An Emotion Markup Language (EML or EmotionML) has first been defined by the W3C Emotion Incubator Group (EmoXG) as a general-purpose emotion annotation and representation language, which should be usable in a large variety of technological contexts where emotions need to be represented. Emotion-oriented computing (or "affective computing") is gaining importance as interactive technological systems become more sophisticated. Representing the emotional states of a user or the emotional states to be simulated by a user interface requires a suitable representation format; in this case a markup language is used. EmotionML version 1.0 was published by the group in May 2014. == Example == Here is an example of an EmotionML document describing emotions expressed in a video recording of the interaction between a teacher, Alice, and a student, Bob. == History == In 2006, a first W3C Incubator Group, the Emotion Incubator Group (EmoXG), was set up "to investigate a language to represent the emotional states of users and the emotional states simulated by user interfaces" with the final Report published on 10 July 2007. In 2007, the Emotion Markup Language Incubator Group (EmotionML XG) was set up as a follow-up to the Emotion Incubator Group, "to propose a specification draft for an Emotion Markup Language, to document it in a way accessible to non-experts, and to illustrate its use in conjunction with a number of existing markups." The final report of the Emotion Markup Language Incubator Group, Elements of an EmotionML 1.0, was published on 20 November 2008. The work then was continued in 2009 in the frame of the W3C's Multimodal Interaction Activity, with the First Public Working Draft of "Emotion Markup Language (EmotionML) 1.0" being published on 29 October 2009. The Last Call Working Draft of "Emotion Markup Language 1.0", was published on 7 April 2011. The Last Call Working Draft addressed all open issues that arose from feedback of the community on the First Call Working Draft as well as results of a workshop held in Paris in October 2010. Along with the Last Call Working Draft, a list of vocabularies for EmotionML has been published to aid developers using common vocabularies for annotating or representing emotions. Annual draft updates were published until the 1.0 version was finished in 2014. == Reasons for defining an emotion markup language == A standard for an emotion markup language would be useful for the following purposes: To enhance computer-mediated human-human or human-machine communication. Emotions are a basic part of human communication and should therefore be taken into account, e.g. in emotional Chat systems or emphatic voice boxes. This involves specification, analysis and display of emotion related states. To enhance systems' processing efficiency. Emotion and intelligence are strongly interconnected. The modeling of human emotions in computer processing can help to build more efficient systems, e.g. using emotional models for time-critical decision enforcement. To allow the analysis of non-verbal behavior, emotion, mental states that can be provided using web services to enable data collection, analysis, and reporting. Concrete examples of existing technology that could apply EmotionML include: Opinion mining / sentiment analysis in Web 2.0, to automatically track customer's attitude regarding a product across blogs; Affective monitoring, such as ambient assisted living applications, fear detection for surveillance purposes, or using wearable sensors to test customer satisfaction; Wellness technologies that provide assistance according to a person's emotional state with the goal to improve the person's well-being; Character design and control for games and virtual worlds; Building web services to capture, analysis, and report data of non-verbal behavior, emotion and mental states of an individual or group across the internet using standard web technologies such as HTML5 and JSON. Social robots, such as guide robots engaging with visitors; Expressive speech synthesis, generating synthetic speech with different emotions, such as happy or sad, friendly or apologetic; expressive synthetic speech would for example make more information available to blind and partially sighted people, and enrich their experience of the content; Emotion recognition (e.g., for spotting angry customers in speech dialog systems, to improve computer games or e-Learning applications); Support for people with disabilities, such as educational programs for people with autism. EmotionML can be used to make the emotional intent of content explicit. This would enable people with learning disabilities (such as Asperger syndrome) to realise the emotional context of the content; EmotionML can be used for media transcripts and captions. Where emotions are marked up to help deaf or hearing impaired people who cannot hear the soundtrack, more information is made available to enrich their experience of the content. The Emotion Incubator Group has listed 39 individual use cases for an Emotion markup language. A standardised way to mark up the data needed by such "emotion-oriented systems" has the potential to boost development primarily because data that was annotated in a standardised way can be interchanged between systems more easily, thereby simplifying a market for emotional databases, and the standard can be used to ease a market of providers for sub-modules of emotion processing systems, e.g. a web service for the recognition of emotion from text, speech or multi-modal input. == The challenge of defining a generally usable emotion markup language == Any attempt to standardize the description of emotions using a finite set of fixed descriptors is doomed to failure, as there is no consensus on the number of relevant emotions, on the names that should be given to them or how else best to describe them. For example, the difference between ":)" and "(:" is small, but using a standardized markup it would make one invalid. Even more basically, the list of emotion-related states that should be distinguished varies depending on the application domain and the aspect of emotions to be focused. Basically, the vocabulary needed depends on the context of use. On the other hand, the basic structure of concepts is less controversial: it is generally agreed that emotions involve triggers, appraisals, feelings, expressive behavior including physiological changes, and action tendencies; emotions in their entirety can be described in terms of categories or a small number of dimensions; emotions have an intensity, and so on. For details, see the Scientific Descriptions of Emotions in the Final Report of the Emotion Incubator Group. Given this lack of agreement on descriptors in the field, the only practical way of defining an emotion markup language is the definition of possible structural elements and to allow users to "plug in" vocabularies that they consider appropriate for their work. An additional challenge lies in the aim to provide a markup language that is generally usable. The requirements that arise from different use cases are rather different. Whereas manual annotation tends to require all the fine-grained distinctions considered in the scientific literature, automatic recognition systems can usually distinguish only a very small number of different states and affective avatars need yet another level of detail for expressing emotions in an appropriate way. For the reasons outlined here, it is clear that there is an inevitable tension between flexibility and interoperability, which need to be weighed in the formulation of an EmotionML. The guiding principle in the following specification has been to provide a choice only where it is needed, and to propose reasonable default options for every choice. == Applications and web services benefiting from an emotion markup language == There are a range of existing projects and applications to which an emotion markup language will enable the building of webservices to measure capture data of individuals non-verbal behavior, mental states, and emotions and allowing results to be reported and rendered in a standardized format using standard web technologies such as JSON and HTML5. One such project is measuring affect data across the Internet using EyesWeb.
PatchMatch
PatchMatch is an algorithm used to quickly find correspondences (or matches) between small square regions (or patches) of an image. It has various applications in image editing, such as reshuffling or removing objects from images or altering their aspect ratios without cropping or noticeably stretching them. PatchMatch was first presented in a 2011 paper by researchers at Princeton University. == Algorithm == The goal of the algorithm is to find the patch correspondence by defining a nearest-neighbor field (NNF) as a function f : R 2 → R 2 {\displaystyle f:\mathbb {R} ^{2}\to \mathbb {R} ^{2}} of offsets, which is over all possible matches of patch (location of patch centers) in image A, for some distance function of two patches D {\displaystyle D} . So, for a given patch coordinate a {\displaystyle a} in image A {\displaystyle A} and its corresponding nearest neighbor b {\displaystyle b} in image B {\displaystyle B} , f ( a ) {\displaystyle f(a)} is simply b − a {\displaystyle b-a} . However, if we search for every point in image B {\displaystyle B} , the work will be too hard to complete. So the following algorithm is done in a randomized approach in order to accelerate the calculation speed. The algorithm has three main components. Initially, the nearest-neighbor field is filled with either random offsets or some prior information. Next, an iterative update process is applied to the NNF, in which good patch offsets are propagated to adjacent pixels, followed by random search in the neighborhood of the best offset found so far. Independent of these three components, the algorithm also uses a coarse-to-fine approach by building an image pyramid to obtain the better result. === Initialization === When initializing with random offsets, we use independent uniform samples across the full range of image B {\displaystyle B} . This algorithm avoids using an initial guess from the previous level of the pyramid because in this way the algorithm can avoid being trapped in local minima. === Iteration === After initialization, the algorithm attempted to perform iterative process of improving the N N F {\displaystyle NNF} . The iterations examine the offsets in scan order (from left to right, top to bottom), and each undergoes propagation followed by random search. === Propagation === We attempt to improve f ( x , y ) {\displaystyle f(x,y)} using the known offsets of f ( x − 1 , y ) {\displaystyle f(x-1,y)} and f ( x , y − 1 ) {\displaystyle f(x,y-1)} , assuming that the patch offsets are likely to be the same. That is, the algorithm will take new value for f ( x , y ) {\displaystyle f(x,y)} to be arg min ( x , y ) D ( f ( x , y ) ) , D ( f ( x − 1 , y ) ) , D ( f ( x , y − 1 ) ) {\displaystyle \arg \min \limits _{(x,y)}{D(f(x,y)),D(f(x-1,y)),D(f(x,y-1))}} . So if f ( x , y ) {\displaystyle f(x,y)} has a correct mapping and is in a coherent region R {\displaystyle R} , then all of R {\displaystyle R} below and to the right of f ( x , y ) {\displaystyle f(x,y)} will be filled with the correct mapping. Alternatively, on even iterations, the algorithm search for different direction, fill the new value to be arg min ( x , y ) { D ( f ( x , y ) ) , D ( f ( x + 1 , y ) ) , D ( f ( x , y + 1 ) ) } {\displaystyle \arg \min \limits _{(x,y)}\{D(f(x,y)),D(f(x+1,y)),D(f(x,y+1))\}} . === Random search === Let v 0 = f ( x , y ) {\displaystyle v_{0}=f(x,y)} , we attempt to improve f ( x , y ) {\displaystyle f(x,y)} by testing a sequence of candidate offsets at an exponentially decreasing distance from v 0 {\displaystyle v_{0}} u i = v 0 + w α i R i {\displaystyle u_{i}=v_{0}+w\alpha ^{i}R_{i}} where R i {\displaystyle R_{i}} is a uniform random in [ − 1 , 1 ] × [ − 1 , 1 ] {\displaystyle [-1,1]\times [-1,1]} , w {\displaystyle w} is a large window search radius which will be set to maximum picture size, and α {\displaystyle \alpha } is a fixed ratio often assigned as 1/2. This part of the algorithm allows the f ( x , y ) {\displaystyle f(x,y)} to jump out of local minimum through random process. === Halting criterion === The often used halting criterion is set the iteration times to be about 4~5. Even with low iteration, the algorithm works well.
Cybernetics
Cybernetics is the transdisciplinary study of circular causal processes such as feedback and recursion, where the effects of a system's actions (its outputs) return as inputs to that system, influencing subsequent actions. It is concerned with general principles that are relevant across multiple contexts, including engineering, ecological, economic, biological, cognitive and social systems and also in practical activities such as designing, learning, and managing. Cybernetics' transdisciplinary character means that it intersects with a number of other fields, resulting in a wide influence and diverse interpretations. The field is named after an example of circular causal feedback—that of steering a ship (the ancient Greek κυβερνήτης (kybernḗtēs) refers to the person who steers a ship). In steering a ship, the position of the rudder is adjusted in continual response to the effect it is observed as having, forming a feedback loop through which a steady course can be maintained in a changing environment, responding to disturbances from cross winds and tide. Cybernetics has its origins in exchanges between numerous disciplines during the 1940s. Initial developments were consolidated through meetings such as the Macy conferences and the Ratio Club. Early focuses included purposeful behaviour, neural networks, heterarchy, information theory, and self-organising systems. As cybernetics developed, it became broader in scope to include work in design, family therapy, management and organisation, pedagogy, sociology, the creative arts and the counterculture. == Definitions == Cybernetics has been defined in a variety of ways, reflecting "the richness of its conceptual base". One of the best known definitions is that of the American scientist Norbert Wiener, who characterised cybernetics as concerned with "control and communication in the animal and the machine". Another early definition is that of the Macy cybernetics conferences, where cybernetics was understood as the study of "circular causal and feedback mechanisms in biological and social systems". Margaret Mead emphasised the role of cybernetics as "a form of cross-disciplinary thought which made it possible for members of many disciplines to communicate with each other easily in a language which all could understand". Other definitions include: "the art of governing or the science of government" (André-Marie Ampère); "the art of steersmanship" (Ross Ashby); "the study of systems of any nature which are capable of receiving, storing, and processing information so as to use it for control" (Andrey Kolmogorov); and "a branch of mathematics dealing with problems of control, recursiveness, and information, focuses on forms and the patterns that connect" (Gregory Bateson). == Etymology == The Ancient Greek term κυβερνητικός (kubernētikos, '(good at) steering') appears in Plato's Republic and Alcibiades, where the metaphor of a steersman is used to signify the governance of people. The French word cybernétique was also used in 1834 by the physicist André-Marie Ampère to denote the sciences of government in his classification system of human knowledge. According to Norbert Wiener, the word cybernetics was coined by a research group involving himself and Arturo Rosenblueth in the summer of 1947. It has been attested in print since at least 1948 through Wiener's book Cybernetics: Or Control and Communication in the Animal and the Machine. In the book, Wiener states: After much consideration, we have come to the conclusion that all the existing terminology has too heavy a bias to one side or another to serve the future development of the field as well as it should; and as happens so often to scientists, we have been forced to coin at least one artificial neo-Greek expression to fill the gap. We have decided to call the entire field of control and communication theory, whether in the machine or in the animal, by the name Cybernetics, which we form from the Greek κυβερνήτης or steersman. Moreover, Wiener explains, the term was chosen to recognize James Clerk Maxwell's 1868 publication on feedback mechanisms involving governors, noting that the term governor is also derived from κυβερνήτης (kubernḗtēs) via a Latin corruption gubernator. Finally, Wiener motivates the choice by steering engines of a ship being "one of the earliest and best-developed forms of feedback mechanisms". == History == === First wave === The initial focus of cybernetics was on parallels between regulatory feedback processes in biological and technological systems. Two foundational articles were published in 1943: "Behavior, Purpose and Teleology" by Arturo Rosenblueth, Norbert Wiener, and Julian Bigelow – based on the research on living organisms that Rosenblueth did in Mexico – and the paper "A Logical Calculus of the Ideas Immanent in Nervous Activity" by Warren McCulloch and Walter Pitts. The foundations of cybernetics were then developed through a series of transdisciplinary conferences funded by the Josiah Macy, Jr. Foundation, between 1946 and 1953. The conferences were chaired by McCulloch and had participants that included Ross Ashby, Gregory Bateson, Heinz von Foerster, Margaret Mead, John von Neumann, and Norbert Wiener. In the UK, similar focuses were explored by the Ratio Club, an informal dining club of young psychiatrists, psychologists, physiologists, mathematicians and engineers that met between 1949 and 1958. Wiener introduced the neologism cybernetics to denote the study of "teleological mechanisms" and popularized it through the book Cybernetics: Or Control and Communication in the Animal and the Machine. During the 1950s, cybernetics was developed as a primarily technical discipline, such as in Qian Xuesen's 1954 "Engineering Cybernetics". The text was quickly translated into multiple languages and became a foundational text on automation. In the Soviet Union, Cybernetics was initially considered with suspicion but became accepted from the mid to late 1950s. By the 1960s and 1970s, however, cybernetics' transdisciplinarity fragmented, with technical focuses separating into separate fields. Artificial intelligence (AI) was founded as a distinct discipline at the Dartmouth workshop in 1956, differentiating itself from the broader cybernetics field. After some uneasy coexistence, AI gained funding and prominence. Consequently, cybernetic sciences such as the study of artificial neural networks were downplayed. Similarly, computer science became defined as a distinct academic discipline in the 1950s and early 1960s. === Second wave === The second wave of cybernetics came to prominence from the 1960s onwards, with its focus shifting away from technology toward social, ecological, and philosophical concerns. It was still grounded in biology, notably Maturana and Varela's autopoiesis, and built on earlier work on self-organising systems and the presence of anthropologists Mead and Bateson in the Macy meetings. The Biological Computer Laboratory, founded in 1958 and active until the mid-1970s under the direction of Heinz von Foerster at the University of Illinois at Urbana–Champaign, was a major incubator of this trend in cybernetics research. Focuses of the second wave of cybernetics included management cybernetics, such as Stafford Beer's biologically inspired viable system model; work in family therapy, drawing on Bateson; social systems, such as in the work of Niklas Luhmann; epistemology and pedagogy, such as in the development of radical constructivism. Cybernetics' core theme of circular causality was developed beyond goal-oriented processes to concerns with reflexivity and recursion, notably in Mead's invocation at the inaugural meeting of the American Society for Cybernetics (ASC) to apply cybernetics to the activities of the ASC itself. This focus on reflexivity was especially prominent in the development of second-order cybernetics (or the cybernetics of cybernetics), developed and promoted by Heinz von Foerster, which focused on questions of observation, cognition, epistemology, and ethics. The 1960s onwards also saw cybernetics begin to develop exchanges with the creative arts, design, and architecture, notably with the Cybernetic Serendipity exhibition (ICA, London, 1968), curated by Jasia Reichardt, and the unrealised Fun Palace project (London, unrealised, 1964 onwards), where Gordon Pask was consultant to architect Cedric Price and theatre director Joan Littlewood. In 1962, Qian Xuesen recruited Song Jian and Guan Zhaozhi to establish China's first cybernetics laboratory with him. Following the Sino-Soviet split, cybernetics was deemed disreputable in China. The field was again favored in the 1970s and 1980s following Deng Xiaoping's emphasis on modernisation. === Third wave === From the 1990s onwards, there has been a renewed interest in cybernetics from a number of directions. Early cybernetic work on artificial neural networks has been returned to as a paradigm in machine learning and artifi
Clips (software)
Clips is a discontinued mobile video editing software application created by Apple Inc. It was released onto the iOS App Store on April 6, 2017, for free. Initially, it was only available on 64-bit devices running iOS 10.3 or later; as of version 3.1.3, it requires iOS 16.0 or later. Apple describes it as an app for "making and sharing fun videos with text, effects, graphics, and more.". Its final release was on May 9, 2024 before was removed from the App Store on October 10, 2025. == Features == After launching of the app, the user sees the view of the front-facing camera. The app allows the user to create a new clip by tapping on a red record button, or use photos or videos from the device's photo library. Once a clip is recorded, it can be added to a project timeline shown at the bottom of the screen. The user can share their project on social media platforms. The user can also add filters and effects to the project. "Live Titles" (available in several styles) can also be created by dictating to the device.
Prosthesis
In medicine, a prosthesis (pl.: prostheses; from Ancient Greek: πρόσθεσις, romanized: prósthesis, lit. 'addition, application, attachment'), or a prosthetic implant, is an artificial device that replaces a missing body part, which may be lost through physical trauma, disease, or a condition present at birth (congenital disorder). Prostheses may restore the normal functions of the missing body part, or may perform a cosmetic function. A person who has undergone an amputation is sometimes referred to as an amputee, Rehabilitation for someone with an amputation is primarily coordinated by a physiatrist as part of an inter-disciplinary team consisting of physiatrists, prosthetists, nurses, physical therapists, and occupational therapists. Prostheses can be created by hand or with computer-aided design (CAD), a software interface that helps creators design and analyze the creation with computer-generated 2-D and 3-D graphics as well as analysis and optimization tools. == Types == A person's prosthetic device should be designed and assembled to meet their individual appearance and functional needs. Depending on personal circumstances, co-morbidities, budget or health insurance coverage, and access to medical care, decisions may need to balance aesthetics and function. In addition, for some individuals, a myoelectric device, a body-powered device, or an activity-specific device may be appropriate options. The person's future goals and vocational aspirations and potential capabilities may help them choose between one or more devices. Craniofacial prostheses include intra-oral and extra-oral prostheses. Extra-oral prostheses are further divided into hemifacial, auricular (ear), nasal, orbital and ocular. Intra-oral prostheses include dental prostheses, such as dentures, obturators, and dental implants. Prostheses of the neck include larynx substitutes, trachea and upper esophageal replacements, Some prostheses of the torso include breast prostheses which may be either single or bilateral, full breast devices or nipple prostheses. Penile prostheses are used to treat erectile dysfunction, perform phalloplasty procedures in men, and to build a new penis in female-to-male gender reassignment surgeries. === Limb prostheses === Limb prostheses include both upper- and lower-extremity prostheses. Upper-extremity prostheses are used at varying levels of amputation: forequarter, shoulder disarticulation, transhumeral prosthesis, elbow disarticulation, transradial prosthesis, wrist disarticulation, full hand, partial hand, finger, partial finger. A transradial prosthesis is an artificial limb that replaces an arm missing below the elbow. Upper limb prostheses can be categorized in three main categories: Passive devices, Body Powered devices, and Externally Powered (myoelectric) devices. Passive devices can either be passive hands, mainly used for cosmetic purposes, or passive tools, mainly used for specific activities (e.g. leisure or vocational). An extensive overview and classification of passive devices can be found in a literature review by Maat et.al. A passive device can be static, meaning the device has no movable parts, or it can be adjustable, meaning its configuration can be adjusted (e.g. adjustable hand opening). Despite the absence of active grasping, passive devices are very useful in bimanual tasks that require fixation or support of an object, or for gesticulation in social interaction. According to scientific data a third of the upper limb amputees worldwide use a passive prosthetic hand. Body Powered or cable-operated limbs work by attaching a harness and cable around the opposite shoulder of the damaged arm. A recent body-powered approach has explored the utilization of the user's breathing to power and control the prosthetic hand to help eliminate actuation cable and harness. The third category of available prosthetic devices comprises myoelectric arms. This particular class of devices distinguishes itself from the previous ones due to the inclusion of a battery system. This battery serves the dual purpose of providing energy for both actuation and sensing components. While actuation predominantly relies on motor or pneumatic systems, a variety of solutions have been explored for capturing muscle activity, including techniques such as Electromyography, Sonomyography, Myokinetic, and others. These methods function by detecting the minute electrical currents generated by contracted muscles during upper arm movement, typically employing electrodes or other suitable tools. Subsequently, these acquired signals are converted into gripping patterns or postures that the artificial hand will then execute. In the prosthetics industry, a trans-radial prosthetic arm is often referred to as a "BE" or below elbow prosthesis. Lower-extremity prostheses provide replacements at varying levels of amputation. These include hip disarticulation, transfemoral prosthesis, knee disarticulation, transtibial prosthesis, Syme's amputation, foot, partial foot, and toe. The two main subcategories of lower extremity prosthetic devices are trans-tibial (any amputation transecting the tibia bone or a congenital anomaly resulting in a tibial deficiency) and trans-femoral (any amputation transecting the femur bone or a congenital anomaly resulting in a femoral deficiency). A transfemoral prosthesis is an artificial limb that replaces a leg missing above the knee. Transfemoral amputees can have a very difficult time regaining normal movement. In general, a transfemoral amputee must use approximately 80% more energy to walk than a person with two whole legs. This is due to the complexities in movement associated with the knee. In newer and more improved designs, hydraulics, carbon fiber, mechanical linkages, motors, computer microprocessors, and innovative combinations of these technologies are employed to give more control to the user. In the prosthetics industry, a trans-femoral prosthetic leg is often referred to as an "AK" or above the knee prosthesis. A transtibial prosthesis is an artificial limb that replaces a leg missing below the knee. A transtibial amputee is usually able to regain normal movement more readily than someone with a transfemoral amputation, due in large part to retaining the knee, which allows for easier movement. Lower extremity prosthetics describe artificially replaced limbs located at the hip level or lower. In the prosthetics industry, a transtibial prosthetic leg is often referred to as a "BK" or below the knee prosthesis. Prostheses are manufactured and fit by clinical prosthetists. Prosthetists are healthcare professionals responsible for making, fitting, and adjusting prostheses and for lower limb prostheses will assess both gait and prosthetic alignment. Once a prosthesis has been fit and adjusted by a prosthetist, a rehabilitation physiotherapist (called physical therapist in America) will help teach a new prosthetic user to walk with a leg prosthesis. To do so, the physical therapist may provide verbal instructions and may also help guide the person using touch or tactile cues. This may be done in a clinic or home. There is some research suggesting that such training in the home may be more successful if the treatment includes the use of a treadmill. Using a treadmill, along with the physical therapy treatment, helps the person to experience many of the challenges of walking with a prosthesis. In the United Kingdom, 75% of lower limb amputations are performed due to inadequate circulation (dysvascularity). This condition is often associated with many other medical conditions (co-morbidities) including diabetes and heart disease that may make it a challenge to recover and use a prosthetic limb to regain mobility and independence. For people who have inadequate circulation and have lost a lower limb, there is insufficient evidence due to a lack of research, to inform them regarding their choice of prosthetic rehabilitation approaches. Lower extremity prostheses are often categorized by the level of amputation or after the name of a surgeon: Transfemoral (Above-knee) Transtibial (Below-knee) Ankle disarticulation (more commonly known as Syme's amputation) Knee disarticulation (also see knee replacement) Hip disarticulation, (also see hip replacement) Hemi-pelvictomy Partial foot amputations (Pirogoff, Talo-Navicular and Calcaneo-cuboid (Chopart), Tarso-metatarsal (Lisfranc), Trans-metatarsal, Metatarsal-phalangeal, Ray amputations, toe amputations). Van Nes rotationplasty ==== Prosthetic raw materials ==== Prosthetic are made lightweight for better convenience for the amputee. Some of these materials include: Plastics: Polyethylene Polypropylene Acrylics Polyurethane Wood (early prosthetics) Rubber (early prosthetics) Lightweight metals: Aluminum Composites: Carbon fiber reinforced polymers Wheeled prostheses have also been used extensively in the rehabilitation of injured domestic animals, including dogs, cats, pigs, rabbits, and
SMBGhost
SMBGhost (or SMBleedingGhost or CoronaBlue) is a type of security vulnerability, with wormlike features, that affects Windows 10 computers and was first reported publicly on 10 March 2020. == Security vulnerability == A proof of concept (PoC) exploit code was published 1 June 2020 on GitHub by a security researcher. The code could possibly spread to millions of unpatched computers, resulting in as much as tens of billions of dollars in losses. Microsoft recommends all users of Windows 10 versions 1903 and 1909 and Windows Server versions 1903 and 1909 to install patches, and states, "We recommend customers install updates as soon as possible as publicly disclosed vulnerabilities have the potential to be leveraged by bad actors ... An update for this vulnerability was released in March [2020], and customers who have installed the updates, or have automatic updates enabled, are already protected." Workarounds, according to Microsoft, such as disabling SMB compression and blocking port 445, may help but may not be sufficient. According to the advisory division of Homeland Security, "Malicious cyber actors are targeting unpatched systems with the new [threat], ... [and] strongly recommends using a firewall to block server message block ports from the internet and to apply patches to critical- and high-severity vulnerabilities as soon as possible."
Co–Star
Co–Star is an American astrological social networking service founded in 2017, and headquartered in New York City. Users enter the date, time and place they were born to generate an astrological chart and daily horoscopes, which can be compared with those of other users. == History == The concept for Co-Star began in 2015 when Banu Guler created an astrological chart as a gift. The idea later developed into a mobile application with collaborators Anna Kopp and Ben Weitzman. The app publicly launched in 2017. The app includes astrological readings, charts, and daily push notifications that have been noted for their unconventional tone. In early 2018, the company raised a $750,000 pre-seed round from Female Founders Fund. In 2019, Co–Star raised a $5.2 million seed round from Maveron, Aspect, and 14W. In January 2020, Co–Star for Android was launched to a 120,000-person waitlist—two years after their iOS version. In April 2021, the company announced a $15 million Series A, led by Spark Capital. As of that date, Co–Star reported more than 20 million downloads and increased adoption among young women in the United States. == Features == Co–Star employs artificial intelligence to analyze publicly accessible NASA JPL data and find patterns in a user's transits. Co–Star's algorithm maps human-written snippets of text to planetary movements to display personalized content for each user. That content has been called “slightly robotic,” “wildly beautiful,” “truly insane," “brutally honest,” and compared to “a free therapy session.” In July 2023, Co–Star released an in-app service called The Void that allows users to ask open-ended questions and receive answers informed by Co–Star's astrological database.