vision
Contents
7.3. vision#
7.3.1. neural signals#
ion channel properties
gating energy - how is the channel activated
ionic selectivity - which ions pass through
high ap velocity when these are high
channel density
channel kinetics
axon diameter
axon surface resistance
\(\lambda = \sqrt{\frac{r_m}{r_a}}\)
synapse types
electrical
chemical
postsynaptic receptors
ionotropic receptors - directly gated
metabotropic receptors - indirectly gated through 2nd messengers
If ion reversal potential is 0 (ex. \(E_{Cl}\) sometimes) then shunting = divisive inhibition
7.3.2. electrophysiology#
EEG - whole brain
ERG (electroretinogram) - whole retina
single cell
sharp micro-electrode - has problems
high resistance
poor seal with membrane
mechanically unstable
patch recording - uses suction to overcome powers
Flaw: bad for studying second-messenger systems because inside of electrode / cell fuse
different types (whole cell, outside-out, inside-out)
clamp
types
whole-cell
cell-attached
inside-out
outside-out
whole cell perforated - generally better, but difficult
recording types
current-clamp - record potential
voltage clamp - record current - most common
conductance-clamp - complicated
IV curve - measured with voltage clamp
V - voltage clamped at
I - maximal current evoked by clamping at this voltage
7.3.3. recording + imaging#
<IP_ADDRESS>. electrode arrays#
multi-electrode array recording (MEA)
well-suited for retina
can now get several thousand electrodes
put retina onto MEA to record ganglion cells
waves of activity spread accross retina during development - probably important for wiring retina
patch ~ 500µm accross
spike sorting
cluster spikes from different neurons based on amplitude, wave shape, refractory period violations
spatiotemporal white noise stimulu - sequence of stimulus frames with randomly assigned pixel intensities (Bernoulli or Gaussian)
spike-triggered average stimulus - averagin frames that correspond to spikes - yields receptive field
requires finding timing (too short and spike won’t fire, too long and won’t repeatedly fire)
retinal cell ganglion classification
cluster by STA timecourse and autocorrelation pca
after clustering, receptive fields of any cluster don’t overlap too much
cell mosaics - ganglion cells tile entire retina
ganglion cell receptive field instead of one blob is several small blobs (the cone array)
each blob corresponds to one cone cell
cones: red, green, blue cones are random
ganglion midget cells contain color information - make red-green connections
this is found in STA
connects to broad set of cells, not just closest
<IP_ADDRESS>. imaging - voltage#
voltage-sensitive dyes - would be great
could provide spatially localized, non invasive recordings
doesn’t exists - usually toxic and inefficient (small fluorescence change / voltage change)
APs short and small area limiting number of photons
subthreshold PSPs (postsynaptic potentials) only have small voltage change
electrochromic - fast, low-sensitivity
quenching/FRET - slow, high capacitance
photo-induced electron transfer - fast, high sensitivity, low capacitance
currently being developed by evan miller at berkeley in chemistry
problem - lights up all the cells - trying to target a cell with genetics
calcium imaging
calcium influxes into cell through a variety of mechanisms
calcium indicators
original: aequorin - bioluminescent protein from jellyfish
calcium indicator - calcium binds to fluorophore and changes its shape, which changes its fluorescence
fret-based - calcium brings together two proteins
now most common: GCaMPs
two-photon imaging in the retina
infrared stimulus to drive laser (can’t use light, would stimulate retina)
T. Euler has been leader in this field
can simultaneously attach electrode and measure single spikes while calcium imaging
Ca signal slower than electrical signal - can lose some things
lots of functional types of retinal ganglions cells (>32?)
respond to different stimuli
different morphology
7.3.4. rod and cone photoreceptor function#
retina - large metabolic rate
at the back of the eye, fairly regular array
~1.2 mil optic nerve fibers
pigment absorbs stray photons to reduce noise
cones
~6 mil cones
low sensitivity
fast responses
don’t saturate
selective for the direction of light rays
rods
~120 mil
high sensitivity to light
slow responses
saturate
photo-transduction - converts photons into voltage-changes
terminals
cone pedicle
rod spherule
glutamate release modulated by voltage + Ca
7.3.5. horizontal cells - outer retinal signaling and lateral inhibition#
on-center - responds to white small circle
off-center - responds to black small circle
horizontal cells - if you make circle to big, these inhibit the photoreceptors
adjust for mean by shifting calcium with hc surround antagonism (5 possible biphysical mechanisms)
extracellular pH
ephaptic mechanism
natural scenes contain strong spatial correlations
predictive coding - use surrounding regions to predict the center value
subtract predicted value from actually measured value
send nothing if you could have predicted, otherwise info you send is interesting
7.3.6. signaling pathways through the retina - amacrine cells and inner retinal processing of visual information#
bipolar cells begin parallel signalling in the visual system
amacrine cells - generally modulate bipolar cells / ganglion cells
very structurally diverse: glycinergic = narrow-field, GABAergic = wide-field
way more cones than ganglion cells
ganglion cells have object motion sensitivity
generated by lateral inhibition
starburst amacrine cells - generate directional signals in the retina
7.3.7. retinal ganglion cells#
takes inputs from bipolar cells
<IP_ADDRESS>. primate retina#
primate retina has 2 major types of ganglion cells
midget ganglion cells (~75%) - majority, high spatial acuity
parvocellular pathway
parasol ganglion cells (~15%) - high temporal resolution
magnocellular pathway
little known about most other types of ganglion cells
we don’t have aliasing because visual system filters out the high frequencies
color
red-green keeps center surround
blue-yellow doesn’t
direction-selective ganglion cells
activated when the image moves on the retina
specific allele can get rid of these (affects GABAergic starburst amacrine cells)
different dendrites represent different directions (inputs are pretty symmetrical)
7.3.8. non-neuronal retina stuff#
<IP_ADDRESS>. retinal glia#
glia greek for “glue”
3 types
Muller cells
from multiplotent retinal progenitor cells (same that make neurons)
in fish, with damage muller cells can become neurons (forced in mammals)
abundant, tile the retina
functions
mechanical support
energy storage
clearing waste products
neurotransmitter recycling (ex. glutamate-glutamine cycling)
metabolism
K+ homeostasis (uptake and redistribution)
disease - gliosis - upregulation of intermediate filaments common in many retinal diseases
astrocytes
originate from brain, enter via optic nerve
look star shaped
functions: lots of neurovascular
cell bodies don’t move, but processes constantly move
disease - become reactive in many retinal diseases
microglia
myeloid origin
concentrated in synaptic layers
immune cells - phagocytosis
disease - activation occurs with / before retinal cell death
microglial depletion alters retinal synapses \(\implies\) microglia maintain synapses (wang et al. 2016 j neurosci)
microglia are highly motile and respond dynamically to stimuli (e.g. neurotransmitters)
microglia respond dynamically to injury
<IP_ADDRESS>. retinal pigment epithelium (rpe)#
monolayer of pigmented, hexagonally-shaped epithelium cells
surround outer segments of photoreceptors
cells have tight junctions
functions
main: light absorption
epithelial transport - require water + photoreceptor cycling + oxygen
photo-oxidation causes damage - photoreceptor tip constantly being phagocytosed, base regenerated - completely renewed in 11 days
visual cycle - recycling retinoids
number of diseases involve this
others: phagocytosis, secretion, glia
<IP_ADDRESS>. retinal blood supply#
retina has highest metabolic demand of any tissue
vasculature - eye is only place we can noninvasively view vasculature
often diagnose stuff like hypertension / diabetes fom eye
2 major blood supplies (non-overlapping)
outer retinal blood supply (past RPE) = posterior ciliary arteries
outer 1/3
bruch’s membrane
choroid - helps provide nutrients, cool retina
inner retinal blood supply = central retinal artery
inner 2/3
no inner capillaries in foveal avascular zone - area near fovea (these would block light)
blood-retinal barriers (BRB)
outer BRB - tight junctions between RPE cells
fenestrated - leaky
inner BRB - tight junctions between capillary endothelial cells
non-fenestrated
autoregulation of retinal blood supply
outer - under sympathetic control
inner - autoregulated by neuronal demands (neurovascular coupling)
7.3.9. retinal diseases#
these are the 3 retinal diseases deepmind is studying
<IP_ADDRESS>. diabetic retinopathy#
leaky blood vessels - often diagnose diabetes through retinopathy
diabetes types
type 1 - autoimmune reaction destroys pancreating \(\beta\) cells that produces insulin: hyperglycemia + hypoglycemia
type 2 - reduced insulin sensitivity: mainly hyperglycemia
need insulin injection
diabetic retinopathy types
proliferative
very bad
non-proliferative
microangiopathy - mainly affects capillaries
patients probably won’t notice this unless its in the macula - this is why diagnosis w/ ml could be useful
neuronal changes seem to precede vascular changes
treatment - intravitreal injections of anti-VEGF treatments
<IP_ADDRESS>. glaucoma#
optic neuropathy with ganglion cell death and visual field loss
people say “pressure in the eye” - but this is just a risk factor
lose your periphery slowly
optic nerve
1-2.2 million ganglion cell axons
~40% of total afferent input to the brain/
cup - region where there are no axons
7.3.10. experimental methods#
7 dimensions
spatial res.
temporal res.
depth - how deep in can you image
toxicity - does it damage the cells
spatial field - how big a region can you see
temporal duration (how long it can stay in)
invasiveness
<IP_ADDRESS>. single cell#
fluorescence imaging
gfp - protein, gets spine-level precision
calcium imaging - not a protein, but still does fluorescence
microelectrode recording
extracellular recording
can measure local field potentials - sum of local currents (most of the volume is in the dendrites - not a great proxy for spikes)
can go deep
spikes are less ambiguous than in calcium imaging
intracellular recording
can measure membrane potential much more precisely
strengths
great temporal resolution
weaknesses
invasive
single neurons has potential biases
80 neurons are not representative
more likely to record from excitatory, bigger neurons
unnatural stimuli
<IP_ADDRESS>. alteration#
optogenetic probes
light-sensitive opsins are genetically modified - when light shines on it, does something (depolarize, hyperpolarize, alter intracellular signaling)
delivery
viral infection
transgenic animals
electroporation
transcranial magnetic stimulation
coil sits on head, induces current
pulse is brief - 1 ms
functional effects are long - milliseconds, minutes, days…
uses
enhance neural function
probe excitability
explore functional anatomy
“virtual lesions” - but lingers, …
local microstimulation with invasive electrodes possible
<IP_ADDRESS>. measure electromagnetic signals#
EEG (electroencephalography)
recorded on scalp (only gets synchronous activity)
can analyze frequencies (higher frequencies like gamma are attenuated)
delta
theta
alpha
beta
gamma
0.5-4 (Hz)
4-8
8-13
13-30
30-50
can analyze event-related potentials (when the signals peak)
ECOG = electrocorticography - put electrodes on brain (for patients)
MEG (magenetoencephalography)
measure magnetic fields generated by active neurons
fMRI type setup
higher spatial resolution
signal is not really distorted by skull (magnetic field goes through better)
<IP_ADDRESS>. dyes#
voltage-sensitive dyes
leaks over everything - can’t select for single neurons / spikes
looks at large spatial field, but can’t resolve single cells
dyes are toxic to neurons over time
<IP_ADDRESS>. blood#
intrinsic signal optical imaging
shine in light and see what’s reflected - oxygenated is more reddish, deoxygenated more bluish
have to expose surface of brain (but can do pre-surgical imaging in humans)
spatial res limited by capillaries (100 micrometers)
temporal res - slow because neurovascular coupling is slow
fMRI
applied magnetic field - very large, homogenous magnetic field
pulse of energy in radiofrequency range excite protons
protons emit energy at resonance frequency proportional to local magnetic field strength (this called nuclear magnetic resonance)
magnetic field gradients allow for spatial localization of MR signal in 3d
rate of energy decay in brain depends on local biochemistry + oxygenated hemoglobin
BOLD = blood oxygenation level-dependent signal
blood oxygenation has linear relationship with decay
pipeline
activity -> more oxygen use + cerebral blood flow -> magnetic field distortions -> MRI signal intensity
activity make oxygen go down real quick then blood flow over compensates, then saturates
can’t separate excitatory / inhibitory
LFP predicts BOLD well (only slightly better than MUA)
all are really pretty well correlated
fMRI analysis
block design - usually have block of nothing between stimuli blocks
activation statistics - compare activation between conditions
event-related design - all trials analyzed over time
<IP_ADDRESS>. PET#
PET
fMRI type setup
radioactive thing (positron) put into brain
as it decays, can triangulate things
cells with most FDG (a tracer) are using the most glucose
now used for studying neurotransmitter maps
good for studying specific radiolabeled tracers
relatively poor spatial res (1 cm), temporal res is minutes
minor risk
<IP_ADDRESS>. mapping visual cortex#
cortical flat mapping
gray matter has ~ 100,000 somas / mm^3, ~3km axon /mm^3
folds in cortical topology make some things much farther than they seem
most stuff is white matter
sulci - negative curvature (indent)
gyri - postive curvature (outdent)
some sulci are common among all people (landmarks)
columns are organized into “pinwheels” - columns in a circle prefer different orientations in spinning pattern
weaknesses
expensive
poor temporal / spatial resolution
topographic mapping
pimary visual cortex - visual maps seem to be replicated
history
gun people - between franco-prussian war, russo-japanese war
sir joseph whitworth
alfred drupp
william ellis metford
faster bullets would leave, cauterize wound, more local cuts
tatsuji inouye - studied soldiers after gunshot wounds (1909) - learned map of retina in the back of the brain
mapping
each visual areas has some retinotopic mapping
moving points
periodic mapping stimuli - rotating wedge / other things (fig left) \(\implies\) between visual areas (e.g. v1 + v2) mirror image tiling of locations (borders between regions are like mirrors)
eccentricity mapping (fig right) - rings spread out evenly - no mirrors
cortical magnification - way more v1 for central locations
more than proportion of retina dedicated to v1
somehow this is offset to give us our normal perception
population receptive field mapping
predefine each fMRI voxel will respond to Gaussian
over time show stimulus
optimization model finds best point point for each voxel
more efficient than periodic mapping…
<IP_ADDRESS>.1. magnetic resonance spectroscopy (mrs)#
just like NMR - put in something like water
gives chemical composition of a region of brain
slow, low resolution
basically pick something (like GABA) and just look at where that is
certain molecules appear the same though….
7.3.11. info processing in the visual system#
vision is ill-posed problem
light reflection depends on multiple things
light source
material
angle
atmospheric properties
lens sorts EM waves by direction
also lets you get in more light compared to pinhole camera
brain must use priors to create repr. of world
ex. image of a cow - data on retina is messy but you perceive something better
ex. mooney faces - can create stories from images after shading
ex. brown and orange are same hue, but different brightness
3d shadow helps make this better
color constancy - see same colors under different lighting conditions
very large individual differences in ratios of l, m, s cones
eyes develop very suddenly around cambrian explosion ~500 mya
eyes takes ~500k years (very fast) - nilsson & pelger 1994
fish have evolved spherical lens several times independently
can understand lenses if we understand optics
can understand brain if we understand principles…
compound eyes (ex. fly) repeat dots each get slightly shifted version of world
collects lots of light - operates at very high speed (e.g. fly h1 neuron bialek 2001)
sand wasp can find its nest based on pattern of stuff that surrounds its nest
jumping spider has interesting visual system
7.3.12. dynamic range of rods + cones#
spontaneous isomerizations determine lower limit of light detection
rods
saturate
cones
turn on then off
lateral inhibition - improves contrast
horizontal cells
HI (type B)
dendrites contact rods, M/L cones
connected via gap junctions
HII (type A)
dendrites contact S, M/L cones
bipolar cells
rod v. cone
on v. off
midget v. diffuse
amacrine cells
link bipolar cells to ganglion cells
7.3.13. whitening and tiling#
redundancy reduction (horace barlow 1961) - pixels are correlated, want to compress info
second-order statistics (auto-correlation function) - pixel correlation vs. spatial separation of pixels
power spectrum of natural images - average power over angles
power goes down for higher spatial frequencies (white noise would be flat)
seems to go down as ~1/freq universally
optic nerve should send unpredictable signals ~look like white noise, decorrelated
shouldn’t be able to predict one nerve from the others
this is because nerves + spikes are expensive
multiply by filter = frequency to get filter that is flat (whitening - atick & redlich 1992)
this is a lowpass filter
this filter looks like center-surround 2D filter (looks like edges)
wasn’t feasible to do this experiment (measuring optic nerve) until recently but has been shown to be true
people have characterized spatiotemporal power spectrum of natural scenes
ex. dan et al. 1996 - LGN neurons whiten time-varying natural images but not white noise
efficient coding model (karklin + simoncelli 2012)
why need on and off type - duplicates each cone
hypothesis: RGCs send spikes which are discretized (not continuous like all the signals within retina)
differences need to be able to send positive/negative rates - this requires having 2 cells
alternatively could have a baseline and send more or less but baseline signal is wasteful because usually send 0
still don’t send 0 as 0 firing rate because too slow to send a 0
try to maximize information out of firing rates - penalty on firing rates
simulation with some neurons on natural images
yields on-center / off-center neurons
optic nerve has pretty equal firing rates - don’t get pca type solns
tiling - ratio varies with eccentricity (higher cone:ganglion ratio in periphery)
bipolar:ganglion is 1:1 in fovea
dendritic field diameter increases linearly with eccentricity
cones also increase
RGCs limit peripheral vision not cones
picture here!!! smoothing and subsampling by RGCs
scale-invariant sampling lattice - same amount of RGCs regardless of how far smth is
but more photoreceptors per ganglion cell (maybe higher signal to noise ratio)
ex. with letters getting bigger in surround
7.3.14. visual pathways#
<IP_ADDRESS>. visual pathways#
organization
thalamus + cortex always work together = thalamocortical system
midbrain - reptilian, old
all sensory must pass through thalamus to get to cortex
vision must pass through LGN (also stuff goes back to pulvinar)
different RGCs that do different things have different dendritic field diameters
6 main targets
LGN (thalamus)
has 6 layers
superior colliculus (midbrain)
eye movements - sensory map on top of motor map - can make eyes move to a location
map centered at eye location
thought to be reflexive
without LGN, get blindsight - can catch a ball, dodge things, …
suprachiasmatic nucleus
circadian rhythm
gets input from special photosensitive RGCs that are big + slow
accessory optic system
pretectum - pupillary light reflex
pregeniculate
<IP_ADDRESS>. lgn#
has six layers
parvocellular are upper 4 layers
subdivided by on/off, left/right
inputs from midget cells
magnocellular are bottom 2 layers
inputs from parasol cells
losing magno seems to lose spatial frequency, control different temporal frequencies, parvo gives you color
important - different spatial/temporal frequencies - differentiate from the beginning
<IP_ADDRESS>. color#
L, M, S cones (red, green, blue)
have learned more from psychophysics than from neural recording
psychophysics: adapt to one axis of color
people habituate to directions based on changes of cones (ex. adapting to S cone doesn’t affect L & M cones)
color oponnency in LGN (derrington et al. 1984) - 2 types of color cells in LGN
cone response distributions
L and M correlate a lot
L and S correlate a little
pca goes to luminance, \(\alpha\), \(\beta\)
luminance dominates
color really takes only ~10% more space than black-and-white
decompose into luminance image and 2 color difference images
non-luminance image requires less bits (can be blurred, less bits)
<IP_ADDRESS>. eye movements#
saccades - old, human vision is fundamentally dynamic
head also moves while eye moves and they help counteract each other
when in bite bar, saccadic movements are bigger
fixational eye movements
new paper - motion helps you see by sampling cone array
could also be that image fades on retina (ex. troxler fading)
michael land did lots of cool things
7.3.15. v1 (primary)#
pathways
right part of eyes go to right brain (sees left visual field)
corpus collosum connects left/right brains but nothing else
when you lose it, like there are 2 people within person
very difficult stitching problem…
left and right lgn, superior colliculus, …
regions defined by having a topographic map
for later areas, histological differences, connectivity, physiological properties
connections are all bidirectional
retinotopy
v1 has map of retina that’s flattened (proportional to ganglion cells)
probably no “fixed up” image of the world somewhere
could understand perception in terms of action
LGN wires don’t interact with each other too much
some inhibitory interneurons
cortex
~2mm thick
layer 1 - mostly axons
layer 2/3 - association layer
lots of neurons connect to each other
layer 4 - input layer
further subdivided (parvo goes to one, magno to another)
layer 5 - output layer (usually to motor)
comes back tho LGN (adam sillito)
unclear what it does - experiments with cooling cortex don’t yield too many profound changes
neurons from different eyes go to different regions of layer 4 (and then mix in layer 2/3)
ocular dominance columns (only within layer 4) - one eye has bands, other eye has other bands
hypercolumn - put 2 eyes together
lots of metabolic blobs tiles this
contains lots of neurons with different orientation selectivities
monkey has ~1k hypercolumns in V1
contains 100k neurons
14 x 14 pixel array (coming in from thalamus)
1 mm^2 of cortex contains 100k neurons
v1 is highly overcomplete - way more neurons than needed
electrophysiology
electrode pics up microvolts
v1 properties not in LGN
orientation selectivity
direction selectivity
simple cells sum LGN inputs
retina and other things are wired up before birth
standard model of V1
neurons have oriented receptive fields (inhibited by bars around bar) with some temporal component
response normalization, based on neighboring neurons
pointwise non-linearity
bruno doesn’t really believe this leads to perception
neurons are highly nonlinear
recurrent circuits of neurons are even more nonlinear
there is no general method for characterizing nonlinear systems
good model
should be in the structure of layers
why do we need so many neurons
problems
biased sampling - single units, ignore inhibitory, only find neurons that fire for what you want
biased stimuli - bars/spots/etc.
biased theories - data-driven vs. theory
interdependence and context of scene
ecological deviance
power spectrum
horizontal spatial frequency of 0 - vertical grating
fft function assumes image at boundary is tiled - artifacts giving artificial edges (spatial frequencies of 0)
could attenuate function at edges to fix this
7.3.16. extrastriate cortex#
striate cortex - v1 (has some kind of stripe - not striatum)
extrastriate cortex - everything else
all areas have one part on each hemisphere
orientation columns - columns have similar orientation preferences
doesn’t have to do with ocular dominance columns
laterally within layers get all orientations in very small area
repeated - one orientation will be represented lots of times
different columns represent different xy coordinates
overview
dorsal stream - where
mt (middle temporal area)
spatial visual pathway - positional relationships
vision for action pathway
ventral stream - what
v4
object recognition pathway
high resolution and form
10x more feedback, no strict motor areas, but lots of visuomotor areas
V2 / V3 aren’t clearly in either stream
<IP_ADDRESS>. dorsal stream#
adaptation - like psychophysicist’s electrode
area MT (middle temporal of macaque, although farther back in human)
has preferred motion orientation columns
visual area STS (superior temporal sulcus) responds to biological motion
ex. 12 dots look like people
parietal cortex also important for spatial attention
biomotionlab is cool
<IP_ADDRESS>. ventral stream#
V1 -> V4 -> IT ->LGN
we’re constantly adjusting for changes in illumination
v4
v4 seems to correspond to perceived colors not wavelengths
damage to v4 stops you from seeing in color
selectivity of v4 responses
mixed magno and parvo inputs (ferrera et al. 1994)
IT
single column thing…
jennifer aniston cell
hand
Halle Berry cell
columnar architecture in IT also though….
pseudo semantic columnar architecture (ex. facial perspective)
face perception orientation can be discriminated by newborn baby (meltzoff)
can also imitate faces
babies have trouble resolving high frequencies
areas
FFA - face selective, fusiform face area
might not be faces, could be expertise
PPA - places, parahippocampal place area (surrounds hippocampus)
things are assymetric in unclear ways (although they contain representations of different visual fields)
7.3.17. sparse coding#
THIS ISN”T REALLY SPARSE CODING MOVE ELSEWHERE: power spectrum falls off with frequency as \(1/f^2\)(amplitude falls as 1/f)
want to decorrelate - multiply by frequency that’s \(f^2\)
you can’t do this for very high frequencies otherwise you amplify noise
in spatial domain, looks like center surround similar to finding edges
v1 has map of space, magnified at fovea
want to explain how center-surround ganglion cells -> elongated orientation selective receptive fields
representation - complete repr. with minimum number of possible neurons
deeper in cortex - cells become more silent
codes: insert pic!!!!
dense -> sparse -> local (grandmother) codes
sparse coding has questionable empirical evidence
lgn fibers around 20 spikes / sec
layer 4 fires ~ 1 spike /sec
we don’t know this at other layers
sparseness seems pretty constant as you go deeper (Rust & DiCarlo)
tradeoff between complexity and invariance
V1 simple cells are oriented, localized, bandpass
projection pursuit (Field 1994) - project distr. onto low dim: you should get gaussian
want to find axes that maximize non-gaussianity
project idea
look at sparsity in different layers
sparse coding is v1 + retina!!! basis transformation
7.3.18. object recognition#
gabor function - convolve Gaussian with sinusoids of different frequency
from dennis gabor
in gabor transform, each basis function has same number of wobbles (self-similar)
at top of visual system goes to entorhinal cortex then to hippocampus
map sizes
V2 little bigger than V1
they fold over so that map of V1 goes 1-1 with map of V2
neocognitron is unsupervised
comments:
“vision is about more than object recognition so deep nets don’t work”
turing test for vision
affordance = prior
not like deep net which is a top box
lots of outputs from intermediate areas
perception as inference
generative model we’re trying to fit data too
bayes rules: \(P(E|D) \propto P(D|E) \cdot P(E)\) where E is environment and D is data about environment
lee + mumford, 2003 - hierarchical bayesian inference in visual cortex
each area makes guesses and higher areas send back corrections
mumford - fields medalist
in real life, we are constantly guessing and trying to resolve ambiguities
7.3.19. top-down modulation#
attention is most-studied (refers to some different things)
endogenous attention - voluntary, slow, effortful, interruptible
exogenous attention - involuntary, fast effortless, disruptive
attention is about more than where the eye is pointing
change blindness - blind to things you’re not attending to
invisible gorrila, door study
covert attention - posner cueing task
<IP_ADDRESS>. endogenous#
better studied because it’s hard to disentangle stimulus vs. attention in exogenous case
v4 very filtered by attention (ex. reynolds + chelazzi 04)
also effects of attention in area v1 (ex. pick a side to attend to while fixating in center)
IPS1 seem to have maps of attended stimuli (but ignore other stimuli)
frontal eye fields - microstimulation forces eye movement
can stimulate enough to attend, but not to saccade
<IP_ADDRESS>. exogenous#
inhibition of return - if we have attended a region, less like we return to that region
<IP_ADDRESS>. visual search#
having more similar objects makes it difficult
feature-integration theory - different visual features are coded in parallel in separate feature maps (orientation, size, color)
conjunction search - conjunction of features (ex. red circle) takes longer
plenty of other areas
ADD, alzheimers, intermodal attention, applied attention, attentional tracking, neurochemistry of attention, feature- and object-based attention…
ex. driving - people in car will stop talking in serious situations unlike on phone
predictive coding - unpredicted response evokes larger response
fits with bayesian method - only need large response when you don’t predict what’s going to happen
in this way, prediction is opposite to attention
7.3.20. visual neuropsychology#
blindsight - damage to V1; aren’t aware of visual stimuli but can do tasks in forced-choice paradigms
retina goes through some things (ex. pulvinar) that aren’t V1 to get to higher order areas (ex. MT)
dorsal pathway
MT monkey lesions in monkeys impair motion perception but not contrast detection
damage to MT causes motion blindness - life is a set of snapshots
ventral pathway
damage to V4 causes loss of color perception, can’t even imagine colors
patient DF (well-known) - can only do vision for perception + action, couldn’t describe it = visual agnosia - perception as an object is impaired
lots of different kinds
prosopagnosia - can’t recognize faces - FFA, PPA (up to 1%)
spatial neglect - failure to acknowledge objects in field contralateral to the lesion
sometimes group things and only look at right sides of groups
very weird - has strange reference frames
functionally very similar to having blindness on one side
7.3.21. visual cortical development + plasticity#
development things
neurons of right types generated in appropriate places
migrate to final positions
differentiate into final forms
axons must follow right paths
neurons must refine synaptic connections
brain must remain flexible
neurons ride up glial fibers until it stops - cortical layers develop inside-first (tracked)
axon projections - some axons have to travel very far to connect (ex. LGN -> V1)
follows chemical signal (even if it starts somewhere diff ends same place) - roger sperry 1943
3 stages in development of retina-lgn-v1 pathways
experience-independent development - can occur prenatally
ex. segregation in eye-specific layers
retinal waves - spontaneous activity go accross the entire retina
critical period of refinement of connections within and between cortical columns
extremeley sensitive to abnormal experience
competition to decide who connects where
maturation and plasticity in adult life
7.3.22. adult plasticity#
these are due to “fatigue” of stimulated neurons
color adaptation - afterim aimages + complementary colors
slight tilt aftereffect as well
problems with fatigue hypothesis
doesn’t account for long-lasting adaptation effect (ex. McCulloch effect lasts very long time)
don’t see optic flow adaptation in driving, even though we see this in the lab
there is a clear critical period for plasticity, although auditory / somatosensory don’t
braille reading in blind subject activates “visual cortex” area
unclear it there is a critical period for this
perceptual learning - can learn new visual tasks
very sensitive to eye, etc.
trying to reat amblyopia
very unclear how much perceptual learning generalizes
7.3.23. alzheimer’s#
manifests in the eye
two biomarkers - can find with PET or MRI
amyloid \(A\beta\) plaque proteins
pTau proteins
bunch of things in retina
ex. RGC loss, NFL atrophy, blood flow rate, inflammation…all exist in other
main thing - look for AB plaques with stains - requires someone is dead
goal: diagnose alzheimer’s via noninvasive retinal imaging + visual function assessment
imaging: modified spectralis HRA + OCT should look at biomarkers (+ other things ex. look at retinal structure deficits)


Home
About
Contact
Blog
Contact Us
If you have any enquiries, please fill out the form below
First Name
Last Name
Message


In scene1.c
Allow the character to float.
Find:
zCtrlForce = -fallSpeed;
And replace with:
if (jumpUp)
fallSpeed = gravity_force;
zCtrlForce = -fallSpeed;
Compile and run.
Allow the character to fly when the 'Z' key is pressed.
Find:
zCtrlForce = -fallSpeed;
And replace with:
if (keyDown[90])// Z
{
jumpUp = TRUE;
fallSpeed = -gravity_force;
}
zCtrlForce = -fallSpeed;
Compile and run.
In initialize.c
Make the character larger.
In initialize.c
Find:
copyTransFromTo(&character_TRANS[0], &character_STATICTRANS[0], character_MESHCNT);
And replace with:
globalResize(&character_TRANS[0], character_MESHCNT, 0, 0, 0, 3, 3, 3);
globalResize(&run1_TRANS[0], run1_MESHCNT, 0, 0, 0, 3, 3, 3);
globalResize(&run2_TRANS[0], run2_MESHCNT, 0, 0, 0, 3, 3, 3);
globalResize(&run3_TRANS[0], run3_MESHCNT, 0, 0, 0, 3, 3, 3);
globalResize(&run4_TRANS[0], run4_MESHCNT, 0, 0, 0, 3, 3, 3);
globalResize(&jump_TRANS[0], jump_MESHCNT, 0, 0, 0, 3, 3, 3);
globalResize(&liftUp_TRANS[0], liftUp_MESHCNT, 0, 0, 0, 3, 3, 3);
copyTransFromTo(&character_TRANS[0], &character_STATICTRANS[0], character_MESHCNT);
Compile and run.
Increase the blast size for the tanks.
In initialize.c
Find:
// resize meshes
globalResize(&arena_TRANS[0], arena_MESHCNT, 0, 0, 0, 20, 20, 20);
And replace with:
// resize meshes
globalResize(&arena_TRANS[0], arena_MESHCNT, 0, 0, 0, 20, 20, 20);
globalResize(&arena_TRANS[arena_explodeYellow*TRSZ], 1, 0, 0, 0, 3, 3, 3);
Compile and run.


Select report type
Booking
Location
Cars
Employees
Name:
Source:
Dest:
Filter
Clear
Location:
Filter
Clear


Randomly Selected Video
Random |
Latest |
Being Watched Now
Recently Favorited Videos
Feature Suggestions
VidLii Live
November 22-23 2018
Suggestion by: krtcdrc
Longer videos
videos of up to 1 hour should be allowed so we could have alot more creativity.
Suggestion by: spodermen2
4K videos are broken (mp4 265)
4k videos are broken, I uploaded one (it's in my channel) and it is entirely broken.
Suggestion by: PokemonOnline
Custom Thumbnails
Custom thumbnails would be really appreciated. I upload my videos on other sites and tend to draw thumbnails for them, so I'd love to be able to do that here too.
Suggestion by: PonderSprocket
Full subscriptions view
I'm not sure is this was back in 200X but bigger subscribed users videos view can be helpful
Suggestion by: piotrexMaster
Support VidLii
Donate to let us be able to improve video quality and add more awesome features
Or donate bitcoin directly:
17CTYVZhsBiySwf4sAtGUPVt1SuW7vkt5X
Recent Comments


It’s not that easy…
We highly recommend installing Jekyll to preview your site and help troubleshoot failed Jekyll builds.
They repeat this throughout GitHub’s documentation and they really mean it. There’s no easy way to set up a Jekyll blog on GitHub Pages without either setting up Jekyll locally or forking an existing Jekyll blog.
Jekyll is not officially supported for Windows. For more information, see “Jekyll on Windows” in the official Jekyll documentation.
I know, I know… I shouldn’t be using Windows. I used to be a devoted Linux user and I called myself an Open Source Software enthusiast. Well, I forgot. It’s a thing I’m not going to get into right now, but when I started to think again the computer in front of me had Windows on it. This is currently my only option.
I did the reading. This setup requires Jekyll on Ruby on Bash on Ubuntu on Windows and every step of the process comes with vague warnings about issues. Even if I manage to bypass any issues, I have to set up and maintain Ruby on Windows, learn how to use the Gem package manager, setup and maintain Bash on Ubuntu on Windows, learn YAML, and learn the Liquid Templating Engine.
Enough is enough!  What happened to just writing my thoughts in markdown?! I’m busy learning more important things right now. I’m just about ready to throw in the towel and become dependent on WordPress again. One last try… I’ll look for a tutorial.
I found what I needed; An excellent article at Smashing Magazine entitled “Build A Blog With Jekyll And GitHub Pages” promises to tell me exactly how to set up my own blog with Jekyll and host it with GitHub Pages. That sounds perfect! It is just a little bit misleading, though. The article doesn’t actually tell me how to set up my own blog. It tells me how to fork the author’s blog boilerplate and change some variables to show my information instead of his.
You know what… I don’t care. It got the job done and now I have a Jekyll-driven blog. For now, I can post my thoughts and share them however I want to. When I decide to upgrade, either by learning and building Jekyll locally or by diving back into the WordPress ecosystem, I’ll have very little difficulty. My markdown files can very easily be copied over or imported into any system I choose.


Your registeration was successful!
Please pay INR 300 at the counter or log on to www.pvrcinemas.com and get your exclusive PVR Privilege e-card


Estimate parameters of univariate probability distributions with maximum likelihood and Bayesian methods.
| Version: | 1.3.5 |
| Depends: | mvtnorm |
| Imports: | graphics, stats, utils |
| Suggests: | FAdist |
| Published: | 2017-06-12 |
| Author: | Francois Aucoin |
| Maintainer: | Thomas Petzoldt <thomas.petzoldt at tu-dresden.de> |
| License: | GPL-2 |
| URL: | https://github.com/tpetzoldt/FAmle |
| NeedsCompilation: | yes |
| CRAN checks: | FAmle results |
| Reference manual: | FAmle.pdf |
| Vignettes: |
Univariate Frequency Analysis Using FAmle |
| Package source: | FAmle_1.3.5.tar.gz |
| Windows binaries: | r-devel: FAmle_1.3.5.zip, r-release: FAmle_1.3.5.zip, r-oldrel: FAmle_1.3.5.zip |
| OS X binaries: | r-release: FAmle_1.3.5.tgz, r-oldrel: FAmle_1.3.5.tgz |
| Old sources: | FAmle archive |
Please use the canonical form https://CRAN.R-project.org/package=FAmle to link to this page.


A website is kind of like building a house Html is your construction material and is what it is built from DOM is the builders puting everything togeather and making sure its all correct CSS is the interior decorating making your house (or webpage) look good
Boxifying design is when you look at a webpage and break down its individual elements into boxs to show how the page is layed out
the model is four things content, padding, margin and border each lets you create space around elements, inside elements and inbetween elements.


Welcome to GooglePeopleSearch !
(It's actually just Google Search. I wrote a scraper to extract data from the carousel and sidebar and display it as the result itself. Thank you for your perfection, Google !)
You can search for
Specific people. Examples: Sachin Tendulkar (or) Lionel Messi
The title of a person. Example, Coach of the Los Angeles Clippers (or) CEO of Google


This incredible Moto Z4 deal will only cost you $10 a month
by James RogersonThis incredible Moto Z4 deal will only cost you $10 a month
Get 5G on a budget
The Moto Z4 (Image credit: Future)
5G is typically expensive but it doesn’t have to be, as right now you can get the Moto Z4 for the remarkably low price of just $10 per month over 24 months.
That price is for the Moto Z4 from Verizon in Frost White or Flash Gray. It’s down from a standard price of $20.83 per month – so it’s less than half price, coming out at just $240 overall.
Technically, the Moto Z4 isn’t actually a 5G phone, but it can be – all you have to do is attach the 5G Moto Mod (sold separately for $349.99).  In fact, even at full price we declared this the cheapest of 2019’s 5G-capable handsets in our review.
- There are lots of other 5G phones
- Check out the best Motorola phones
- Here's everything you need to know about 5G
And with or without 5G, the Moto Z4 is worth considering for your next phone. This is a decent mid-range handset, with a Snapdragon 675 chipset, 4GB of RAM, a 6.4-inch 1080 x 2340 OLED screen, a 48MP rear camera, and a 25MP front-facing one.
It also supports all Motorola’s other Moto Mods, so if you want an extra battery, better speakers, or a number of other things, you can clip them on.
It’s not clear how long this $10 per month offer will last, so if you’ve been eyeing up the Moto Z4 or a 5G phone in general, now’s the time to buy, and with Christmas around the corner it could also make a great present.
- Don't need 5G? Check out our Moto G7 Plus review


Failed to check out!
Please try again.
Code/message: {{checkoutError}}
Please pay this invoice with your Lightning Network enabled wallet
by scanning the QR code or by clicking on it if your wallet is on
the same device.
Total: CHF {{order.amountChf | number : '1.2-2'}}
{{order.amount | number : '1.0-0'}} Satoshi
{{order.exchangeRate | number : '1.2-2'}} CHF/BTC
Summary
CHF {{row.price | number : '1.2-2'}}
Thank you for your payment of CHF {{order.amountChf | number : '1.2-2'}}
({{order.amount | number : '1.0-0'}} Satoshi).
You can claim your order by mentioning the name: {{order.orderName}}
Enjoy your beer, Lightning Network pioneer!
Shop is closed!
We are sorry, but the shop is currently closed for online orders.
Please come back tomorrow.


Across: 7. see 24 8,19dn. "Opener" is man at the wicket first, handy batter possessing ultimate in patience? (7 6) 10. Shored arrangement for crowds (6) 11. 15's job made coin - spectacularly! (8) 12. In control mostly, Lulu frets: a marked turnaround! (9) 13. see 1 down 15. He has cobra on can, dancing, as he doesn't mind some twisted toilet humour (5 5 5) 18. 8 19's main tool, and his peak and troughs (5) 20. Age of which featured 1 13 as new inclusion among once nice characters (9) 23. Broken plate may help with occupying a child (8) 24,7. She appeared on TV doing uni anime program (6 7) 25. Nice gal, oddly sweet (7) 26. 7s rock... and no minor sort of rock! (7) Down: 1,13ac. Successfully wager on a mobile Derry player (6 5) 2. Bend before trough for Clare, performing from down under (8) 3. Sign off on overpriced underwater vessels, and cheat France ultimately (9) 4. Loud weapons, or "scare guns", mostly used in such places (5) 5. Conservative putting "foreign dog" in place of "pig" (6) 6. Lee's confused about a sort of bean and varieties of cucumber ang eggplant (8) 9. These things happen... core of cancer's contained by crazy crone's cure! (11) 14. Musician composed good song using piano (5 4) 16. Helping Galvani experiment with one (8) 17. Flower in buttonhole and eruptions in heart (8) 19. see 8 across 21. It's done with company (it's about union) (6) 22. Fast 14's job and identity (5)


I'm a programmer, nothing special. I make projects in a variety of different languages. Check out my GitHub for more info.
First things first... I like to go by 'Agoldfish', So uhh... Yeah. I'm a programmer, real name Michael. Uh. I live in the United States, Ohio to be more exact. Um. I'm in EsperNet IRC a lot. And I like to play GTA and make programs with ComputerCraft. I take requests, so, contact me on one of my social media outlets, I'm on the ComputerCraft forum a lot, that is where you are more likely to reach me. Kbai.


A centralized webpage to keep track of all your devices, sensors, and online services, accounts, and authentications.
Your HomeAS is able to be self-configured to include devices and sensors logging data around your home. HomeAS enables more complete collection, management, and automated decision-making about your data. HomeAS includes:
| Date and Time | Temperature, C | Temperature, F | Intruder Alert |
|---|---|---|---|
| Sun, 21 Jan 2018 22:46:22 GMT | Test | Test | Test |


Connect
2
Grow
Navbar Link
Navbar Link
menu
Founder
?[founder]
grade
person_pin_circle
Location
?[ort]
grade
business_center
Branch
?[branch]
grade
multiline_chart
Trends
?[trands]
grade
?[name]
What we are doing.
?[info]
Tags: ?[tagdiv]
Technologie Trend
Tech Trend
Best Practices


Module nodetree
This file (nodetree.lua
) is part of the LuaTeX package
'nodetree'.
nodetree uses the annotation system of the lua-language-server. Install the type definitions for LuaTeX or the Visual Studio Code Extension.
The LDoc support is deprecated.
Nodes in LuaTeX are connected. The nodetree view distinguishes between list and field connections.
- list: Nodes that are doubly connected by next and
previous
fields. - field: Connections to nodes by other fields than next and
previous
fields, e.g.,head
,pre
.
Tables
| default_options | The default options. |
| options | The current options. |
| prev_options | The previous options. |
| tree_state | tree_state: |
Fields
| utf8 | Lua 5.1 does not have the utf8 library (Lua 5.1 is the default version in LuajitTeX). |
| example_counter | A counter for the compiled TeX examples. |
| have_output | A flag to indicate that something has been emitted by nodetree. |
Format functions
| format.underscore (input) | |
| format.escape (input) | |
| format.function (input) | |
| format.whitespace (count) | |
| format.color_code (code) | |
| format.color_tex (color, mode) | |
| format.node_begin () | |
| format.node_end () | |
| format.new_line (count) | |
| format.type_id (id) | |
| nodetree_print (text) | Print the output to stdout or write it into a file (output_file ). |
Template functions
| template.color (color, mode, background) | SGR (Select Graphic Rendition) parameters. |
| template.char (head) | Format the char field of a node. |
| template.line (length) | |
| template.branch (connection_type, connection_state, last) | |
| template.fill (number, order, field) | |
| template.colored_string (text, color, mode, background) | Colorize a text string. |
| template.length (input) | Format a scaled point input value into dimension string (12pt ,
1cm ) |
| get_all_table_data (data, previous_data) | Get all data from a table including metatables. |
| template.table_inline (table) | Convert a Lua table into a format string. |
| template.key_value (key, value, typ, color) | Format a key-value pair (key: value, ). |
| template.type (type, id) | |
| template.callback (callback_name, variables, where) | |
| template.branches (level, connection_type) | Format the branching tree for one output line. |
Node library extensions
| node_extended.node_id (n) | Get the ID of a node. |
| get_node_subtypes () | A table of all node subtype names. |
| node_extended.subtype (n) |
Node tree building functions
| tree.format_field (head, field) | |
| tree.format_attributes (head) | Attributes are key-value number pairs. |
| tree.set_state (level, connection_type, connection_state) | |
| tree.analyze_fields (fields, level) | |
| tree.analyze_node (head, level) | |
| tree.analyze_list (head, level) | Recurse over the current node list. |
| tree.analyze_callback (head) | The top-level internal entry point. |
Callback wrappers
Messages, options
| message (why, where, what, help) | Emit a warning or error message. |
| set_option (key, value) | Set a single-option key-value pair. |
| set_options (opts) | Set multiple key-value option pairs using a table. |
Callback management
| template.get_print_position (cb) | |
| template.no_callback (name, internal) | |
| check_callback_name (callback_name) | Check whether the given callback name exists. |
| get_callback_name (alias) | Get the real callback name from an alias string. |
| register_callback (cb) | Register a callback. |
| unregister_callback (cb) | Unregister a callback. |
Exported functions
| export.register_callbacks () | |
| export.unregister_callbacks () | |
| export.compile_include (tex_markup) | Compile a TeX snippet. |
| export.check_shell_escape (what, is_command) | Check for --shell-escape within a command or environment. |
| export.print (head, opts) | Print a node tree. |
| export.format_dim (sp) | Format a scaled point value as a formatted string. |
| export.get_default_option (key) | Get a default option that is not changed. |
| export.push_options () | Push current options. |
| export.pop_options () | Pop previous options. |
| export.input (filename) | Read a LaTeX input file and emit it immediately, obeying options
firstline and lastline . |
| export.analyze | Set to export.print. |
Tables
- default_options
-
The default options.
Fields:
- callback
- channel
- color
- decimalplaces
- unit
- verbosity
- firstline
- lastline
- options
- The current options.
- prev_options
-
The previous options.
We need this for functions push_options and pop_options so that
the effects of the
\setkeys
commands innodetree-embed.sty
(which directly manipulates the options table) stay local. - tree_state
-
tree_state: *
1
(level): *list
:continue
*field
:stop
*2
: *list
:continue
*field
:stop
...
Fields
- utf8
- Lua 5.1 does not have the utf8 library (Lua 5.1 is the default version in LuajitTeX). LuaJitTeX does include the slnunicode library.
- example_counter
- A counter for the compiled TeX examples. Some TeX code snippets a written into files, wrapped with some TeX boilerplate code. These written files are compiled later on.
- have_output
- A flag to indicate that something has been emitted by nodetree.
Format functions
- format.underscore (input)
-
Parameters:
- input string
Returns:
- format.escape (input)
-
Parameters:
- input string
Returns:
- format.function (input)
-
Parameters:
- input number
Returns:
-
number
- format.whitespace (count)
-
Parameters:
- count number # How many spaces should be output.
Returns:
- format.color_code (code)
-
Parameters:
- code number
Returns:
- format.color_tex (color, mode)
-
Parameters:
- color string
- mode string
Returns:
- format.node_begin ()
-
Returns:
- format.node_end ()
-
Returns:
- format.new_line (count)
-
Parameters:
- count number # How many new lines should be output.
Returns:
- format.type_id (id)
-
Parameters:
- id number
Returns:
- nodetree_print (text)
-
Print the output to stdout or write it into a file (
output_file
). New text is appended.Parameters:
- text string # A text string.
Template functions
- template.color (color, mode, background)
-
SGR (Select Graphic Rendition)
parameters.
attributes
| color |code| |------------|----| | reset | 0 | | clear | 0 | | bright | 1 | | dim | 2 | | underscore | 4 | | blink | 5 | | reverse | 7 | | hidden | 8 |
foreground
| color |code| |------------|----| | black | 30 | | red | 31 | | green | 32 | | yellow | 33 | | blue | 34 | | magenta | 35 | | cyan | 36 | | white | 37 |
background
| color |code| |------------|----| | onblack | 40 | | onred | 41 | | ongreen | 42 | | onyellow | 43 | | onblue | 44 | | onmagenta | 45 | | oncyan | 46 | | onwhite | 47 |
Parameters:
- color ColorName # A color name.
- mode ColorMode
- background boolean # If set, colorize the background instead of the text.
Returns:
- template.char (head)
-
Format the char field of a node. Try to find a textual representation that
corresponds with the number stored in the char field.
LuaTeX’s
node.char
values are not really characters; they are font glyph indices which sometimes happen to match valid Unicode characters. HarfBuzz shapers differentiate between glyph IDs and characters by adding to 0x120000 to glyph IDs.The code of this function is borrowed from the function
get_glyph_info(n)
of the luaotfload package. The harfbuzz mode in luaotfload uses this function to embed text in a PDF file and for messages that show textual representation of the nodes like over/underfull messages. It will not result in an error in other modes, but it might not give proper text representation, but that is a limitation of these modes.It should be understood what the glyph nodes represent. Before processing by luaotfload they represent one-to-one mapping of the input characters. After processing, they represent font glyphs with potentially complicated relationship with input characters.
Relation between input characters and output glyphs are many-to-many. An input character may be represented by one or more glyphs, and output glyph might represent one or more input characters, and in some cases (e.g. when there is reordering) a group of input characters are represented by a group of output glyphs. In the 2nd and 3rd cases, the first glyph node will have a
glyph_info
property with all the characters of the group, and subsequent glyph nodes in the group will have emptyglyph_info
properties.It should also noted that this mapping is not unique, the same glyph can represent different characters in different context.
Parameters:
- head Node # The head node of a node list.
Returns:
- template.line (length)
-
Parameters:
- length
string # If
long
, emit a longer line.
Returns:
- length
string # If
- template.branch (connection_type, connection_state, last)
-
Parameters:
- connection_type ConnectionType
- connection_state ConnectionState
- last boolean
Returns:
- template.fill (number, order, field)
-
Parameters:
- number number
- order number
- field string
Returns:
- template.colored_string (text, color, mode, background)
-
Colorize a text string.
Parameters:
- text string # A text string.
- color ColorName # A color name.
- mode ColorMode
- background boolean # If set, colorize the background instead of the text.
Returns:
- template.length (input)
-
Format a scaled point input value into dimension string (
12pt
,1cm
)Parameters:
- input number
Returns:
- get_all_table_data (data, previous_data)
-
Get all data from a table including metatables.
Properties will reside in a metatable if nodes were copied using an operation like box copy: (\copy). The LuaTeX manual states this: “If the second argument of
set_properties_mode
is true, then a metatable approach is chosen: the copy gets its own table with the original table as metatable.”Source: StackOverflow – this works if
__index
returns a table, which it should as per LuaTeX manual.Parameters:
- data table A Lua table.
- previous_data table The data of a Lua table of a previous recursive call.
Returns:
-
table
A merged table.
- template.table_inline (table)
-
Convert a Lua table into a format string.
Parameters:
- table table # A table to generate an inline view of.
Returns:
- template.key_value (key, value, typ, color)
-
Format a key-value pair (
key: value,
).Parameters:
- key string # A key.
- value string|number # A value.
- typ string # A node type.
- color ColorName # A color name.
Returns:
- template.type (type, id)
-
Parameters:
- type string
- id number
Returns:
- template.callback (callback_name, variables, where)
-
Parameters:
- callback_name string
- variables table
- where
string #
'before'
or'after'
Returns:
- template.branches (level, connection_type)
-
Format the branching tree for one output line.
Parameters:
- level number
- connection_type ConnectionType
Returns:
Node library extensions
- node_extended.node_id (n)
-
Get the ID of a node.
We have to convert the node into a string and then to extract the ID from this string using a regular expression. If you convert a node into a string it looks like:
<node nil < 172 > nil : hlist 2>
.Parameters:
- n Node # A node.
Returns:
- get_node_subtypes ()
-
A table of all node subtype names.
Nodes without subtypes:
ins
(3)local_par
(9)penalty
(14)unset
(15)style
(16)choice
(17)fraction
(20)math_char
(23)sub_box
(24)sub_mlist
(25)math_text_char
(26)delim
(27)margin_kern
(28)align_record
(30)pseudo_file
(31)pseudo_line
(32)page_insert
(33)split_insert
(34)expr_stack
(35)nested_list
(36)span
(37)attribute
(38)glue_spec
(39)attribute_list
(40)temp
(41)align_stack
(42)movement_stack
(43)if_stack
(44)unhyphenated
(45)hyphenated
(46)delta
(47)passive
(48)shape
(49)
Returns:
- node_extended.subtype (n)
-
Parameters:
- n Node
Returns:
Node tree building functions
- tree.format_field (head, field)
-
Parameters:
- head Node # The head node of a node list.
- field string
Returns:
- tree.format_attributes (head)
-
Attributes are key-value number pairs. They are printed as an inline
list. The attribute
0
with the value0
is skipped because this attribute is in every node by default.Parameters:
- head Node # The head node of a node list.
Returns:
- tree.set_state (level, connection_type, connection_state)
-
Parameters:
- level
number #
level
is an integer beginning with 1. - connection_type ConnectionType
- connection_state ConnectionState
- level
number #
- tree.analyze_fields (fields, level)
-
Parameters:
- fields table
- level number # The current recursion level.
- tree.analyze_node (head, level)
-
Parameters:
- head Node # The head node of a node list.
- level number # The current recursion level.
- tree.analyze_list (head, level)
-
Recurse over the current node list.
Parameters:
- head Node # The head node of a node list.
- level number # The current recursion level.
- tree.analyze_callback (head)
-
The top-level internal entry point.
Parameters:
- head Node # The head node of a node list.
Callback wrappers
Nodetree uses luatexbase's functions to manage callbacks if available. Otherwise a simplistic approach is taken by prepending or appending nodetree's diagnostic callbacks to the existing ones (and also removing them again if requested).
Each function in the callbacks
table consists of three parts:
- before-callback inspection
- original callback or default function call
- after-callback inspection
- callbacks.contribute_filter (extrainfo)
-
Parameters:
- extrainfo string
- callbacks.buildpage_filter (extrainfo)
-
Parameters:
- extrainfo string
- callbacks.build_page_insert (n, i)
-
Parameters:
- n string
- i string
Returns:
-
number
- callbacks.pre_linebreak_filter (head, groupcode)
-
Parameters:
- head Node # The head node of a node list.
- groupcode string
Returns:
-
boolean
- callbacks.linebreak_filter (head, is_display)
-
Parameters:
- head Node # The head node of a node list.
- is_display boolean
Returns:
-
boolean
- callbacks.append_to_vlist_filter (box, locationcode, prevdepth, mirrored)
-
Parameters:
- box Node
- locationcode string
- prevdepth number
- mirrored boolean
Returns:
- Node
- number
- callbacks.post_linebreak_filter (head, groupcode)
-
Parameters:
- head Node # The head node of a node list.
- groupcode string
Returns:
-
boolean
- callbacks.hpack_filter (head, groupcode, size, packtype, direction, attributelist)
-
Parameters:
- head Node # The head node of a node list.
- groupcode string
- size number
- packtype string
- direction string
- attributelist Node
Returns:
-
boolean
- callbacks.vpack_filter (head, groupcode, size, packtype, maxdepth, direction, attributelist)
-
Parameters:
- head Node # The head node of a node list.
- groupcode string
- size number
- packtype string
- maxdepth number
- direction string
- attributelist Node
Returns:
-
boolean
- callbacks.hpack_quality (incident, detail, head, first, last)
-
Parameters:
- incident string
- detail number
- head Node # The head node of a node list.
- first number
- last number
Returns:
-
Node
- callbacks.vpack_quality (incident, detail, head, first, last)
-
Parameters:
- incident string
- detail number
- head Node # The head node of a node list.
- first number
- last number
- callbacks.process_rule (head, width, height)
-
Parameters:
- head Node # The head node of a node list.
- width number
- height number
- callbacks.pre_output_filter (head, groupcode, size, packtype, maxdepth, direction)
-
Parameters:
- head Node # The head node of a node list.
- groupcode string
- size number
- packtype string
- maxdepth number
- direction string
Returns:
-
boolean
- callbacks.hyphenate (head, tail)
-
Parameters:
- head Node # The head node of a node list.
- tail Node
- callbacks.ligaturing (head, tail)
-
Parameters:
- head Node # The head node of a node list.
- tail Node
- callbacks.kerning (head, tail)
-
Parameters:
- head Node # The head node of a node list.
- tail Node
- callbacks.insert_local_par (local_par, location)
-
Parameters:
- local_par Node
- location string
- callbacks.mlist_to_hlist (head, display_type, need_penalties)
-
Parameters:
- head Node # The head node of a node list.
- display_type string
- need_penalties boolean
Returns:
-
Node
Messages, options
- message (why, where, what, help)
-
Emit a warning or error message.
This works for plain TeX, Texinfo, and LaTeX (for plain TeX and Texinfo we make the message look identical to the LaTeX case). Note that a full stop gets appended to
what
.Parameters:
- why
string #
'error'
or'warning'
. - where string # In which package the error happened.
- what string # The warning message to emit.
- help string # Additional help text for errors.
- why
string #
- set_option (key, value)
-
Set a single-option key-value pair.
Parameters:
- key string # The key of the option pair.
- value number|string # The value of the option pair.
- set_options (opts)
-
Set multiple key-value option pairs using a table.
Parameters:
- opts table # Options.
Callback management
- template.get_print_position (cb)
-
Parameters:
- cb string # The name of a callback.
Returns:
- template.no_callback (name, internal)
-
Parameters:
- name string
- internal string
- check_callback_name (callback_name)
-
Check whether the given callback name exists.
Throw an error if it doesn’t.
Parameters:
- callback_name string # The name of a callback to check.
Returns:
-
string
The unchanged input of the function.
- get_callback_name (alias)
-
Get the real callback name from an alias string.
Parameters:
- alias string # The alias of a callback name or the callback name itself.
Returns:
-
string
The real callback name.
- register_callback (cb)
-
Register a callback.
Doing this for plain TeX is simple; we have access to LuaTeX's base function
callback.register
and thus can easily add our callback wrapper, which in turn calls nodetree's functions at the very beginning and/or at the very end.Enter LaTeX. It comes with its own callback management that can register multiple callbacks, also taking care of the calling order. Unfortunately, however, it is also more restrictive: for example, some callbacks like linebreak_filter are tagged as 'exclusive', only accepting a single callback. While this makes sense for the end user, it complicates the situation for nodetree to install its non-intrusive, observing-only callbacks.
We thus take the following route.
If there is no function for callback
<foo>
installed, registercallbacks.<foo>
.If there is a (single) function for callback
<foo>
of type three ('exclusive'), remove it, wrap it intocallbacks.<foo>
(viaorig_callbacks
) and installcallbacks.<foo>
.Otherwise register
callbacks.<foo>
as the first and last function of callback<foo>
.
Parameters:
- cb string # The name of a callback.
- unregister_callback (cb)
-
Unregister a callback.
We follow the same logic as with register_callback.
Parameters:
- cb string # The name of a callback.
Exported functions
- export.register_callbacks ()
- export.unregister_callbacks ()
- export.compile_include (tex_markup)
-
Compile a TeX snippet.
Write some TeX snippets into a temporary LaTeX file, compile this file using
latexmk
, read the generated*.nttex
file, and return its content.Parameters:
- tex_markup string
- export.check_shell_escape (what, is_command)
-
Check for
--shell-escape
within a command or environment.Parameters:
- what string # The name of the command or environment.
- is_command
boolean # Set if
what
is a command.
- export.print (head, opts)
-
Print a node tree.
Parameters:
- head Node # The head node of a node list.
- opts table # Options as a table.
- export.format_dim (sp)
-
Format a scaled point value as a formatted string.
Parameters:
- sp number # A scaled point value.
Returns:
- export.get_default_option (key)
-
Get a default option that is not changed.
Parameters:
- key string # The key of the option.
Returns:
-
string, number or boolean
- export.push_options ()
- Push current options.
- export.pop_options ()
- Pop previous options.
- export.input (filename)
-
Read a LaTeX input file and emit it immediately, obeying options
firstline
andlastline
.Parameters:
- filename string
- export.analyze
- Set to export.print.


 Please validate that all required fields have been filled out
{{ errors.body }}
{{ errors }}
Full name: {{ travelForm.fullName }}
Email: {{ travelForm.mailingAddress }}
Number of people: {{ travelForm.groupMembers }}
Accepted reimbursement amount: ${{ response?.amount }}


Disclaimer: You probably won't do any permanent damage to your phone (but don't take my word for it) if you do anything wrong here, but you can waste a lot of time and get very frustrated. I've modded 4 different androids many times over the last 5 years or so, and I've never bricked a phone.
I modded my Nexus 4 (‘Mako’ is the codename for it) today, switching to Cyanogen 10.2 and enabling LTE.
Speedtest.net results:
I want to get the steps down in writing to help others do the same and future me when I need to reformat my phone. There no single solution to getting this done, so I’ll describe the process I took. I did this using Windows 7, but most of the steps should be similar regardless of the OS.
1) Tools that are included in the latest Android SDK (adt bundle), called adb and fastboot, are necessary to access the phone. Unzip wherever.
2) If you haven’t enabled Developer Mode on your phone, you’ll have to in order to enable USB debugging. Go to Settings and click “Build number” 7 times. You’ll see a visual confirmation. Now enable USB debugging.
3) Even getting your computer to recognize your Nexus 4 can be a pain sometimes. If you have issues with Windows, uninstall the current drivers through the device manager, and install this driver. If that link breaks, here is the forum post.
Make sure to add the whatever Cyanogenmod (we’ll call it CM hereon), Google apps, radio zips to your phone’s /sdcard/
directory (whether a real sdcard or on the internal memory). Plug your phone by USB into your computer and either drag and drop files or use the adb “push” commands (in the ‘platform-tools’ directory of the Android SDK you installed). Sample command:
4a) If you want CM with LTE enabled, go here (10.2 is latest stable version as of this post).
And get a radio.
Mmultiple choices here under “Resources”, but I used the .33-.84 Hybrid radio, and it’s working fine so far. Others have reported the other radios working too.
4b) OR for the standard CM, get it here.
And get a radio in case Wifi/Bluetooth don’t work (mine didn’t by default).
5) Get Google apps (make sure to match to appropriate CM version):
6) Also get the ClockwordMod Recovery image.
I made sure to take whatever I wanted to off my phone before doing this, so make sure you back up whatever you want. I have no recommendations for how to do this, but there are tools out there.
7) Now with your phone connected by USB to your computer, run this command from the terminal:
8) This will boot the phone into the bootloader screen. FYI, in general, volume up and down control direction and the power button selects. Now in your terminal, you’ll want to unlock the phone (visually confirmed). I believe this step wipes the phone:
8) Restart the phone if it didn’t already. You may need to re-enable USB debugging. Now get to the bootloader screen again. Make sure the ClockwordMod Recovery image is in the same directory as the adb and fastboot (makes it easier) and run these two commands:
9) Now you should be in the CWM Recovery screen.
Choose to factory wipe / reset your phone.
Now choose to install a zip and navigate to the sdcard and to the /0/
directory or wherever you put your zips.
10) Install the CM zip first. Then the Google apps zip. Then the radio zip.
Now restart your phone! If you chose the LTE enabled CM, there are just a few more steps - otherwise, you’re done!
11) Don’t worry if your signal bars look different now or have a weaker signal. Go to the phone dialer and enter:
Go to Phone Information, and select “LTE/GSM/CDMA auto” from the drop-down menu.
12) Lastly, navigate to Settings > More > Mobile Networks > Access Point Names, and choose this network configuration:
You can create a new one or modify the IPv6 version to include the IPv4/IPv6 option. Remember to hit save.
You should be good to go!
I JUST did this process myself, so I have no feedback about bugs or if this helped or hurt my Nexus 4 experience. Will try to remember to update. Let me know about your experiences! : )


Dwarf Explorer Droph:
You have collected all 3
Cave Exploration Texts
! Ah, now we can summon Hekaton Prime using them.
Are you ready?
Summon Hekaton Prime.


Accessing your device camera is required to be able to use AR Click Scan, please enable your camera when asked.
Reload the site after allowing camera access to view the contents in AR.
We use WebRTC technology to be able to show you augmented reality experiences, sadly this window browser doesn't support WebRTC, please try again with Google Chrome.
We use WebRTC technology to be able to show you augmented reality experiences, sadly this window browser doesn't support WebRTC, please click the button to copy website link and to open Safari.
We would request you to switch to chrome for best experience of Augmented Reality in your browser.


--- layout: post permalink: iseemecom.html Tags: Array NameOfCoupon: ISeeMe.com Cash Back Summary: See our ISeeMe.com Coupon Code, ISeeMe.com Rebate, ISeeMe.com Promo Code, ISeeMe.com Discount, ISeeMe.com Coupon Cash Back Rebate here. MetaKeywords: ISeeMe.com Coupon Code, ISeeMe.com Rebate, ISeeMe.com Promo Code, ISeeMe.com Cash Back Rebate, ISeeMe.com Discount, ISeeMe.com Coupon Image: images/25926.gif LinkURL: http://www.shareasale.com/r.cfm?b=214478&u=412002&m=25926 affiliate_url_link: http://www.icouponblog.com/jane.php?id=218 thesis_readmore: See the ISeeMe.com Coupon thesis_thumb: http://www.icouponblog.com/tn.php?merchantURL=iseeme.com Title: Save 5% - ISeeMe.com Coupon Code for iseeme.com. ISeeMe.com Rebate & Discount thesis_description: Our ISeeMe.com Discount Coupon Code for iseeme.com. We have the best ISeeMe.com Rebate anywhere. thesis_keywords: ISeeMe.com, ISeeMe.com Coupon Code, ISeeMe.com Discount, ISeeMe.com Rebate product_name: ISeeMe.com hop_url: http://www.icouponblog.com/hop.php?id=218 ---
Are you looking to buy a special present for a little person in your life? Let ISeeMe.com help you find a unique and personal present a child is sure to love. ISeeMe.com offers high quality personalized children’s books and gifts. ISeeMe.com products have been grabbed attention on a national level, being features in People, In Touch and Parents magazines. The reviews describe ISeeMe.com items as unique gifts for newborns and young children.
ISeeMe.com offers a personalized book called, “My very own name.” One by one, entertaining animals bring in letters that spell out the child’s first and last names. This not only grabs your child’s personal attention, but also allows them to learn how to spell their name. ISeeMe.com believes in making you’re kid the star of their own story. You can also choose from a theme that is most appealing to your child. Perhaps they like mysterious and magical fairies or prefer lively and energetic animals. No matter what your child’s tastes or interests, a present from ISeeMe.com will put a smile on their face!
You’ll also have a smile on your face once you use the ISeeMe.com Discount above. The ISeeMe.com Coupon offers you great savings on your next ISeeMe.com purchase. So don’t hesitate. Get a gift that’s distinguished from the rest.  In fact, it’s something they’ll enjoy to look at once they’re grown too. Make it a part of your child’s life today. And use your ISeeMe.com Rebate for wonderful savings!


Here I Am
This is me inside the Swimming Pool. It is the first picture of me ever taken. I don't know how they did it, because I'm alone in the Swimming Pool and I haven't had any visitors. It looks like humans are clever beings. I'm glad I'll also be a human soon.


Calories and Steps
Cumulative Sleep
Cumulative Distance
Totals (aggregate values)
Steps:
52
Calories:
1000
Distance:
52


Codebook
sign in
Create Your Codebook Account
Name
Choose Your Username
Create a Password
Gender
Male
Female
Create account


Page maintained by Ke-Sen Huang.
If you have additions or changes, send an e-mail.
Note that when possible I link to the page containing the link to the actual PDF or PS of the preprint.
I prefer this as it gives some context to the paper and avoids possible copyright problems with direct linking.
Thus you may need to search on the page to find the actual document.
the Eurographics Digital Library Link for the paper
Paper Abstract
Author Preprint
Paper Video
Paper Presentation
Paper Images
Paper Data
Demo Program or Source Code
Related Links
Changelog
Coordinates and Fields
- Designing N-PolyVector Fields with Complex Polynomials
-
Olga Diamanti,
Amir Vaxman,
Daniele Panozzo,
Olga Sorkine-Hornung
- Discrete 2-Tensor Fields on Triangulations
-
Fernando de Goes,
Beibei Liu,
Max Budninskiy,
Yiying Tong,
Mathieu Desbrun
- Efficient Encoding of Texture Coordinates Guided by Mesh Geometry
-
Libor Vasa,
Guido Brunnett
- Compressed Manifold Modes for Mesh Processing
-
Thomas Neumann,
Kiran Varanasi,
Christian Theobalt,
Marcus Magnor,
Markus Wacker
Reconstruction
- Feature-Preserving Surface Completion Using Four Points
-
Gur Harary,
Ayellet Tal,
Eitan Grinspun
- Piecewise-Planar 3D Reconstruction with Edge and Corner Regularization
-
Alexandre Boulch,
Martin de La Gorce,
Renaud Marlet
- 3D Motion Completion in Crowded Scenes
-
Niv Gafni,
Andrei Sharf
- Real-time Bas-Relief Generation from Depth-and-Normal Maps on GPU
-
Zhongping Ji,
Xianfang Sun,
Shi Li,
Yigang Wang
Segmentation and Structure
- 3D Shape Segmentation and Labeling via Extreme Learning Machine
-
Zhige Xie,
Kai Xu,
Ligang Liu,
Yueshan Xiong
- Robust Region Detection via Consensus Segmentation of Deformable Shapes
-
Emanuele Rodola,
Samuel Rota Bulo,
Daniel Cremers
- Transductive 3D Shape Segmentation using Sparse Reconstruction
-
Weiwei Xu,
Zhouxu Shi, Mingliang Xu,
Kun Zhou,
Jingdong Wang,
Bin Zhou, Jinrong Wang, Zhenming Yuan
- Clever Support: Efficient Support Structure Generation for Digital Fabrication
-
Juraj Vanek,
Jorge A. G. Galicia,
Bedrich Benes
Matching
- Supervised Learning of Bag-of-features Shape Descriptors Using Sparse Coding
-
Roee Litman,
Alex Bronstein,
Michael Bronstein,
Umberto Castellani
- An Efficient Approach to Correspondences between Multiple Non-Rigid Parts
-
Gary K.L. Tam,
Ralph Martin,
P.L. Rosin,
Yu-Kun Lai
- Learnt Real-time Meshless Simulation
-
Kirill A. Sidorov,
A. David Marshall
- What Makes London Work Like London?
-
Sawsan AlHalawani,
Yong-Liang Yang,
Peter Wonka,
Niloy J. Mitra
Meshing
- Blue-Noise Remeshing with Farthest Point Optimization
-
Dong-Ming Yan,
Jianwei Guo,
Xiaohong Jia,
Xiaopeng Zhang,
Peter Wonka
- Pattern-Based Quadrangulation for N-Sided Patches
-
Kenshi Takayama,
Daniele Panozzo,
Olga Sorkine-Hornung
- Freeform Honeycomb Structures
-
Caigui Jiang,
Jun Wang,
Johannes Wallner,
Helmut Pottmann
Point Clouds
- A Variational Taxonomy for Surface Reconstruction from Oriented Points
(TR)
-
Christopher Schroers,
Simon Setzer,
Joachim Weickert
- SUPER 4PCS: Fast Global Pointcloud Registration via Smart Indexing
-
Nicolas Mellado,
Dror Aiger,
Niloy J. Mitra
Surfaces and Shells
- Semi-sharp Creases on Subdivision Curves and Surfaces
-
Jiri Kosinka,
Malcolm A. Sabin,
Neil A. Dodgson
- Pseudo-Spline Subdivision Surfaces
-
Chongyang Deng,
Kai Hormann
- Functional Fluids on Surfaces
-
Omri Azencot,
Steffen Weissmann,
Maks Ovsjanikov,
Max Wardetzky,
Ben-Chen, Mirela
- Exploring the Geometry of the Space of Shells
-
Behrend Heeren,
Martin Rumpf,
Peter Schroder,
Max Wardetzky,
Benedikt Wirth
Maps
- As-Conformal-As-Possible Surface Registration
-
Yusuke Yoshiyasu,
Wan-Chun Ma,
Eiichi Yoshida,
Fumio Kanehiro
- Remeshing-assisted Optimization for Locally Injective Mappings
-
Yao Jin,
Jin Huang,
Ruofeng Tong
- Cross-Collection Map Inference by Intrinsic Alignment of Shape Spaces
-
Nitzan Shapira,
Ben-Chen, Mirela
kesen.huang@gmail.com


This Privacy Policy governs the manner in which XYSec Labs Pte. Ltd. collects, uses, maintains and discloses information collected from users (each, a "User") of the devknox.io website ("Site"). This privacy policy applies to the Site and all products and services offered by XYSec Labs Pte. Ltd.
Personal identification informationWe may collect personal identification information from Users in a variety of ways, including, but not limited to, when Users visit our site, register on the site, subscribe to the newsletter, respond to a survey, fill out a form, and in connection with other activities, services, features or resources we make available on our Site. Users may be asked for, as appropriate, name, email address, credit card information. Users may, however, visit our Site anonymously. We will collect personal identification information from Users only if they voluntarily submit such information to us. Users can always refuse to supply personally identification information, except that it may prevent them from engaging in certain Site related activities.
Non-personal identification information
We may collect non-personal identification information about Users whenever they interact with our Site. Non-personal identification information may include the browser name, the type of computer and technical information about Users means of connection to our Site, such as the operating system and the Internet service providers utilized and other similar information.
Web browser cookies
Our Site may use "cookies" to enhance User experience. User's web browser places cookies on their hard drive for record-keeping purposes and sometimes to track information about them. User may choose to set their web browser to refuse cookies, or to alert you when cookies are being sent. If they do so, note that some parts of the Site may not function properly.
How we use collected information
XYSec Labs Pte. Ltd. may collect and use Users personal information for the following purposes:
- - To improve customer service
Information you provide helps us respond to your customer service requests and support needs more efficiently. - - To personalize user experience
We may use information in the aggregate to understand how our Users as a group use the services and resources provided on our Site. - - To improve our Site
We may use feedback you provide to improve our products and services. - - To process payments
We may use the information Users provide about themselves when placing an order only to provide service to that order. We do not share this information with outside parties except to the extent necessary to provide the service. - - To run a promotion, contest, survey or other Site feature
To send Users information they agreed to receive about topics we think will be of interest to them. - - To send periodic emails
We may use the email address to send User information and updates pertaining to their order. It may also be used to respond to their inquiries, questions, and/or other requests. If User decides to opt-in to our mailing list, they will receive emails that may include company news, updates, related product or service information, etc. If at any time the User would like to unsubscribe from receiving future emails, we include detailed unsubscribe instructions at the bottom of each email.
We adopt appropriate data collection, storage and processing practices and security measures to protect against unauthorized access, alteration, disclosure or destruction of your personal information, username, password, transaction information and data stored on our Site.
Sensitive and private data exchange between the Site and its Users happens over a SSL secured communication channel and is encrypted and protected with digital signatures. Our Site is also in compliance with PCI vulnerability standards in order to create as secure of an environment as possible for Users.
Sharing your personal information
We do not sell, trade, or rent Users personal identification information to others. We may share generic aggregated demographic information not linked to any personal identification information regarding visitors and users with our business partners, trusted affiliates and advertisers for the purposes outlined above.We may use third party service providers to help us operate our business and the Site or administer activities on our behalf, such as sending out newsletters or surveys. We may share your information with these third parties for those limited purposes provided that you have given us your permission.
Third party websites
Users may find advertising or other content on our Site that link to the sites and services of our partners, suppliers, advertisers, sponsors, licensors and other third parties. We do not control the content or links that appear on these sites and are not responsible for the practices employed by websites linked to or from our Site. In addition, these sites or services, including their content and links, may be constantly changing. These sites and services may have their own privacy policies and customer service policies. Browsing and interaction on any other website, including websites which have a link to our Site, is subject to that website's own terms and policies.
Changes to this privacy policy
XYSec Labs Pte. Ltd. has the discretion to update this privacy policy at any time. When we do, we will revise the updated date at the bottom of this page. We encourage Users to frequently check this page for any changes to stay informed about how we are helping to protect the personal information we collect. You acknowledge and agree that it is your responsibility to review this privacy policy periodically and become aware of modifications.
Your acceptance of these terms
By using this Site, you signify your acceptance of this policy. If you do not agree to this policy, please do not use our Site. Your continued use of the Site following the posting of changes to this policy will be deemed your acceptance of those changes.
Contacting us
If you have any questions about this Privacy Policy, the practices of this site, or your dealings with this site, please contact us at:
XYSec Labs Pte. Ltd.
79 Ayer Rajah Crescent, #04-09
Singapore 139951
+65 3158 5898
<EMAIL_ADDRESS>This document was last updated on September 30, 2016


WITNESS LIST
Emerging Issues In Texas Law Enforcement, Select Committee
April 30, 2015 Upon final adjourn./recess
For:
McCollough, W. Scott (Self; Data Foundry, Inc.)
Yokubaitis, Ronald Barron (Self; Data Foundry & Golden Frog)
Against:
Clark, Mark (Houston Police Officers' Union & Dallas Police Association)
Registering, but not testifying:
For:
Donahue, Steven (Self; Texans for Accountable Government)
Hanna, Steve (Self)
Hanna, Steve (Self)
Henson, Scott (Texas Electronic Privacy Coalition)
Isom, Jon (Self; Restore the Fourth Austin)
James, Claire (Self)
Simpson, Matt (ACLU of Texas)
Swift, Kat (Self; Green Party of Texas)
Torrence, Stephen (Self; EFF-Austin)
Against:
Anderson, Jessica (Houston Police Department)
Craft, Lon (TMPA)
Crowell, Aaron (Self; Baytown municipal police assoc)
Hawthorne, Brian (Self; Sheriffs' Association of Texas)
Shannon, Kelley (Freedom of Information Foundation of Texas)
On:
Preston, Terry (Texas Department Of Public Safety)
1


This is a song name
This is a band name
skip_previous
pause_circle_filled
skip_next
volume_down
volume_up
Show Controls
Visualization
Sample Count Multiplier
equalizer
Audio Logarithmic Scale
show_chart
Enable Bezier Visualization
Use Waveform Data
Enable Parallax
Select Theme
arrow_drop_down
Audio
Playback Speed
fast_forward
Delay
access_alarm
Select Song
arrow_drop_down
Upload Song
Image Pixel Manipulation
Invert Color
Generate Noise
Redshift
Blueshift
Greenshift
Made by Daniel Timko


TICKETS CENTRE
On this page you have the ability to log new tickets via the "Create a Ticket" button. Please note that when you create a new ticket the system will automatically assign an owner from one of our five customer service operators.
Displayed beneath this button is a table of current tickets along with their status and owner. These tickets can be update or deleted using the "Update" and "Delete" buttons. Please note that you cannot reassign the owner.


Apple making iPhone XR in India, expanding operations: Ravi Shankar Prasad
India's overall mobile as well as components exports are expected to cross $1.6 billion each in 2019-20,
by Press Trust of IndiaTech titan Apple has started production of iPhone XR in India for domestic market and exports, underlying the government's efforts to bolster mobile manufacturing in the country, IT Minister Ravi Shankar Prasad said on Monday.
The minister also said that Salcomp, the world's largest manufacturer and a major supplier of chargers to Apple for iPhone, has reached agreement to take over closed facility of Nokia in a SEZ near Chennai.
Salcomp will revive the unit, which was closed for nearly 10 years, and the facility will become operational from March 2020. The unit will produce charger and other equipment, and will boast of a diversified portfolio. This will entail an investment of Rs 2,000 crore in five years.
"The big SEZ of Nokia which was closed for 10 years will get into life again. As many as 10,000 people will get jobs directly and 50,000 jobs will be generated indirectly," Prasad said.
India's overall mobile as well as components exports are expected to cross USD 1.6 billion each in 2019-20, he said.


PyCon India - 2020 Devsprint Mentor
Competition information
- Category: Python Conference
- Organizer:Python Org
- Topic:Python project, Open Source Contribution
- Competition date:Oct,2020
- Competition URL: PyCon 2020 -Devsprint
Devsprints are a 2 day event being organised at PyCon India 2020 to promote open-source contribution and getting people started with it.
At devsprints, mentors/contributors of popular OSS projects are there to guide you through the end-to-end process of making your very first open-source contribution. Right from setting up the build environments, to developing and testing code, to submitting patches for bugs and features. If you are stuck anywhere, mentors will be available right on the stream to help you figure out issues and resolve them as best as they can! From past experiences, we’ve seen a lot of people start off contributing to FOSS through PyCon India Devsprints and later go on to become regular contributors to various projects across the web.
I have joined as a speaker and a maintainer for my project 'eduAlgo' in this devsprint event.


The Great Covid Survey is a survey given to people who have tested positive for Covid 19 to see if we can tease out correlations between lifestyle, diet, self-treatments and adverse reactions to the disease.
If we can get better data on what lifestyles, preconditions and demographics correlate to better outcomes we may be able to find better treatments and do a better job of separating out sensitive and non-sensitive people.
The survey is for people who have tested positive for the CoronaVirus through an official test, ideally people who recently received a positive result.
Enter your phone number in the form above. You will receive a total of 4 survey requests via text message over the course of two weeks (one main and 3 short followups).
Be a real-world hearo and save lives. The data from your experiences may help us unlock what causes mild and tragic Covid outcomes.
Our mission is to save lives by helping identify whether specific types of lifestyles, supplements and medications may correlate with better and worse outcomes. We are hoping that by gathering data from thousands of people who have tested positive we can analyize the data and find correlations that help save lives.
We would love to have some insights in a matter of weeks. Our goal is to have thousands of people fill out the data and analyze it shortly after we receive outcome data.
Anonymous data may be shared with researchers and data analysts to help determine correlations. All personal information is separated out. Our system generates a unique random ID for each participant and all analysis will be done using those anonymous ids. We do store your telephone number in a separate contact database and any linkage between the id and the telephone number will be removed at the conclusion of the surveys.
You are correct. Surveys are never perfect and there are a number of biases that may skew survey results as well as issues with causation vs correlation and co-factors. Our hope is in analysis we can control for some of these biases. We also plan only to use the results as a way to spur on more research. Given the situation we feel some skewed information is better than no information.
If you are a data scientist or someone who wishes to help with the project please email info@thegreatcovidsurvey.com


Anyone who’s used images from Docker Hub will likely have noticed that there can be quite a few old and stale images up there. People will post an image to help them achieve a goal but then might not remember to maintain it, which reduces the usefulness for others over time as software versions get outdated and projects that are incorporated into the image move on. I’m guilty of this myself with quite a few images up on Hub that haven’t been updated since I initially uploaded them.
Luckily, one of the handy things that you can do with Docker is to have automated builds on Docker Hub. This lets you create images based on a Github repository and have Docker manage the build process for you, and with a bit of scripting you can automate the update process so that your images stay nice and fresh and ready for use.
To get this working for your build, go into the “build settings” tab on the Docker Hub page and scroll down to “Build Triggers”. If you click the “Activate Triggers” button Docker Hub will helpfully provide you with a token and trigger URL which can trigger your image to be rebuilt, and will also provide example curl commands to trigger the build.
So for example for one of my images the curl request to trigger a build on it would look like this (with the [TOKEN] replaced with the trigger token for the build)
curl -H "Content-Type: application/json" --data '{"build": true}' -X POST https://registry.hub.docker.com/u/raesene/alpine-nettools/trigger/[TOKEN]/
Now we have the method for updating our image, we just need to set up some mechanism for this to be carried out periodically. The obvious way to do this is just to make use of a Linux host and cron but I’m sure there’s a variety of other ways of triggering an HTTP post on a regular basis.
So just add your curl command to a crontab file somewhere and away you go.
The last thing to watch for is obviously there’s a risk that your automated build will fail, so you should turn on the option in Docker Hub to notify you of any build failures. That can be found in Settings-->Notifications
. Just tick the box “Notify me when an automated build fails” and you should get an e-mail if something goes wrong.


| bump_version {xfun} | R Documentation |
Increase the last digit of version numbers, e.g., from 0.1
to
0.2
, or 7.23.9
to 7.23.10
.
bump_version(x)
x |
A vector of version numbers (of the class |
A vector of new version numbers.
xfun::bump_version(c("0.1", "91.2.14"))


{% if not teacher %}
{% if object.use_groups and enrolled %}
In this exercise you're submitting as a group.
{% if my_group %}
Members in your group: {{ my_group.student_usernames }}
{% else %}
You have not been assigned to any group! Contact course personnel to make a submission.
{% endif %}
{% elif not enrolled %}
You have to enroll to the course in order to submit.
You can enroll from the course page.


Some content 1
Some content 2
Some content 3
Some content 4
Some content 5
Some content 6
Some content 7
Some content 8
Some content 9
Some content 10
Some content 11
Some content 12
Some content 13
Some content 14
Some content 15
Some content 16
Some content 17
Some content 18
Some content 19
Some content 20
Some content 21
Some content 22
Some content 23
Some content 24
Some content 25
Some content 26
Some content 27
Some content 28
Some content 29
Some content 30


--- title: Program Analysis and Synthesis ref: pa2017 description: Graduate course on program analysis and synthesis for discrete and probabilistic programs. semester: Spring 2017 number: 263-2910-00L lecturer: Martin Vechev ta: Dimitar Dimitrov, Petar Tsankov assistants: edoz: http://www.vvz.ethz.ch/Vorlesungsverzeichnis/lerneinheitPre.do?lerneinheitId=112813&semkez=2017S&lang=en session-time-place: lecture-time-place: Mon 13-16, CAB G51 exercise-time-place: credits: 6 image: assets/images/gears.jpeg ---
The last decade has seen an explosion in modern program analysis and synthesis techniques. These techniques are increasingly being used to reason about a vast range of computational paradigms, from finding security flaws in systems software (e.g., drivers) to automating the construction of programs (e.g., for end user programming), to programmable networks, to reliable machine learning models (e.g., probabilistic programming). This course provides a comprehensive introduction to these methods. The course consists of 3 parts:
Part I: Theory and Practice of Static Analysis Static analysis is the science of creating precise and scalable finite approximations of potentially infinite behaviors so to enable a machine to automatically reason about these. These behaviors may come from programs but also other dynamic systems (e.g., biological). Hence the theory and principles of static analysis are widely applicable. We will cover:
Part II: Theory and Practice of Synthesis Modern program synthesis is an approach for automating the construction of programs from (partial) user intent. Recent years have seen exciting breakthroughs in techniques and algorithms that discover complex programs purely from input/output examples, natural language, partial programs (sketches) and many others forms of supervision and intent. Modern program synthesis can be seen as a path towards the ultimate goal of explainable machine learning. We will cover:
Part III: Programming Languages (PL) and Machine Learning (ML) We will cover the latest and most exciting developments bridging the areas of machine learning and programming languages. These trends include both directions: (i) PL techniques applied to ML problems, and (ii) ML techniques applies to PL tasks (e.g., reasoning about a program). Here, we will cover:
Connections between Topics In each of the 3 parts, we will also see how the parts connect. For instance, techniques from Part I are used to compute abstractions that are then fed to techniques from Part III. Similarly, the synthesis techniques from Part II connect with Part III in applications such as enforcing security policies with certain probabilistic guarantees (this has applications to privacy preservation, secure machine learning and other problems).
Course Project To gain a deeper understanding of how to apply these techniques, the course involves a hands-on programming project.


{% extends "ospi/base_home.html" %} {% load humanize %} {% block content_detail %} {% for account in object_list %}
| # | Station Information | Actions |
|---|---|---|
| {{ station.number }} |
{{ station.name }} Editheads: {{ station.heads }} (~{{ station.gph|floatformat:"-2" }} gph){% comment %}
pump?: {{ station.pump|yesno }}
ignore rain?: {{ station.ignore_rain|yesno }}
soil type: {{ station.soil_type }}
{% endcomment %}
|
{% comment %} {% if station.status %} {# It's on, allow the user to turn it off. #} {% elif station.status|default_if_none:"None" == "None" %} {# We aren't able to contact the station. No soup for you! #} {% else %} {# The station must be off, allow the user to turn it on. #} {% endif %} {% endcomment %} |
TODO: - Current status of the RPi unit. x Overview of the stations and their current status. - Time of last watering. - Weather data? - Action buttons to manually enable/disable a particular station.


It's beta, so things may break, go missing or just plain not work. But they should work, and I'd be very interested if they don't work, so please drop me a line at<EMAIL_ADDRESS>and I'll take a look
This application is designed for use with the openstreetmap project. There are legal and ethical arguments not to use Google Maps when rectifiying and processing out of copyright maps. However, in my opinion you can use Google Satellite, so you may see a way to add this later.
Grab the wms link and paste that into the relevant place using the wmsplugin. Have a look on the openstreetmap wiki for more info.
Yes. you can get the forked code here: https://github.com/maning/mapwarper/ it's currently MIT licenced.
By uploading images to the website you agree that you allow anyone to view and use them,
including changing control points etc.
So we recommend that you have permission to upload them.
Uploading and making images available does not grant a copyright license to others.
There is no guarantee about privacy or protection of images and metadata uploaded
(see note below about personal data).
If you suspect a copyright infringement, please mail<EMAIL_ADDRESS>Generally, make sure the image is free to distribute or copy - and please make a note in the
description if there
are any special conditions.
Soon, users will be able to make maps private.
We hate spam - your personal data (used for logging in etc) will be kept private,
and they will never be shared with any third party, and will only be used in the rare occasion
if we need to contact you.
If you want your details deleted, please mail emmanuel.sambale@gmail.com.


Game Server Tracker
Player Issue and Ban Tools
Item #1 | Item #2 | Item #3 | Item #4
Body
Footer Section Place Holder