7:00 – 8:00 Research
- Scheduled a meeting with Don for Monday at 4:00
- Working on finding submission formats for my top 3
- Physical Review E
- Author page
- Here’s the format
- My guess is that there will have to be equations for neighbor calculation (construct a vector from visible neighbors and slew heading and speed) plus maybe a table for the figure 8? Not sure how to do that since the populations had no overlap.
- Length FAQ Looks like 4500 words
Rapid Communication 4500 words Comment / Reply 3500 words - Include:
- Any text in the body of the article
- Any text in a figure caption or table caption
- Any text in a footnote or an endnote
- I’m at 3073 words in the content.
- Here’s the figure word eqivalents:
figure xsize ysize aspect one col two cols 10 6.69 4.03 1.66 110.26 401.04 9 8.13 2.85 2.85 72.56 250.24 8 6.28 5.14 1.22 142.79 531.14 7 8.78 2.94 2.98 70.31 241.23 6 6.64 3.97 1.67 109.74 398.97 5 6.80 3.89 1.75 105.79 383.15 4 8.13 2.85 2.85 72.56 250.24 3 8.13 2.85 2.85 72.56 250.24 2 8.13 2.85 2.85 72.56 250.24 1 7.26 5.44 1.33 132.40 489.59 961.52 3446.08 - So it looks like the word count is between 4,034 and 6,519
- IEEE Transactions on Automatic Control
- Instructions for full papers
- Manuscript style is in section C. References are like ACM
- Normally 12 pages and no more than 16
- A mandatory page charge is imposed on all accepted full papers exceeding 12 Transactions formatted pages including illustrations, biographies and photos . The charge is $125 per page for each page over the first 12 pages and is a prerequisite for publication. A maximum of 4 such additional pages (for a total of 16 pages) is allowed.
- Note that the authors will be asked to submit a single-column double-spaced version of their paper as well, under Supplementary Materials
- To enhance the appearance of your paper on IEEEXplore®, a Graphical Abstract can be displayed along with traditional text. The Graphical Abstract should provide a clear, visual summary of your paper’s findings by means of an image, animation, video, or audio clip. NOTE: The graphical abstract is considered a part of the technical content of the paper, and you must provide it for peer review during the paper submission process.
- Submission policy
- MSWord template and Instructions on How to Create Your Paper
- Guidelines for graphics and charts
- Instructions for full papers
- Journal of Political Philosophy (Not sure if it makes sense, but this was where The Law of Group Polarization was published)
- Author Guidelines
- Manuscripts accepted for publication must be put into JPP house style, as follows:
- SPELLING AND PUNCTUATION: Authors may employ either American or English forms, provided that style is used consistently throughout their submission.
- FOOTNOTES: Should be numbered consecutively. Authors may either:
- employ footnotes of the traditional sort, containing all bibliographic information within them; or else
- collect all bibliographic information into a reference list at the end of the article, to which readers should be referred by footnotes (NOT in-text reference) of the form ‘Barry 1965, p. 87’.
- BIBLIOGRAPHIC INFORMATION: should be presented in either of the following formats:
- If incorporated into the footnotes themselves:
Jürgen Habermas, Legitimation Crisis, trans. Thomas McCarthy (London: Heinemann, 1976), p. 68.
Louise Antony, ‘The socialization of epistemology’, Oxford Handbook of Contextual Political Analysis, ed. by Robert E. Goodin and Charles Tilly (Oxford: Oxford University Press, 2006, pp.58-77, at p. 62.
John Rawls ‘Justice as fairness’, Philosophical Review, 67 (1958), 164-94 at p. 185. - If collected together in a reference list at the end of the article:
Habermas, Jurgen. 1976. Legitimation Crisis, trans. Thomas McCarthy. London: Heinemann.
Antony, Louise. 2006. The socialization of epistemology. Pp. 58-77 in Oxford Handbook of Contextual Political Analysis, ed. by Robert E. Goodin and Charles Tilly. Oxford: Oxford University Press.
Rawls, John. 1958. Justice as Fairness. Philosophical Review, 67, 164-94. - In footnotes/references, spelling should follow the original while punctuation should conform to the style adopted in the body of the text, being either American (double quotation marks outside closing commas and full stops) or English (single quotation marks inside them).For Survey Articles or Debates, option (ii) – i.e., the reference list at the end of the article, together with the corresponding footnote style – is preferred.
- If incorporated into the footnotes themselves:
- Nature (Yeah, I know. But as a letter?)
- Letters are 4 pages, articles are 5
- ‘For authors’ site map
- Presubmission enquiries are not required for Articles or Letters, and can be difficult to assess reliably; Nature editors cannot make an absolute commitment to have a contribution refereed before seeing the entire paper.
- Editorial process
- Letters
- Letters are short reports of original research focused on an outstanding finding whose importance means that it will be of interest to scientists in other fields.
They do not normally exceed 4 pages of Nature, and have no more than 30 references. They begin with a fully referenced paragraph, ideally of about 200 words, but certainly no more than 300 words, aimed at readers in other disciplines. This paragraph starts with a 2-3 sentence basic introduction to the field; followed by a one-sentence statement of the main conclusions starting ‘Here we show’ or equivalent phrase; and finally, 2-3 sentences putting the main findings into general context so it is clear how the results described in the paper have moved the field forwards.
Please refer to our annotated example to see how the summary paragraph for a Letter should be constructed.
The rest of the text is typically about 1,500 words long. Any discussion at the end of the text should be as succinct as possible, not repeating previous summary/introduction material, to briefly convey the general relevance of the work.
Letters typically have 3 or 4 small display items (figures or tables).
Word counts refer to the text of the paper. References, title, author list and acknowledgements do not have to be included in total word counts
- Letters are short reports of original research focused on an outstanding finding whose importance means that it will be of interest to scientists in other fields.
- Physical Review E
8:30 – 5:30 BRC
- Just read Gregg’s response to the white paper. He seems to think that TF is just deep NN. Odd
- Working through fully_connected_feed.py from the TF Mechanics 101 tutorial
- Multiple returns works in python:
def placeholder_inputs(batch_size): images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, Mnist.IMAGE_PIXELS)) labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size)) return images_placeholder, labels_placeholder images_placeholder, labels_placeholder = placeholder_inputs(FLAGS.batch_size) - The logit (/ˈloʊdʒɪt/ loh-jit) function is the inverse of the sigmoidal “logistic” function or logistic transform used in mathematics, especially in statistics. When the function’s parameter represents a probability p, the logit function gives the log-odds, or the logarithm of the odds p/(1 − p).[1]
- In this case,
logits = Tensor("softmax_linear/add:0", shape=(100, 10), dtype=float32) - Here are some of the other variables:
images_placeholder = Tensor("Placeholder:0", shape=(100, 784), dtype=float32) labels_placeholder = Tensor("Placeholder_1:0", shape=(100,), dtype=int32) logits = Tensor("softmax_linear/add:0", shape=(100, 10), dtype=float32) loss = Tensor("xentropy_mean:0", shape=(), dtype=float32) train_op = name: "GradientDescent" op: "AssignAdd" input: "global_step" input: "GradientDescent/value" attr { key: "T" value { type: DT_INT32 } } attr { key: "_class" value { list { s: "loc:@global_step" } } } attr { key: "use_locking" value { b: false } } eval_correct = Tensor("Sum:0", shape=(), dtype=int32) summary = Tensor("Merge/MergeSummary:0", shape=(), dtype=string) - Note that everything is a Tensor except the train_op, which is declared as follows
# Add to the Graph the Ops that calculate and apply gradients. train_op = Mnist.training(loss, FLAGS.learning_rate) print("train_op = ", train_op) - It looks like dictionaries are the equivalent of may labeled matrices
def fill_feed_dict(data_set, images_pl, labels_pl): """Fills the feed_dict for training the given step. A feed_dict takes the form of: feed_dict = { : , .... } Args: data_set: The set of images and labels, from input_data.read_data_sets() images_pl: The images placeholder, from placeholder_inputs(). labels_pl: The labels placeholder, from placeholder_inputs(). Returns: feed_dict: The feed dictionary mapping from placeholders to values. """ # Create the feed_dict for the placeholders filled with the next # `batch size` examples. images_feed, labels_feed = data_set.next_batch(FLAGS.batch_size, FLAGS.fake_data) feed_dict = { images_pl: images_feed, labels_pl: labels_feed, } return feed_dict - lookup_ops seems to have the pieces we want. Now I just have to make it run…
Training?
- https://www.udacity.com/course/deep-learning–ud730
- Started the above. So far so good. Nice softmax example
- Finished one-hot
Last-second proposal writing
Exploit-Exploit 
Explore-Exploit 
Explore-Explore
viztales 
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