dataesr/scientific-paragraphs-categorization huggingface dataset:
III. 2 Redistributing The ubiquity of digital images results in part from the efficiency with which media logistics enable instantaneous distribution. At the same time, however, it is an effect of another distribution: the proliferative expansion of various networked things that are equipped with imaging technology. The agency of the ›ambient‹ image infrastructure is distributed over a number of key players, ranging from technological agents of visual capture by sensors to agents of data mobilization, processing, and visualization. The ›visual culture‹ of the expanding Internet of Things follows calculations that are likewise algorithmically formalized and thus dependent on the general datafication of the image. Nonetheless, noticeably less logistical energy is invested here in the production and transportation of visual surfaces.
Even under the conditions of »ubiquitous sensing,«728 of course, the image does not completely disappear as a phenomenal ›data exterior. ‹ It remains an option to display certain data states, to make them specifically intelligible. With regard to the attention economy as well as operationally, however, a shift in the direction of the periphery is taking place. Here, image data circulate as sensor data, which are passed on more or less directly-that is, without visual mediation-to further information-technological processing. Fully in line with Weiser's vision, the distributed image acts as a muted signal that remains effective and is able to mediate operational decisions according to protocol without further raising the level of the general information overload. As ambient signals, distributed images thus serve to create structure, opening and closing fields of action, but generally withdraw from visible zones of (conventional) visual culture.
While the language of vision perseveres, it is important not to assume a direct correlation between vision as a sense and visualization as an object and practice. Married initially to psychology, and now digital computation and algorithmic logic, the substrate and content of this practice has often had little to do with human sense perception or the content of this practice has often had little to do with human sense perception or the optic system. Moreover, with the rise of emphasis on haptic interactions and interactivity, visualizations also often take multisensorial modes. Vision cannot be taken, therefore, as an isolated form of perception, but rather must be understood as inseparable from other senses«  (Halpern, Beautiful Data, 21). The Internet of Things, which has emerged over various stages of development, nonetheless operates as a network that seems just as hard to imagine without the data acquisitions of image sensors as is a contemporary social media platform whose usage spectrum would refrain from positioning effortless image distribution on a massive scale as a central interface and practice option.
Thus in the field of security and surveillance technologies, extensive video intelligence has come into use, which subjects the masses of data acquired through image sensors to automatic analyses in order to render them pragmatically manageable. The typical range of services offered by a provider like SeeQuestor aims first and foremost at increasing efficiency in terms of the economics of time, with an optimized searchability of extensive video data banks. From thousands of hours of video material, which might come for instance from a multicamera system installed by the police in a metro station, scenes that contain certain patterns of movement within the image can be filtered out, as well as a gallery of the faces captured in this material. If one of them is marked as a »person of interest,« SeeQuestor promises to automatically extract all scenes in which the facial recognition algorithms detect the same face.
Similar assistive scanning operations aim to use »attributes of search,« which refers to sorting the galleries of faces according to criteria such as male/female, glasses/no glasses, etc. 754 The boundaries between application areas are fluid. For instance, the California company IntelliVision, which specializes in video analytics and smart cameras, operates its range of products-divided into the market-driven categories of smart security, smart home, and smart retail-with interchangeable or expandable technological solutions. Motion detectors and facial recognition algorithms, which inform concerned homeowners about the activities of burglars and pets or open the garage door upon positive identification via a database comparison, come into contact here with video-analytical processes 754 Cf.
759 If a database comparison determines that the correct face, authorized per data image, is standing in front of the door, it will be opened without the need for an observer to deal with the images continually produced by the home security system. If a smart camera recognizes that a certain suitcase is left unattended on a railway platform for a long period of time, a security alarm will be triggered as a precaution with no human assessment of the situation: »These images are not fixed representations of environments, but rather are temporal markers of visual sensing data within larger data sets  […]. «760 The intelligent evaluation of a video feed is meant to detect persons, objects, situations in real time-in certain surveillance systems even »in anticipation«761-and thus to make constant distinctions within the incoming image data that define what counts as a relevant 759 »To test the Nest Cam IQ, I set it up in the Financial Time's San Francisco bureau a few weeks ago.
It did not eliminate false positives altogether, although it reduced them to a handful a week. When placed in one location, propped up on a tripod, I realised that it was mistaking its own reflection in a meeting room's glass wall for a human head and shoulders. […] Other new features that mark the Nest Cam IQ out over the original device include the ability to zoom in when you are viewing live footage, thanks to that 4K sensor. Its Supersight software means that the video pans and zooms to follow a person around the room, even though the camera itself does not physically move« (Tim Bradshaw, »Review: Nest's Sharp Upgrade to Home Security Camera,« Financial Times, July 14, 2017). 760 Gabrys, Program Earth, 69. 761 »Crucial to these emerging surveillance systems is a radically new politics of anticipatory seeing.
For the overarching feature of the new, militarized, surveillance push, whether its ›targets‹ are located in Manhattan and Baghdad, London and Fallujah, is an attempt to build systems of technological vision in which computer code itself is, along with databases of real or imaged ›targets‹, delegated with the agency of tracking and identifying ›abnormal‹ ›targets‹ from the background ›normality‹ of a homeland or war-zone city. Crucial here is the adaptation of the commercial practices of ›data mining‹ or ›predictive analytics‹ where algorithms are developed to look for patterns in the swathes of captured data, identify or profile behaviours or characteristics deemed to be ›unusual‹ or signal, what should remain in the working memory ready to be displayed, and what is judged to be noise, which can then be erased or transferred to more distant, less accessible storage.
The image circuits, managed quasi-autonomously by non-human actors, that emerge in this process-and the effects of the sorting capacities operationalized by them-have been described by Kristin Veel using the example of Smartgate, a security system installed in Australian airports that works with facial recognition software: [Smartgate] takes an image of a traveller's face and matches it with a digitized image stored in the traveller's ePassport. If the match is successful, the traveller is cleared through customs and immigration control. The technique obviously allows for fast processing of the increasing number of travellers in a globalized world. However, it is also likely to have a significant impact on travelling patterns and on what has been termed ›software-sorted societies‹. […] In the case of software sorting, we are thus seeing an example of calm surveillance technologies, which are undeniably countering the risk of overload inherent in border controls, but which have a huge impact on people's lives and the structure of society.
Taken individually, these devices and technologies are of course vastly less complex-by many orders of magnitude-than human bodyminds; cumulatively, however, and with their capacity to operate almost entirely without interruption and across a vast range of scales, they have already begun to dwarf us in their ability to gather and generate sensory data. 765 At least the suggestion of a qualitatively new range of ambient sensor data streams-which no longer need be output in forms that are viewable, readable to the senses in order to have an effect on human actors766-seems completely the sensory impact of ubicomp as a question of an expansion of the visible to include the imperceptible: a new correlation of media and sensation that occurs beneath the temporal frame definitive of both image and conscious perception.
On one hand, the permeation of the environment by sensor technology involves ever more sophisticated modes of its computerized controllability. On the other, automation leads the microtemporal rhythm and the logistics of the corresponding processes shrouded in the ambient background-which are meant to transport, calculate, master the ubiquitously collected environmental and behavioral data-to a reduction in perceptible, pragmatically accessible margins for intervention. Under these conditions, the redistribution of the image outlined here thus arises above all in connection with the increasing significance of visual actors that capture environmental data through sensors and algorithmically process it, without requiring that visual input be stabilized and displayed in the form of an image. This general ›imagelessness‹ in applications of imaging technology-as has been shown: understood as the effect of a redistribution that defers phenomenal materializations of images in favor of image data that are automatically transported further767-is manifest somewhat more ambivalently in the application context of so-called Augmented Reality (AR).
Turn, and the onscreen view turns with you, tracked (after whose framing is movable, is provisional. Transmission and processing occur instantaneously, in the imperceptible, microtemporal rhythms of ›real time‹ imparted by media technology, but they are also connected to media-logistical storage processes: on the one hand, with databases in which the superimposed information-such as additional virtual objects-are deposited as data sets, and on the other hand with those data returns that are an integral component of delivery protocols, especially in commercial applications. For the AR interface image as well, which ostensibly is fully immersed in its liveness and transparency, there is what John Durham Peters has described as »a boom in data«: »In an online world every act leaves a trace, a record of some sort, and such documentation provides potent data to those who can access and read it.
In The Moderators,781 the algorithmic channeling of the global flow of images encounters the precarious socio-economic distributive reality of human imaging work. »You have to judge data«-with this sentence a trainer standing in front of a group of young Indians (with only one woman among them) summarizes the job requirements of a »content moderator. « As is revealed in the course of the training week, documented here on film, the data in need of judgment include primarily digital images that are distributed in mass quantities via the interfaces of social media platforms, but should neither remain there nor be distributed further. The moderators-to-be are prepared for the script-based purgation of transmission channels ›flooded‹ with image data by means of casuistic exercises. The practical form of the »Human Intelligence Tasks« (HIT) to be performed here turns out to be filter factory work.
In the configuration of large-scale networked systems, this process can cascade. «784 The reason that this repetitive, psychically onerous image work-invisibilized as »microwork« in platform capitalism and accordingly poorly compensated-cannot be delegated, is justified in The Moderators with technological skepticism: »You can't be dependent on automation in this thing. Without humans it's not possible, for now at least. « In the digital present, ubiquitous imaging technologies that can be activated with little effort and transmitted in real time are not only fueling the proliferation and differentiation of sensor networks, but also continue to feed the excesses of image distribution on social media. The visual culture modulated in this way is obviously anything but ›calm. ‹ Even if it is not intended to diffuse completely unfiltered, its economic calculus is still aimed at unbridled growth, at a logic of escalated distribution and replication at will: more users, more profiles, more images, more ads-bigger and bigger data that can be aggregated, distributed further, explored, and managed.
As The Moderators presents paradigmatically, between the production and the usage of distributed digital images-that is, in the primary purview of logistics-there is still an astonishingly large amount of human work that handles image data as images and is targeted at erasing the information they contain. This human image work is also distributed-according to the guidelines of a calculation distributed in the global economy: from acquiring raw materials, which is accomplished for example in Congolese cobalt mines, in order to be able to supply smartphones-and their versatile imaging technology-with cheap lithium-ion batteries,785 to ›manual‹ channel filtering, which responds The fact that »the South of this planet« loses out with regard to »the at least two-hundred-year-old problem of information versus energy, algorithms versus raw materials«789 only continues, with no metaphorical detours, in relation to the distribution of digital image work.
795 This reveals a structural level of the digital divide that cannot be understood solely through questions of network connectivity or exclusion and enables Western media companies to outsource the remaining human data processing work that is still necessary for the lowest wages possible, until, in the vision of a proximate future that is perhaps not at all so free of ambivalence, processes of non-human storage readings ultimately channel the streams autonomously through information technology. The click farms, the hubs of the Global South that employ moderators or »ads quality raters,« where the global emergence of images actually passes through not as an abstract mass of numbers, but in enormous volumes of materialized image forms, in fact also produce data packets that are meant to ›rationalize‹ this filtering process in a logistically different way.
136 Gitelman, Jackson, »Introduction,« 9. David Gugerli summarizes the relational database model outlined by Edgar F. Codd in the 1970s as follows: »All data in a relational database system must be able to be represented by a connected set of clearly designated tables, so-called relations. Within each relation there would be clearly designated columns. Described and developed since 1970, relational databases organize data into separate stables (›relational vari-ables‹) in such a way that new data and new kinds of data can be added or subtracted without making earlier arrangements obsolete. Data are effectively made independent of their organization, and users who perform logical operations on the data are thus ›protected‹ from having to know how the data is organized. 136 This »selective protection of knowledge«137 also determines, as David Gugerli has explained, what is required for data to be searchable, how they can be 134 Cf.
The newly hired visual database programmer sought out technological solutions for how photographic grayscale images could be compared with existing facial composite sketches in the form of graphics,335 how pattern recognition-instrumentalized as pattern Die Tode der Fotografie I, 508. 333 More on this below in Part III. 334 Cf. Gugerli, Suchmaschinen,[52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69] 62. 331 Cf. Lorraine Daston, Peter Galiston, »Das Bild der Objektivität,« in: Peter Geimer (ed. ), Ordnungen der Sichtbarkeit. Fotografie in Wissenschaft, Kunst und Technologie, Frankfurt am Main: Suhrkamp, 2002, 29-99. 332 Sykora, ). 333 As David Gugerli has shown, the actions of the Bundeskriminalamt (BKA, the Federal Criminal Police Office of Germany) under the direction of Horst Herold-whom the news magazine Der Spiegel once dubbed »Commissioner Computer« in view of his »negative dragnet investigation« during the events of the German Autumn 1977-can be understood here as an intermediate stage on the path to more recent procedures of digital dataveillance.
345 Cf. Sante, »A Roomful of Death and Destruction. « 346 On the economy of visibility in this boom, Anthony Hamber notes: »[…] while Web-based digital image databases and catalogues are in some respects rendering ›analogue‹ photographic collections redundant, thereby increasing the threats to their existence, information and communication technology has also acted as a catalyst that has opened up and highlighted the richness and diversity of 19th century photographic collections. Such interest has led to the re-discovery of significant but little known photographic archives ranging from major institutions to individuals« (Anthony Hamber, »Archives, Museums, and Collections of Photographs,« in: John Hannavy (ed. ), Encyclopedia of Nineteenth-Century Photography (Vol. 1), London: Routledge, 2009, 64-69, here: 64).
So in the present time, every private filming of a large-scale event is potentially a document of forensic investigation, a component of security police action. Secret services with unrestricted access to the image data banks of online memory would not even (or don't even) have to ask for permission« (Dietmar Kammerer, »Videoüberwachung,« POP. Kultur und Kritik 9 (Autumn 2016), 16-32, here: 32). 720 Agre, »Surveillance and Capture,« 108-109. 721 Ibid. , 114.
87. 237 This is especially the case for digital archival objects with no analog equivalent. Cf. Annet Dekker (ed. ), Archive 2020-Sustainable Archiving of Born-Digital Cultural Content, Virtueel Platform 2010. In the context of the PEVIAR Project (Persistent Visual Archive), Rudolf Gschwind, the director of the Imaging & Media Lab in Basel, has attempted to work out the degree to which analog material can be utilized for long-term archiving of digital or digitized information: »A permanent medium that is very familiar is photographic film, microfilm, which has a high data density; this could be a commercial consideration. Films today exposed to rapid aging tests show that they can last a few hundred Simon Rothöhler -978-3-8467-6777-1 Downloaded from Brill. com 06/03/2024 05:31:31PM via Open Access.
Gugerli, Suchmaschinen, 71. This principle becomes generalized in web-based database operation, as Martin Warnke writes: »Databases facilitate access by many, they compensate for a disadvantage of the web as it was designed by Sir Tim Berners-Lee. With the appropriate technologies on the part of the large database operators, one can participate without having to know a lot about technology. A content management system ensures that users can make entries by means of web forms, that these find their way into databases and are then displayed on web pages, where they can be seen by others« (Warnke, »Datenbanken als Zitadellen,« 133.
This can give rise to spuriously large proper motions (although the uncertainties are also larger). In many cases, these solutions are rejected by the astrometric solutions (to give the so-called 2p solutions; see  Lindegren et al. 2021  for the definitions), meaning that many galaxies lack parallaxes and proper motions and are therefore not processed by Allosmod. Allosmod models the distribution of the data, and so it provides likelihoods. When combined with the class prior, this gives posterior class probabilities, which are the output from Allosmod. Specmod, in contrast, is a tree-based model that does not strictly provide posterior probabilities. Moreover, its output is L. Delchambre et al. :  Gaia DR3: Apsis III -Non-stellar content and source classification Fig. 1. Dependency of the OA, UGC, QSOC, and TGE modules on the DSC combined probabilities for the selection of the sources to be processed (classprob_dsc_combod, see Section 3 for a definition).
For each module, we provide a synthetic view of their input and output, and the number of sources for which the module produces results in Gaia DR3. In the case of TGE, we provide the number of extinction estimates that were computed in level 9 HEALPixes (see Section 7). Unlike the other modules described here, TGE additionally relies on the General Stellar Parametrizer from Photometry (GSP-Phot) for its source selection and processing, which is described in  Andrae et al. (2022). influenced by the distribution in the training data (see below). However, by using the simple method described in the online documentation we can adjust the outputs from Specmod so that they are analogous to posterior probabilities that incorporate our desired class prior. Allosmod is described in more detail in  Bailer-Jones et al.
Training data DSC is trained empirically, meaning it is trained on a labelled subset of the actual Gaia data it will be applied to (except for binary stars). The classes were defined by selecting sources of each class from an external database and cross-matching them to Gaia DR3. The sources used to construct the training setsand which therefore define the classes-are as follows ( see the online documentation and Bailer-Jones (2021) for more details): -Quasars: 300 000 spectroscopically confirmed quasars from the fourteenth release of the Sloan Digital Sky Survey (SDSS) catalogue, SDSS-DR14  (Pâris et al. 2018 ). -Galaxies: 50 000 spectroscopically confirmed galaxies from SDSS-DR15  (Aguado et al. 2019 ). -Stars: 720 000 objects drawn at random from Gaia DR3 that are not in the quasar or galaxy training sets.
Strictly speaking, this is therefore an 'anonymous' class. But as the vast majority of sources in Gaia are stars, and the majority of those will appear in (spectro)photometry and astrometry as single stars, we call this class 'stars'. -White dwarfs: 40 000 white dwarfs from the Montreal White Dwarf Database 2  that have coordinates and that are not   2. DSC class prior. The first row gives these as fractions relative to the stars, and the second row gives their decimal values summing to 1. 0. This is the class prior for Specmod. The prior for the star class in Allosmod is the sum of star, white dwarf, and physical binary star. quasar galaxy star white dwarf physical binary star ∝ 1/1000 1/5000 1 1/5000 1/100 = 0. 000989 0. 000198 0. 988728 0. 000198 0. 009887 known to be binaries using the flag provided in that table.
However, for both Specmod and Allosmod, we remove this influence to ensure that their priors correspond to our class prior. We are therefore free to choose as many training examples in each class as we need, or can obtain, in order to learn the data distributions. We note that for the common classes between Specmod and Allosmod, that is, quasars, galaxies, and stars, a common sample with complete input data was used to train both modules. In particular, this means that even though Specmod does not require parallaxes and proper motions as inputs, its training sample is restricted to those sources that do have parallaxes and proper motions. This is important because Specmod is also applied to L. Delchambre et al. :  Gaia DR3: Apsis III -Non-stellar content and source classification sources that lack parallaxes and proper motions, meaning that some of its results are on types of objects that are not represented in its training set.