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Appendix 1 thesis 100%

2/11/2016 NCBI Blast:c2 BLAST ® Basic Local Alignment Search Tool NCBI/ BLAST/ blastn suite/ Formatting Results ­ BT5KXG9C015 Formatting options Download Blast report description c2 RID Query ID Description Molecule type Query Length BT5KXG9C015 (Expires on 02­12 21:11 pm) lcl|Query_21457 c2 nucleic acid 77 Database Name Description Program nr Nucleotide collection (nt) BLASTN 2.3.1+ Graphic Summary Distribution of 100 Blast Hits on the Query Sequence http://blast.ncbi.nlm.nih.gov/Blast.cgi 1/7 2/11/2016 NCBI Blast:c2 Descriptions Sequences producing significant alignments:

https://www.pdf-archive.com/2016/02/12/appendix-1-thesis/

12/02/2016 www.pdf-archive.com

Appendix2 99%

2/12/2016 NCBI Blast:HMF1AA_dt74b_5 sequences (IYWV7OX01CEBZW) BLAST ® Basic Local Alignment Search Tool NCBI/ BLAST/ blastn suite/ Formatting Results ­ BUJTM9P9015 Formatting options Download Blast report description HMF1AA_dt74b_5 sequences (IYWV7OX01CEBZW) RID Query ID Description Molecule type Query Length BUJTM9P9015 (Expires on 02­13 10:02 am) lcl|Query_189085 IYWV7OX01CEBZW nucleic acid 199 Database Name Description Program nr Nucleotide collection (nt) BLASTN 2.3.1+ Graphic Summary Distribution of 136 Blast Hits on the Query Sequence http://blast.ncbi.nlm.nih.gov/Blast.cgi 1/8 2/12/2016 NCBI Blast:HMF1AA_dt74b_5 sequences (IYWV7OX01CEBZW) Descriptions Sequences producing significant alignments:

https://www.pdf-archive.com/2016/02/12/appendix2/

12/02/2016 www.pdf-archive.com

9780817682880-c2 98%

Chapter 2 Sequences and Series 2.1 Sequences A sequence is a function from the positive integers (possibly including 0) to the reals.

https://www.pdf-archive.com/2015/09/30/9780817682880-c2/

30/09/2015 www.pdf-archive.com

CanIndifferenceVindicateInduction 98%

    Fool Me Once: Can Indifference Vindicate Induction?  Roger White (2015) sketches an ingenious new solution to the problem of induction. It argues on  a priori ​  grounds that the world is more likely to be induction­friendly than induction­unfriendly.  The argument relies primarily on the principle of indifference, and, somewhat surprisingly,  assumes little else. If inductive methods could be vindicated in anything like this way, it would  be quite a groundbreaking result. But there are grounds for pessimism about the envisaged  approach. This paper shows that in the crucial test cases White concentrates on, the principle of  indifference actually renders induction no more accurate than random guessing. It then diagnoses  why the indifference­based argument seems so intuitively compelling, despite being ultimately  unsound.  1 An Indifference­Based Strategy  White begins by imagining that we are “apprentice demons” tasked with devising an  induction­unfriendly world ​  – a world where inductive methods tend to be unreliable. To  simplify, we imagine that there is a single binary variable that we control (such as whether the  sun rises over a series of consecutive days). So, in essence, the task is to construct a binary  sequence such that – if the sequence were revealed one bit at a time – an inductive reasoner  would fare poorly at predicting its future bits. This task, it turns out, is surprisingly difficult. To  see this, it will be instructive to consider several possible strategies for constructing a sequence  that would frustrate an ideal inductive predictor.  Immediately, it is clear that we should avoid uniformly patterned sequences, such as:   00000000000000000000000000000000   or  01010101010101010101010101010101.  ­1­      Sequences like these are quite kind to induction. Our inductive reasoner would quickly latch onto  the obvious patterns these sequences exhibit. A more promising approach, it might seem, is to  build an apparently patternless sequence:  00101010011111000011100010010100  ​ But, importantly, while induction will not be particularly ​ ​ reliable at predicting the terms of this  sequence, it will not be particularly ​unreliable here either. Induction would simply be silent  about what a sequence like this contains. As White puts it, “ In order for... induction to be  applied, our data must contain a salient regularity of a reasonable length” (p. 285). When no  pattern whatsoever can be discerned, presumably, induction is silent. (We will assume that the  inductive predictor is permitted to suspend judgment whenever she wishes.) The original aim  was not to produce an induction­neutral sequence, but to produce a sequence that elicits errors  from induction. So an entirely patternless sequence will not suffice. Instead, the  induction­unfriendly sequence will have to be more devious, building up seeming patterns and  then violating them. As a first pass, we can try this:  00000000000000000000000000000001  Of course, this precise sequence is relatively friendly to induction. While our inductive predictor  will undoubtedly botch her prediction of the final bit, it is clear that she will be able to amass a  long string of successes prior to that point. So, on balance, the above sequence is quite kind to  induction – though not maximally so.   In order to render induction unreliable, we will need to elicit more errors than correct  predictions. We might try to achieve this as follows:  00001111000011110000111100001111  ­2­      The idea here is to offer up just enough of a pattern to warrant an inductive prediction, before  pulling the rug out – and then to repeat the same trick again and again. Of course, this precise  sequence would not necessarily be the way to render induction unreliable: For, even if we did  manage to elicit an error or two from our inductive predictor early on, it seems clear that she  would eventually catch on to the exceptionless higher­order pattern governing the behavior of  the sequence.  The upshot of these observations is not that constructing an induction­unfriendly sequence is  impossible. As White points out, constructing such a sequence should be possible, given any  complete description of how exactly induction works (p. 287). Nonetheless, even if there are a  few special sequences that can frustrate induction, it seems clear that such sequences are fairly  few and far between. In contrast, it is obviously very easy to ​corroborate induction (i.e. to  construct a sequence rendering it thoroughly reliable). So induction is relatively  un­frustrate­able. And it is worth noting that this property is fairly specific to induction. For  example, consider an inferential method based on the gambler’s fallacy, which advises one to  predict whichever outcome has occurred less often, overall. It would be quite easy to frustrate  this method thoroughly (e.g. ​00000000…​).   So far, we have identified a highly suggestive feature of induction. To put things roughly, it  can seem that:   * Over a large number of sequences, induction is thoroughly reliable.   * Over a large number of sequences, induction is silent (and hence, neither reliable nor unreliable).  * Over a very small number of sequences (i.e. those specifically designed to thwart induction),  induction is unreliable (though, even in these cases, induction is still silent much of the time).  ­3­      Viewed from this angle, it can seem reasonable to conclude that there are ​a priori grounds for  confidence that an arbitrary sequence is not induction­unfriendly. After all, there seem to be far  more induction­friendly sequences than induction­unfriendly ones. If we assign equal probability  to every possible sequence, then the probability that an arbitrary sequence will be  induction­friendly is going to be significantly higher than the probability that it will be  induction­unfriendly. So a simple appeal to the principle of indifference seems to generate the  happy verdict that induction can be expected to be more reliable than not, at least in the case of  binary sequences.   Moreover, as White points out, the general strategy is not limited to binary sequences. If we  can show ​a priori that induction over a binary sequence is unlikely to be induction­unfriendly,  then it’s plausible that a similar kind of argument can be used to show that we are justified in  assuming that an arbitrary ​world is not induction­unfriendly. If true, this would serve to fully  vindicate induction.  2 Given Indifference, Induction Is not Reliable   However, there are grounds for pessimism about whether the strategy is successful even in the  simple case of binary sequences. Suppose that, as a special promotion, a casino decided to offer  Fair Roulette. The game involves betting $1 on a particular color – black or red – and then  spinning a wheel, which is entirely half red and half black. If wrong, you lose your dollar; if  right, you get your dollar back and gain another. If it were really true that induction can be  expected to be more reliable than not over binary sequences, it would seem to follow that  induction can serve as a winning strategy, over the long term, in Fair Roulette. After all, multiple  spins of the wheel produce a binary sequence of reds and blacks. And all possible sequences are  ­4­      equally probable. Of course, induction cannot be used to win at Fair Roulette – past occurrences  of red, for example, are not evidence that the next spin is more likely to be red. This suggests that  something is amiss. Indeed, it turns out that no inferential method – whether inductive or  otherwise – can possibly be expected to be reliable at predicting unseen bits of a binary  sequence, if the principle of indifference is assumed. This can be shown as follows.  Let ​S be an unknown binary sequence of length ​n. ​S is to be revealed one bit at a time,  starting with the first.   S: ​? ? ? ? ? ? … ?​ ​:​S    n bits  Let ​f be an arbitrary predictive function that takes as input any initial subsequence of ​S and  outputs a prediction for the next bit: ‘0’, ‘1’, or ‘suspend judgment’.   A  predictive  function’s  accuracy  is measured as follows: +1 for each correct prediction; ­1 for  each  incorrect  prediction;  0  each  time ‘suspend judgment’ occurs. (So the maximum accuracy of  a  function  is  ​n;  the  minimum  score  is  –​n.)  Given  a  probability  distribution  over  all  possible  sequences,  the  ​expected  accuracy  of  a  predictive  function  is  the  average  of  its  possible  scores  weighted by their respective probabilities.  Claim: ​If we assume indifference (i.e. if we assign equal probability to every possible sequence), then  – no matter what ​S is – each of​ f’s predictions​ will be expected to contribute 0 to ​f’s accuracy. And, as  a consequence of this, ​f has 0 expected accuracy more generally.  Proof: ​For some initial subsequences, ​f will output ‘suspend judgment’. The contribution of such  predictions will inevitably be 0. So we need consider only those cases where ​f makes a firm  prediction (i.e. ‘0’ or ‘1’; not ‘suspend judgment’).  Let ​K be a ​k­length initial subsequence for which ​f makes a firm prediction about the bit in   ­5­ 

https://www.pdf-archive.com/2017/02/19/canindifferencevindicateinduction/

19/02/2017 www.pdf-archive.com

ACL73 98%

ON SYMMETRIC BASIC SEQUENCES IN LORENTZ SEQUENCE SPACES BY ZVI ALTSHULERt, P.

https://www.pdf-archive.com/2016/01/01/acl73/

01/01/2016 www.pdf-archive.com

FoolMeOnce 98%

So, in essence, the task is to construct a binary sequence such that – if the sequence were revealed one bit at a time – an inductive reasoner would fare poorly at predicting its future bits.

https://www.pdf-archive.com/2017/02/19/foolmeonce/

19/02/2017 www.pdf-archive.com

Paper ICT2014 96%

On the Design of Spreading Sequences for CDMA Systems with Nonlinear OQPSK-type Modulations Ângelo da Luz (1,2), Francisco Cercas (1,2), Pedro Sebastião (1,2) and Rui Dinis (2,3) (1) ISCTE-IUL, Lisbon University Institute, Portugal IT, Instituto de Telecomunicações, Portugal (3) FCT-UNL, Monte da Caparica, Portugal (2) Abstract—The main focus of this paper is to present an analytical method for calculate the correlation between OQPSK (Offset Quadrature Phase-Shift Keying) signals modulated by several spreading sequence families.

https://www.pdf-archive.com/2014/09/23/paper-ict2014/

23/09/2014 www.pdf-archive.com

the-game-of-chess-and-searches-in-protein-sequence-space 95%

BIOTOPIC The game of chess and searches in protein sequence space The victory of the computer Deep Blue 2 over the world chess champion, Gary Kasparov, symbolizes the triumph of sophisticated calculations on a superpowerful computer over some of the activities in which the human mind was thought to excel.

https://www.pdf-archive.com/2016/10/24/the-game-of-chess-and-searches-in-protein-sequence-space/

24/10/2016 www.pdf-archive.com

project management system ADD v1.7 95%

Sequence Diagrams .............................................................................................................30 3.1.1.

https://www.pdf-archive.com/2011/04/01/project-management-system-add-v1-7/

01/04/2011 www.pdf-archive.com

parades 94%

The winners from each side will form form an an identically coloured sequence and then parade together receiving great honour and tribute identically coloured sequence and then parade together receiving great honour and tribute from the the two two presidents.

https://www.pdf-archive.com/2018/03/29/parades/

29/03/2018 www.pdf-archive.com

deepnorm-deep-learning 94%

We present a novel approach in which the prediction of our classification algorithm is used by our sequence to sequence model to predict the normalized text of the input token.

https://www.pdf-archive.com/2018/01/06/deepnorm-deep-learning/

06/01/2018 www.pdf-archive.com

AMP 2012 HIVposterV3 SM ES CC 93%

We analyzed the effect of various library preparation protocols on the ability of the Illumina MiSeqTM instrument to detect viral sequence reads, and performed sensitivity analysis for detection of human immunodeficiency virus (HIV) in human plasma.

https://www.pdf-archive.com/2012/10/29/amp-2012-hivposterv3-sm-es-cc/

29/10/2012 www.pdf-archive.com

ExpectedValueThing 93%

OK, so what we are interested in is a number of tosses E after which we can expect a sequence of x heads in a row, given that the specific coin we are tossing lands on heads with a probability p.

https://www.pdf-archive.com/2016/08/14/expectedvaluething/

14/08/2016 www.pdf-archive.com

Genetic Algorithms 92%

Because of redundancies within the DNA sequence, many changes can occur that go unnoticed as they have no net effect.

https://www.pdf-archive.com/2020/04/20/genetic-algorithms/

20/04/2020 www.pdf-archive.com

Work in Evidence of Talent 91%

This sequence is derived by starting with a single object, and adding rows of increasing numbers of such objects in the form of an equilateral triangle.

https://www.pdf-archive.com/2017/01/04/work-in-evidence-of-talent/

04/01/2017 www.pdf-archive.com

SST49LF020A 91%

19 SOFTWARE COMMAND SEQUENCE . ... Program Command Sequence (LPC Mode) .

https://www.pdf-archive.com/2013/05/04/sst49lf020a/

04/05/2013 www.pdf-archive.com

Quality Control Software for Manufacturing - Metis Automation 91%

Enjoy using the Metest Sequence Editor to simply customise your own product tests in a few clicks and revel in the cost savings.

https://www.pdf-archive.com/2018/08/16/quality-control-software-for-manufacturing---metis-automation/

16/08/2018 www.pdf-archive.com

qunatsym 91%

any other sequence can be obtained from the author's one by swapping any two elements of the sequence one or more times.

https://www.pdf-archive.com/2015/09/06/qunatsym/

06/09/2015 www.pdf-archive.com

IP MH3D DUAL EN 91%

PRODUCT INFO Three-dimensional Coordinate Measuring Machine TESA MICRO-HITE 3D DUAL UNIQUE FEATURES –– 2 integrated modes (manual and automatic) Patent-registered –– User-friendly automation –– Smart TESA-REFLEX software –– Quick and easy to handle –– Automatic reproduction of a manually created measuring sequence SWISS MADE PREMIUM PRODUCT SOFT TOUCH QUICK ALIGNMENT EN ISO 10360-2 EASY LEARNING ACCURATE AND RELIABLE CONVENIENT HANDLING 2 MACHINES IN 1 FOR MORE FLEXIBILITY WHEN IN USE From now on, it is no longer necessary to choose between the manual or the automatic world of metrology!

https://www.pdf-archive.com/2014/05/26/ip-mh3d-dual-en/

26/05/2014 www.pdf-archive.com

Urban Cycle Count Worksheet MARCH18 90%

Cycle Count Worksheet Sequence Class - Vendor - Style 1 0530-021019-0006249 Vendor:

https://www.pdf-archive.com/2014/03/18/urban-cycle-count-worksheet-march18/

18/03/2014 www.pdf-archive.com

m140003 90%

Also, certain functions and explored few of their we functions until date that includes vital functions in sequence-specific transcription factors and their the transcriptional and translational regulation of binding domain, effector molecules that function at protein-coding sequences.

https://www.pdf-archive.com/2015/07/27/m140003/

27/07/2015 www.pdf-archive.com

Maffullo ComputationAlgorithmCellsDNA 89%

This process is mostly executed by biological mechanisms called enzymes, enzymes are active protein that catalyze (permit/speed up) a reaction, each enzyme has its own unique sequence of amino acids, which is determined by the genes in the cells.

https://www.pdf-archive.com/2016/10/07/maffullo-computationalgorithmcellsdna/

07/10/2016 www.pdf-archive.com

2013 Cumberworth BiochemJ 88%

The fact that these ID regions are folded in the complex with their partners may explain why a number of studies have found that whereas the sequence outside of the interfaces in transient hubs is enriched in disorder, the interface itself does not show such enrichment [33,34,41].

https://www.pdf-archive.com/2013/09/12/2013-cumberworth-biochemj/

12/09/2013 www.pdf-archive.com

course-description 88%

Graded activities a) Sequence analysis Teams of 2 to 3 students choose a film extract they would like to share with the class.

https://www.pdf-archive.com/2013/09/11/course-description/

11/09/2013 www.pdf-archive.com

Day 11-Pascals triangle 88%

Exercise 5 Fibonacci’s sequence can also be located in Pascal’s triangle.

https://www.pdf-archive.com/2014/01/24/day-11-pascals-triangle/

24/01/2014 www.pdf-archive.com