1.1 --- a/thesis/cortex.org	Wed Mar 26 20:38:17 2014 -0400
     1.2 +++ b/thesis/cortex.org	Wed Mar 26 22:17:42 2014 -0400
     1.3 @@ -211,7 +211,8 @@
     1.4     #+caption: full circle. Imagine how you would replicate this functionality
     1.5     #+caption: using low-level pixel features such as HOG filters!
     1.6     #+name: grand-circle-intro
     1.7 -   #+begin_listing clojure
     1.8 +   #+attr_latex: [htpb]
     1.9 +#+begin_listing clojure
    1.10     #+begin_src clojure
    1.11  (defn grand-circle?
    1.12    "Does the worm form a majestic circle (one end touching the other)?"
    1.13 @@ -400,7 +401,7 @@
    1.14       :muscles (movement! model)}))
    1.15    #+end_src
    1.16    #+end_listing
    1.17 -  
    1.18 +
    1.19  ** Embodiment factors action recognition into managable parts
    1.20  
    1.21     Using empathy, I divide the problem of action recognition into a
    1.22 @@ -436,7 +437,8 @@
    1.23     #+caption: independent and ignores vermopomorphic differences such as 
    1.24     #+caption: worm textures and colors.
    1.25     #+name: curled
    1.26 -   #+begin_listing clojure
    1.27 +   #+attr_latex: [htpb]
    1.28 +#+begin_listing clojure
    1.29     #+begin_src clojure
    1.30  (defn curled?
    1.31    "Is the worm curled up?"
    1.32 @@ -451,7 +453,9 @@
    1.33     #+caption: Program for summarizing the touch information in a patch 
    1.34     #+caption: of skin.
    1.35     #+name: touch-summary
    1.36 -   #+begin_listing clojure
    1.37 +   #+attr_latex: [htpb]
    1.38 +
    1.39 +#+begin_listing clojure
    1.40     #+begin_src clojure
    1.41  (defn contact
    1.42    "Determine how much contact a particular worm segment has with
    1.43 @@ -476,7 +480,8 @@
    1.44     #+caption: floor. Note that this function contains no references to 
    1.45     #+caption: proprioction at all.
    1.46     #+name: resting
    1.47 -   #+begin_listing clojure
    1.48 +   #+attr_latex: [htpb]
    1.49 +#+begin_listing clojure
    1.50     #+begin_src clojure
    1.51  (def worm-segment-bottom (rect-region [8 15] [14 22]))
    1.52  
    1.53 @@ -495,7 +500,8 @@
    1.54     #+caption: I am able to both use a previous action predicate (=curled?=)
    1.55     #+caption: as well as the direct tactile experience of the head and tail.
    1.56     #+name: grand-circle
    1.57 -   #+begin_listing clojure
    1.58 +   #+attr_latex: [htpb]
    1.59 +#+begin_listing clojure
    1.60     #+begin_src clojure
    1.61  (def worm-segment-bottom-tip (rect-region [15 15] [22 22]))
    1.62  
    1.63 @@ -525,7 +531,8 @@
    1.64     #+caption: wiggle but can't. Frustrated wiggling is very visually different 
    1.65     #+caption: from actual wiggling, but this definition gives it to us for free.
    1.66     #+name: wiggling
    1.67 -   #+begin_listing clojure
    1.68 +   #+attr_latex: [htpb]
    1.69 +#+begin_listing clojure
    1.70     #+begin_src clojure
    1.71  (defn fft [nums]
    1.72    (map
    1.73 @@ -565,7 +572,8 @@
    1.74     #+caption: Use the action predicates defined earlier to report on 
    1.75     #+caption: what the worm is doing while in simulation.
    1.76     #+name: report-worm-activity
    1.77 -   #+begin_listing clojure
    1.78 +   #+attr_latex: [htpb]
    1.79 +#+begin_listing clojure
    1.80     #+begin_src clojure
    1.81  (defn debug-experience
    1.82    [experiences text]
    1.83 @@ -683,7 +691,8 @@
    1.84     #+caption: Program to convert an experience vector into a 
    1.85     #+caption: proprioceptively binned lookup function.
    1.86     #+name: bin
    1.87 -   #+begin_listing clojure
    1.88 +   #+attr_latex: [htpb]
    1.89 +#+begin_listing clojure
    1.90     #+begin_src clojure
    1.91  (defn bin [digits]
    1.92    (fn [angles]
    1.93 @@ -741,7 +750,8 @@
    1.94     #+caption: and finding the longest (ie. most coherent) interpretation
    1.95     #+caption: of the data.
    1.96     #+name: longest-thread
    1.97 -   #+begin_listing clojure
    1.98 +   #+attr_latex: [htpb]
    1.99 +#+begin_listing clojure
   1.100     #+begin_src clojure
   1.101  (defn longest-thread
   1.102    "Find the longest thread from phi-index-sets. The index sets should
   1.103 @@ -783,7 +793,8 @@
   1.104     #+caption: Fill in blanks in sensory experience by replicating the most 
   1.105     #+caption: recent experience.
   1.106     #+name: infer-nils
   1.107 -   #+begin_listing clojure
   1.108 +   #+attr_latex: [htpb]
   1.109 +#+begin_listing clojure
   1.110     #+begin_src clojure
   1.111  (defn infer-nils
   1.112    "Replace nils with the next available non-nil element in the
   1.113 @@ -804,19 +815,21 @@
   1.114     
   1.115     To use =EMPATH= with the worm, I first need to gather a set of
   1.116     experiences from the worm that includes the actions I want to
   1.117 -   recognize. The =generate-phi-space= program (listint
   1.118 +   recognize. The =generate-phi-space= program (listing
   1.119     \ref{generate-phi-space} runs the worm through a series of
   1.120     exercices and gatheres those experiences into a vector. The
   1.121     =do-all-the-things= program is a routine expressed in a simple
   1.122 -   muscle contraction script language for automated worm control.
   1.123 +   muscle contraction script language for automated worm control. It
   1.124 +   causes the worm to rest, curl, and wiggle over about 700 frames
   1.125 +   (approx. 11 seconds).
   1.126  
   1.127     #+caption: Program to gather the worm's experiences into a vector for 
   1.128     #+caption: further processing. The =motor-control-program= line uses
   1.129     #+caption: a motor control script that causes the worm to execute a series
   1.130     #+caption: of ``exercices'' that include all the action predicates.
   1.131     #+name: generate-phi-space
   1.132 -   #+attr_latex: [!H]
   1.133 -   #+begin_listing clojure 
   1.134 +   #+attr_latex: [htpb]
   1.135 +#+begin_listing clojure 
   1.136     #+begin_src clojure
   1.137  (def do-all-the-things 
   1.138    (concat
   1.139 @@ -843,7 +856,8 @@
   1.140     #+caption: Use longest thread and a phi-space generated from a short
   1.141     #+caption: exercise routine to interpret actions during free play.
   1.142     #+name: empathy-debug
   1.143 -   #+begin_listing clojure
   1.144 +   #+attr_latex: [htpb]
   1.145 +#+begin_listing clojure
   1.146     #+begin_src clojure
   1.147  (defn init []
   1.148    (def phi-space (generate-phi-space))
   1.149 @@ -891,7 +905,8 @@
   1.150     #+caption: Determine how closely empathy approximates actual 
   1.151     #+caption: sensory data.
   1.152     #+name: test-empathy-accuracy
   1.153 -   #+begin_listing clojure
   1.154 +   #+attr_latex: [htpb]
   1.155 +#+begin_listing clojure
   1.156     #+begin_src clojure
   1.157  (def worm-action-label
   1.158    (juxt grand-circle? curled? wiggling?))
   1.159 @@ -931,6 +946,7 @@
   1.160  
   1.161     #+caption: Program to generate \Phi-space using manual training.
   1.162     #+name: manual-phi-space
   1.163 +   #+attr_latex: [htpb]
   1.164     #+begin_listing clojure
   1.165     #+begin_src clojure
   1.166  (defn init-interactive []
   1.167 @@ -949,11 +965,174 @@
   1.168  
   1.169    After about 1 minute of manual training, I was able to achieve 95%
   1.170    accuracy on manual testing of the worm using =init-interactive= and
   1.171 -  =test-empathy-accuracy=. The ability of the system to infer sensory
   1.172 -  states is truly impressive.
   1.173 +  =test-empathy-accuracy=. The majority of errors are near the
   1.174 +  boundaries of transitioning from one type of action to another.
   1.175 +  During these transitions the exact label for the action is more open
   1.176 +  to interpretation, and dissaggrement between empathy and experience
   1.177 +  is more excusable.
   1.178  
   1.179  ** Digression: bootstrapping touch using free exploration
   1.180  
   1.181 +   In the previous section I showed how to compute actions in terms of
   1.182 +   body-centered predicates which relied averate touch activation of
   1.183 +   pre-defined regions of the worm's skin. What if, instead of recieving
   1.184 +   touch pre-grouped into the six faces of each worm segment, the true
   1.185 +   topology of the worm's skin was unknown? This is more similiar to how
   1.186 +   a nerve fiber bundle might be arranged. While two fibers that are
   1.187 +   close in a nerve bundle /might/ correspond to two touch sensors that
   1.188 +   are close together on the skin, the process of taking a complicated
   1.189 +   surface and forcing it into essentially a circle requires some cuts
   1.190 +   and rerragenments.
   1.191 +   
   1.192 +   In this section I show how to automatically learn the skin-topology of
   1.193 +   a worm segment by free exploration. As the worm rolls around on the
   1.194 +   floor, large sections of its surface get activated. If the worm has
   1.195 +   stopped moving, then whatever region of skin that is touching the
   1.196 +   floor is probably an important region, and should be recorded.
   1.197 +   
   1.198 +   #+caption: Program to detect whether the worm is in a resting state 
   1.199 +   #+caption: with one face touching the floor.
   1.200 +   #+name: pure-touch
   1.201 +   #+begin_listing clojure
   1.202 +   #+begin_src clojure
   1.203 +(def full-contact [(float 0.0) (float 0.1)])
   1.204 +
   1.205 +(defn pure-touch?
   1.206 +  "This is worm specific code to determine if a large region of touch
   1.207 +   sensors is either all on or all off."
   1.208 +  [[coords touch :as touch-data]]
   1.209 +  (= (set (map first touch)) (set full-contact)))
   1.210 +   #+end_src
   1.211 +   #+end_listing
   1.212 +
   1.213 +   After collecting these important regions, there will many nearly
   1.214 +   similiar touch regions. While for some purposes the subtle
   1.215 +   differences between these regions will be important, for my
   1.216 +   purposes I colapse them into mostly non-overlapping sets using
   1.217 +   =remove-similiar= in listing \ref{remove-similiar}
   1.218 +
   1.219 +   #+caption: Program to take a lits of set of points and ``collapse them''
   1.220 +   #+caption: so that the remaining sets in the list are siginificantly 
   1.221 +   #+caption: different from each other. Prefer smaller sets to larger ones.
   1.222 +   #+name: remove-similiar
   1.223 +   #+begin_listing clojure
   1.224 +   #+begin_src clojure
   1.225 +(defn remove-similar
   1.226 +  [coll]
   1.227 +  (loop [result () coll (sort-by (comp - count) coll)]
   1.228 +    (if (empty? coll) result
   1.229 +        (let  [[x & xs] coll
   1.230 +               c (count x)]
   1.231 +          (if (some
   1.232 +               (fn [other-set]
   1.233 +                 (let [oc (count other-set)]
   1.234 +                   (< (- (count (union other-set x)) c) (* oc 0.1))))
   1.235 +               xs)
   1.236 +            (recur result xs)
   1.237 +            (recur (cons x result) xs))))))
   1.238 +   #+end_src
   1.239 +   #+end_listing
   1.240 +
   1.241 +   Actually running this simulation is easy given =CORTEX='s facilities.
   1.242 +
   1.243 +   #+caption: Collect experiences while the worm moves around. Filter the touch 
   1.244 +   #+caption: sensations by stable ones, collapse similiar ones together, 
   1.245 +   #+caption: and report the regions learned.
   1.246 +   #+name: learn-touch
   1.247 +   #+begin_listing clojure
   1.248 +   #+begin_src clojure
   1.249 +(defn learn-touch-regions []
   1.250 +  (let [experiences (atom [])
   1.251 +        world (apply-map
   1.252 +               worm-world
   1.253 +               (assoc (worm-segment-defaults)
   1.254 +                 :experiences experiences))]
   1.255 +    (run-world world)
   1.256 +    (->>
   1.257 +     @experiences
   1.258 +     (drop 175)
   1.259 +     ;; access the single segment's touch data
   1.260 +     (map (comp first :touch))
   1.261 +     ;; only deal with "pure" touch data to determine surfaces
   1.262 +     (filter pure-touch?)
   1.263 +     ;; associate coordinates with touch values
   1.264 +     (map (partial apply zipmap))
   1.265 +     ;; select those regions where contact is being made
   1.266 +     (map (partial group-by second))
   1.267 +     (map #(get % full-contact))
   1.268 +     (map (partial map first))
   1.269 +     ;; remove redundant/subset regions
   1.270 +     (map set)
   1.271 +     remove-similar)))
   1.272 +
   1.273 +(defn learn-and-view-touch-regions []
   1.274 +  (map view-touch-region
   1.275 +       (learn-touch-regions)))
   1.276 +   #+end_src
   1.277 +   #+end_listing
   1.278 +
   1.279 +   The only thing remining to define is the particular motion the worm
   1.280 +   must take. I accomplish this with a simple motor control program.
   1.281 +
   1.282 +   #+caption: Motor control program for making the worm roll on the ground.
   1.283 +   #+caption: This could also be replaced with random motion.
   1.284 +   #+name: worm-roll
   1.285 +   #+begin_listing clojure
   1.286 +   #+begin_src clojure
   1.287 +(defn touch-kinesthetics []
   1.288 +  [[170 :lift-1 40]
   1.289 +   [190 :lift-1 19]
   1.290 +   [206 :lift-1  0]
   1.291 +
   1.292 +   [400 :lift-2 40]
   1.293 +   [410 :lift-2  0]
   1.294 +
   1.295 +   [570 :lift-2 40]
   1.296 +   [590 :lift-2 21]
   1.297 +   [606 :lift-2  0]
   1.298 +
   1.299 +   [800 :lift-1 30]
   1.300 +   [809 :lift-1 0]
   1.301 +
   1.302 +   [900 :roll-2 40]
   1.303 +   [905 :roll-2 20]
   1.304 +   [910 :roll-2  0]
   1.305 +
   1.306 +   [1000 :roll-2 40]
   1.307 +   [1005 :roll-2 20]
   1.308 +   [1010 :roll-2  0]
   1.309 +   
   1.310 +   [1100 :roll-2 40]
   1.311 +   [1105 :roll-2 20]
   1.312 +   [1110 :roll-2  0]
   1.313 +   ])
   1.314 +   #+end_src
   1.315 +   #+end_listing
   1.316 +
   1.317 +
   1.318 +   #+caption: The small worm rolls around on the floor, driven
   1.319 +   #+caption: by the motor control program in listing \ref{worm-roll}.
   1.320 +   #+name: worm-roll
   1.321 +   #+ATTR_LaTeX: :width 12cm
   1.322 +   [[./images/worm-roll.png]]
   1.323 +
   1.324 +
   1.325 +   #+caption: After completing its adventures, the worm now knows 
   1.326 +   #+caption: how its touch sensors are arranged along its skin. These 
   1.327 +   #+caption: are the regions that were deemed important by 
   1.328 +   #+caption: =learn-touch-regions=. Note that the worm has discovered
   1.329 +   #+caption: that it has six sides.
   1.330 +   #+name: worm-touch-map
   1.331 +   #+ATTR_LaTeX: :width 12cm
   1.332 +   [[./images/touch-learn.png]]
   1.333 +
   1.334 +   While simple, =learn-touch-regions= exploits regularities in both
   1.335 +   the worm's physiology and the worm's environment to correctly
   1.336 +   deduce that the worm has six sides. Note that =learn-touch-regions=
   1.337 +   would work just as well even if the worm's touch sense data were
   1.338 +   completely scrambled. The cross shape is just for convienence. This
   1.339 +   example justifies the use of pre-defined touch regions in =EMPATH=.
   1.340 +
   1.341  * Contributions
   1.342  
   1.343