Autodesk provides download and install instructions for individuals and administrators. Your available downloads appear in Autodesk Account. Find your product, select a version, platform, language, and download method. For more information, visit the Autodesk Knowledge Network.
Autodesk 3ds Max 2018 Crack Plus Product Key Full Version Free Download
3ds Max is used to model, animate, and render detailed 3D characters, photorealistic designs, and complex scenes for film and TV, games, and design visualization projects.\r\n"}]},"@type":"Question","name":"Who uses 3ds Max?","acceptedAnswer":["@type":"Answer","text":"3ds Max is used by 3D modelers, animators, and lighting artists for game development, film and TV productions, and design visualization projects.\r\n"],"@type":"Question","name":"3ds Max vs Maya","acceptedAnswer":["@type":"Answer","text":"3ds Max and Maya are used by creative studios around the world for animation, modeling, visual effects, and rendering. Learn when to choose 3ds Max and when to choose Maya.\n"],"@type":"Question","name":"How do I download 3ds Max?","acceptedAnswer":["@type":"Answer","text":"Autodesk provides download and install instructions for individuals and administrators. Your available downloads appear in Autodesk Account. Find your product, select a version, platform, language, and download method. For more information, visit the Autodesk Knowledge Network.\n"],"@type":"Question","name":"Can I install 3ds Max on multiple computers?","acceptedAnswer":["@type":"Answer","text":"With a subscription to 3ds Max software, you can install it on up to 3 computers or other devices. However, only the named user can sign in and use that software on a single computer at any given time. Please refer to the\u202fSoftware License Agreement for more information.\r\n"],"@type":"Question","name":"How do I convert my 3ds Max free trial to a paid subscription?","acceptedAnswer":["@type":"Answer","text":"Launch your trial software and click Subscribe Now on the trial screen or buy 3ds Max here. When buying your subscription, enter the same email address and password combination you used to sign in to your trial. Learn more about\u202fconverting a trial to a paid subscription.\r\n"],"@type":"Question","name":"How much does a 3ds Max subscription cost?","acceptedAnswer":["@type":"Answer","text":"The price of an annual 3ds Max subscription is\u202f\u202fand the price of a monthly 3ds Max subscription is\u202f. The price of a 3-year 3ds Max subscription is\u202f. If you have infrequent users and are interested in a pay-as-you-go option, please visit www.autodesk.com/flex to learn more.\r\n"]],"@type":"FAQPage","@context":" "} Autodesk Company overview Careers Investor relations Newsroom Diversity and belonging
Autodesk Foundation Sustainability Contact us Students and educators Affiliate program Autodesk Research How to buy
Customers with an active subscription of the products listed below are eligible to download and use previous releases* of Autodesk software. To download a previous version, see Download previous versions.
Summary: Due to the availability of new sequencing technologies, we are now increasingly interested in sequencing closely related strains of existing finished genomes. Recently a number of de novo and mapping-based assemblers have been developed to produce high quality draft genomes from new sequencing technology reads. New tools are necessary to take contigs from a draft assembly through to a fully contiguated genome sequence. ABACAS is intended as a tool to rapidly contiguate (align, order, orientate), visualize and design primers to close gaps on shotgun assembled contigs based on a reference sequence. The input to ABACAS is a set of contigs which will be aligned to the reference genome, ordered and orientated, visualized in the ACT comparative browser, and optimal primer sequences are automatically generated. Availability and Implementation: ABACAS is implemented in Perl and is freely available for download from Contact: sa4@sanger.ac.uk PMID:19497936
Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI. Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech signals, first manually (using the Praat software), and then automatically, with an automatic speech recognition (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features. The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process - that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%. The temporal analysis of spontaneous speech
"Objective" methods to monitor physical activity and sedentary patterns in free-living conditions are necessary to further our understanding of their impacts on health. In recent years, many software solutions capable of automatically identifying activity types from portable accelerometry data have been developed, with promising results in controlled conditions, but virtually no reports on field tests. An automatic classification algorithm initially developed using laboratory-acquired data (59 subjects engaging in a set of 24 standardized activities) to discriminate between 8 activity classes (lying, slouching, sitting, standing, walking, running, and cycling) was applied to data collected in the field. Twenty volunteers equipped with a hip-worn triaxial accelerometer performed at their own pace an activity set that included, among others, activities such as walking the streets, running, cycling, and taking the bus. Performances of the laboratory-calibrated classification algorithm were compared with those of an alternative version of the same model including field-collected data in the learning set. Despite good results in laboratory conditions, the performances of the laboratory-calibrated algorithm (assessed by confusion matrices) decreased for several activities when applied to free-living data. Recalibrating the algorithm with data closer to real-life conditions and from an independent group of subjects proved useful, especially for the detection of sedentary behaviors while in transports, thereby improving the detection of overall sitting (sensitivity: laboratory model = 24.9%; recalibrated model = 95.7%). Automatic identification methods should be developed using data acquired in free-living conditions rather than data from standardized laboratory activity sets only, and their limits carefully tested before they are used in field studies. Copyright 2015 the American Physiological Society. 2ff7e9595c