When Should I Mention the Source Or Attribute on My Blog for a Fact I've Written?

When should I mention the source or attribute on my blog for a fact I've written?

When Should I Mention the Source Or Attribute on My Blog for a Fact I've Written? 1

Part of attributing a source is how much of that source you actually use. In general, if you take any direct quotes or ideas, it is appropriate to attribute the source. You can do this at the bottom, via end notes, or using APA, use in in text citation. For a listing of how to use APA citing, check out Purdue University's OWL lab: Purdue OWL A2A

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Open source technical infrastructure

CUNY Academic Commons is built entirely with open source software: WordPress with Multisite, BuddyPress and MediaWiki. It uses MYSQL as a database and runs under Linux. BuddyPress, a powerful WordPress plug-in which transforms a multi-user WordPress site into a social network, serves as the site's hub. As one of its spokes, MediaWiki is seamlessly integrated into the site and lets users collaborate on projects and build knowledge bases. Users are allowed to create as many blogs and groups as they want. As Lamb & Groom (2011) write in Educause: "the jaw-dropping CUNY Academic Commons ... seamlessly integrates the open-source ... platforms into an appealing and highly sustainable environment. ".

When Should I Mention the Source Or Attribute on My Blog for a Fact I've Written? 2

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How to detect source of smell in the house and how serious it can be?

Until we know what the cause of the odor is, we CAN'T know if a home inspector will be able to determine the source. My recommendation would be to include a contingency clause in any offer you make requiring the seller to correct the odor. You should also have a contingency clause for a home inspection. SOME odors are symptoms of issues that would cost more to repair than the home is worth.

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Free and open source

HA TrinityX from ClusterVision Solutions is an open source, highly-scalable ansible-based HA management environment for HPC, AI and other general IT infrastructures Apache Mesos, from the Apache Software Foundation Kubernetes, founded by Google Inc, from the Cloud Native Computing Foundation Heartbeat, from Linux-HA Docker Swarm Red Hat cluster suite Nomad, from HashiCorp non-HA oneSIS OpenHPC Rocks Cluster Distribution Stacki, from StackIQ YARN, distributed with Apache Hadoop xCAT Warewulf Foreman

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Borodin source material

According to Richard E. Rodda in his 2008 liner notes to recordings of Borodin works, Robert Wright and George Forrest specialized in "turning melodies from classical music into film scores and popular songs". The following Borodin works were used as musical sources for Kismet: In the Steppes of Central Asia ("Sands of Time") Symphony No. 2, Movement 1 ("Fate") "Polovtsian Dances" from Prince Igor ("Bazaar of Caravans", "Not Since Nineveh", "Stranger in Paradise", "He's in Love", "Samaris' Dance") String Quartet No. 2, Movement 2 ("Baubles, Bangles and Beads"), Movement 3 ("And This Is My Beloved") String Quartet No. 1 [it], Movement 4 ("Was I Wazir?") Symphony No. 1 [it], Movement 4 ("Gesticulate") "Serenade" from the Petite Suite ("Night of My Nights") Act III trio from Prince Igor ("The Olive Tree") 'Aria of Khan Konchak" from Prince Igor (Introduction to "Gesticulate") "Aria of Vladimir Galitsky" from Prince Igor ("Zubbediya") Act II scene with Ovlur from Prince Igor ("My Magic Lamp")

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Proof: Source coding theorem

Given X is an i.i.d. source, its time series X1, ., Xn is i.i.d. with entropy H(X) in the discrete-valued case and differential entropy in the continuous-valued case. The Source coding theorem states that for any ε > 0, i.e. for any rate H(X) ε larger than the entropy of the source, there is large enough n and an encoder that takes n i.i.d. repetition of the source, X1:n, and maps it to n(H(X) ε) binary bits such that the source symbols X1:n are recoverable from the binary bits with probability of at least 1 − ε. Proof of Achievability. Fix some ε > 0, and let p ( x 1 , … , x n ) = Pr [ X 1 = x 1 , ⋯ , X n = x n ] . displaystyle p(x_1,ldots ,x_n)=Pr left[X_1=x_1,cdots ,X_n=x_n

ight]. The typical set, Aεn, is defined as follows: A n ε = ( x 1 , ⋯ , x n ) : | − 1 n log ⁡ p ( x 1 , ⋯ , x n ) − H n ( X ) | ight|ight. The Asymptotic Equipartition Property (AEP) shows that for large enough n, the probability that a sequence generated by the source lies in the typical set, Aεn, as defined approaches one. In particular, for sufficiently large n, P ( ( X 1 , X 2 , ⋯ , X n ) ∈ A n ε ) displaystyle P((X_1,X_2,cdots ,X_n)in A_n^varepsilon ) can be made arbitrarily close to 1, and specifically, greater than 1 − ε displaystyle 1-varepsilon (See AEP for a proof). The definition of typical sets implies that those sequences that lie in the typical set satisfy: 2 − n ( H ( X ) ε ) ≤ p ( x 1 , ⋯ , x n ) ≤ 2 − n ( H ( X ) − ε ) displaystyle 2^-n(H(X)varepsilon )leq pleft(x_1,cdots ,x_n

ight)leq 2^-n(H(X)-varepsilon ) Note that: The probability of a sequence ( X 1 , X 2 , ⋯ X n ) displaystyle (X_1,X_2,cdots X_n) being drawn from Aεn is greater than 1 − ε. | A n ε | ≤ 2 n ( H ( X ) ε ) displaystyle left|A_n^varepsilon

ight|leq 2^n(H(X)varepsilon ) , which follows from the left hand side (lower bound) for p ( x 1 , x 2 , ⋯ x n ) displaystyle p(x_1,x_2,cdots x_n) . | A n ε | ≥ ( 1 − ε ) 2 n ( H ( X ) − ε ) displaystyle left|A_n^varepsilon

ight|geq (1-varepsilon )2^n(H(X)-varepsilon ) , which follows from upper bound for p ( x 1 , x 2 , ⋯ x n ) displaystyle p(x_1,x_2,cdots x_n) and the lower bound on the total probability of the whole set Aεn.Since | A n ε | ≤ 2 n ( H ( X ) ε ) , n ( H ( X ) ε ) displaystyle left|A_n^varepsilon

ight|leq 2^n(H(X)varepsilon ),n(H(X)varepsilon ) bits are enough to point to any string in this set. The encoding algorithm: The encoder checks if the input sequence lies within the typical set; if yes, it outputs the index of the input sequence within the typical set; if not, the encoder outputs an arbitrary n(H(X) ε) digit number. As long as the input sequence lies within the typical set (with probability at least 1 − ε), the encoder does not make any error. So, the probability of error of the encoder is bounded above by ε. Proof of Converse. The converse is proved by showing that any set of size smaller than Aεn (in the sense of exponent) would cover a set of probability bounded away from 1.

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