►
Description
DevoWorm meeting: January 27, 2020. Attendees: Richard Gordon, Vinay Varma, Devansh Batra, Yash Agarwal, Bradly Alicea, and Jesse Parent
B
C
Okay,
you
don't
know
what
else
is
gonna
show
up,
but
welcome
to
the
meeting
for
today
today
we're
gonna
have
the
ashen
finish
present
on
the
deep
learning
architectures,
so
they're
gonna
tell
us
what
they
have
found
in
their
you
know
in
research
orientation,
so
I
don't
know
else
is
gonna
show
up,
but
you
know
they
can
come
in
as
they
come
in
and
so
any
is
there
any
news
from
anyone
over
the
last
week.
Anything
they
want
to
bring
up
issues,
deadlines,
other
things.
A
A
A
D
C
C
C
C
B
C
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
Also
in
a
break
even
snow
in
displacements,
but
introducing
a
subnetwork
specializing
on
snow,
so
they
fear
of
snow
notions
where
they
are
you've
seen
that
there
is
no
notion,
but
the
objects
are
moving
quite
slowly,
so
they
were
not
captured
earlier
in
the
original
unit.
So
they
introduced
a
special
subnet
architecture
which
data
soon
so.
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
E
C
Okay,
great
well,
that's
very
good!
Thank
you.
Let's
take
a
look
at
the
comments
here
in
the
chat,
we're
talking
about
the
video,
so
you
can
share
the
link
with
us,
so
yeah
I,
like
the
presentation,
if
he
could
send
me
a
copy
of
the
slides
and
link
to
the
video
I,
can
make
them
available
to
the
rest
of
the
group
just
as
a
link
so
actually
loop.
If
we're
gonna
have
a
chat
about
some
of
the
slides,
could
you
share
your
screen
again?
C
E
E
C
In
the
diatom
paper
that
we
recently
did
and
we
did
deep
learning
analysis
of
data,
we
also
did
a
like
a
template
version
of
the
analysis
of
the
vassal
area.
So
you
know
the
the
cells
were
segmented
and
then
they
were
tracked
using
templates
of
the
cells.
What
you're
suggesting
here
is,
we
could
do
something
similar
to
that,
but
just
using
a
deep
learning
architecture.
So
you
would
have-
and
you
probably
don't
know
this
paper,
but
what
we
had
was
we
had
a
colony
of
multi
colony
of
single
cells
that
operate
together.
C
It's
called
it's
a
diatom,
so
they're
sort
of
you
know
it's
very:
it's
not
a
mic.
It's
a
micro
organism,
but
it's
not
a
unicellular
organism,
the
existing
colonies.
That
are
where
the
cells
are,
these
long
filaments
that
are
stacked
together
and
they
move
in
a
way
that
is
collective
in
nature.
So
the
cells
move
against
one
another
and
they
have
pretty
complicated,
oh
yeah.
So
we
did
this
paper
on
this
organism
called
Bessel
area,
that's
a
diatom
and
they're
marine
or
micro
organisms,
but
they're,
you
know
multicellular
organisms.
C
So
my
question
is
from
that
is
that
in
that
analysis,
that
we
did
of
those
organisms
we
did
sort
of
where
we
had
we
segmented
the
cells.
And
then
we
didn't
use
this
each
cell
as
a
template
and
then
looked
at
their
relative
motion.
But
in
this
case
you
can
actually
combine
that
templating
method
with
a
deep
learning
method
to
identify
the
cell
or
identify
the
features
and
then
identify
the
motion
of
the
features
relative
to
one
another.
A
C
So
yeah,
so
you
could
well
okay,
so
you
could
do
that
and
it
would
be
maybe
more
efficient,
maybe
now
we'd
have
to
find
out,
but
so
you
can
also
you
were
talking
about.
Pre
train
models,
a
lot
and
as
opposed
to
like
a
conventional
training
method.
So
could
you
elaborate
a
little
bit
more
on
that?
What's,
the
is
is
like
part
of
the
envision
pre-trained
models
in
this
literature.
Is
it
like?
Is
this
sort
of
standard
that
they
do,
or
is
this
like
something
that
they
compare
with
like
regular
training?
E
E
E
We
could
like
produce
a
completely
mobile
camera,
which
is
which
has
bogus
temporal
and
spatial
parents,
along
with
lots
of
weights
of
base
combination,
which
may
or
may
not
be
misunderstood,
so
that
can
be
throughout
for
that.
So,
as
I
mentioned,
there
were
four
phases
for
ending
with
these
four
phases
of
training.
This
the
first
frame,
the
third
and
fourth
actually
work
on
the
target,
intercept
the
first
two
phases,
which
are
done
differently
for
temporal
coherence,
branch
and
special
governance
runs
until
in
use.
E
E
E
E
E
C
Yeah
I
mean
that
that
sounds
good
in
the
reason,
I
well
I
mean
I
guess.
The
thing
I
would
point
out
is
that
in
a
lot
of
data
and
a
lot
of
biological
data,
you
kind
of
have
a
mix
of
temporal
and
spatial
features
so
like,
let's
take
something
like
cell
division,
if
you're
observing
cell
division
in
a
time
series
that's
out
of
it,
you
know
you
can't
necessarily.
C
You
know,
use
like
past
images
to
predict
the
future
cleanly.
There's
gonna,
be
some
correction.
You're
gonna
have
to
make
I
mean
I,
guess
you
could
use
it
to
predict
the
future,
but
the
image
you
know
it's.
It
differs
over
time
in
terms
of
what's
there
you
have
maybe
like
fewer
cells
and
more
cells
over
time,
but
there
are
these
division
events
that
I
don't
know
how.
C
E
C
C
Error
behind
all
this
I
know
with
vinay,
for
example,
we
had
some
trial
and
error
last
summer
with
you
know,
getting
things
to
run.
You
know
in
terms
of
just
computational
resources
that
he
had
at
his
disposal.
He
talked
about
going
to
like
a
supercomputer
and
doing
it
in
a
high-performance
computing.
C
Resource,
so
you
know
they're
these
near
these
considerations.
You
have
to
make
when
considering
these
architectures
but
I
like
I,
like
the
idea
of
you
know
having
like
spatial
and
temporal
streams
that
can
be
done.
You
know
so
you're
doing
like
a
spatial
you're
doing
segmentation
and
combining
those
two.
H
H
E
F
C
C
E
And
certainly
it
is
shape
in
between
Afeni
so
to
deconvolution.
We
have
done
these
kinds
of
even
less
is
that
there
is
a
bond
moving
from
left
to
right
and
such
a
nice
shape,
but
you
can
think
that
there
are
currents
that
will
Forex
shell
cheat
and
get
one.
So
in
the
mega
layers,
this
type
of
complex
dependencies
can
be
done
very
easily,
so
yeah
about
the
ocean.
Yes,.
C
C
Otherwise,
next
week
which
wall
is
going
to
present
on
DNA
barcoding
and
then,
if
you
anyone
has
any
other
announcements
or
things
they
want
to
bring
up,
you
welcome
to
do
so
again.
We
have
think
we
have
slots
to
fill
for
the
rest
of
the
spring.
So
if
you
want
to
do
something
longer,
like
a
paper
review
or
something
you're
welcome
to
schedule
it,
we
can
talk
about
that
offline.
So,
thanks
for
attending
everyone
have
a
good
week
and
talk
to
you
next
week
or
offline.