Jews Control U.S.A., Therefore the World – Is That a Good Thing?
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Sexo, Amor Y Cine Por Salvador Sainz Introducción
Sexo, amor y cine por Salvador Sainz Introducción: En la última secuencia de El dormilón (The Sleeper, 1973), Woody Allen, desengañado por la evolución polí tica de una hipotética sociedad futura, le decí a escéptico a Diane Keaton: “Yo sólo creo en el sexo y en la muerte” . Evidentemente la desconcertante evolución social y polí tica de la última década del siglo XX parecen confirmar tal aseveración. Todos los principios é ticos del filósofo alemá n Hegel (1770-1831) que a lo largo de un siglo engendraron movimientos tan dispares como el anarquismo libertario, el comunismo autoritario y el fascismo se han desmoronado como un juego de naipes dejando un importante vací o ideológico que ha sumido en el estupor colectivo a nuestra desorientada generación. Si el siglo XIX fue el siglo de las esperanzas el XX ha sido el de los desengaños. Las creencias má s firmes y má s sólidas se han hundido en su propia rigidez. Por otra parte la serie interminable de crisis económica, polí tica y social de nuestra civilización parece no tener fin. Ante tanta decepción sólo dos principios han permanecido inalterables: el amor y la muerte. Eros y Tá natos, los polos opuestos de un mundo cada vez má s neurótico y vací o. De Tánatos tenemos sobrados ejemplos a cada cual má s siniestro: odio, intolerancia, guerras civiles, nacionalismo exacerbado, xenofobia, racismo, conservadurismo a ultranza, intransigencia, fanatismo… El Sé ptimo Arte ha captado esa evolución social con unas pelí culas cada vez má s violentas, con espectaculares efectos especiales que no nos dejan perder detalle de los aspectos má s sombrí os de nuestro entorno. -
Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos
Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos Rui Hou, Chen Chen, Mubarak Shah Center for Research in Computer Vision (CRCV), University of Central Florida (UCF) [email protected], [email protected], [email protected] Abstract Deep learning has been demonstrated to achieve excel- lent results for image classification and object detection. However, the impact of deep learning on video analysis has been limited due to complexity of video data and lack of an- notations. Previous convolutional neural networks (CNN) based video action detection approaches usually consist of two major steps: frame-level action proposal generation and association of proposals across frames. Also, most of these methods employ two-stream CNN framework to han- dle spatial and temporal feature separately. In this paper, we propose an end-to-end deep network called Tube Con- volutional Neural Network (T-CNN) for action detection in videos. The proposed architecture is a unified deep net- work that is able to recognize and localize action based on 3D convolution features. A video is first divided into equal length clips and next for each clip a set of tube pro- Figure 1: Overview of the proposed Tube Convolutional posals are generated based on 3D Convolutional Network Neural Network (T-CNN). (ConvNet) features. Finally, the tube proposals of differ- ent clips are linked together employing network flow and spatio-temporal action detection is performed using these tracking based approaches [32]. Moreover, in order to cap- linked video proposals. Extensive experiments on several ture both spatial and temporal information of an action, two- video datasets demonstrate the superior performance of T- stream networks (a spatial CNN and a motion CNN) are CNN for classifying and localizing actions in both trimmed used. -
Render for CNN: Viewpoint Estimation in Images Using Cnns Trained with Rendered 3D Model Views
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views Hao Su∗, Charles R. Qi∗, Yangyan Li, Leonidas J. Guibas Stanford University Abstract Object viewpoint estimation from 2D images is an ConvoluƟonal Neural Network essential task in computer vision. However, two issues hinder its progress: scarcity of training data with viewpoint annotations, and a lack of powerful features. Inspired by the growing availability of 3D models, we propose a framework to address both issues by combining render-based image synthesis and CNNs (Convolutional Neural Networks). We believe that 3D models have the potential in generating a large number of images of high variation, which can be well exploited by deep CNN with a high learning capacity. Figure 1. System overview. We synthesize training images by overlaying images rendered from large 3D model collections on Towards this goal, we propose a scalable and overfit- top of real images. CNN is trained to map images to the ground resistant image synthesis pipeline, together with a novel truth object viewpoints. The training data is a combination of CNN specifically tailored for the viewpoint estimation task. real images and synthesized images. The learned CNN is applied Experimentally, we show that the viewpoint estimation from to estimate the viewpoints of objects in real images. Project our pipeline can significantly outperform state-of-the-art homepage is at https://shapenet.cs.stanford.edu/ methods on PASCAL 3D+ benchmark. projects/RenderForCNN 1. Introduction However, this is contrary to the recent finding — features learned by task-specific supervision leads to much better 3D recognition is a cornerstone problem in many vi- task performance [16, 11, 14]. -
Calls, Clicks & Smes
Interactive Local Media A TKG Continuous Advisory Service White Paper #05-03 November 1, 2005 From Reach to Targeting: The Transformation of TV in the Internet Age By Michael Boland Copyright © 2005 The Kelsey Group All Rights Reserved. This published material may not be duplicated or copied in any form without the express prior written consent of The Kelsey Group. Misuse of these materials may expose the infringer to civil or criminal liabilities under Federal Law. The Kelsey Group disclaims any warranty with respect to these materials and, in addition, The Kelsey Group disclaims any liability for direct, indirect or consequential damages that may result from the use of the information and data contained in these materials. 600 Executive Drive • Princeton • NJ • 08540 • (609) 921-7200 • Fax (609) 921-2112 Advisory Services • Research • Consulting • [email protected] Executive Summary The Internet Protocol Television (IPTV) revolution is upon us. The transformation of television that began with the VCR and gained significant momentum with TiVo is now accelerating dramatically with “on-demand” cable, video search and IPTV. Billions of dollars are at stake, and a complex and competitive landscape is also starting to emerge. It includes traditional media companies (News Corp., Viacom), Internet search engines and portals (America Online, Google, Yahoo!), telcos (BellSouth, SBC, Verizon) and the cable companies themselves (Comcast, Cox Communications). Why is all this happening now? While there was a great deal of hype in the 1990s about TV-Internet convergence, or “interactive TV,” previous attempts failed in part because of low broadband penetration, costly storage and inferior video-streaming technologies. -
Browser Security
Transcript of Episode #38 Browser Security Description: Steve and Leo discuss the broad topic of web browser security. They examine the implications of running "client-side" code in the form of interpreted scripting languages such as Java, JavaScript, and VBScript, and also the native object code contained within browser "plug- ins" including Microsoft’s ActiveX. Steve outlines the "zone-based" security model used by IE and explains how he surfs with high security under IE, only "lowering his shields" to a website after he’s had the chance to look around and decide that the site is trustworthy. High quality (64 kbps) mp3 audio file URL: http://media.GRC.com/sn/SN-038.mp3 Quarter size (16 kbps) mp3 audio file URL: http://media.GRC.com/sn/sn-038-lq.mp3 Leo Laporte: Bandwidth for Security Now! is provided by AOL Radio at AOL.com/podcasting. This is Security Now! with Steve Gibson, Episode 38 for May 4, 2006: Browser Security. Security Now! is brought to you by Astaro, makers of the Astaro Security Gateway, on the web at www.astaro.com. So finally the sun is shining in beautiful California. We have summer, or at least spring finally arrived. And, look, Steve Gibson’s wearing a tank top and shorts. No, he’s not. Steve Gibson: Well, it’s dry in Toronto, Leo. You were all chapped; I was all, like, chapped just after one day. Leo: I am. And, you know, they complain in Toronto that it’s too humid because they have the lake effect. But I guess it gets humid some other time because I – chapped is the word, exactly. -
D-Link Announce Alliance to Offer Expanded Entertainment Options for the Digital Home
PRESS RELEASE AMERICA ONLINE AND D-LINK ANNOUNCE ALLIANCE TO OFFER EXPANDED ENTERTAINMENT OPTIONS FOR THE DIGITAL HOME Taipei, January 8, 2004 D-Link, the global leader in the design and development of connectivity and communications technologies for the digital home and the small to medium business markets, and America Online, Inc., the world's leading interactive services company, today announced a new alliance to jointly offer expanded entertainment options for the Digital Home. The companies are collaborating to develop and deliver products and services that will allow broadband consumers to access secure, high-quality video, music and photo content, on-demand, from virtually any room in the home. In the first step of this new relationship, D-Link and AOL are extending access to the #1 Internet broadcasting service, Radio@AOL, beyond the computer and into any room with a TV or stereo. A new line of D-Link Wireless Media Players, including the Wireless Network Player with DVD and the D-Link Wireless Network Player with 5-in-1 Flash Card Reader stream multimedia content such as audio, photos and video across a home network to a home entertainment center for an unparalleled entertainment experience. The D-Link Wireless Media Players will immediately enhance and extend an AOL for Broadband member's experience by allowing them to listen to over 175 CD-quality Radio@AOL stations on any TV or stereo in the home. D-Link and AOL are also working together to extend AOL's popular You've Got Pictures in the same way, allowing AOL for Broadband members to view their personal photos and slideshows, and exclusive photos from AOL's various photo galleries by using the AOL You've Got Pictures service on any TV in the home. -
Finding a Partner Model with Broadcasters at Tunein.Com.” Stanford Casepublisher 204-2011-1
S TANFORD U NIVERSITY 2 0 1 1 - 2 0 4 - 1 S CHOOL OF E NGINEERING C ASEP UBLISHER DRAFT Rev. May 17, 2011 DRAFT COPY DO NOT CITE: R. Chen et al. “Finding a Partner Model with Broadcasters at Tunein.com.” Stanford CasePublisher 204-2011-1. 17 May 2010. DRAFT COPY DO NOT CITE FINDING A PARTNER MODEL WITH BROADCASTERS AT TUNEIN.COM T ABLE OF C ONTENTS Introduction 1. Overview of Online Broadcast Industry 1.1. Industry Trends 1.2. Industry Participants 2. TuneIn Background 2.1. RadioTime 2.2. Tunein Radio 3. Business Model 3.1. Metrics 3.2. Market Reaction 4. Conclusions 5. Discussion 6. Questions 7. Exhibits 8. References 9. Authors Professors Micah Siegel (Stanford University) and Fred Gibbons (Stanford University) guided the de- velopment of this case using the CasePublisher service as the basis for class discussion rather than to il- lustrate either effective or ineffective handling of a business situation. S TANFORD 204-2011-01 Finding a Partner Model at TuneIn.com Introduction !Founded in 2002, TuneIn delivers free Internet radio from all over the world. The application streams radio stations from 50,000 local, international, and Internet stations spanning 140 countries and 55 di"erent languages in over 150 products.1) It provides users with access to live radio content through a variety of media including websites, smart- phones, home entertainment devices, and auto in-dash receivers. 2) The application also al- lows users to pause, rewind, and record live radio programs. Although there are numerous online applications that o"er music streaming, TuneIn di"erentiates itself by focusing on radio programming. -
An Introduction to Internet Radio
NB: This version was updated with new Internet Radio products on 26 October 2005 (see page 8). INTERNET RADIO AnInternet introduction to Radio Franc Kozamernik and Michael Mullane EBU This article – based on an EBU contribution to the WBU-TC Digital Radio Systems Handbook – introduces the concept of Internet Radio (IR) and provides some technical background. It gives examples of IR services now available in different countries and provides some guidance for traditional radio broadcasters on how to adapt to the rapidly changing multimedia environment. Traditionally, audio programmes have been available via dedicated terrestrial networks broad- casting to radio receivers. Typically, they have operated on AM and FM terrestrial platforms but, with the move to digital broadcasting, audio programmes are also available today via DAB, DRM and IBOC (e.g. HD Radio in the USA). However, this paradigm is about to change. Radio programmes are increasingly available not only from terrestrial networks but also from a large variety of satellite, cable and, indeed, telecommunications networks (e.g. fixed telephone lines, wire- less broadband connections and mobile phones). Very often, radio is added to digital television plat- forms (e.g. DVB-S and DVB-T). Radio receivers are no longer only dedicated hi-fi tuners or portable radios with whip aerials, but are now assuming the shape of various multimedia-enabled computer devices (e.g. desktops, notebooks, PDAs, “Internet” radios, etc.). These sea changes in radio technologies impact dramatically on the radio medium itself – the way it is produced, delivered, consumed and paid-for. Radio has become more than just audio – it can now contain associated metadata, synchronized slideshows and even short video clips. -
Precise and High-Throughput Analysis of Maize Cob Geometry Using Deep
Kienbaum et al. Plant Methods (2021) 17:91 https://doi.org/10.1186/s13007-021-00787-6 Plant Methods METHODOLOGY Open Access DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics Lydia Kienbaum1, Miguel Correa Abondano1, Raul Blas2 and Karl Schmid1,3* Abstract Background: Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. Results: Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy ( r = 0.99 ). We inte- grated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identifed key training parameters for efcient iterative model updating. -
Julian E. Zelizer
Julian E. Zelizer Julian E. Zelizer Department of History and Woodrow Wilson School Princeton University 136 Dickinson Hall Princeton, NJ 08544-1174 Phone: 609-258-8846 Cell Phone: 609-751-4147 Department FAX: 609-258-5326 Faculty Appointments Professor of History and Public Affairs, Princeton University, 2007-Present. Faculty Associate, Center for the Study for the Study of Democratic Politics, 2007-Present. Professor of History, Boston University, 2004-2007. Faculty Associate, Center for American Political Studies, Harvard University, 2004-2007. Associate Professor, Department of Public Administration and Policy, State University of New York at Albany, 2002-2004. Joint appointment with the Department of Political Science. Affiliated Faculty, Center of Policy Research, State University of New York at Albany, 2002- 2004. Associate Professor, Department of History, State University of New York at Albany, 1999- 2002. Joint Appointment with Department of Public Administration and Policy, 1999-2002. Assistant Professor, Department of History, State University of New York at Albany, 1996- 1999. Education Ph.D., Department of History, The Johns Hopkins University, 1996. M.A., with four Distinctions, Department of History, The Johns Hopkins University, 1993. B.A., Summa Cum Laude with Highest Honors in History, Brandeis University, 1991. Editorial Positions Co-Editor, Politics and Society in Twentieth Century America book series, Princeton University Press, 2002-Present. Editorial Board, The Journal of Policy History, 2002-Present. Books Jimmy Carter (New York: Times Books, Forthcoming, Fall 2010). 2 Conservatives in Power: The Reagan Years, 1981-1989 (Boston: Bedford, Forthcoming, Fall 2010). Arsenal of Democracy: The Politics of National Security--From World War II to the War on Terrorism (New York: Basic Books, 2010). -
Render for CNN: Viewpoint Estimation in Images Using Cnns Trained with Rendered 3D Model Views
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views Hao Su⇤, Charles R. Qi⇤, Yangyan Li, Leonidas J. Guibas Stanford University Abstract Object viewpoint estimation from 2D images is an ConvoluƟonal Neural Network essential task in computer vision. However, two issues hinder its progress: scarcity of training data with viewpoint annotations, and a lack of powerful features. Inspired by the growing availability of 3D models, we propose a framework to address both issues by combining render-based image synthesis and CNNs (Convolutional Neural Networks). We believe that 3D models have the potential in generating a large number of images of high variation, which can be well exploited by deep CNN with a high learning capacity. Figure 1. System overview. We synthesize training images by overlaying images rendered from large 3D model collections on Towards this goal, we propose a scalable and overfit- top of real images. CNN is trained to map images to the ground resistant image synthesis pipeline, together with a novel truth object viewpoints. The training data is a combination of CNN specifically tailored for the viewpoint estimation task. real images and synthesized images. The learned CNN is applied Experimentally, we show that the viewpoint estimation from to estimate the viewpoints of objects in real images. Project our pipeline can significantly outperform state-of-the-art homepage is at https://shapenet.cs.stanford.edu/ methods on PASCAL 3D+ benchmark. projects/RenderForCNN 1. Introduction However, this is contrary to the recent finding — features learned by task-specific supervision leads to much better 3D recognition is a cornerstone problem in many vi- task performance [16, 11, 14]. -
HTTP: IIS "Propfind" Rem HTTP:IIS:PROPFIND Minor Medium
HTTP: IIS "propfind"HTTP:IIS:PROPFIND RemoteMinor DoS medium CVE-2003-0226 7735 HTTP: IkonboardHTTP:CGI:IKONBOARD-BADCOOKIE IllegalMinor Cookie Languagemedium 7361 HTTP: WindowsHTTP:IIS:NSIISLOG-OF Media CriticalServices NSIISlog.DLLcritical BufferCVE-2003-0349 Overflow 8035 MS-RPC: DCOMMS-RPC:DCOM:EXPLOIT ExploitCritical critical CVE-2003-0352 8205 HTTP: WinHelp32.exeHTTP:STC:WINHELP32-OF2 RemoteMinor Buffermedium Overrun CVE-2002-0823(2) 4857 TROJAN: BackTROJAN:BACKORIFICE:BO2K-CONNECT Orifice 2000Major Client Connectionhigh CVE-1999-0660 1648 HTTP: FrontpageHTTP:FRONTPAGE:FP30REG.DLL-OF fp30reg.dllCritical Overflowcritical CVE-2003-0822 9007 SCAN: IIS EnumerationSCAN:II:IIS-ISAPI-ENUMInfo info P2P: DC: DirectP2P:DC:HUB-LOGIN ConnectInfo Plus Plus Clientinfo Hub Login TROJAN: AOLTROJAN:MISC:AOLADMIN-SRV-RESP Admin ServerMajor Responsehigh CVE-1999-0660 TROJAN: DigitalTROJAN:MISC:ROOTBEER-CLIENT RootbeerMinor Client Connectmedium CVE-1999-0660 HTTP: OfficeHTTP:STC:DL:OFFICEART-PROP Art PropertyMajor Table Bufferhigh OverflowCVE-2009-2528 36650 HTTP: AXIS CommunicationsHTTP:STC:ACTIVEX:AXIS-CAMERAMajor Camerahigh Control (AxisCamControl.ocx)CVE-2008-5260 33408 Unsafe ActiveX Control LDAP: IpswitchLDAP:OVERFLOW:IMAIL-ASN1 IMail LDAPMajor Daemonhigh Remote BufferCVE-2004-0297 Overflow 9682 HTTP: AnyformHTTP:CGI:ANYFORM-SEMICOLON SemicolonMajor high CVE-1999-0066 719 HTTP: Mini HTTP:CGI:W3-MSQL-FILE-DISCLSRSQL w3-msqlMinor File View mediumDisclosure CVE-2000-0012 898 HTTP: IIS MFCHTTP:IIS:MFC-EXT-OF ISAPI FrameworkMajor Overflowhigh (via