text:
Published on Digi-Sign, The Certificate Corporation (https://www.digi-sign.com)
Front page of Digi-Sign, The Certificate Corporation [1] [2]
Books at Digi-Sign, The Certificate Corporation.
Identity & Authentication [3]
Identity & Validation Layers [4]
Proving Identity & Authentication [4]
Validation Issues [5]
Simple Questions about Online Security [6]
The Triple Check Approach [7]
The Business Case [8]
Digi-CA™ Validations [9]
Countries [10]
Digital Certificates & Signatures : Digi-Sign.com [11]
The Basics [12]
Replacing an Existing CA [13]
Online Security Questions [14]
Online Security & Digital Certificates [15]
Why Digital Certificates are needed [16]
How Digital Certificates Work [17]
Digital Certificate Policy [18]
Three Types of Digital Certificate [19]
Digi-Sign Digital Certificates [20]
Two Factor Authentication [21]
Certificate Authority [CA] Systems [22]
Understanding Online Security [23]
Trusted Root Restriction [24]
CA Systems [25]
Shared CA [26]
Selecting the Correct CA for You [27]
Selecting Your CA System [28]
Certificate Policy [29]
Certificate Authority Types [30]
Guidelines [31]
Key Management [32]
Replication [33]
Digi-ID™ Options [34]
Digi-ID™ Benefits [35]
Considerations [36]
Digi-ID™ Delivery [37]
Digi-ID™ Storage [38]
Digi-ID™ Storage Types [39]
Storage Considerations [40]
Digi-CA™ Service In Detail [41]
Competing Technology [42]
Server [43]
Server Requirements [44]
Documenting [45]
Certificate Authority [CA] Software [47]
Managed Certificate Authority [CA] [48]
Shared Certificate Authority [CA] [49]
Traditional CAs [50]
Microsoft® CA [51]
The Modern CA - Digi-CA™ [52]
SSL Security Guide [53]
What is SSL? [54]
Displaying SSL [55]
For Apache [56]
Certificate SSL [59]
Step by Step instructions [60]
Online Certificate [61]
Apply online Certificate II [62]
Certificate Classes [63]
Extended Validation [64]
Digi-ISP™ Service [65]
Service [66]
Using SSL [67]
For IIS [68]
Benefits Displaying SSL [71]
Step by Step [73]
Apply online [74]
SSL Certificate Installation [75]
Reasons for Choosing Digi-SSL™ Service [76]
Upgrading/Migrating [77]
Introducing Digi-SSL™ [78]
Digi-SSL™ - Site Seal [79]
ARP Network : Digi-Sign.com [80]
The Benefits of ARP Membership [81]
The Strength of the ARP Network : Digital Certificate : Certificate Authority : PKI : Public Key Cryptography : Digi-Sign.com [82]
List of Current ARP Members [83]
Partners [84]
Resellers [85]
Affiliates [86]
Overview of ARP Solutions & Services [87]
How to Get Digi-SSL™ Certificates [88]
The Complete ARP Network Program [89]
ARP Membership Rules [90]
ARP Network Online Demonstrations : Two Factor Authentication : Digital Certificate : Digi-Sign.com [91]
ARP Network Dedicated Support & Help [92]
ARP Network Dedicated Help & Support [93]
Placing ARP Orders using Shopping Cart [94]
Shopping Cart Discount Code [95]
About the ARP Extranet [96]
ARP Ordering Instructions [97]
ARP Online Accounts & Statements [98]
Example of a Dedicated ARP Section [99]
Demonstrations : Two Factor Authentication : Digital Certificate : Digi-Sign.com [100]
Digi-Access™ Demonstration Instructions [101]
Introducing Digi-Access™ for Online Banking [102]
Digi-Access™ Online Banking Demonstration Instructions [103]
Digi-CA™ Manuals [104]
Introducing Digi-CA™ [105]
Project Approach [106]
'Fast Track' Approach [107]
Technical Overview [108]
Algorithms & Compatibility [109]
Performance Data [110]
Using Digi-IDs™ [111]
Root Key Ceremony [112]
Processes [113]
Applying for Digital Certificates [114]
Digital Signature Overview [115]
Issuing Digital Signatures [116]
Sample Enrolment Forms [117]
Issuing Options [118]
Enrolment Policy [119]
Sample Activation Forms [120]
Viewing Your Digital Signature [121]
Deleting Your Digital Signature [122]
Revoking Digital Signatures [123]
S/MIME Technology [124]
Flexibility & Capability [125]
Replace an Existing CA [126]
System Design [127]
Information Database [128]
Preparation & Configuration [129]
Service Setup [130]
Server Setup [131]
Server Xs Requirements [132]
Server Xp Requirements [133]
Server Xg Requirements [134]
Sample Project [135]
System Installation [136]
Customisation [137]
Interface Customization [138]
Installation Guidelines [139]
Single Server Digi-CA™ Server Xs Installation Diagram [140]
Single Server Digi-CA™ Server Xs Technical Diagram [141]
Single Server Digi-CA™ Server Xs Installation Schedule [142]
Single Server Digi-CA™ Sever Xs & HSM Installation Diagram [143]
Single Server Digi-CA™ Server Xs & HSM Technical Diagram [144]
Dual Server Digi-CA™ Server Xp Installation Diagram [145]
Dual Server Digi-CA™ Server Xp Technical Diagram [146]
Dual Server Digi-CA™ Server Xp Installation Schedule [147]
Dual Server Digi-CA™ Server Xp & HSM Technical Diagram [148]
Dual Server Digi-CA™ Server Xp HSM Installation Schedule [149]
Multi-Server Digi-CA™ Server Xg Full Redundancy Technical Diagram [150]
Multi-Server Digi-CA™ Server Xg Full Redundancy Installation Schedule [151]
Delivery by an ARP Member : Public Key Cryptography : PKI : Certification Authority : Digi-Sign.com [152]
Buyer's Guide [153]
Introduction [154]
Benefits [155]
Choosing [156]
Versions [157]
Service [158]
Server [159]
Server Deployment [160]
Shared [161]
Summary [162]
System [163]
Cryptographic Service [164]
Entity Registration [165]
System Components [166]
Unique Service [167]
Automated Service [168]
Available Versions [169]
RAMC Automation [170]
TimeStamping [171]
Gateway [172]
Recovery [173]
Key Ceremony [175]
Deployment Guide [176]
Deployment Introduction [178]
services [179]
Software [180]
Modules [181]
Models [182]
Service Modules : Public Key Cryptography : Digi-Sign.com [184]
Configuration [185]
Management [186]
CRL Dissemination [188]
Time Stamp [189]
Online Certificate [190]
CA Application [191]
Standalone [192]
Admininistrators Manual [193]
System Details [195]
Core Principals [196]
Selection [197]
Class Distinction [198]
Digi-CAST™ [199]
Digi-CA™ Server Xs & Xp [200]
Digi-CA™ Server Xg [201]
Digi-CA™ System Components [202]
Installations Time Lines [203]
Certificate Authority [CA] Implementation Plan [204]
Existing CA Transition Plan [205]
Certificate Authority [CA] Implementation Schedule [207]
Management Consoles & Interfaces [208]
Digi-CA™ Technical Specifications [209]
Digi-CA™ System Architecture [210]
Digi-CA™ System Modules [211]
Preparing for Digi-CA™ [213]
Selecting Your Digi-CA™ [214]
Reasons for Choosing Digi-CA : X509 Certificate : Digi-Sign.com [215]
Customising Your Digi-CA™ [216]
Digi-CA™ RA/API [217]
The Digi-CA™ Certificate Enrolment API [218]
Installing Dig-CA™ Server [219]
Digi-CA™ Server Xs Requirements [220]
Digi-CA™ Server Xp Requirements [221]
Digi-CA™ Server Xg Requirements [222]
Server Xg Physical Environment [223]
Digi-CA™ Installation Guide [224]
Digi-CA™ Hierarchy [225]
Server Preparation [226]
Sendmail 8.x SMTP server [227]
cURL 7.16.1 [228]
GNU mHash 0.9.9 [229]
MySQL 5.0.37 SQL [230]
PHP 5.2.3 [231]
PKI Introduction [232]
Guide [233]
Approach [234]
Digital Certificates [237]
Replacing CA [238]
Agreement [239]
Application [240]
Authentication [241]
CA Software [242]
CA-systems [244]
Certificate Authority [245]
Certificate Policy [246]
Certificate Revocation [247]
Certificate Storage [248]
Certificate Uses [249]
Certificates [250]
Code Signing [251]
Communication [252]
Delivery [255]
Demonstration [256]
Design Principals [257]
Digi-Access™ [258]
Digi-CA™ Service [259]
Dual Server [260]
End Entity Certificate [261]
Examples [262]
Fast Track [263]
Groups [264]
Key Management [265]
Key Management Options [266]
Language Capabilities [267]
Mail Service [268]
Managed-CA [269]
Methodology [270]
Multi Server [271]
Online Certificates [272]
Policy Documentation [274]
Public keys [275]
Questions [276]
Root Key [277]
Root Certificate : Digi-Sign.com [278]
Secure Socket [279]
Selection Guidelines [280]
Server Overview [281]
Service Approach [282]
Service Overview [283]
Signature [284]
Single Server [285]
Standards [286]
Steps [287]
System Consoles [288]
Technology [289]
Trust Management [290]
Types of Certificate [291]
User Storage [292]
Validations [293]
Why Digital Certificates [294]
Training Manuals [295]
Digi-CA™ Administration Management Console [CA AMC] [296]
Digi-CA™ RA Management Console [RA MC] [297]
Digi-CA™ Limited RA [LRA] - Control Centre [298]
Digi-CA™ RA Registration Service [RA RS] [299]
Two Factor Authentication [300]
Digi-Access™ Videos [301]
View All Versions [302]
Buy Online Now [303]
On Line Quotation [303]
Digi-Access™ Demos [304]
Demo' Instructions [305]
View Buyer's Guide [306]
View User's Guide [307]
Digi-Access™ Support [308]
Fast, Simple Installation [309]
Compliance / Standards [310]
'Try Before You Buy' [311]
Related Considerations [312]
Digi-SSL™ [313]
Cell-OTP™ [314]
Flash Presentation [315]
Why Choose Digi-CA™? [215]
Download the Manual [318]
Digi-CA™ Buyers Guide [319]
Digi-CA™ Deployment [320]
Replace Your System [126]
Consultancy [322]
TTM™ Service [323]
ID Verification [324]
CA Root Signing [325]
Digi-Card™ [326]
Digi-Token™ [327]
OCSP Gateway [328]
Roaming Solution [330]
ICAO MRTD [332]
Digi-ID™ [334]
Digi-Seal™ [335]
Digi-Mail™ [336]
Digi-ID™ Videos [337]
EU Qualified Signatures [339]
Digi-ID™ & Digi-Seal™ [335]
Digi-Seal™ Service [340]
Consulting Services [322]
How to Issue a Digi-ID [116]
Try Before You Buy [341]
Digi-Mail™ Videos [342]
Demo Digi-Mail™ [345]
Instant Advice [346]
Read the Help Pages [348]
Compliance [310]
Download Case Studies [349]
Download Admin' Manual [193]
Support [348]
Digi-Seal™ Videos [337]
How Digi-Seal™ Works [352]
Online Live Demo' [353]
Using Digi-Seal™ [354]
Digi-Seal™ Keystore [355]
Browsers Supported [356]
Why Choose Digi-SSL™? [76]
Consultant Advice [347]
Selection Advice [346]
SSL Certificates [357]
2 Factor Authentication [308]
Secure Email [348]
Knowledgebase [358]
Source URL: https://www.digi-sign.com/sitemap
[1] https://www.digi-sign.com/
[2] https://www.digi-sign.com/rss.xml
[3] https://www.digi-sign.com/identity%20authentication
[4] https://www.digi-sign.com/identity%20authentication/proof
[5] https://www.digi-sign.com/digi-ca/total%20trust%20management/validations
[6] https://www.digi-sign.com/identity%20authentication/examples
[7] https://www.digi-sign.com/identity%20authentication/validations
[8] https://www.digi-sign.com/aacd/validations/business%20case
[9] https://www.digi-sign.com/digi-ca/validations
[10] https://www.digi-sign.com/idv/countries
[11] https://www.digi-sign.com/digital%20certificate
[12] https://www.digi-sign.com/digital%20certificate/introduction
[13] https://www.digi-sign.com/digital%20certificate/migrating%20certificate%20authority
[14] https://www.digi-sign.com/digital%20certificate/online%20security
[15] https://www.digi-sign.com/digital%20certificate/security%20digital%20certificate
[16] https://www.digi-sign.com/digital%20certificate/uses
[17] https://www.digi-sign.com/digital%20certificate/how%20digital%20certificates%20work
[18] https://www.digi-sign.com/digital%20certificate/digital%20certificate%20policy
[19] https://www.digi-sign.com/digital%20certificate/digital%20certificate%20types
[20] https://www.digi-sign.com/digital%20certificate/digi-sign%20digital%20certificate
[21] https://www.digi-sign.com/two%20factor%20authentication
[22] https://www.digi-sign.com/certificate%20authority
[23] https://www.digi-sign.com/certificate%20authority/introduction
[24] https://www.digi-sign.com/certificate%20authority/trusted%20root%20restriction
[25] https://www.digi-sign.com/certificate%20authority/certificate%20authority%20systems
[26] https://www.digi-sign.com/certificate%20authority/shared%20ca
[27] https://www.digi-sign.com/certificate%20authority/selecting%20certificate%20authority
[28] https://www.digi-sign.com/ca%20selection
[29] https://www.digi-sign.com/digi-ca/introduction1
[30] https://www.digi-sign.com/digi-ca/certificate%20authority%20types
[31] https://www.digi-sign.com/digi-ca/certificate%20authority%20selection%20guidelines
[32] https://www.digi-sign.com/digi-ca/key%20management
[33] https://www.digi-sign.com/digi-ca/database%20replication
[34] https://www.digi-sign.com/digi-ca/key%20management%20options
[35] https://www.digi-sign.com/digi-ca/key%20management%20benefits
[36] https://www.digi-sign.com/digi-ca/key%20management%20considerations
[37] https://www.digi-sign.com/digi-ca/digital%20certificate%20delivery
[38] https://www.digi-sign.com/digi-ca/digital%20certificate%20storage
[39] https://www.digi-sign.com/digi-ca/digital%20certificate%20storage%20types
[40] https://www.digi-sign.com/digi-ca/digital%20certificate%20storage%20considerations
[41] https://www.digi-sign.com/digi-ca/certificate%20authority%20service%20detail
[42] https://www.digi-sign.com/digi-ca/certificate%20authority%20competing%20technology
[43] https://www.digi-sign.com/digi-ca/certificate%20authority%20server%20detail
[44] https://www.digi-sign.com/digi-ca/certificate%20authority%20server%20requirements
[45] https://www.digi-sign.com/digi-ca/certificate%20policy%20documentation
[46] https://www.digi-sign.com/digi-ca/certificate%20policy%20control
[47] https://www.digi-sign.com/certificate%20authority/software
[48] https://www.digi-sign.com/certificate%20authority/managed
[49] https://www.digi-sign.com/certificate%20authority/shared
[50] https://www.digi-sign.com/certificate%20authority/traditional%20ca
[51] https://www.digi-sign.com/certificate%20authority/microsoft%20ca
[52] https://www.digi-sign.com/certificate%20authority/modern%20ca
[53] https://www.digi-sign.com/ssl
[54] https://www.digi-sign.com/digi-ssl/security%20guide/what%20ssl1
[55] https://www.digi-sign.com/digi-ssl/security%20guide/displaying%20ssl1
[56] https://www.digi-sign.com/digi-ssl/apache%20guide%20
[57] https://www.digi-sign.com/digi-ssl/apache%20guide/what%20ssl3
[58] https://www.digi-sign.com/digi-ssl/apache/displaying%20ssl5
[59] https://www.digi-sign.com/digi-ssl/apache/certificate%20ssl
[60] https://www.digi-sign.com/digi-ssl/apache/step%20instructions%20apache1
[61] https://www.digi-sign.com/digi-ssl/apache/apply%20online%20certificate1
[63] https://www.digi-sign.com/digi-ssl/certificate%20classes
[64] https://www.digi-sign.com/digi-ssl/extended%20ssl
[65] https://www.digi-sign.com/digi-ssl/isp%20services
[66] https://www.digi-sign.com/digi-ssl/service
[67] https://www.digi-sign.com/digi-ssl/using%20ssl
[68] https://www.digi-sign.com/digi-ssl/iis%20guide
[69] https://www.digi-sign.com/digi-ssl/iis%20guide/what%20ssl2
[70] https://www.digi-sign.com/digi-ssl/iis%20guide/displaying%20ssl3
[72] https://www.digi-sign.com/digi-ssl/iis%20guide/step%20instructions%20Microsoft%20iss5x%201
[74] https://www.digi-sign.com/digi-ssl/iis%20guide/apply%20online%20digi-ssl1
[75] https://www.digi-sign.com/digi-ssl/iis%20guide/ssl%20certificate%20installation
[76] https://www.digi-sign.com/digi-ssl/benefits
[77] https://www.digi-sign.com/digi-ssl/migration
[78] https://www.digi-sign.com/digi-ssl/introduction
[79] https://www.digi-sign.com/digi-ssl/site%20seal
[80] https://www.digi-sign.com/arp
[81] https://www.digi-sign.com/arp/benefits
[82] https://www.digi-sign.com/arp/reasons
[83] https://www.digi-sign.com/arp/members
[84] https://www.digi-sign.com/arp/members/partners
[85] https://www.digi-sign.com/arp/members/resellers
[86] https://www.digi-sign.com/arp/members/affiliates
[87] https://www.digi-sign.com/arp/solutions
[88] https://www.digi-sign.com/arp/digi-ssl
[89] https://www.digi-sign.com/arp/complete%20program
[90] https://www.digi-sign.com/arp/rules
[91] https://www.digi-sign.com/arp/demos
[92] https://www.digi-sign.com/arp/support
[93] https://www.digi-sign.com/arp/help
[94] https://www.digi-sign.com/arp/website%20ordering
[95] https://www.digi-sign.com/products/discounts
[96] https://www.digi-sign.com/arp/extranet
[97] https://www.digi-sign.com/arp/ordering
[98] https://www.digi-sign.com/arp/accounts
[99] https://www.digi-sign.com/arp/intro%202x
[100] https://www.digi-sign.com/demos
[101] https://www.digi-sign.com/demos/instructions/digi-access
[102] https://www.digi-sign.com/demos/introductions/online%20banking
[103] https://www.digi-sign.com/demos/instructions/online%20banking
[104] https://www.digi-sign.com/digi-ca/manuals
[105] https://www.digi-sign.com/digi-ca/introduction
[106] https://www.digi-sign.com/digi-ca/introduction/project%20approach
[107] https://www.digi-sign.com/digi-ca/introduction/fast-track
[108] https://www.digi-sign.com/digi-ca/introduction/technical%20information
[109] https://www.digi-sign.com/digi-ca/introduction/compatibility
[110] https://www.digi-sign.com/digi-ca/introduction/performance
[111] https://www.digi-sign.com/digi-ca/introduction/two%20factor%20authentication
[112] https://www.digi-sign.com/digi-ca/introduction/root-key%20ceremony
[113] https://www.digi-sign.com/digi-ca/introduction/processes
[114] https://www.digi-sign.com/digi-ca/introduction/application
[115] https://www.digi-sign.com/digi-ca/introduction/issuing
[116] https://www.digi-sign.com/digi-id/distribute
[117] https://www.digi-sign.com/digi-id/distribute/enrol
[118] https://www.digi-sign.com/digi-id/distribute/invite
[119] https://www.digi-sign.com/digi-id/distribute/policy
[120] https://www.digi-sign.com/digi-id/distribute/activate
[121] https://www.digi-sign.com/digi-id/view
[122] https://www.digi-sign.com/digi-id/delete
[123] https://www.digi-sign.com/digi-ca/introduction/revocation
[124] https://www.digi-sign.com/digi-ca/introduction/email
[125] https://www.digi-sign.com/digi-ca/introduction/flexibility
[126] https://www.digi-sign.com/digi-ca/introduction/migration
[127] https://www.digi-sign.com/digi-ca/introduction/design
[128] https://www.digi-sign.com/digi-ca/introduction/database
[129] https://www.digi-sign.com/digi-ca/introduction/preparation
[130] https://www.digi-sign.com/digi-ca/introduction/service
[131] https://www.digi-sign.com/digi-ca/introduction/server
[132] https://www.digi-sign.com/digi-ca/introduction/server/xs
[133] https://www.digi-sign.com/digi-ca/introduction/server/xp
[134] https://www.digi-sign.com/digi-ca/introduction/server/xg
[135] https://www.digi-sign.com/digi-ca/introduction/sample%20project%20
[136] https://www.digi-sign.com/digi-ca/introduction/installation
[137] https://www.digi-sign.com/digi-ca/introduction/customisation
[138] https://www.digi-sign.com/digi-ca/introduction/customisation/interface
[139] https://www.digi-sign.com/digi-ca/installation
[140] https://www.digi-sign.com/digi-ca/installation%20guidelines/server%20xs%20installation%20guidelines
[141] https://www.digi-sign.com/digi-ca/installation%20guidelines/server%20xs%20technical%20diagram
[142] https://www.digi-sign.com/installation%20guidelines/server%20xs%20installation%20schedule%20
[143] https://www.digi-sign.com/digi-ca/installation%20guidelines/server%20xs%20%2526%20hsm%20installation%20schedule%20
[144] https://www.digi-sign.com/digi-ca/installation%20guidelines/server%20xs%20%2526%20hsm%20technical%20diagram%20
[145] https://www.digi-sign.com/digi-ca/installation%20guidelines/server%20xp%20installation%20diagram
[146] https://www.digi-sign.com/digi-ca/installation%20guidelines/server%20xp%20technical%20diagram
[147] https://www.digi-sign.com/digi-ca/installation%20guidelines/server%20xp%20installation%20schedule
[148] https://www.digi-sign.com/digi-ca/installation%20guidelines/server%20xp%20hsm%20technical%20diagram
[149] https://www.digi-sign.com/digi-ca/installation%20guidelines/server%20xp%20hsm%20installation%20schedule
[150] https://www.digi-sign.com/digi-ca/installation%20guidelines/server%20xg%20full%20redundancy%20technical%20diagram
[151] https://www.digi-sign.com/digi-ca/installation%20guidelines/server%20xg%20full%20redundancy%20installation%20schedule
[152] https://www.digi-sign.com/about/arp%20co-operation
[153] https://www.digi-sign.com/digi-ca/buyer
[154] https://www.digi-sign.com/digi-ca/buyer/introduction
[155] https://www.digi-sign.com/digi-ca/buyer/benefits
[156] https://www.digi-sign.com/digi-ca/buyer/choosing
[157] https://www.digi-sign.com/digi-ca/buyer/versions
[158] https://www.digi-sign.com/digi-ca/buyer/versions/service
[159] https://www.digi-sign.com/digi-ca/buyer/versions/server
[160] https://www.digi-sign.com/digi-ca/buyer/versions/server/considerations
[161] https://www.digi-sign.com/digi-ca/buyer/versions/shared
[162] https://www.digi-sign.com/digi-ca/buyer/versions/summary
[163] https://www.digi-sign.com/digi-ca/buyer/components
[164] https://www.digi-sign.com/digi-ca/buyer/components/primary
[165] https://www.digi-sign.com/digi-ca/buyer/components/user
[166] https://www.digi-sign.com/digi-ca/buyer/components/optional
[167] https://www.digi-sign.com/digi-ca/buyer/ttm
[168] https://www.digi-sign.com/digi-ca/buyer/digi-ca-service
[169] https://www.digi-sign.com/digi-ca/buyer/certificates
[170] https://www.digi-sign.com/digi-ca/buyer/ramc/automation
[171] https://www.digi-sign.com/digi-ca/buyer/eers/tsa
[172] https://www.digi-sign.com/digi-ca/buyer/eers/ocsp
[173] https://www.digi-sign.com/digi-ca/buyer/disaster%20recovery
[174] https://www.digi-sign.com/digi-ca/buyer/performance
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[344] https://www.digi-sign.com/quote/digi-mail
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[356] https://www.digi-sign.com/digi-seal/requirements
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Clarke, Rotnei: 29 mins, 6-11 fg, 5-8 3pt, 1 ast, 17pts.
Thanks, gosh. Hope the kid continues to keep lighting it up.
I have, however, given a mighty cheer for ex-Zags who for one reason or another didn't stick. They at least had the foresight to try and be a Zag. So here's to you, Calum, P-Mac and Theo. All the best out there.
Edited to say: I'm a sour####, but even I recognize all student athletes deserve some props, some more than others.
It's always a bummer to lose recruits, but I also think it's great when they do well. That means that GU recruiting is on the right track.
Especially for some, like Deon Thompson, where we were in it to the very end.
We went down to the wire against UNC for him. Look at how well Vasquez is doing at MD and tell me that in the event that Bouldin never came here, the coaching staff didn't have a solid plan B lined up.
When zag-recruits do well (whether they attend Gonzaga or not), it's definitely a compliment/bragging point for GU coaches and fans.


text:
import numpy as np
from scipy.linalg import eigh, LinAlgError
from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
from sklearn.metrics.pairwise import pairwise_kernels, euclidean_distances
from sklearn.utils import check_random_state
from sklearn.utils.multiclass import check_classification_targets
from sklearn.utils.validation import check_is_fitted, check_X_y

class BaseEigenPro(BaseEstimator):
    """
    Base class for EigenPro iteration.
    """

    def __init__(
        self,
        batch_size="auto",
        n_epoch=2,
        n_components=1000,
        subsample_size="auto",
        kernel="rbf",
        gamma="scale",
        degree=3,
        coef0=1,
        kernel_params=None,
        random_state=None,
    ):
        self.batch_size = batch_size
        self.n_epoch = n_epoch
        self.n_components = n_components
        self.subsample_size = subsample_size
        self.kernel = kernel
        self.gamma = gamma
        self.degree = degree
        self.coef0 = coef0
        self.kernel_params = kernel_params
        self.random_state = random_state

    def _kernel(self, X, Y):
        """Calculate the kernel matrix

        Parameters
        ---------
        X : {float, array}, shape = [n_samples, n_features]
            Input data.

        Y : {float, array}, shape = [n_centers, n_targets]
            Kernel centers.

        Returns
        -------
        K : {float, array}, shape = [n_samples, n_centers]
            Kernel matrix.
        """
        if (
            self.kernel != "rbf"
            and self.kernel != "laplace"
            and self.kernel != "cauchy"
        ):
            if callable(self.kernel):
                params = self.kernel_params or {}
            else:
                params = {
                    "gamma": self.gamma_,
                    "degree": self.degree,
                    "coef0": self.coef0,
                }
            return pairwise_kernels(
                X, Y, metric=self.kernel, filter_params=True, **params
            )
        distance = euclidean_distances(X, Y, squared=True)
        bandwidth = np.float32(1.0 / np.sqrt(2.0 * self.gamma_))
        if self.kernel == "rbf":
            distance = -self.gamma_ * distance
            K = np.exp(distance)
        elif self.kernel == "laplace":
            d = np.maximum(distance, 0)
            K = np.exp(-np.sqrt(d) / bandwidth)
        else:  # self.kernel == "cauchy":
            K = 1 / (1 + 2.0 * self.gamma_ * distance)
        return K

    def _nystrom_svd(self, X, n_components):
        """Compute the top eigensystem of a kernel
        operator using Nystrom method

        Parameters
        ----------
        X : {float, array}, shape = [n_subsamples, n_features]
            Subsample feature matrix.

        n_components : int
            Number of top eigencomponents to be restored.

        Returns
        -------
        E : {float, array}, shape = [k]
            Top eigenvalues.

        Lambda : {float, array}, shape = [n_subsamples, k]
            Top eigenvectors of a subsample kernel matrix (which can be
            directly used to approximate the eigenfunctions of the kernel
            operator).
        """
        m, _ = X.shape
        K = self._kernel(X, X)

        W = K / m
        try:
            E, Lambda = eigh(W, eigvals=(m - n_components, m - 1))
        except LinAlgError:
            # Use float64 when eigh fails due to precision
            W = np.float64(W)
            E, Lambda = eigh(W, eigvals=(m - n_components, m - 1))
            E, Lambda = np.float32(E), np.float32(Lambda)
        # Flip so eigenvalues are in descending order.
        E = np.maximum(np.float32(1e-7), np.flipud(E))
        Lambda = np.fliplr(Lambda)[:, :n_components] / np.sqrt(
            m, dtype="float32"
        )

        return E, Lambda

    def _setup(self, feat, max_components, mG, alpha):
        """Compute preconditioner and scale factors for EigenPro iteration

        Parameters
        ----------
        feat : {float, array}, shape = [n_samples, n_features]
            Feature matrix (normally from training data).

        max_components : int
            Maximum number of components to be used in EigenPro iteration.

        mG : int
            Maximum batch size to fit in memory.

        alpha : float
            Exponential factor (< 1) for eigenvalue ratio.

        Returns
        -------
        max_S : float
            Normalized largest eigenvalue.

        max_kxx : float
            Maximum of k(x,x) where k is the EigenPro kernel.

        E : {float, array}, shape = [k]
            Preconditioner for EigenPro

        Lambda : {float, array}, shape = [n_subsamples, k]
            Top eigenvectors of a subsample kernel matrix
        """
        alpha = np.float32(alpha)

        # Estimate eigenvalues (S) and eigenvectors (V) of the kernel matrix
        # corresponding to the feature matrix.
        E, Lambda = self._nystrom_svd(feat, max_components)
        n_subsamples = feat.shape[0]

        # Calculate the number of components to be used such that the
        # corresponding batch size is bounded by the subsample size and the
        # memory size.
        max_bs = min(max(n_subsamples / 5, mG), n_subsamples)
        n_components = np.sum(np.power(1 / E, alpha) < max_bs) - 1
        if n_components < 2:
            n_components = min(E.shape[0] - 1, 2)

        Lambda = Lambda[:, :n_components]
        scale = np.power(E[0] / E[n_components], alpha)

        # Compute part of the preconditioner for step 2 of gradient descent in
        # the eigenpro model
        D = (1 - np.power(E[n_components] / E[:n_components], alpha)) / E[
            :n_components
        ]

        max_S = E[0].astype(np.float32)
        kxx = 1 - np.sum(Lambda**2, axis=1) * n_subsamples
        return max_S / scale, np.max(kxx), D, Lambda

    def _initialize_params(self, X, Y, random_state):
        """
        Validate parameters passed to the model, choose parameters
        that have not been passed in, and run setup for EigenPro iteration.
        Parameters
        ----------
        X : {float, array}, shape = [n_samples, n_features]
            Training data.

        Y : {float, array}, shape = [n_samples, n_targets]
            Training targets.

        random_state : RandomState instance
            The random state to use for random number generation

        Returns
        -------
        Y : {float, array}, shape = [n_samples, n_targets]
            Training targets. If Y was originally of shape
            [n_samples], it is now [n_samples, 1].

        E : {float, array}, shape = [k]
            Preconditioner for EigenPro

        Lambda : {float, array}, shape = [n_subsamples, k]
            Top eigenvectors of a subsample kernel matrix

        eta : float
            The learning rate

        pinx : {int, array}, shape = [sample_size]
            The rows of X used to calculate E and Lambda
        """
        n, d = X.shape
        n_label = 1 if len(Y.shape) == 1 else Y.shape[1]
        self.centers_ = X

        # Calculate the subsample size to be used.
        if self.subsample_size == "auto":
            if n < 100000:
                sample_size = 4000
            else:
                sample_size = 12000
        else:
            sample_size = self.subsample_size
        sample_size = min(n, sample_size)

        n_components = min(sample_size - 1, self.n_components)
        n_components = max(1, n_components)

        # Approximate amount of memory that we want to use
        mem_bytes = 0.1 * 1024**3
        # Memory used with a certain sample size
        mem_usages = (d + n_label + 2 * np.arange(sample_size)) * n * 4
        mG = np.int32(np.sum(mem_usages < mem_bytes))

        # Calculate largest eigenvalue and max{k(x,x)} using subsamples.
        pinx = random_state.choice(n, sample_size, replace=False).astype(
            "int32"
        )
        if self.gamma == "scale":
            self.gamma_ = np.float32(1.0 / (X.var() * d))
        else:
            self.gamma_ = self.gamma
        max_S, beta, E, Lambda = self._setup(
            X[pinx], n_components, mG, alpha=0.95
        )
        # Calculate best batch size.
        if self.batch_size == "auto":
            bs = min(np.int32(beta / max_S), mG) + 1
        else:
            bs = self.batch_size
        self.bs_ = min(bs, n)

        # Calculate best step size.
        if self.bs_ < beta / max_S + 1:
            eta = self.bs_ / beta
        elif self.bs_ < n:
            eta = 2.0 * self.bs_ / (beta + (self.bs_ - 1) * max_S)
        else:
            eta = 0.95 * 2 / max_S
        # Remember the shape of Y for predict() and ensure it's shape is 2-D.
        self.was_1D_ = False
        if len(Y.shape) == 1:
            Y = np.reshape(Y, (Y.shape[0], 1))
            self.was_1D_ = True
        return Y, E, Lambda, np.float32(eta), pinx

    def validate_parameters(self):
        """
        Validate the parameters of the model to ensure that no unreasonable
        values were passed in.
        """
        if self.n_epoch <= 0:
            raise ValueError(
                "n_epoch should be positive, was " + str(self.n_epoch)
            )
        if self.n_components < 0:
            raise ValueError(
                "n_components should be non-negative, was "
                + str(self.n_components)
            )
        if self.subsample_size != "auto" and self.subsample_size < 0:
            raise ValueError(
                "subsample_size should be non-negative, was "
                + str(self.subsample_size)
            )
        if self.batch_size != "auto" and self.batch_size <= 0:
            raise ValueError(
                "batch_size should be positive, was " + str(self.batch_size)
            )
        if self.gamma != "scale" and self.gamma <= 0:
            raise ValueError(
                "gamma should be positive, was " + str(self.gamma)
            )

    def _raw_fit(self, X, Y):
        """Train eigenpro regression model

        Parameters
        ----------
        X : {float, array}, shape = [n_samples, n_features]
            Training data.

        Y : {float, array}, shape = [n_samples, n_targets]
            Training targets.

        Returns
        -------
        self : returns an instance of self.
        """
        X, Y = check_X_y(
            X,
            Y,
            dtype=np.float32,
            multi_output=True,
            ensure_min_samples=3,
            y_numeric=True,
        )
        Y = Y.astype(np.float32)
        random_state = check_random_state(self.random_state)

        self.validate_parameters()
        """Parameter Initialization"""
        Y, D, V, eta, pinx = self._initialize_params(X, Y, random_state)

        """Training loop"""
        n = self.centers_.shape[0]

        self.coef_ = np.zeros((n, Y.shape[1]), dtype=np.float32)
        step = np.float32(eta / self.bs_)
        for _ in range(0, self.n_epoch):
            epoch_inds = random_state.choice(
                n, n // self.bs_ * self.bs_, replace=False
            ).astype("int32")

            for batch_inds in np.array_split(epoch_inds, n // self.bs_):
                batch_x = self.centers_[batch_inds]
                kfeat = self._kernel(batch_x, self.centers_)
                batch_y = Y[batch_inds]

                # Update 1: Sampled Coordinate Block.
                gradient = np.dot(kfeat, self.coef_) - batch_y

                self.coef_[batch_inds] -= step * gradient

                # Update 2: Fixed Coordinate Block
                delta = np.dot(
                    V * D, np.dot(V.T, np.dot(kfeat[:, pinx].T, gradient))
                )
                self.coef_[pinx] += step * delta
        return self

    def _raw_predict(self, X):
        """Predict using the kernel regression model

        Parameters
        ----------
        X : {float, array}, shape = [n_samples, n_features]
            Samples.

        Returns
        -------
        Y : {float, array}, shape = [n_samples, n_targets]
            Predicted targets.
        """
        check_is_fitted(
            self, ["bs_", "centers_", "coef_", "was_1D_", "gamma_"]
        )
        X = np.asarray(X, dtype=np.float64)

        if len(X.shape) == 1:
            raise ValueError(
                "Reshape your data. X should be a matrix of shape"
                " (n_samples, n_features)."
            )
        n = X.shape[0]

        Ys = []
        for batch_inds in np.array_split(range(n), max(1, n // self.bs_)):
            batch_x = X[batch_inds]
            kfeat = self._kernel(batch_x, self.centers_)

            pred = np.dot(kfeat, self.coef_)
            Ys.append(pred)
        Y = np.vstack(Ys)
        if self.was_1D_:
            Y = np.reshape(Y, Y.shape[0])
        return Y

    def _get_tags(self):
        tags = super()._get_tags()
        tags["multioutput"] = True
        return tags

class EigenProRegressor(RegressorMixin, BaseEigenPro):
    """Regression using EigenPro iteration.

    Train least squared kernel regression model with mini-batch EigenPro
    iteration.

    Parameters
    ----------
    batch_size : int, default = 'auto'
        Mini-batch size for gradient descent.

    n_epoch : int, default = 2
        The number of passes over the training data.

    n_components : int, default = 1000
        the maximum number of eigendirections used in modifying the kernel
        operator. Convergence rate speedup over normal gradient descent is
        approximately the largest eigenvalue over the n_componentth
        eigenvalue, however, it may take time to compute eigenvalues for
        large n_components

    subsample_size : int, default = 'auto'
        The number of subsamples used for estimating the largest
        n_component eigenvalues and eigenvectors. When it is set to 'auto',
        it will be 4000 if there are less than 100,000 samples
        (for training), and otherwise 12000.

    kernel : string or callable, default = "rbf"
        Kernel mapping used internally. Strings can be anything supported
        by scikit-learn, however, there is special support for the
        rbf, laplace, and cauchy kernels. If a callable is given, it should
        accept two arguments and return a floating point number.

    gamma : float, default='scale'
        Kernel coefficient. If 'scale', gamma = 1/(n_features*X.var()).
        Interpretation of the default value is left to the kernel;
        see the documentation for sklearn.metrics.pairwise.
        For kernels that use bandwidth, bandwidth = 1/sqrt(2*gamma).

    degree : float, default=3
        Degree of the polynomial kernel. Ignored by other kernels.

    coef0 : float, default=1
        Zero coefficient for polynomial and sigmoid kernels.
        Ignored by other kernels.

    kernel_params : mapping of string to any
        Additional parameters (keyword arguments) for kernel function
        passed as callable object.

    random_state : int, RandomState instance or None, (default=None)
        The seed of the pseudo random number generator to use when
        shuffling the data.  If int, random_state is the seed used by the
        random number generator; If RandomState instance, random_state is
        the random number generator; If None, the random number generator
        is the RandomState instance used by `np.random`.

    References
    ----------
    * Siyuan Ma, Mikhail Belkin
      "Diving into the shallows: a computational perspective on
      large-scale machine learning", NIPS 2017.

    Examples
    --------
    >>> from sklearn_extra.kernel_methods import EigenProRegressor
    >>> import numpy as np
    >>> n_samples, n_features, n_targets = 4000, 20, 3
    >>> rng = np.random.RandomState(1)
    >>> x_train = rng.randn(n_samples, n_features)
    >>> y_train = rng.randn(n_samples, n_targets)
    >>> rgs = EigenProRegressor(n_epoch=3, gamma=.5, subsample_size=50)
    >>> rgs.fit(x_train, y_train)
    EigenProRegressor(gamma=0.5, n_epoch=3, subsample_size=50)
    >>> y_pred = rgs.predict(x_train)
    >>> loss = np.mean(np.square(y_train - y_pred))
    """

    def __init__(
        self,
        batch_size="auto",
        n_epoch=2,
        n_components=1000,
        subsample_size="auto",
        kernel="rbf",
        gamma="scale",
        degree=3,
        coef0=1,
        kernel_params=None,
        random_state=None,
    ):
        super().__init__(
            batch_size=batch_size,
            n_epoch=n_epoch,
            n_components=n_components,
            subsample_size=subsample_size,
            kernel=kernel,
            gamma=gamma,
            degree=degree,
            coef0=coef0,
            kernel_params=kernel_params,
            random_state=random_state,
        )

    def fit(self, X, Y):
        return self._raw_fit(X, Y)

    def predict(self, X):
        return self._raw_predict(X)

class EigenProClassifier(ClassifierMixin, BaseEigenPro):
    """Classification using EigenPro iteration.

    Train least squared kernel classification model with mini-batch EigenPro
    iteration.

    Parameters
    ----------
    batch_size : int, default = 'auto'
        Mini-batch size for gradient descent.

    n_epoch : int, default = 2
        The number of passes over the training data.

    n_components : int, default = 1000
        the maximum number of eigendirections used in modifying the
        kernel operator. Convergence rate speedup over normal gradient
        descent is approximately the largest eigenvalue over the
        n_componenth eigenvalue, however, it may take time to compute
        eigenvalues for large n_components

    subsample_size : int, default = 'auto'
        The size of subsamples used for estimating the largest
        n_component eigenvalues and eigenvectors. When it is set to
        'auto', it will be 4000 if there are less than 100,000 samples
        (for training), and otherwise 12000.

    kernel : string or callable, default = "rbf"
        Kernel mapping used internally. Strings can be anything supported
        by scikit-learn, however, there is special support for the
        rbf, laplace, and cauchy kernels. If a callable is given, it should
        accept two arguments and return a floating point number.

    gamma : float, default='scale'
        Kernel coefficient. If 'scale', gamma = 1/(n_features*X.var()).
        Interpretation of the default value is left to the kernel;
        see the documentation for sklearn.metrics.pairwise.
        For kernels that use bandwidth, bandwidth = 1/sqrt(2*gamma).

    degree : float, default=3
        Degree of the polynomial kernel. Ignored by other kernels.

    coef0 : float, default=1
        Zero coefficient for polynomial and sigmoid kernels. Ignored by
        other kernels.

    kernel_params : mapping of string to any
        Additional parameters (keyword arguments) for kernel function
        passed as callable object.

    random_state : int, RandomState instance or None (default=None)
        The seed of the pseudo random number generator to use when
        shuffling the data.  If int, random_state is the seed used by
        the random number generator; If RandomState instance,
        random_state is the random number generator;
        If None, the random number generator is the RandomState
        instance used by `np.random`.

    References
    ----------
    * Siyuan Ma, Mikhail Belkin
      "Diving into the shallows: a computational perspective on
      large-scale machine learning", NIPS 2017.

    Examples
    --------
    >>> from sklearn_extra.kernel_methods import EigenProClassifier
    >>> import numpy as np
    >>> n_samples, n_features, n_targets = 4000, 20, 3
    >>> rng = np.random.RandomState(1)
    >>> x_train = rng.randn(n_samples, n_features)
    >>> y_train = rng.randint(n_targets, size=n_samples)
    >>> rgs = EigenProClassifier(n_epoch=3, gamma=.01, subsample_size=50)
    >>> rgs.fit(x_train, y_train)
    EigenProClassifier(gamma=0.01, n_epoch=3, subsample_size=50)
    >>> y_pred = rgs.predict(x_train)
    >>> loss = np.mean(y_train != y_pred)
    """

    def __init__(
        self,
        batch_size="auto",
        n_epoch=2,
        n_components=1000,
        subsample_size="auto",
        kernel="rbf",
        gamma=0.02,
        degree=3,
        coef0=1,
        kernel_params=None,
        random_state=None,
    ):
        super().__init__(
            batch_size=batch_size,
            n_epoch=n_epoch,
            n_components=n_components,
            subsample_size=subsample_size,
            kernel=kernel,
            gamma=gamma,
            degree=degree,
            coef0=coef0,
            kernel_params=kernel_params,
            random_state=random_state,
        )

    def fit(self, X, Y):
        """Train eigenpro classification model

        Parameters
        ----------
        X : {float, array}, shape = [n_samples, n_raw_feature]
            The raw input feature matrix.

        Y : {float, array}, shape =[n_samples]
            The labels corresponding to the features of X.

        Returns
        -------
        self : returns an instance of self.
        """
        X, Y = check_X_y(
            X,
            Y,
            dtype=np.float32,
            force_all_finite=True,
            multi_output=False,
            ensure_min_samples=3,
        )
        check_classification_targets(Y)
        self.classes_ = np.unique(Y)

        loc = {}
        for ind, label in enumerate(self.classes_):
            loc[label] = ind

        class_matrix = np.zeros((Y.shape[0], self.classes_.shape[0]))

        for ind, label in enumerate(Y):
            class_matrix[ind, loc[label]] = 1
        self._raw_fit(X, class_matrix)
        return self

    def predict(self, X):
        """Predict using the kernel classification model

        Parameters
        ----------
        X : {float, array}, shape = [n_samples, n_features]
            Samples.

        Returns
        -------
        y : {float, array}, shape = [n_samples]
            Predicted labels.
        """
        Y = self._raw_predict(X)
        return self.classes_[np.argmax(Y, axis=1)]

redpajama_set_name: RedPajamaGithub


text:
Strange "ghostly" particles found inside a mountain in Italy could unlock the Sun's mysteries
ALBERTO PIZZOLI/AFP/Getty Images
Researchers believe they may have discovered direct evidence of a particle that plays a part in fueling massive stars that are bigger than our Sun. These particles, called CNO neutrinos, were detected in an underground laboratory located in the mountains of Italy by an international team of physicists called the Borexino Collaboration. The team presented their observations last month during the Neutrino 2020 conference and are currently awaiting peer-review of their study before publication. If confirmed, their findings could help scientists around the world expand their understanding of how the Sun works.
The Sun is powered through two different nuclear fusion processes—the proton-proton chain (p-p chain) and the CNO (carbon, nitrogen, oxygen) cycle. Our Sun primarily relies on the p-p chain for its energy, but researchers hypothesize that much larger stars use the CNO cycle for their energy instead. Both processes fuse hydrogen into helium, which creates a great amount of energy for the Sun's core, and releases different kinds of neutrinos.
The neutrinos that are emitted during the processes are 'ghostly' particles that zip through the air at the speed of light. They don't usually interact with masses—billions of them are passing through your body right now—making them very hard to detect. To make things more difficult, scientists learned the hard way that there are different types of neutrinos that all require different calibrations for detectors to detect. Before they knew better, scientists were only picking up a portion of the neutrinos all around us, not all.
Building machines that are sensitive enough to spot neutrinos isn't easy, but a detector housed in the Gran Sasso Laboratory in Italy has managed to produce excellent results for the Borexino Collaboration. In 2014, the Borexino team was the first to detect neutrinos emitted from the p-p chain.
The current detector is a round tank full of a liquid that flashes whenever electrons within it interact with neutrinos. The flash is a burst of energy, which presumably comes from CNO neutrinos. Borexino's CNO neutrino discovery can help other scientists answer questions about the Sun, such as what elements it's made of.
"With this outcome, Borexino has completely unraveled the two processes powering the sun," physicist Gioacchino Ranucci told Live Science. The team is pretty confident about their findings, calculating that the probability they detected something different was a 1 in 3.5 million chance. But other scientists believe that number is still too high for their liking, and are hoping to see the experiment continue to make further observations.
This hallucinogen is looking pretty promising for depression treatment
These activities may boost your memory and brain function

redpajama_set_name: RedPajamaCommonCrawl


Brazilian graphic designer Paula Rúpolo recently challenged our senses and our expectations by swapping the colors of competing brands' logos. In her experiment, which she shared in a post called, The Brand Colour Swap: What's in a Colour?, she paired well known companies with their competitors in a series of interactive graphics: mousing over each pair allows you to see their logos before and after the switch.
The results are enlightening, and provoke thought. Her observations about color can be applied to many other situations, such as shop exteriors and product design, where appearance must spark interest, trust and recognition.
While reminding us that colors have primal human meanings, and that some color meanings are quite different for different cultures, she presents an array of logo/color pairings that sometimes jar and other times seem to be no different at all.
What do these color tests mean for business and design?
I'm curious what conclusions you draw from this experiment. It turns out mine differ somewhat from Rúpolo's.
Having spent years beginning with shape in logo design, I've recently begun determining colors first (in some situations), to great effect. By choosing color first, we can sometimes tap its intuitive connections to touch the core identity of a business, its values and its vision. Knowing that first can inspire unexpected, powerful possibilities for a logo's shape. I would emphasize that what I don't mean is running with color preferences mentioned during the first couple of project meetings. In those situations, Rúpolo is spot on.
I would counter that black is a color, and both brands use it with impressive attention to detail! Have a look at the Nike site, where black both anchors and dominates the brand experience.
Beyond that, however, I highly recommend having a look at the whole collection of color swaps in the original post. In particular, I challenge you to spend a little longer with switches of similar colors. See if you can detect subtler, more intuitive reactions in yourself to logos that are "both blue". You can also leave requests in the comments for future swaps you'd like to see!
Thanks to the Fastcodesign blog for bringing my attention to this cool project.
3 Responses to Designing to sell: does color always reach us first?
Thanks for weighing in, Julie!


Album The Fabulous Chi Ali when he was 14. Now serving a 14 year sentence for killing a man.
Younger than Jennifer Connelly by less than 1 year.
Younger than Martha Fiennes by 5 years.
Younger than Ralph Fiennes by 7 years.
Older than Budgie by less than 1 year.
Younger than Scott Bakula by 2 years.
Movies Enemy Mine, Jaws 3-D, An Officer and a Gentleman, Iron Eagle, Iron Eagle II, Aces: Iron Eagle III, Iron Eagle IV. TV series Extant, The Lazarus Syndrome, Roots.. Hollywood Walk of Fame: A star at 7000 Hollywood Boulevard for contributions to the motion picture industry; ceremony: 20 May 1992.
Prolific and popular science fiction writer. Short story I Have no Mouth, and I Must Scream. Writer for movie Logan's Run. Science Fiction and Fantasy Hall of Fame 2011.
Shot in the back of the head while playing poker. In Wild West show of Buffalo Bill Cody. Texas Trail of Fame 2002.
Railroad financier in the second half of the 19th century. Stock and gold manipulator, called a "robber baron."


You are probably fed up with the same marketing schemes. It allows you to connect with a new way to get to more people. Read on to discover some tips to make your video marketing campaign a successful one.
Video marketing allows you to reach your audience in a very real way. You should ask customers to send you their questions about your products or on topics related to your industry and answer the best questions in a short weekly video. If possible, offer a prize or incentive to customers whose questions were used in the clip.
TIP! Marketing through videos helps maintain contact with clients. Let your customers know that they can send in questions about your business's services or products.
Video marketing is a way to keep in contact with your audience.
You should include screenshots of your web pages in your video. This allows users to see how your website is set up. Screenshots are also a great tool to use when showing a how-to video. Just save the screenshot and utilize video editing programs to incorporate it into a video.
When creating videos, don't become obsessed with your content's production quality. In most cases, it's not the technical brilliance of the clip that achieves the desired results. Large corporations often use videos produced by their employees, rather than hiring actors and directors.
TIP! Don't spend to much time worrying about production value when it comes to generating video marketing content. In most cases, it's not the technical brilliance of the clip that achieves the desired results.
A proper title will attract more viewers. Take time to think of creative titles for successful video marketing.
You have a limited amount of time to capture the attention right away if you want them to view your entire video. The initial 15 seconds is the most crucial. You should use an unusual perspective or hook to draw them in the beginning of the video.
If your video's objective is pushing a product to the individuals watching, make certain that you include some hyperlink near the bottom of the frame or the page's margin. Better yet, place the link within the video itself. This way, this link will be there in case people end up sharing the video somewhere else.
Make sure your videos remain focused and focused. It is easy to go off topic when you are not prepared. Try making a pre-written outline of your video to be about and keep to it while filming. Keep your eye on the prize and stick with what the topic is teaching if you will have success at video marketing.
You will probably have the opportunity to speak to experts or simply document your experience. If you do a public talk, ask someone to record it.
If you have a lot of people that you work with, see if they'll help you with the video marketing campaign you're working on. The ideal person is someone personable and good in front of the camera. Having a variety of people in your videos can make them more interesting.
TIP! Does your business have any employees or colleagues? If so, then involve them in your videos. Choose an upbeat person who can perform well on camera and be verbally clear.
Keep content engaging and fresh so that viewers to return. Boring content will only drive people angry. Leave viewers longing for more and anticipating what you come up with next. If you have really interesting content, the viewers are sure to follow.
You should think of a greeting in your viewers off. You need to indicate who you work for, your company, and the topic of the video.
Decide on a consistent tone for your marketing videos. Humor can be used in silly spoofs or educational videos, alike. Consider both the product or service that you want to market, as well as the demographic you want to reach. Consider the product and the tone of your other advertising.
TIP! Use a consistent approach in each of your videos. Serious how-to videos or funny videos both work equally well.
You are dreaming if you think you can upload a video to YouTube and it will go viral on its own! You need to promote it as much as you can with social media sites and email marketing. You need to get the word out there for people to see.
You don't need high-quality equipment to market with video. Most computers are capable of the task. You can even use your smartphone if you need to.
Make sure your content is fresh and interesting. Boring content will only drive potential customers away. Leave viewers longing for more and anticipating what you'll do next. Intriguing content attracts more viewers.
TIP! Your can keep your audience interested by creating more quality content. Uninteresting content is of little use, aside from sending visitors someplace else.
Voice overs can be great for shy people. You may have wanted to create a video because being on the screen makes you uncomfortable. You might just want to show your products and then talk over it. Just record what you're saying and then put it together with your video footage.
Write a script for your introduction and closing of each video. Information to always include is who you are, your business name and the video topic. At the end, go over your company's name again with any call to action you have to get your viewer to your website or to where they can buy the product.
TIP! Make a script for your intro and outro for the video. You will have to say who you are, what company you represent and what the video is about to start.
Successful video marketers understand the importance of responding to comments. There are times that questions need quick responses.
What would you think of about your product if asked? Your customers have the same things. Provide these answers in the videos for fun and easy facts. The more people like your videos, the more shares and customers you will receive.
Do not take the whole burden upon yourself. It is not easy to constantly be creative and come up with new ideas. Your best bet is to brainstorm with a group of creative people and then come up with a list. Do brainstorming with these people whenever you can so that you're able to advertise more effectively.
TIP! Do not try to do it all yourself. It is a little hard to think of ideas and things when you're making a lot of marketing videos.
Humor can be a great tool in video marketing.Your customers will not original and funny. Funny commercials or even controversial ones are the best eye-grabbers. Try to change things up with your videos.A video that is filled with interesting and makes people chuckle will help your video marketing campaign succeed.
If videos are not your thing, you can make it so others make them for you. Hold a contest, and hand out prizes to the best video makers. For the prize, tell them that the best video can be used as an ad for an upcoming project.
TIP! If you aren't good at videos, recruit outside help. Hold a contest, and hand out prizes to the best video makers.
Watch videos from other companies before you make your next video. Find out what you like as a buyer.Ask those you to try to do this as well. Use what you make a great video.
If you are paying for your videos to be shared, pay for hosting. Don't use services that will use your videos on sites that aren't related to your content or that make your videos pop up. Use surveys to find out what kinds of sites your current customers go to frequently.
Tripods are a video marketer's best friend. Skip the jerky camera movements and artistic shots. It is important to keep your camera steady so that it is easier to watch. It is within the initial seconds that viewers make their decision to continue watching, which means you need to make it worthwhile.
TIP! Consider purchasing a tripod in order to make your videos appear professional. Using a shaky camera typically is not appealing.
The more you understand about video marketing the further you can take it. You can do many things with video marketing that can help you gain customers. Video marketing is one of the most effective ways to generate leads and grow your business.
Embed the video within your domain to add a little extra attention on top of the usual YouTube traffic. That way, folks can see the video on your own website, in close proximity to your product offerings. Don't worry about lost views since Google counts views no matter if the video is on YouTube or embedded on other sites.
TIP! On your website, embed your video, as well as putting it on YouTube. People will be able to view your video and they will get great information on your business.


text:
The Entire Original Maupassant Short Stories
by Guy De Maupassant
Original Maupassant Short Stories, Volume 1
Original Maupassant Short Stories, Volume 10
Pierre and Jean
"Mark Twain's Letters Volume 2" includes the correspondences of Mark Twain from 1673-1875.... More
The Entire Original Maupassant Short
"I entered literary life as a meteor, and I shall leave it like a thunderbolt." These words of Maupassant to Jose Maria de Heredia on the occasion of a memorable meeting are, in spite of their morbid solemnity, not an inexact summing up of the brief career during which, for ten years, the writer, by... More
The Necklace and Other Short Stories
The French author Guy de Maupassant (1850-1893) was a master of the short story, creating detailed character studies and brief but moving dramas well suited to the genre.The nine stories in this collection provide a vivid portrait gallery of his typical subjects — from simple peasants and prostitute... More
A Parisian Bourgeois' Sunday and Other Stories
A collection of stories by one of France's greatest short-story writers--the long title piece never having been available in English before.... More
A Parisian Affair and other Stories
They focus on the relationships between men and women, as in the poignant fantasy of 'A Parisian Affair', between brothers and sisters, and between masters and servants.

Through these relationships, Maupassant explores the dualistic nature of the human character and his stories reveal both n... More
Mademoiselle Perle and Other Stories (riverrun editions): a new selection of the sharp, sensitive and much-revered stories
A priest receives an unexpected visitor from his past.A triumphant celebration ends in murder.A doctor tells of an unrequited love that only ended with death.Maupassant's direct treatment of sex and sexuality, and his insistence that the artist's primary duty was faithfulness to his own perceptions,... More
Best Short Stories: A Dual-Language Book (Dover Dual Language French)
New edition features 7 of the most popular tales of one of the greatest of all short-story writers. Included are "La Parure," "Mademoiselle Fifi," "La Maison Tellier," "La Ficelle," "Miss Harriet," "Boule de Suif" and "Le Horla," all reflecting Maupassant's intimate familiarity with Paris and the u... More
Bel Ami, written at the height of Guy de Maupassant's powers, is a classic novel of seduction, intrigue, and ruthless social climbing in belle époque Paris. Georges Duroy is a down-and-out journalist from a humble background who engineers a stunning rise to the top of Parisian society through his re... More
My Uncle Jules and Other Stories/Mon oncle Jules et autres contes: A Dual-Language Book
by Guy De Maupassant • Stanley Appelbaum
Admired as "the greatest French short story writer," and emulated by Somerset Maugham and O. Henry, Guy de Maupassant (1850-1893) was a master of plot construction and a keen recorder of life, translating his observations into finely crafted short stories that spoke entire volumes.This edition featu... More
Language: ENG FRE
Bel Ami: The History of a Scoundrel
by Henri Rene Guy De Maupassant
Returning from three years of military service in Algeria, journalist Georges Duroy takes advantage of his mistresses to achieve success. He wields his charm with unscrupulous ambition, becoming one of the most powerful men in Paris. In his 1885 novel Bel Ami, Maupassant constructs a cynical portrai... More