tion machine (Clark, 2013), continually attempting to predict the external causes ... empathy (Blake and Shiffrar, 2007). Neu- rophysiological and neuroimaging ...
the paucity of functional differences in protein-coding re- gions of the genome .... to test differences in SAGE and Tag-Seq data, specifically, when there are small ... distribution. Python code used to perform all enrichments is available at:.
Marcus Nyströmâ, Jerry D. Gibsonâ and John B. Andersonâ. â. Dept. of Electrical and Information Technology, Lund University, Sweden. â . Dept. of Electrical ...
Each stimulus was presented 40 times with an interstim- ulus interval of 1.5 seconds. The order of aided and unaided conditions was counterbalanced across ...
Synthetic Aperture Radar (SAR) interferometry has the po- ... If confirmed in the coming years, this .... the radar viewing angle and the active shadow when the.
May 10, 2012 - C ACS publications. solid-state nanopore. Readout speed and length, therefore, are not enzyme limited. The use of optical sensing to detect a.
A modified account, however, suggests that it may not ... 800 £ 600 pixels, 100 Hz refresh) controlled by an Apple G4 P
Apr 14, 2008 - 1University of Pennsylvania School of Medicine, Department of Microbiology, 3610 Hamilton Walk, ... we describe the use of DNA bar coding and pyrosequenc- ...... parallel pyrosequencing reveals association with epigenetic.
Aug 2, 2012 - Armel Salmon,*,1 Joshua A. Udall,â Jeffrey A. Jeddeloh,â¡ and ..... the target region(s) are enriched is a direct measure of the efficacy of.
Yo-Sung Ho and Jung-Ah Choi. Gwangju Institute of Science and Technology (GIST). 261 Cheomdan-gwagiro, Buk-gu,. Gwangju 500-712, Republic of Korea.
Synthetic Aperture Radar (SAR) interferometry has the po- tential to measure temperate glacier displacement with a very high precision compared to terrestrial ...
Feb 3, 2016 - that sequence coding at the single-cell and ensemble level was linked to ... Science Foundation (Awards IOS-1150292 and BCS-1439267 to.
Lip Synchronisation for the iCub Talking Head. Ingo Keller ... Phoneme mapping. http://www.annosoft.com/docs/Visemes17.html, 2015. ... Email: [email protected].
We give new deterministic and randomized algorithms for the wireless model ... fixed arbitrarily to nonzero values, and the remaining coefficients can be deter-.
Jan 18, 2008 ... The Physical Geography Lab builds on your knowledge of physical ... Darrel
Hess, Laboratory Manual – 8th Edition Physical Geography – A ...
Sep 14, 2011 - job markets in the various domains like cognitive flexibility, creativity, knowledge .... 10 Nose plier (small). 1. 60. 60. 11 Hook up wire ..... Conference, F2A-7, Saratoga Springs, N, October 22 â 25,. 2008. [11] Jean-Claude ...
Line Coding: Output of the multiplexer (TDM) is coded into electrical pulses or ....
Line codes are used for digital base-band modulation in data communication
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Stretch Coding and Block Coding: Two New Strategies to Represent. Questionably Aligned DNA Sequences. Daniel L. Geiger. Research Associate, Santa ...
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The ATmega16 microcontroller generally used in IITM is a 40- pin wide DIP (Dual
In Line) package chip of the atmega series by Atmel. This chip is robust, and ...
redundancy in digital communications allows transmission at arbitrarily low bit ...
Coding techniques are classified as either block codes or convolutional codes ...
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Arithmetic coding with integer representation. 1. Representation of numbers. Let's
consider a fixed point representation of numbers in [0-1) interval.
Arithmetic coding with integer representation 1. Representation of numbers Let’s consider a fixed point representation of numbers in [0-1) interval 0.bbbbbbbbbbbb... We can consider 1 = 0.11111111111111.... (an infinite number of 1) We use a finite length representation by keeping (in an integer variable) only the first 32 bits. 0. bbbbbbbbb...bbb bbbbbbbbbbbb... keep first 32 bits
drop the rest
We use in the arithmetic coder: Low – unsigned integer value on 32 bits High – unsigned integer value on 32 bits Range – unsigned integer value on 64 bits We initialize: High = 0xFFFFFFFF and keep in mind that it is followed by an infinite number of 1’s Low = 0x00000000 and keep in mind that it is followed by an infinite number of 0’s In that case: • the first bit of the representation describes the half of the [0-1) interval where the value lies • the first 2 bits of the representation describes the quarter of the [0-1) interval where the value lies:
0bbbb... 00bbbb...
1bbbb... 01bbbb...
10bbbb...
11bbbb...
2. Shiftings to be done in order to keep Range big enough.
2.1. First shifting : when both high and low are in the same half before: High 0h1h2h3h4...h31 Low 0l1l2l3l4...l31
or
action: send 0 to output, shift left and add a bit after: High h1h2h3h4...h 311 Low l1l2l3l4...l 310
High Low
1h1h2h3h4...h31 1l1l2l3l4...l31
send 1 to output, shift left and add a bit
or
High Low
h1h2h3h4...h 311 l1l2l3l4...l 310
2.2. Second shifting (underflow): when high and low are in 2’nd and 3’th quarters before: High 10h1h2h3h4...h30 Low 01l1l2l3l4...l30 action: extract the second bit, increment a counter, shift 1 bit left the last 30 bits and add a bit after: High 1h1h2h3h4...h301 Low 0l1l2l3l4...l300 The exact value of the extracted bit will be known only at the next first shifting, when the corresponding number of bits (according to the counter) will be written to output.
3. Representation of MODEL The simplest representation could be one based on 2 vectors: counts[] and sums[] indexed by the symbol. An example... symbol counts[] sums[] count[] sums[] S
0 2 0
1 5 2
2 3 7
3 2 10
... ... ...
NrSymb-1 ... ...
NrSymb S
= the count for each symbol (frequency of occurrence of the symbol). = the sum of counts of symbols before the symbol. = the sum of counts for all the symbols.
The range for a symbol is now given by: - for symbol 2 (by example) 7 / S – 10 / S - generally sums[symbol]/sums[NrSymb] – sums[symbol+1]/sums[symbol] The standard floating point arithmetic coder updating formulas: High = Low + Range * LimitH(symbol) Low
= Low + Range * LimitL(symbol)
become: High = Low + Range * sums[symbol+1]/sums[NrSymb]-1 Low
= Low + Range * sums[symbol
]/sums[NrSymb]
4. Closing the output stream In theory, at the end a value (any) from the range [Low-High) should be send to the decoder. The simplest case is to send the whole 32 bits representation of Low.
We can save 30 bits by sending only a 2 bit value corresponding to the left margin of the quarter that lies between Low and High (we have 3 cases described in the next figure). The decoder must provide 0’s after the input stream ends (to complete the 32 bit required value). Case
First quarter 00...
Second quarter 01...
Third quarter 10...
1
Low
Send 01
High
Low
Send 10
2 3
Low
Send 01 or 10
Forth quarter 11...
High High
Implementation requirements: • • • • • •
• •
High, Low on 32 bits. The number of symbols should be configurable (at least by a constant value). Adding an EOF symbol can be useful to inform the decoder about the end of the stream. The model should change after each symbol (i.e. we have to implement a dynamic approach). Fixed initialization of the model with all counts 1 is acceptable/enough. (You can try also to use a precomputed initialization). Encapsulate the coder/model in a class in order to easy implement later a contextual approach with different coders for different contexts in a data stream. Use only bitwise operations (shifting, logical) not arithmetic operations (multiplication, division).
Implementation advices: Start with a static approach until it works and only after that put code in an Update method. If you need debugging choose a small number of symbols in the alphabet.