Jpred v.4 is the latest version of the JPred Protein Secondary Structure Prediction Server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction, that has existed since 1998 in different versions. In addition to protein secondary structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 134 000 jobs per month and has carried out over 2 million predictions in total for users in 179 countries. == JPred 2 == The static HTML pages of JPred 2 are still available for reference. == JPred 3 == The JPred v3 followed on from previous versions of JPred developed and maintained by James Cuff and Jonathan Barber (see JPred References). This release added new functionality and fixed many bugs. The highlights are: New, friendlier user interface Retrained and optimised version of Jnet (v2) - mean secondary structure prediction accuracy of >81% Batch submission of jobs Better error checking of input sequences/alignments Predictions now (optionally) returned via e-mail Users may provide their own query names for each submission JPred now makes a prediction even when there are no PSI-BLAST hits to the query PS/PDF output now incorporates all the predictions == JPred 4 == The current version of JPred (v4) has the following improvements and updates incorporated: Retrained on the latest UniRef90 and SCOPe/ASTRAL version of Jnet (v2.3.1) - mean secondary structure prediction accuracy of >82%. Upgraded the Web Server to the latest technologies (Bootstrap framework, JavaScript) and updating the web pages – improving the design and usability through implementing responsive technologies. Added RESTful API and mass-submission and results retrieval scripts - resulting in peak throughput above 20,000 predictions per day. Added prediction jobs monitoring tools. Upgraded the results reporting – both, on the web-site, and through the optional email summary reports: improved batch submission, added results summary preview through Jalview results visualization summary in SVG and adding full multiple sequence alignments into the reports. Improved help-pages, incorporating tool-tips, and adding one-page step-by-step tutorials. Sequence residues are categorised or assigned to one of the secondary structure elements, such as alpha-helix, beta-sheet and coiled-coil. Jnet uses two neural networks for its prediction. The first network is fed with a window of 17 residues over each amino acid in the alignment plus a conservation number. It uses a hidden layer of nine nodes and has three output nodes, one for each secondary structure element. The second network is fed with a window of 19 residues (the result of first network) plus the conservation number. It has a hidden layer with nine nodes and has three output nodes.
Deluxe Paint Animation
DeluxePaint Animation is a 1990 graphics editor and animation creation package for MS-DOS, based on Deluxe Paint for the Amiga. It was adapted by Brent Iverson with additional animation features by Steve Shaw and released by Electronic Arts. The program requires VGA graphics, MS-DOS 2.1 or higher, and a mouse. == Features == Listed from the back of the box. Complete selection of painting tools — Draw any shape you want, any way you want. Turn any image into a brush. You can rotate, flip, shear, resize, smear, and shade it. 7 levels of magnification — Paint in magnified mode if you want. Use variable zoom for detailed editing at the pixel level. 3-D perspective — Move and rotate images in full 3-D, automatically. Use color cycling and gradient fills to create great special effects. Stencils — Protect your designs from the slip of the hand or a bad idea. A stencil masks your image so you can paint "behind" and "in front of" it. Use the handy Move Dialog to animate brushes in full 3-D — automatically! Ideal for creating spinning titles for low-cost videos. 37 multi-sized fonts
Nanonetwork
A nanonetwork or nanoscale network is a set of interconnected nanomachines (devices a few hundred nanometers or a few micrometers at most in size) which are able to perform only very simple tasks such as computing, data storing, sensing and actuation. Nanonetworks are expected to expand the capabilities of single nanomachines both in terms of complexity and range of operation by allowing them to coordinate, share and fuse information. Nanonetworks enable new applications of nanotechnology in the biomedical field, environmental research, military technology and industrial and consumer goods applications. Nanoscale communication is defined in IEEE P1906.1. == Communication approaches == Classical communication paradigms need to be revised for the nanoscale. The two main alternatives for communication in the nanoscale are based either on electromagnetic communication or on molecular communication. === Electromagnetic === This is defined as the transmission and reception of electromagnetic radiation from components based on novel nanomaterials. Recent advancements in carbon and molecular electronics have opened the door to a new generation of electronic nanoscale components such as nanobatteries, nanoscale energy harvesting systems, nano-memories, logical circuitry in the nanoscale and even nano-antennas. From a communication perspective, the unique properties observed in nanomaterials will decide on the specific bandwidths for emission of electromagnetic radiation, the time lag of the emission, or the magnitude of the emitted power for a given input energy, amongst others. For the time being, two main alternatives for electromagnetic communication in the nanoscale have been envisioned. First, it has been experimentally demonstrated that is possible to receive and demodulate an electromagnetic wave by means of a nanoradio, i.e., an electromechanically resonating carbon nanotube which is able to decode an amplitude or frequency modulated wave. Second, graphene-based nano-antennas have been analyzed as potential electromagnetic radiators in the terahertz band. === Molecular === Molecular communication is defined as the transmission and reception of information by means of molecules. The different molecular communication techniques can be classified according to the type of molecule propagation in walkaway-based, flow-based or diffusion-based communication. In walkway-based molecular communication, the molecules propagate through pre-defined pathways by using carrier substances, such as molecular motors. This type of molecular communication can also be achieved by using E. coli bacteria as chemotaxis. In flow-based molecular communication, the molecules propagate through diffusion in a fluidic medium whose flow and turbulence are guided and predictable. The hormonal communication through blood streams inside the human body is an example of this type of propagation. The flow-based propagation can also be realized by using carrier entities whose motion can be constrained on the average along specific paths, despite showing a random component. A good example of this case is given by pheromonal long range molecular communications. In diffusion-based molecular communication, the molecules propagate through spontaneous diffusion in a fluidic medium. In this case, the molecules can be subject solely to the laws of diffusion or can also be affected by non-predictable turbulence present in the fluidic medium. Pheromonal communication, when pheromones are released into a fluidic medium, such as air or water, is an example of diffusion-based architecture. Other examples of this kind of transport include calcium signaling among cells, as well as quorum sensing among bacteria. Based on the macroscopic theory of ideal (free) diffusion the impulse response of a unicast molecular communication channel was reported in a paper that identified that the impulse response of the ideal diffusion based molecular communication channel experiences temporal spreading. Such temporal spreading has a deep impact in the performance of the system, for example in creating the intersymbol interference (ISI) at the receiving nanomachine. In order to detect the concentration-encoded molecular signal two detection methods named sampling-based detection (SD) and energy-based detection (ED) have been proposed. While the SD approach is based on the concentration amplitude of only one sample taken at a suitable time instant during the symbol duration, the ED approach is based on the total accumulated number of molecules received during the entire symbol duration. In order to reduce the impact of ISI a controlled pulse-width based molecular communication scheme has been analysed. The work presented in showed that it is possible to realize multilevel amplitude modulation based on ideal diffusion. A comprehensive study of pulse-based binary and sinus-based, concentration-encoded molecular communication system have also been investigated.
CloudLibrary
CloudLibrary (stylized as "cloudLibrary") is a cloud-based software system through which libraries lend electronic books; it is also the name of the app that users download to access the e-books. CloudLibrary was created in 2011 by 3M as part of its library systems unit as a competitor to OverDrive, Inc.; in 2015 3M sold the North American part of that unit to Bibliotheca Group GmbH, a company founded in 2011 that was funded by One Equity Partners Capital Advisors, a division of JP Morgan Chase. By 2019, Bibliotecha had tried, unsuccessfully, to negotiate with Amazon to add Kindle-ebook compatibility to cloudLibrary - something that, as of then, Amazon had only made available to Overdrive. In that year, cloudLibrary, along with hoopla offered by Midwest Tape, ODILO, and Baker & Taylor’s Axis 360, were the main competitors to the Overdrive and Libby apps offered by OverDrive, Inc. in the library e-book market. In April 2024, Bibliotheca sold cloudLibrary to the nonprofit cooperative OCLC. By that time, cloudLibrary was used by around 500 libraries in around 20 countries in around 50 languages, and was used to lend audiobooks, digital magazines, newspapers, and comics, and streaming media, along with e-books.
CU-RTC-WEB
Customizable, Ubiquitous Real Time Communication over the Web is an API definition being drafted by Bernard Aboba at Microsoft. It is a competing standard to WebRTC, which drafted by a World Wide Web Consortium working group since May 2011. As of 2024, CU-RTC-WEB is still in the drafting phase, with ongoing discussions and contributions from various stakeholders in the tech community. Bernard Aboba, who serves as a co-chair of the W3C WebRTC Working Group, is actively involved in both CU-RTC-WEB and WebRTC, indicating a commitment to advancing real-time communication standards across platforms.
Machine translation software usability
The sections below give objective criteria for evaluating the usability of machine translation software output. == Stationarity or canonical form == Do repeated translations converge on a single expression in both languages? I.e. does the translation method show stationarity or produce a canonical form? Does the translation become stationary without losing the original meaning? This metric has been criticized as not being well correlated with BLEU (BiLingual Evaluation Understudy) scores. == Adaptive to colloquialism, argot or slang == Is the system adaptive to colloquialism, argot or slang? The French language has many rules for creating words in the speech and writing of popular culture. Two such rules are: (a) The reverse spelling of words such as femme to meuf. (This is called verlan.) (b) The attachment of the suffix -ard to a noun or verb to form a proper noun. For example, the noun faluche means "student hat". The word faluchard formed from faluche colloquially can mean, depending on context, "a group of students", "a gathering of students" and "behavior typical of a student". The Google translator as of 28 December 2006 doesn't derive the constructed words as for example from rule (b), as shown here: Il y a une chorale falucharde mercredi, venez nombreux, les faluchards chantent des paillardes! ==> There is a choral society falucharde Wednesday, come many, the faluchards sing loose-living women! French argot has three levels of usage: familier or friendly, acceptable among friends, family and peers but not at work grossier or swear words, acceptable among friends and peers but not at work or in family verlan or ghetto slang, acceptable among lower classes but not among middle or upper classes The United States National Institute of Standards and Technology conducts annual evaluations [1] Archived 2009-03-22 at the Wayback Machine of machine translation systems based on the BLEU-4 criterion [2]. A combined method called IQmt which incorporates BLEU and additional metrics NIST, GTM, ROUGE and METEOR has been implemented by Gimenez and Amigo [3]. == Well-formed output == Is the output grammatical or well-formed in the target language? Using an interlingua should be helpful in this regard, because with a fixed interlingua one should be able to write a grammatical mapping to the target language from the interlingua. Consider the following Arabic language input and English language translation result from the Google translator as of 27 December 2006 [4]. This Google translator output doesn't parse using a reasonable English grammar: وعن حوادث التدافع عند شعيرة رمي الجمرات -التي كثيرا ما يسقط فيها العديد من الضحايا- أشار الأمير نايف إلى إدخال "تحسينات كثيرة في جسر الجمرات ستمنع بإذن الله حدوث أي تزاحم". ==> And incidents at the push Carbuncles-throwing ritual, which often fall where many of the victims - Prince Nayef pointed to the introduction of "many improvements in bridge Carbuncles God would stop the occurrence of any competing." == Semantics preservation == Do repeated re-translations preserve the semantics of the original sentence? For example, consider the following English input passed multiple times into and out of French using the Google translator as of 27 December 2006: Better a day earlier than a day late. ==> Améliorer un jour plus tôt qu'un jour tard. ==> To improve one day earlier than a day late. ==> Pour améliorer un jour plus tôt qu'un jour tard. ==> To improve one day earlier than a day late. As noted above and in, this kind of round-trip translation is a very unreliable method of evaluation. == Trustworthiness and security == An interesting peculiarity of Google Translate as of 24 January 2008 (corrected as of 25 January 2008) is the following result when translating from English to Spanish, which shows an embedded joke in the English-Spanish dictionary which has some added poignancy given recent events: Heath Ledger is dead ==> Tom Cruise está muerto This raises the issue of trustworthiness when relying on a machine translation system embedded in a Life-critical system in which the translation system has input to a Safety Critical Decision Making process. Conjointly it raises the issue of whether in a given use the software of the machine translation system is safe from hackers. It is not known whether this feature of Google Translate was the result of a joke/hack or perhaps an unintended consequence of the use of a method such as statistical machine translation. Reporters from CNET Networks asked Google for an explanation on January 24, 2008; Google said only that it was an "internal issue with Google Translate". The mistranslation was the subject of much hilarity and speculation on the Internet. If it is an unintended consequence of the use of a method such as statistical machine translation, and not a joke/hack, then this event is a demonstration of a potential source of critical unreliability in the statistical machine translation method. In human translations, in particular on the part of interpreters, selectivity on the part of the translator in performing a translation is often commented on when one of the two parties being served by the interpreter knows both languages. This leads to the issue of whether a particular translation could be considered verifiable. In this case, a converging round-trip translation would be a kind of verification.
The Holocaust and social media
The representation of the Holocaust on social media has been a subject of scholarly inquiry and media attention. == Selfies at Holocaust memorial sites == Some visitors take selfies at Holocaust memorials, which has been the subject of controversy. In 2018, Rhian Sugden, a British model, received criticism after posting a selfie at the Memorial to the Murdered Jews of Europe in Berlin with the caption "ET phone home". She later removed the caption, but defended taking the photograph. Other celebrities have also been criticised for photographs at the Berlin memorial, including Indian actress Priyanka Chopra and US politician Pete Buttigieg, whose husband posted a photograph of him at the memorial on a personal social media account. The Israeli artist and satirist Shahak Shapira set up the website yolocaust.de in 2017 to expose people who take inappropriate selfies at the Holocaust memorial in Berlin. Shapira went through thousands of selfies posted to social media sites such as Facebook, Instagram, Tinder, and Grindr, choosing the twelve that he found most offensive. When the images were moused over, the website replaces the memorial backdrop with black and white images of Nazi victims. "Yolocaust" is a portmanteau of "Holocaust" and YOLO, an acronym for "you only live once". The website went viral, receiving 1.2 million views in the first 24 hours after its launch. Shapira honored requests to take down all of the photographs, which he had used without permission, and the website remains with only a textual documentation of the project. In an analysis of comments by Internet users on the project, Christoph Bareither estimated that 75% were positive. However, the memorial's architect, Peter Eisenman, criticized the website. In his 2018 book Postcards from Auschwitz, Grinnell professor Daniel P. Reynolds defends the practice of selfie-taking at Holocaust sites. In 2019, the Auschwitz-Birkenau State Museum requested that visitors not take inappropriate selfies, although the museum's staff acknowledged that other visitors take selfies in a thoughtful and respectful manner, which they did not criticize. In an academic paper, Gemma Commane and Rebekah Potton analyze the use of Instagram to share tourist photographs at Holocaust sites and conclude that "Instagram encourages conversation and empathy, keeping the Holocaust visible in youth discourses". According to their analysis, most images are tagged with respectful hashtags such as #tragic, #remembrance, and #sadness. The Auschwitz museum has an official Instagram account, auschwitzmemorial, which it uses to share selected appropriate Instagram posts. However, the image feed for the hashtag "Auschwitz" includes potentially offensive images such as an image of "Nazi Vs. Jews #beerpong". This image, according to the authors, expresses "mockery and contempt" for Holocaust victims. They also document offensive memes using images of Holocaust atrocities and shared on Instagram. Some social media users post in order to criticize what they see as inappropriate behavior at Holocaust sites, with one commenting, "Taking photos posing next to razor wire, selfies with victim's hair in the background, and even group shots in front of the crematoria had to be seen to be believed." == Assessment of tourism == Social media posts have been used by researchers to analyze the phenomenon of Holocaust-related tourism. == Social media groups == People have created groups on Facebook to discuss issues related to the Holocaust. One paper analyses two such groups, "The Holocaust and My Family" and "The Descendants of the Victims and Survivors of the Holocaust" in which people engage in collective trauma processing. == Eva.stories == In 2019, Israeli high-tech entrepreneur Mati Kochavi created a fictitious Instagram account for Eva Heyman, a Hungarian-Jewish girl who was murdered in Auschwitz concentration camp. The project met with mixed reception. Israeli prime minister Benjamin Netanyahu praised the project, saying that it "exposes the immense tragedy of our people through the story of one girl". == Holocaust denial == The issue of Holocaust denial on social media has also attracted attention. In October 2020, Facebook reversed its policy and banned Holocaust denial from the platform. Founder Mark Zuckerberg had previously argued that such content should not be banned on freedom of speech grounds.