobile cloud storage apps are platform-independent. For example, Nasuni's Mobile Access app is available on any Android or iOS device. Most companies offering Cloud Storage have secure website to access files allowing use on any device that can browse the Internet.