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8
8
8
4876
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e
n
tal
d
esig
n
,
in
clu
d
in
g
d
ata
co
llectio
n
,
f
in
e
-
t
u
n
in
g
o
f
th
e
m
o
d
els,
an
d
th
eir
ev
alu
atio
n
.
Sectio
n
6
p
r
esen
ts
r
esu
lts
an
d
k
ey
f
in
d
i
n
g
s
,
alo
n
g
with
p
er
f
o
r
m
an
ce
co
m
p
ar
is
o
n
,
f
r
o
m
r
ea
l
-
life
ex
p
e
r
im
en
t
with
c
o
n
ten
t
g
e
n
er
ated
b
y
th
e
s
y
s
tem
.
Fin
ally
,
s
ec
tio
n
s
7
an
d
8
d
is
cu
s
s
im
p
licatio
n
s
f
o
r
f
u
tu
r
e
d
ir
ec
tio
n
s
an
d
a
co
n
cl
u
s
io
n
th
at
r
ef
lect
o
n
th
e
th
eo
r
etica
l
co
n
tr
ib
u
tio
n
s
an
d
eth
ical
co
n
s
id
er
atio
n
s
o
f
th
is
s
y
s
tem
,
an
d
also
id
ea
s
a
b
o
u
t
h
o
w
it
ca
n
b
e
f
u
r
th
er
e
x
ten
d
e
d
to
o
t
h
er
d
i
g
ital m
ar
k
etin
g
c
h
an
n
els.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
2
.
1
.
AI in
dig
it
a
l ma
rk
e
t
ing
T
h
e
in
teg
r
atio
n
o
f
AI
in
d
ig
ita
l
m
ar
k
etin
g
h
as u
n
d
e
r
g
o
n
e
th
r
o
u
g
h
s
ig
n
if
ican
t
c
h
an
g
es,
f
u
n
d
am
en
tally
ch
an
g
in
g
h
o
w
co
m
p
an
ies
co
m
m
u
n
icate
with
co
n
s
u
m
e
r
s
.
E
ar
ly
AI
im
p
lem
en
tatio
n
s
wer
e
lim
ited
to
r
u
le
-
b
ased
s
y
s
tem
s
f
o
r
c
u
s
to
m
er
s
eg
m
en
tatio
n
a
n
d
em
ail
au
to
m
atio
n
task
s
.
T
h
ese
s
y
s
tem
s
,
th
o
u
g
h
ef
f
icien
t
,
n
ee
d
ed
m
o
r
e
a
d
ap
tab
ilit
y
a
n
d
p
er
s
o
n
aliza
tio
n
.
T
h
e
em
er
g
e
n
ce
o
f
m
ac
h
in
e
lear
n
in
g
(
ML
)
in
th
e
late
1
9
9
0
s
en
ab
led
m
o
r
e
d
y
n
a
m
ic
an
d
d
a
ta
-
d
r
iv
en
s
tr
ateg
ies,
in
clu
d
in
g
tar
g
eted
r
ec
o
m
m
en
d
atio
n
s
,
ex
em
p
lifie
d
b
y
ea
r
l
y
co
llab
o
r
ativ
e
f
ilter
in
g
alg
o
r
ith
m
s
u
s
ed
in
p
latf
o
r
m
s
lik
e
Am
az
o
n
[
1
]
.
T
h
e
ad
v
en
t
o
f
d
ee
p
lea
r
n
in
g
i
n
th
e
2
0
1
0
s
m
ar
k
ed
a
s
ig
n
i
f
ican
t
s
h
if
t
in
AI
’
s
ca
p
ab
ilit
ies,
i
n
tr
o
d
u
ci
n
g
m
o
d
els
s
u
ch
as
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NNs)
an
d
r
ec
u
r
r
e
n
t
n
eu
r
al
n
etwo
r
k
s
(
R
NNs).
T
h
ese
tech
n
o
lo
g
ies
p
o
wer
e
d
in
n
o
v
at
io
n
s
in
im
ag
e
r
ec
o
g
n
itio
n
,
n
a
tu
r
al
lan
g
u
ag
e
p
r
o
ce
s
s
in
g
(
N
L
P),
an
d
p
r
e
d
ictiv
e
an
aly
tics
—
to
d
r
iv
e
ap
p
licatio
n
s
s
u
ch
as
ch
atb
o
ts
a
n
d
ad
g
en
er
atio
n
[
2
]
.
E
s
p
ec
ially
,
c
h
atb
o
ts
s
h
o
wed
th
e
p
o
wer
o
f
h
o
w
NL
P c
an
b
e
in
e
n
h
an
cin
g
cu
s
to
m
er
in
ter
ac
tio
n
s
,
wh
ile
p
r
ed
ictiv
e
an
aly
tics
o
p
tim
ized
ca
m
p
aig
n
s
tr
ateg
ies with
r
eg
ar
d
to
f
i
n
d
in
g
h
ig
h
-
v
alu
e
c
u
s
to
m
er
s
[
3
]
.
I
n
2
0
1
7
,
th
e
in
tr
o
d
u
ctio
n
o
f
tr
an
s
f
o
r
m
e
r
ar
ch
itectu
r
es,
p
ar
ticu
lar
ly
b
id
ir
ec
tio
n
a
l
en
co
d
er
r
ep
r
esen
tatio
n
s
f
r
o
m
tr
an
s
f
o
r
m
er
s
(
B
E
R
T
)
,
r
ev
o
lu
tio
n
ize
d
NL
P.
B
E
R
T
’
s
u
n
d
er
s
tan
d
in
g
o
f
tex
t
u
al
co
n
tex
t
im
p
r
o
v
e
d
ap
p
licatio
n
s
s
u
ch
a
s
s
en
tim
en
t
an
aly
s
is
[
4
]
a
n
d
s
ea
r
ch
en
g
in
e
o
p
tim
izatio
n
[
5
]
.
B
u
ild
in
g
o
n
th
ese
ad
v
an
ce
m
e
n
ts
,
lar
g
e
lan
g
u
a
g
e
m
o
d
els
(
L
L
Ms)
lik
e
Op
en
AI
’
s
GPT
-
3
.
5
an
d
Go
o
g
le’
s
PaL
M
2
e
x
p
an
d
ed
t
h
e
p
o
s
s
ib
ilit
ies
o
f
AI
in
m
ar
k
etin
g
b
y
en
a
b
lin
g
th
e
g
e
n
er
atio
n
o
f
p
er
s
o
n
alize
d
an
d
co
n
te
x
tu
ally
r
elev
an
t
tex
t
[
6
]
.
T
h
ese
m
o
d
els
s
u
p
p
o
r
t
c
r
ea
tin
g
p
er
s
o
n
alize
d
em
ail
ca
m
p
aig
n
s
an
d
ad
co
p
ies
th
at
in
cr
ea
s
e
th
e
r
ate
o
f
cu
s
to
m
er
en
g
ag
em
e
n
t a
n
d
click
-
th
r
o
u
g
h
.
Me
an
wh
ile,
g
en
er
ativ
e
m
o
d
el
s
in
AI
,
s
u
ch
as
g
en
er
ativ
e
a
d
v
er
s
ar
ial
n
etwo
r
k
s
(
GANs)
an
d
d
if
f
u
s
io
n
m
o
d
els,
in
clu
d
in
g
s
tab
le
d
if
f
u
s
io
n
,
h
av
e
tak
en
v
is
u
al
co
n
te
n
t
g
en
er
atio
n
t
o
th
e
n
e
x
t
lev
e
l.
GANs
d
o
m
in
ated
th
is
s
p
ac
e
in
itially
[
7
]
,
b
u
t
th
en
ca
m
e
th
e
d
if
f
u
s
io
n
m
o
d
els,
wh
ich
cr
ea
ted
h
ig
h
-
r
es
o
lu
tio
n
im
ag
es
b
y
iter
ativ
ely
r
ef
in
in
g
n
o
is
y
d
ata
[
8
]
.
W
ith
s
tab
le
d
if
f
u
s
io
n
,
f
o
r
in
s
tan
ce
,
m
ar
k
eter
s
ca
n
g
en
er
ate
b
r
an
d
-
alig
n
e
d
v
is
u
als th
at
wil
l b
e
in
tu
n
e
wit
h
th
e
o
b
jectiv
es o
f
a
ca
m
p
aig
n
,
lead
in
g
to
m
o
r
e
en
g
a
g
em
en
t
an
d
s
tr
o
n
g
er
b
r
an
d
id
en
tity
.
All
o
f
th
is
p
r
o
g
r
ess
en
ab
les
p
e
r
s
o
n
aliza
tio
n
in
m
ar
k
etin
g
wit
h
AI
-
p
o
wer
e
d
tex
t
an
d
v
is
u
al
co
n
ten
t
at
an
u
n
p
r
ec
ed
en
te
d
lev
el.
AI
-
p
o
wer
ed
r
ec
o
m
m
e
n
d
atio
n
e
n
g
in
es,
d
y
n
a
m
ic
p
r
icin
g
m
o
d
els,
an
d
p
r
ed
ictiv
e
an
aly
tics
m
ak
e
ca
m
p
aig
n
o
p
ti
m
izatio
n
p
o
s
s
ib
le
an
d
th
e
d
el
iv
er
y
o
f
p
er
s
o
n
alize
d
cu
s
to
m
er
ex
p
er
ien
ce
s
[
9
]
.
B
u
t,
n
o
twith
s
tan
d
in
g
s
u
ch
p
r
o
g
r
ess
,
ch
allen
g
es
r
em
ain
.
A
d
v
an
ce
d
AI
m
o
d
els
ca
n
b
e
p
r
o
h
ib
itiv
ely
co
s
tly
f
o
r
s
m
aller
b
u
s
in
ess
es
to
im
p
lem
en
t;
b
esid
es,
eth
ical
co
n
s
id
er
a
tio
n
s
ab
o
u
n
d
,
in
clu
d
in
g
alg
o
r
i
th
m
ic
b
ias
[
1
0
]
an
d
en
v
ir
o
n
m
en
tal
im
p
ac
t
[
1
1
]
.
M
o
r
eo
v
e
r
,
h
o
w
a
u
to
m
atio
n
ca
n
b
e
b
alan
ce
d
with
au
th
en
ticity
s
till
r
em
ain
s
a
b
ig
is
s
u
e
f
o
r
m
ar
k
eter
s
.
I
t
i
s
im
p
o
r
tan
t
to
k
n
o
w
th
at
e
v
en
th
o
u
g
h
th
e
ev
e
r
-
ev
o
l
v
in
g
f
ac
e
o
f
AI
h
as
ch
an
g
e
d
d
ig
ital
m
ar
k
etin
g
,
m
o
v
in
g
f
r
o
m
r
u
le
-
b
ased
s
y
s
tem
s
to
co
m
p
lex
ML
,
d
ee
p
lear
n
i
n
g
,
an
d
g
en
e
r
ativ
e
AI
ar
ch
itectu
r
es
,
it
i
s
also
en
g
en
d
er
in
g
m
an
y
ch
allen
g
es wh
ich
m
u
s
t b
e
r
eso
l
v
ed
as th
ese
r
ap
id
ad
v
an
ce
s
p
er
s
is
t.
2
.
2
.
L
a
rg
e
la
ng
ua
g
e
m
o
dels
L
ar
g
e
lan
g
u
a
g
e
m
o
d
els
(
L
L
Ms)
h
av
e
b
ec
o
m
e
a
f
u
n
d
am
e
n
tal
elem
en
t
in
m
o
d
er
n
NL
P,
p
r
im
ar
il
y
d
r
iv
en
b
y
th
e
in
teg
r
atio
n
o
f
th
e
tr
an
s
f
o
r
m
er
ar
c
h
itectu
r
e
f
ir
s
t
p
r
o
p
o
s
ed
b
y
Vaswan
i
et
a
l.
[
1
2
]
.
Un
lik
e
r
ec
u
r
r
en
t
an
d
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
,
tr
an
s
f
o
r
m
er
s
u
s
e
m
ec
h
an
is
m
s
o
f
s
elf
-
atten
t
io
n
to
p
r
o
ce
s
s
in
p
u
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
AI
-
d
r
iven
in
teg
r
a
ted
s
ystem
fo
r
co
mp
r
eh
en
s
ive
ema
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r
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u
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eq
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ly
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wh
ich
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ig
n
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ican
tly
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cr
ea
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es
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ef
f
icien
cy
an
d
s
ca
lab
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o
f
lan
g
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els.
T
h
is
led
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th
e
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ev
elo
p
m
en
t
o
f
th
ese
m
o
d
els
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r
et
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e
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atasets
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i
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tu
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f
o
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ic
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wh
ich
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h
u
g
e
t
u
r
n
ar
o
u
n
d
in
NL
P r
esear
ch
.
T
h
e
in
tr
o
d
u
ctio
n
o
f
B
E
R
T
b
y
Dev
lin
et
a
l.
[
1
3
]
d
em
o
n
s
tr
ated
th
e
ef
f
ec
tiv
en
ess
o
f
b
id
ir
ec
tio
n
al
co
n
tex
t
m
o
d
elin
g
th
r
o
u
g
h
m
a
s
k
ed
lan
g
u
ag
e
m
o
d
elin
g
(
ML
M)
.
B
E
R
T
attain
ed
s
tate
-
of
-
t
h
e
-
ar
t
p
er
f
o
r
m
an
ce
o
n
a
v
ar
iety
o
f
NL
P
b
en
ch
m
ar
k
s
,
wh
ich
f
u
r
th
er
h
i
g
h
lig
h
ted
th
e
b
en
ef
its
o
f
p
r
etr
ain
i
n
g
o
n
lar
g
e
co
r
p
o
r
a
f
o
llo
wed
b
y
task
-
s
p
ec
if
ic
f
i
n
e
-
tu
n
in
g
.
I
ts
ar
ch
itectu
r
e
h
as
s
in
ce
m
o
tiv
ated
f
u
r
th
er
in
n
o
v
ati
o
n
s
in
th
e
f
ield
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et
a
s
tan
d
ar
d
f
o
r
m
u
ltit
ask
lear
n
in
g
with
in
NL
P.
Gen
er
ativ
e
m
o
d
els
s
u
ch
as
GPT
-
3
,
a
g
en
er
ativ
e
p
r
e
-
tr
ain
ed
tr
an
s
f
o
r
m
er
,
in
tr
o
d
u
ce
d
b
y
B
r
o
wn
et
a
l.
[
1
4
]
,
e
x
ten
d
e
d
th
e
ca
p
a
b
ilit
ies
o
f
L
L
Ms
b
y
f
o
c
u
s
in
g
o
n
a
u
to
r
eg
r
ess
iv
e
tex
t
g
en
e
r
atio
n
.
GPT
-
3
’
s
m
ass
iv
e
175
-
b
illi
o
n
-
p
ar
a
m
eter
ar
ch
ite
ctu
r
e
en
ab
led
u
n
p
r
ec
ed
en
te
d
ze
r
o
-
s
h
o
t
an
d
f
ew
-
s
h
o
t
lear
n
i
n
g
,
o
u
tp
er
f
o
r
m
in
g
s
m
aller
m
o
d
els
in
a
v
ar
iety
o
f
g
e
n
er
ativ
e
task
s
with
o
u
t
r
eq
u
ir
in
g
ex
ten
s
iv
e
task
-
s
p
ec
i
f
ic
d
ata.
T
h
e
later
GPT
-
3
.
5
in
tr
o
d
u
ce
d
im
p
r
o
v
e
d
f
in
e
-
g
r
ain
e
d
co
n
tex
t
u
al
u
n
d
e
r
s
tan
d
in
g
an
d
d
em
o
n
s
tr
ated
e
n
h
an
ce
d
r
ea
s
o
n
in
g
ca
p
ab
ilit
ies,
p
r
o
v
in
g
th
e
u
tili
ty
o
f
g
e
n
er
ativ
e
L
L
Ms f
o
r
co
n
v
er
s
atio
n
al
AI
,
co
n
ten
t
g
en
er
ati
o
n
,
an
d
b
ey
o
n
d
.
Mo
r
e
r
ec
en
t
a
d
v
an
ce
m
en
ts
in
clu
d
e
p
ath
way
s
lan
g
u
ag
e
m
o
d
el
2
(
PaL
M
2
)
,
wh
ich
was
d
e
v
elo
p
ed
b
y
Go
o
g
le
[
1
5
]
.
Usi
n
g
m
u
lti
-
m
o
d
al,
m
u
ltil
in
g
u
al
d
ata,
th
e
m
o
d
el
is
s
aid
to
o
u
tp
er
f
o
r
m
th
e
co
m
p
etitio
n
in
task
s
r
elate
d
to
tr
a
n
s
latio
n
,
s
cien
tifi
c
r
ea
s
o
n
in
g
,
an
d
e
v
en
c
o
d
e
g
e
n
er
atio
n
.
I
t
is
g
e
n
er
ally
m
o
r
e
ef
f
icien
t,
b
ala
n
cin
g
co
m
p
u
tin
g
n
ee
d
s
with
h
o
w
well
it
p
er
f
o
r
m
s
.
PaL
M
2
r
ef
lects
co
n
tin
u
ed
r
esear
ch
in
to
L
L
Ms
d
esig
n
ed
f
o
r
s
p
ec
if
ic
f
ield
s
an
d
g
e
n
er
ic
task
s
.
T
h
at
s
ets th
e
ex
am
p
le
f
o
r
t
r
ain
in
g
m
eth
o
d
s
u
s
in
g
r
eso
u
r
c
es e
f
f
icien
tly
.
Desp
ite
th
eir
s
u
cc
ess
,
L
L
Ms
f
ac
e
n
o
tab
le
c
h
allen
g
es.
E
th
i
ca
l
co
n
ce
r
n
s
s
u
r
r
o
u
n
d
in
g
t
h
e
s
e
m
o
d
els
in
clu
d
e
th
e
p
e
r
p
etu
atio
n
o
f
b
iases
em
b
ed
d
ed
in
tr
ai
n
in
g
d
ata,
r
is
k
s
o
f
m
is
in
f
o
r
m
atio
n
p
r
o
p
ag
atio
n
,
an
d
s
u
s
ce
p
tib
ilit
y
to
m
alicio
u
s
u
s
e.
Fo
r
ex
am
p
le,
W
eid
in
g
er
et
a
l.
[
1
6
]
n
o
ted
r
is
k
s
s
u
ch
as
d
is
cr
im
in
atio
n
,
m
is
in
f
o
r
m
atio
n
,
an
d
au
to
m
at
io
n
h
ar
m
s
,
ca
llin
g
f
o
r
ef
f
ec
t
iv
e
way
s
to
m
itig
ate
th
ese
p
r
o
b
lem
s
.
B
esid
es,
tech
n
ical
lim
itatio
n
s
,
s
u
ch
as
h
allu
cin
atio
n
s
,
wh
er
e
m
o
d
els
co
n
f
id
en
tly
g
en
er
ate
in
c
o
r
r
ec
t
o
u
tp
u
ts
,
an
d
h
ig
h
co
m
p
u
tatio
n
al
c
o
s
ts
ar
e
also
s
er
io
u
s
is
s
u
es
th
at
m
u
s
t
b
e
ad
d
r
ess
ed
.
T
h
e
en
v
ir
o
n
m
en
tal
im
p
ac
t
o
f
tr
ain
in
g
a
n
d
d
ep
lo
y
in
g
L
L
Ms,
esp
ec
ially
th
o
s
e
with
b
illi
o
n
s
o
f
p
ar
am
et
er
s
,
r
aises
s
u
s
tain
ab
ilit
y
co
n
ce
r
n
s
,
as
s
tu
d
ied
b
y
B
en
d
er
et
a
l.
[
1
0
]
a
n
d
also
,
Str
u
b
ell
et
a
l.
[
1
7
]
.
L
L
Ms
s
u
ch
as
B
E
R
T
,
GPT
-
3
.
5
,
an
d
PaL
M
2
r
ep
r
esen
t
th
e
f
ast
-
p
ac
ed
e
v
o
lu
tio
n
o
f
AI
in
lan
g
u
a
g
e
u
n
d
er
s
tan
d
i
n
g
an
d
g
en
er
ati
o
n
.
Ho
wev
er
,
th
e
m
o
r
e
th
ese
m
o
d
els
ar
e
p
u
t
in
to
wid
e
u
s
e
ac
r
o
s
s
in
d
u
s
tr
ies,
th
e
m
o
r
e
u
r
g
en
t
it
will
b
e
to
ad
d
r
ess
th
eir
eth
ical,
tech
n
ical,
an
d
s
o
cieta
l
im
p
licatio
n
s
.
T
h
is
will
ex
p
an
d
th
eir
ab
ilit
ies to
th
eir
f
u
lles
t p
o
ten
tial.
2
.
3
.
St
a
ble
diff
us
io
n
m
o
dels
Dif
f
u
s
io
n
m
o
d
els
h
av
e
b
ec
o
m
e
a
k
ey
tech
n
iq
u
e
in
g
en
e
r
a
tiv
e
m
o
d
elin
g
,
with
ap
p
licati
o
n
s
r
an
g
in
g
f
r
o
m
im
ag
e
s
y
n
th
esis
to
v
id
eo
g
en
er
atio
n
a
n
d
b
e
y
o
n
d
.
T
h
e
m
ain
co
n
ce
p
t
o
f
d
if
f
u
s
io
n
m
o
d
els
was
f
ir
s
t
p
r
o
p
o
s
ed
b
y
So
h
l
-
Dick
s
tein
et
a
l.
[
1
8
]
,
a
n
d
i
n
v
o
lv
es
iter
ativ
ely
tr
an
s
f
o
r
m
in
g
d
ata
in
t
o
n
o
is
e
th
r
o
u
g
h
a
f
o
r
war
d
d
if
f
u
s
io
n
p
r
o
ce
s
s
an
d
th
en
r
ev
e
r
s
in
g
t
h
is
p
r
o
ce
s
s
to
r
ec
o
v
er
t
h
e
o
r
ig
in
al
d
ata.
T
h
i
s
r
ev
er
s
e
p
r
o
ce
s
s
is
p
ar
am
eter
ized
b
y
d
ee
p
n
eu
r
al
n
etwo
r
k
s
a
n
d
tr
ain
e
d
to
p
r
o
d
u
ce
h
i
g
h
-
q
u
ality
s
am
p
les
f
r
o
m
n
o
is
e
[
1
8
]
.
T
h
is
tech
n
iq
u
e
g
ain
ed
p
r
o
m
in
e
n
ce
with
th
e
d
ev
elo
p
m
en
t
o
f
de
-
n
o
is
in
g
d
if
f
u
s
io
n
p
r
o
b
ab
ilis
tic
m
o
d
els
(
DDPMs)
i
n
a
p
ap
er
b
y
Ho
et
a
l.
[
1
9
]
th
at
d
em
o
n
s
tr
ated
s
tate
-
of
-
t
h
e
-
ar
t
p
er
f
o
r
m
an
ce
in
im
ag
e
g
en
er
atio
n
b
y
em
p
lo
y
in
g
v
ar
iatio
n
al
in
f
er
en
ce
an
d
an
a
r
ch
itectu
r
e
b
ased
o
n
U
-
Net.
Stab
le
d
if
f
u
s
io
n
,
th
u
s
,
as
a
s
ig
n
if
ican
t
e
x
ten
s
io
n
o
f
DDP
Ms,
in
tr
o
d
u
ce
d
a
n
ef
f
icien
t
a
p
p
r
o
ac
h
to
laten
t
-
s
p
ac
e
d
if
f
u
s
io
n
.
T
h
e
s
tab
le
d
if
f
u
s
io
n
m
o
d
el,
d
ev
elo
p
ed
b
y
R
o
m
b
ac
h
et
a
l.
[
2
0
]
,
p
r
o
jects
d
ata
in
to
a
co
m
p
r
ess
ed
laten
t
s
p
ac
e
u
s
in
g
a
p
r
e
-
tr
ai
n
ed
a
u
to
en
c
o
d
er
,
wh
ic
h
d
r
asti
ca
lly
r
ed
u
ce
s
co
m
p
u
tatio
n
al
r
eq
u
ir
em
e
n
ts
b
o
th
f
o
r
tr
ain
in
g
an
d
in
f
er
e
n
ce
.
T
h
is
in
n
o
v
atio
n
n
o
t
o
n
l
y
m
ak
es
d
if
f
u
s
io
n
m
o
d
els
ac
ce
s
s
ib
le
f
o
r
lar
g
e
-
s
ca
le
task
s
b
u
t
also
en
a
b
les
r
ea
l
-
tim
e
ap
p
licatio
n
s
.
S
tab
le
d
if
f
u
s
io
n
h
as
f
o
u
n
d
wi
d
e
-
s
p
r
ea
d
a
d
o
p
tio
n
b
ec
au
s
e
it
g
e
n
er
ates
p
h
o
to
r
ea
lis
tic
an
d
h
ig
h
-
r
eso
lu
tio
n
im
a
g
es
wh
o
s
e
p
r
o
p
er
ties
ca
n
b
e
co
n
tr
o
lled
,
s
u
ch
as
s
ty
le
an
d
co
n
ten
t sp
ec
if
icity
.
T
h
e
in
co
r
p
o
r
atio
n
o
f
au
x
iliar
y
m
eth
o
d
s
s
u
ch
as
v
ec
to
r
em
b
ed
d
in
g
s
an
d
k
n
o
wled
g
e
g
r
ap
h
s
h
as
f
u
r
th
er
in
c
r
ea
s
ed
th
e
p
o
te
n
tial
o
f
s
tab
le
d
if
f
u
s
io
n
.
Kn
o
w
led
g
e
g
r
ap
h
s
m
o
d
el
s
tr
u
ctu
r
e
d
r
elatio
n
s
am
o
n
g
en
titi
es,
wh
ich
allo
ws
m
o
r
e
co
n
tex
tu
al
r
elev
an
ce
a
n
d
in
ter
p
r
etab
ilit
y
with
g
en
er
ated
co
n
te
n
t
[
2
1
]
.
K
n
o
wled
g
e
g
r
ap
h
s
in
teg
r
ated
in
to
s
tab
le
d
if
f
u
s
io
n
h
av
e
allo
wed
r
ese
ar
ch
er
s
to
p
r
o
d
u
ce
f
r
o
m
s
u
c
h
m
o
d
els’
o
u
tp
u
ts
g
r
o
u
n
d
ed
in
f
ac
tu
al
o
r
d
o
m
ain
-
s
p
ec
if
ic
k
n
o
wled
g
e,
h
e
n
ce
m
ak
i
n
g
th
e
en
d
r
esu
lt
m
o
r
e
r
eliab
le
a
n
d
ap
p
r
o
p
r
iate
f
o
r
s
p
ec
if
ic
u
s
e
in
s
o
m
e
d
o
m
ain
s
s
u
ch
as
p
h
ar
m
ac
eu
tical
d
r
u
g
d
is
co
v
er
y
an
d
s
em
an
tic
p
ar
s
in
g
in
NL
P
[
2
2
]
.
Vec
to
r
em
b
ed
d
i
n
g
s
,
o
n
th
e
o
th
er
h
an
d
,
a
r
e
a
f
u
n
d
am
e
n
tal
r
ep
r
esen
tatio
n
tec
h
n
iq
u
e
in
m
ac
h
in
e
lear
n
in
g
,
as
th
ey
en
co
d
e
h
ig
h
-
d
im
en
s
io
n
al
d
ata
in
to
co
n
tin
u
o
u
s
v
ec
to
r
s
p
ac
es,
p
r
eser
v
in
g
s
em
an
tic
r
elatio
n
s
h
ip
s
.
T
h
is
em
b
ed
d
in
g
en
ab
les
s
tab
le
d
if
f
u
s
io
n
m
o
d
els
to
co
n
d
itio
n
th
eir
o
u
tp
u
ts
o
n
tex
tu
al
p
r
o
m
p
ts
,
wh
ich
is
s
h
o
wn
in
s
y
s
tem
s
s
u
ch
as
I
m
a
g
en
,
wh
ich
is
a
Go
o
g
le
r
esear
ch
tex
t
-
to
-
im
a
g
e
d
if
f
u
s
io
n
m
o
d
el
k
n
o
wn
f
o
r
its
h
ig
h
p
h
o
t
o
r
ea
lis
m
[
2
3
]
,
an
d
DAL
L
-
E
,
a
m
o
d
el
d
ev
e
lo
p
ed
b
y
Op
e
n
AI
th
at
cr
ea
tes
im
ag
es
f
r
o
m
tex
t
p
r
o
m
p
ts
[
2
4
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
8
7
5
-
4
8
8
8
4878
T
h
e
in
teg
r
atio
n
o
f
v
ec
t
o
r
e
m
b
ed
d
in
g
s
an
d
k
n
o
wled
g
e
g
r
ap
h
s
ca
n
o
v
er
co
m
e
two
o
f
th
e
m
o
s
t
s
ig
n
if
ican
t
wea
k
n
ess
es
in
d
if
f
u
s
io
n
m
o
d
els:
lack
o
f
c
o
n
tr
o
l
o
v
er
th
e
co
n
ten
t
g
e
n
er
ate
d
an
d
t
h
e
r
is
k
o
f
ir
r
elev
an
t
o
r
u
n
s
tr
u
ctu
r
ed
o
u
t
p
u
ts
.
Fo
r
in
s
tan
ce
,
Fen
g
[
2
5
]
s
h
o
wed
th
at
em
b
ed
d
in
g
tech
n
iq
u
es,
to
g
eth
er
with
k
n
o
wled
g
e
-
au
g
m
e
n
ted
d
if
f
u
s
io
n
m
o
d
els,
im
p
r
o
v
e
d
th
e
a
lig
n
m
en
t
o
f
g
en
er
ated
v
is
u
a
ls
to
th
eir
tex
tu
al
d
escr
ip
tio
n
s
s
ig
n
if
ican
tly
in
m
ed
ical
im
ag
in
g
.
L
ik
ewise,
k
n
o
wled
g
e
g
r
a
p
h
s
with
i
n
d
if
f
u
s
io
n
-
b
ased
f
r
am
ewo
r
k
s
h
av
e
also
s
h
o
wn
th
eir
p
o
ten
tial
to
alig
n
b
etter
with
d
o
m
ain
-
s
p
ec
if
ic
r
eq
u
i
r
em
en
ts
,
as
r
ec
en
t
wo
r
k
s
o
n
r
ec
o
m
m
e
n
d
atio
n
s
y
s
tem
s
ap
p
lied
to
e
-
c
o
m
m
er
ce
[
2
6
]
an
d
p
r
o
d
u
ct
d
esig
n
[
2
7
]
h
av
e
p
o
in
te
d
o
u
t.
W
h
ile
s
tab
le
d
if
f
u
s
io
n
h
as
r
ev
o
lu
tio
n
ized
g
e
n
er
ativ
e
AI
b
y
co
m
b
in
in
g
co
m
p
u
tatio
n
al
ef
f
i
cien
cy
an
d
cr
ea
tiv
e
f
lex
ib
ilit
y
,
ch
allen
g
e
s
r
em
ain
.
T
h
e
laten
t
s
p
ac
es
ar
e
ty
p
ically
h
ig
h
-
d
im
e
n
s
io
n
al,
wh
ich
p
r
esen
ts
o
p
tim
izatio
n
c
h
allen
g
es;
th
e
m
o
d
els
ar
e
s
en
s
itiv
e
to
ad
v
er
s
ar
ial
p
r
o
m
p
ts
,
wh
ich
m
ay
p
r
o
d
u
ce
u
n
d
esire
d
o
r
m
is
lead
in
g
o
u
tp
u
ts
[
2
8
]
.
M
o
r
eo
v
e
r
,
s
im
ilar
t
o
lar
g
e
-
s
ca
le
m
o
d
els
i
n
g
en
er
al,
eth
ica
l
co
n
ce
r
n
s
p
e
r
s
is
t,
esp
ec
ially
with
r
eg
ar
d
to
in
tellectu
al
p
r
o
p
er
ty
,
m
is
in
f
o
r
m
atio
n
,
an
d
p
o
s
s
ib
le
m
is
u
s
e
f
o
r
g
en
er
atin
g
m
is
lead
in
g
co
n
ten
t
[
2
9
]
.
T
h
ese
is
s
u
es,
ag
ain
,
r
eq
u
ir
e
f
u
r
th
er
d
ev
elo
p
m
en
t
o
f
m
o
d
el
r
o
b
u
s
tn
ess
,
tr
an
s
p
ar
en
cy
,
an
d
g
o
v
er
n
an
ce
f
r
am
ew
o
r
k
s
.
No
n
eth
eless
,
s
tab
le
d
if
f
u
s
io
n
r
ep
r
esen
ts
an
im
p
o
r
tan
t
n
ex
t
s
tep
to
war
d
b
o
th
s
ca
lin
g
an
d
q
u
ality
in
g
en
er
ativ
e
m
o
d
elin
g
.
I
ts
co
m
b
in
atio
n
with
v
ec
to
r
e
m
b
ed
d
in
g
s
an
d
k
n
o
wled
g
e
g
r
ap
h
s
ep
ito
m
izes
a
g
r
o
win
g
tr
en
d
t
o
war
d
im
p
r
o
v
in
g
t
h
e
ca
p
ab
ilit
y
o
f
m
o
d
els
wh
ile
p
a
y
in
g
m
u
c
h
-
n
ee
d
e
d
atten
tio
n
to
th
e
p
r
ac
tical
an
d
eth
ical
asp
ec
ts
o
f
th
e
d
ep
l
o
y
m
en
t
o
f
g
e
n
er
ativ
e
tech
n
o
lo
g
ies.
2
.
4
.
E
x
is
t
ing
g
a
ps
Desp
ite
th
e
f
ast
d
ev
elo
p
m
en
t
o
f
b
o
th
L
L
Ms
an
d
d
if
f
u
s
io
n
m
o
d
els,
o
n
ly
s
o
m
e
a
r
e
th
e
f
r
am
ewo
r
k
s
th
at
u
s
e
b
o
t
h
th
ese
tech
n
o
lo
g
ies
to
s
o
lv
e
p
ar
ticu
la
r
ch
alle
n
g
es,
s
u
ch
as
t
h
o
s
e
r
elate
d
to
em
ail
m
ar
k
etin
g
.
Alth
o
u
g
h
ea
c
h
o
f
th
ese
m
o
d
e
ls
h
as
in
d
iv
id
u
ally
ac
h
ie
v
ed
g
r
ea
t
s
u
cc
ess
in
its
o
wn
d
o
m
ai
n
—
tex
t
g
en
er
atio
n
an
d
v
is
u
al
co
n
ten
t
cr
ea
tio
n
,
r
esp
ec
tiv
ely
—
th
ey
ar
e
r
a
r
el
y
ap
p
lied
to
g
eth
er
i
n
a
s
in
g
le
wo
r
k
f
lo
w.
T
h
is
d
is
co
n
n
ec
tio
n
lim
its
th
e
ex
p
l
o
r
atio
n
o
f
h
o
w
th
eir
co
m
b
in
ed
p
o
te
n
tial
ca
n
e
n
h
an
ce
cr
e
ativ
e
an
d
i
m
p
ac
tf
u
l
m
ar
k
etin
g
s
tr
ateg
ies
[
2
0
]
.
L
ar
g
e
lan
g
u
ag
e
m
o
d
els
lik
e
GPT
-
3
.
5
,
PaL
M
2
,
an
d
B
E
R
T
h
av
e
co
n
s
id
er
ab
ly
e
n
h
an
ce
d
tex
t
g
en
er
atio
n
[
3
0
]
,
p
r
o
d
u
cin
g
f
lu
en
t,
co
h
er
en
t,
an
d
co
n
tex
tu
ally
r
elev
an
t
o
u
tp
u
ts
f
o
r
v
ar
io
u
s
m
ar
k
etin
g
ap
p
licatio
n
s
.
At
t
h
e
s
am
e
tim
e,
s
o
m
e
o
th
er
p
o
wer
f
u
l
m
o
d
els,
in
clu
d
in
g
d
if
f
u
s
io
n
-
b
ased
m
o
d
els
lik
e
s
tab
le
d
if
f
u
s
io
n
,
h
a
v
e
ac
h
iev
ed
s
im
ilar
r
esu
lts
in
g
en
e
r
atin
g
a
p
p
ea
lin
g
,
h
ig
h
-
q
u
ality
i
m
ag
es
th
at
ca
ter
to
wid
e
-
r
an
g
in
g
cr
ea
tiv
e
n
ee
d
s
[
3
1
]
.
H
o
wev
er
,
th
ese
ar
e
b
ein
g
im
p
lem
en
ted
in
d
ep
e
n
d
en
tly
,
with
o
u
t
a
co
m
b
in
ed
s
y
s
tem
th
at
u
n
if
ies
th
eir
f
u
n
ctio
n
alities
in
to
o
n
e
s
o
lid
,
h
ig
h
-
p
er
f
o
r
m
in
g
m
ec
h
an
is
m
f
o
r
m
ar
k
eter
s
.
T
h
at
g
ap
m
u
s
t
b
e
f
illed
b
y
allo
win
g
m
ar
k
eter
s
to
ap
p
ly
f
u
lly
th
e
v
a
r
io
u
s
g
en
e
r
ativ
e
AI
m
o
d
els
i
n
em
ail
m
ar
k
etin
g
ca
m
p
aig
n
s
b
ef
o
r
e
th
e
f
u
ll p
o
ten
tial o
f
AI
in
e
m
ail
m
ar
k
etin
g
will b
e
lev
er
ag
ed
.
An
o
th
er
c
r
itical
g
ap
is
h
o
w
litt
le
th
ese
g
en
er
ativ
e
f
r
am
ewo
r
k
s
in
co
r
p
o
r
ate
co
n
tex
tu
al
an
d
d
o
m
ain
-
s
p
ec
if
ic
k
n
o
wled
g
e.
Kn
o
wled
g
e
g
r
ap
h
s
an
d
v
ec
to
r
em
b
ed
d
in
g
s
,
wh
ich
h
av
e
p
r
o
v
ed
ef
f
ec
ti
v
e
f
o
r
g
r
o
u
n
d
in
g
o
u
tp
u
ts
in
to
d
o
m
a
in
-
s
p
ec
if
ic
co
n
tex
ts
,
ar
e
s
till
v
astl
y
u
n
d
er
u
tili
ze
d
in
s
y
s
tem
s
u
s
in
g
d
if
f
u
s
io
n
m
o
d
els.
Fo
r
ex
am
p
le,
s
tr
u
ctu
r
ed
d
ata
a
n
d
r
elatio
n
s
h
ip
s
f
r
o
m
k
n
o
wled
g
e
g
r
ap
h
s
h
elp
im
p
r
o
v
e
th
e
r
elev
an
ce
o
f
th
e
o
u
t
p
u
ts
p
r
o
v
id
e
d
[
3
2
]
.
At
th
e
s
am
e
tim
e,
v
ec
to
r
em
b
e
d
d
in
g
s
ca
n
e
n
r
ich
d
if
f
u
s
io
n
m
o
d
els
b
y
alig
n
in
g
g
en
er
ated
co
n
te
n
t
with
th
em
es
o
r
b
r
an
d
in
g
r
e
q
u
ir
em
e
n
ts
[
3
3
]
.
No
t
h
av
in
g
th
is
co
n
tex
tu
al
in
teg
r
atio
n
co
n
s
tr
ain
s
th
e
m
o
d
els
to
b
e
ef
f
ec
tiv
ely
ad
a
p
ted
to
th
e
d
o
m
ain
-
s
p
ec
if
ic
n
ee
d
s
,
wh
ic
h
m
ak
es
th
em
less
p
r
ac
tically
ap
p
licab
le
with
in
m
ar
k
etin
g
.
An
o
th
er
u
n
d
er
d
ev
elo
p
ed
ar
ea
r
eg
ar
d
in
g
m
u
lti
-
m
o
d
el
s
y
s
tem
s
is
th
e
ev
alu
atio
n
o
f
g
en
er
ati
v
e
m
o
d
els.
W
h
ile
L
L
Ms’
ev
alu
atio
n
m
etr
ics
ar
e
r
elate
d
to
f
lu
en
cy
,
c
o
h
er
en
ce
,
a
n
d
r
elev
a
n
ce
,
an
d
d
if
f
u
s
io
n
m
o
d
els’
ev
alu
atio
n
a
r
e
m
etr
ics
r
elate
d
to
v
is
u
al
q
u
ality
an
d
s
ty
le
co
n
tr
o
l,
th
er
e
n
ee
d
s
to
b
e
a
u
n
if
ie
d
m
eth
o
d
o
lo
g
y
f
o
r
ass
es
s
in
g
th
eir
co
m
b
in
ed
u
s
e
ca
s
es.
A
f
r
am
ewo
r
k
th
at
ca
p
tu
r
es
th
e
o
v
er
all
e
f
f
ec
tiv
en
ess
o
f
o
u
tp
u
ts
f
o
r
s
p
ec
if
ic
ap
p
licatio
n
s
,
s
u
ch
as
m
ar
k
etin
g
,
r
em
ain
s
a
s
ig
n
if
ica
n
t
r
esear
ch
ch
allen
g
e
[
3
4
]
.
Fin
ally
,
eth
ical
an
d
o
p
er
atio
n
al
co
n
ce
r
n
s
c
r
ea
te
a
m
o
r
e
c
o
m
p
lex
en
v
ir
o
n
m
en
t
f
o
r
a
d
o
p
t
in
g
th
ese
ad
v
an
ce
d
m
o
d
els.
B
o
th
lar
g
e
l
an
g
u
ag
e
an
d
d
if
f
u
s
io
n
m
o
d
els
p
ick
u
p
t
h
e
b
iases
in
th
eir
tr
a
in
in
g
d
ata
an
d
r
is
k
cr
ea
tin
g
u
n
i
n
ten
d
ed
co
n
s
eq
u
e
n
ce
s
in
th
e
o
u
t
p
u
t.
T
h
is
in
cl
u
d
es,
f
o
r
ex
am
p
le,
g
en
er
ated
tex
ts
f
r
o
m
L
L
Ms
r
ein
f
o
r
cin
g
s
ter
eo
ty
p
es
[
3
5
]
an
d
b
iased
o
r
o
th
er
wis
e
in
a
p
p
r
o
p
r
iate
co
n
ten
t
with
d
if
f
u
s
io
n
m
o
d
els
[
3
6
]
.
Ad
d
itio
n
ally
,
th
is
h
ig
h
c
o
m
p
u
tatio
n
al
co
s
t
o
f
tr
ain
in
g
an
d
d
ep
lo
y
in
g
t
h
ese
m
o
d
els
also
p
r
esen
ts
ch
allen
g
es
in
s
u
s
tain
ab
ilit
y
,
m
ain
ly
f
o
r
b
u
s
i
n
ess
es a
im
in
g
to
d
ep
lo
y
s
ca
lab
le
s
o
lu
tio
n
s
[
3
7
]
.
W
h
ile
lar
g
e
lan
g
u
ag
e
a
n
d
d
if
f
u
s
io
n
m
o
d
els
ar
e
e
x
ce
llen
t
in
th
eir
r
esp
ec
tiv
e
d
o
m
ain
s
,
th
er
e
is
s
till
a
v
ast,
u
n
tap
p
ed
p
o
ten
tial
b
etwe
en
th
e
two
as
co
m
p
lem
en
tar
y
tech
n
o
lo
g
ies.
L
ac
k
o
f
in
te
g
r
atio
n
,
co
n
tex
tu
al
g
r
o
u
n
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in
g
,
u
n
if
ied
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v
alu
atio
n
f
r
am
e
-
wo
r
k
s
,
an
d
atten
tio
n
to
eth
ical
an
d
o
p
er
atio
n
al
ch
allen
g
es
ar
e
n
ee
d
ed
f
o
r
an
ap
p
r
o
ac
h
th
at
c
o
n
s
id
er
s
ev
er
y
th
in
g
.
E
n
a
b
lin
g
s
y
s
tem
s
to
in
teg
r
ate
t
h
ese
tech
n
o
lo
g
ies
i
n
to
o
n
e
f
r
am
ewo
r
k
co
u
ld
r
e
v
o
lu
tio
n
ize
a
p
p
licatio
n
s
s
u
ch
as e
m
ail
m
ar
k
etin
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
AI
-
d
r
iven
in
teg
r
a
ted
s
ystem
fo
r
co
mp
r
eh
en
s
ive
ema
il ma
r
ke
tin
g
a
u
to
m
a
tio
n
(
S
o
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ma
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L
o
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kili
)
4879
3.
P
RO
P
O
SE
D
SYS
T
E
M
A
RC
H
I
T
E
CT
U
RE
3
.
1
.
Sy
s
t
e
m
o
v
er
v
iew
T
h
e
p
r
o
p
o
s
ed
s
y
s
tem
ar
ch
itec
tu
r
e
u
s
es
f
in
e
-
tu
n
ed
lar
g
e
lan
g
u
ag
e
m
o
d
els,
wh
ich
will
b
e
r
esp
o
n
s
ib
le
f
o
r
g
en
e
r
atin
g
h
ig
h
q
u
ality
e
m
ail
s
u
b
jects.
I
t
al
s
o
em
p
lo
y
s
a
f
in
etu
n
ed
s
tab
le
d
if
f
u
s
io
n
m
o
d
el,
wh
ich
will
b
e
in
ch
ar
g
e
o
f
g
e
n
er
atin
g
c
o
m
p
ellin
g
em
ail
v
is
u
al
co
n
ten
t.
T
h
is
will
en
s
u
r
e
th
at
co
h
er
en
t,
p
er
s
o
n
alize
d
co
n
ten
t
is
cr
ea
ted
an
d
is
in
lin
e
with
th
e
m
ar
k
etin
g
o
b
jectiv
es
an
d
p
r
o
d
u
ct
d
escr
ip
tio
n
s
p
r
o
v
id
e
d
b
y
th
e
u
s
er
.
T
h
e
ar
ch
itectu
r
e
f
o
ll
o
ws th
ese
k
ey
s
tag
es:
3
.
1
.
1
.
I
np
ut
da
t
a
co
llect
io
n a
nd
prepro
ce
s
s
ing
T
h
e
s
y
s
tem
co
llects
es
s
en
tia
l
in
p
u
t
d
ata
th
r
o
u
g
h
a
f
o
r
m
,
in
clu
d
in
g
th
e
p
r
o
d
u
ct
titl
e,
p
r
o
d
u
ct
d
escr
ip
tio
n
,
d
esire
d
em
ail
to
n
e
(
ch
o
s
en
f
r
o
m
m
u
ltip
le
o
p
tio
n
s
)
,
an
d
p
r
ef
e
r
r
ed
len
g
th
.
Fo
r
im
ag
e
g
en
er
atio
n
,
a
p
r
o
m
p
t
is
co
llected
,
d
etailin
g
h
o
w
th
e
u
s
er
e
n
v
is
io
n
s
th
e
im
ag
e
an
d
th
e
r
elev
an
t
p
r
o
d
u
ct
d
escr
ip
tio
n
.
T
ex
t
d
ata
is
p
r
ep
r
o
ce
s
s
ed
—
to
k
en
iz
ed
,
n
o
r
m
alize
d
,
an
d
clea
n
ed
—
en
s
u
r
in
g
co
n
s
is
ten
cy
an
d
a
cc
u
r
ac
y
b
ef
o
r
e
b
ei
n
g
u
s
ed
f
o
r
g
en
er
atio
n
.
3
.
1
.
2
.
Su
bje
ct
lin
e
g
ener
a
t
io
n
v
ia
L
L
M
m
o
du
le
T
h
e
s
y
s
tem
u
s
es
th
r
ee
f
in
e
-
t
u
n
ed
L
L
Ms
—
GPT
-
3
.
5
,
PaL
M
2
,
an
d
B
E
R
T
—
to
g
en
er
at
e
m
u
ltip
le
s
u
b
ject
lin
e
s
u
g
g
esti
o
n
s
b
ase
d
o
n
th
e
p
r
o
v
id
ed
p
r
o
d
u
ct
d
e
s
cr
ip
tio
n
,
to
n
e,
an
d
len
g
th
p
r
ef
er
en
ce
s
.
B
ec
au
s
e
ev
er
y
m
o
d
el
o
f
f
er
s
a
u
n
iq
u
e
c
o
n
tr
ib
u
tio
n
,
th
e
u
s
er
wo
u
ld
h
a
v
e
a
wid
e
r
an
g
e
o
f
o
p
tio
n
s
f
o
r
s
u
b
ject
lin
es.
T
h
is
allo
ws
f
lex
ib
ilit
y
in
ch
o
o
s
in
g
th
at
b
est
o
p
tio
n
t
h
at
wo
u
l
d
f
it
th
eir
m
ar
k
etin
g
s
tr
ateg
y
s
in
ce
ea
ch
m
o
d
el
o
p
er
ates
o
n
th
e
s
am
e
in
p
u
t
an
d
m
o
ld
s
it
in
to
its
u
n
iq
u
e
s
tr
en
g
th
s
.
T
h
is
m
u
lti
-
m
o
d
el
ap
p
r
o
ac
h
ten
d
s
to
y
ield
a
m
o
r
e
en
g
ag
in
g
s
u
b
ject
lin
e
th
a
t b
est f
its
th
e
tar
g
eted
au
d
ie
n
c
e.
3
.
1
.
3
.
Vis
ua
l
co
nte
nt
g
ener
a
t
io
n v
ia
s
t
a
ble dif
f
us
io
n
m
o
du
le
On
ce
s
u
b
ject
lin
es
ar
e
g
en
er
at
ed
,
o
n
e
ca
n
p
r
o
ce
e
d
to
cr
ea
te
v
is
u
al
co
n
ten
t
-
o
r
v
ice
v
er
s
a.
Her
e,
th
e
s
tab
le
d
if
f
u
s
io
n
m
o
d
u
le
will
g
en
er
ate
im
ag
es
th
at
c
o
m
p
le
m
en
t
th
e
ch
o
s
en
s
u
b
ject
lin
e
in
r
elatio
n
t
o
th
e
p
r
o
d
u
ct
d
escr
ip
tio
n
an
d
u
s
er
-
p
r
o
v
id
e
d
p
r
o
m
p
ts
.
Fo
r
th
is
p
u
r
p
o
s
e,
th
e
m
o
d
el
also
in
co
r
p
o
r
ates
k
n
o
wled
g
e
g
r
ap
h
s
an
d
v
ec
t
o
r
em
b
ed
d
in
g
s
to
m
ak
e
th
e
v
is
u
als
co
r
r
esp
o
n
d
to
th
e
th
e
m
atic
elem
en
ts
o
f
th
e
em
ail
an
d
its
to
n
e.
Qu
ality
,
co
n
tex
t
u
ally
r
el
ev
an
t
v
is
u
als
ar
e
c
r
ea
ted
th
r
o
u
g
h
iter
ativ
e
r
ef
in
e
m
en
ts
th
at
en
h
an
ce
t
h
e
im
p
ac
t
o
f
th
e
em
ail
ca
m
p
aig
n
.
3
.
1
.
4
.
Ca
m
pa
i
g
n
cr
ea
t
io
n a
n
d e
m
a
il deplo
y
m
ent
Af
ter
th
e
g
en
er
atio
n
o
f
s
u
b
je
ct
lin
e
an
d
v
is
u
al
v
ar
iatio
n
s
,
t
h
e
s
y
s
tem
p
r
o
ce
ed
s
with
th
e
cr
ea
tio
n
o
f
ca
m
p
aig
n
s
,
wh
er
e
th
e
u
s
er
d
ec
id
es
u
p
o
n
th
eir
p
r
ef
er
r
ed
co
n
ten
t.
I
n
th
is
ca
s
e,
a
f
in
al
em
ail
p
ac
k
ag
e
is
co
m
p
o
s
ed
,
wh
er
e
em
ail
s
eg
m
en
ts
ar
e
s
elec
ted
b
ased
o
n
th
e
u
s
er
'
s
tar
g
et
au
d
ien
ce
.
T
h
e
s
y
s
tem
in
clu
d
es
in
teg
r
atio
n
s
o
f
s
ev
er
al
e
m
ail
s
er
v
ice
p
r
o
v
id
er
s
(
E
SP
s
)
,
s
u
ch
as
Sen
d
g
r
id
,
Ma
ilg
u
n
,
Ma
ilch
i
m
p
,
Po
s
tm
ar
k
,
an
d
Ma
iljet,
to
n
am
e
a
f
ew,
wh
e
r
e
d
ep
lo
y
m
e
n
t sh
o
u
ld
tak
e
p
lace
.
I
t
is
im
p
o
r
tan
t
th
at
th
e
s
y
s
tem
d
o
es
n
o
t
c
o
m
p
r
is
e
f
ee
d
b
ac
k
l
o
o
p
s
in
r
ea
l
tim
e.
Ho
wev
er
,
th
r
o
u
g
h
th
e
an
aly
tics
to
o
ls
b
y
E
SP
s
,
m
ar
k
eter
s
ca
n
m
o
n
ito
r
k
e
y
m
etr
ics
th
at
g
iv
e
th
em
p
r
ec
is
e
d
ata
ab
o
u
t
th
e
p
er
f
o
r
m
an
ce
.
T
h
ese
in
clu
d
e
o
p
en
r
ates
an
d
click
-
th
r
o
u
g
h
r
a
tes
wh
ich
p
r
o
v
id
es
in
s
ig
h
t
i
n
to
th
e
e
f
f
ec
tiv
en
ess
o
f
th
e
ca
m
p
aig
n
.
3
.
2
.
L
L
M
m
o
du
le
T
h
e
L
L
M
m
o
d
u
le
p
la
y
s
a
v
ita
l
p
ar
t
in
th
is
s
y
s
tem
b
ec
au
s
e
i
t
g
en
er
ates
cu
s
to
m
ized
a
n
d
h
i
g
h
-
im
p
ac
t
em
ail
s
u
b
ject
lin
es
with
th
r
ee
f
in
e
-
tu
n
e
d
m
o
d
els,
n
a
m
ely
G
PT
-
3
.
5
,
PaL
M
2
,
a
n
d
B
E
R
T
.
E
ac
h
h
as
d
if
f
er
e
n
t
p
o
s
itiv
e
f
ea
tu
r
es,
wh
ich
is
a
p
lu
s
p
o
in
t
th
e
s
y
s
tem
tak
es
ad
v
an
tag
e
o
f
.
T
h
e
o
p
p
o
r
tu
n
ity
th
e
s
y
s
tem
o
f
f
er
s
b
y
s
u
g
g
esti
n
g
a
v
ar
iety
o
f
s
u
b
ject
lin
es h
elp
s
m
ee
tin
g
th
e
tar
g
et
au
d
ien
ce
’
s
p
r
e
f
er
en
ce
s
an
d
m
ar
k
et
o
b
jectiv
es.
3
.
2
.
1
.
I
nte
g
ra
t
io
n o
f
m
ultiple LL
M
s
B
y
in
co
r
p
o
r
atin
g
GPT
-
3
.
5
,
Pa
L
M
2
,
an
d
B
E
R
T
,
th
e
s
y
s
tem
g
en
er
ates a
r
an
g
e
o
f
s
u
b
ject
lin
e
o
p
tio
n
s
.
GPT
-
3
.
5
e
x
ce
ls
in
c
r
ea
tiv
e,
h
u
m
an
-
lik
e
o
u
tp
u
ts
,
PaL
M
2
in
c
o
n
v
e
r
s
atio
n
al
to
n
es,
an
d
B
E
R
T
in
h
a
n
d
lin
g
co
m
p
lex
s
em
a
n
tics
.
T
o
g
eth
e
r
,
th
ese
m
o
d
els
en
s
u
r
e
t
h
at
t
h
e
s
u
b
ject
lin
es
ar
e
v
a
r
ied
a
n
d
r
ic
h
in
co
n
tex
t,
in
cr
ea
s
in
g
en
g
a
g
em
en
t
p
o
ten
ti
al.
3
.
2
.
2
.
Su
bje
ct
lin
e
g
ener
a
t
io
n pro
ce
s
s
W
ith
th
e
in
p
u
t
o
f
a
p
r
o
d
u
ct
titl
e,
d
escr
ip
tio
n
,
to
n
e,
a
n
d
s
u
b
ject
len
g
th
,
ea
ch
m
o
d
el
g
en
er
ates
a
d
is
s
im
ilar
s
u
b
ject.
W
h
ile
th
e
in
p
u
t
d
ata
is
th
e
s
am
e,
ea
ch
m
o
d
el
in
ter
p
r
ets
it
in
its
o
w
n
way
g
iv
en
th
eir
ar
ch
itectu
r
e.
I
t
s
h
o
ws
th
e
u
s
er
th
r
ee
s
u
g
g
esti
o
n
s
o
f
a
s
u
b
ject
lin
e,
f
r
o
m
wh
ic
h
a
u
s
er
ca
n
p
ick
th
e
m
o
s
t
r
elev
an
t
to
h
is
ca
m
p
aig
n
.
He
co
u
ld
e
v
en
r
ev
is
e
th
e
o
n
e
in
wh
ich
h
e
s
ee
s
th
e
m
o
s
t
p
o
ten
t
ial,
tak
in
g
elem
en
ts
f
r
o
m
all
th
r
ee
s
u
g
g
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o
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
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2
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8
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I
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&
C
o
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p
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n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
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r
20
25
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4880
3
.
2
.
3
.
M
o
del
f
ine
-
t
un
ing
f
o
r
m
a
r
k
et
ing
re
lev
a
nce
E
ac
h
o
f
t
h
e
L
L
Ms
h
as
b
ee
n
f
in
e
-
tu
n
e
d
o
n
em
ail
m
ar
k
etin
g
-
s
p
ec
if
ic
d
atasets
to
en
s
u
r
e
th
at
th
e
g
en
er
ated
s
u
b
ject
lin
es
h
av
e
r
elev
an
ce
,
d
r
iv
e
en
g
a
g
em
en
t,
an
d
m
ee
t
b
est
m
ar
k
etin
g
p
r
ac
tices.
T
h
is
will
en
s
u
r
e
th
e
s
u
b
ject
lin
es
ar
e
alig
n
ed
with
s
tr
ateg
ies
th
at
tr
ig
g
er
o
p
en
r
ates,
s
u
ch
as
th
e
u
s
e
o
f
u
r
g
e
n
cy
an
d
p
er
s
o
n
aliza
tio
n
.
3
.
3
.
St
a
ble
diff
us
io
n
m
o
du
le
T
h
e
s
t
a
b
l
e
d
i
f
f
u
s
i
o
n
m
o
d
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l
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n
e
r
a
t
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i
g
h
l
y
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g
a
g
i
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v
is
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a
l
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d
u
s
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p
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p
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p
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t
l
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r
a
t
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n
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At
a
n
y
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d
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s
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t
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r
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t
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o
a
d
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m
a
r
k
et
i
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o
b
j
e
c
t
i
v
es
.
O
n
e
o
f
t
h
e
m
a
i
n
e
n
h
a
n
c
e
m
e
n
t
s
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h
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o
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l
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h
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n
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at
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a
p
h
s
a
n
d
v
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ct
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m
b
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d
d
i
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g
s
.
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n
o
wl
e
d
g
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g
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ap
h
s
m
a
p
r
e
l
at
i
o
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s
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p
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et
w
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t
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w
it
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p
r
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c
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d
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r
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p
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n
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e
n
a
b
li
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g
t
h
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y
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t
e
m
t
o
c
r
ea
t
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i
m
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g
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th
a
t
r
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f
l
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c
t
t
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c
a
m
p
a
i
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n
’
s
t
h
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m
a
t
i
c
el
e
m
e
n
ts
—
w
h
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t
h
e
r
b
y
e
m
p
h
a
s
iz
i
n
g
u
n
i
q
u
e
p
r
o
d
u
c
t
f
e
a
t
u
r
e
s
o
r
r
ei
n
f
o
r
c
in
g
b
r
a
n
d
i
d
e
n
t
i
t
y
.
As
f
o
r
v
e
c
t
o
r
e
m
b
e
d
d
i
n
g
s
,
t
h
e
y
t
r
a
n
s
l
at
e
te
x
t
-
b
a
s
e
d
i
n
p
u
t
s
i
n
to
r
i
c
h
n
u
m
e
r
i
c
al
r
e
p
r
es
e
n
t
a
tio
n
s
t
h
a
t
w
il
l
e
n
a
b
l
e
t
h
e
m
o
d
e
l
t
o
c
r
e
a
t
e
v
i
s
u
al
s
c
a
p
t
u
r
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n
g
t
h
e
s
u
b
t
le
t
y
o
f
t
h
e
u
s
e
r
'
s
p
r
o
m
p
t
s
,
i
n
c
l
u
d
i
n
g
s
t
y
l
e
a
n
d
t
o
n
e
,
o
r
s
p
e
c
i
f
i
c
v
is
u
a
l
r
e
q
u
i
r
e
m
e
n
t
s
.
Gen
er
atio
n
o
f
im
ag
es
in
v
o
l
v
e
s
iter
atio
n
r
ef
in
em
en
t
-
a
tech
n
iq
u
e
wh
er
e
in
itial
n
o
is
e
is
r
ef
in
ed
o
v
er
s
u
cc
ess
iv
e
s
tep
s
to
g
en
er
ate
h
ig
h
-
q
u
ality
v
is
u
als.
Ad
o
p
tin
g
th
is
tech
n
iq
u
e
en
s
u
r
es
th
at
th
e
g
e
n
er
ated
f
in
al
im
ag
es
s
tan
d
co
n
tex
tu
ally
in
tu
n
e
with
th
e
p
r
o
d
u
ct
d
escr
ip
tio
n
an
d
m
a
r
k
etin
g
o
b
jectiv
es.
T
h
e
s
y
s
tem
th
e
n
g
en
er
ates
d
if
f
er
en
t
alter
n
ativ
e
s
f
o
r
th
e
u
s
er
,
wh
o
ca
n
s
elec
t
wh
ich
im
ag
e
b
est
s
u
its
h
is
ca
m
p
aig
n
.
I
t
also
m
ak
es su
r
e
th
at
all
v
is
u
als f
it
t
h
e
tech
n
ical
r
eq
u
ir
e
m
en
ts
o
f
th
e
em
ail
p
latf
o
r
m
in
ter
m
s
o
f
f
ile
s
ize,
r
eso
lu
tio
n
,
an
d
asp
ec
t r
atio
,
a
n
d
th
e
ass
et
is
r
ea
d
y
f
o
r
d
ep
l
o
y
m
en
t a
c
r
o
s
s
m
u
ltip
le
d
ev
ices a
n
d
p
latf
o
r
m
s
.
T
h
e
s
tab
le
d
if
f
u
s
io
n
m
o
d
u
le
m
ain
tain
s
s
ca
lab
ilit
y
an
d
is
b
u
ilt
to
h
an
d
le
m
u
ltip
le
ca
m
p
a
ig
n
s
,
alo
n
g
with
lar
g
e
d
atasets
,
with
ef
f
i
cien
cy
.
Kn
o
wled
g
e
g
r
ap
h
s
,
v
ec
to
r
em
b
e
d
d
in
g
s
,
an
d
th
e
p
o
wer
o
f
d
if
f
u
s
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n
m
o
d
els
ass
u
r
e
h
i
g
h
-
q
u
ality
,
co
n
tex
t
u
ally
r
ele
v
an
t
v
is
u
als.
T
h
ese
f
ea
tu
r
es
will
en
h
an
ce
th
e
o
v
er
all
ef
f
ec
tiv
en
ess
o
f
th
e
em
ail
ca
m
p
aig
n
b
y
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ak
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g
it m
o
r
e
co
m
p
ellin
g
to
th
e
c
o
n
s
u
m
er
.
3
.
4
.
I
nte
g
ra
t
io
n
a
nd
wo
rk
f
lo
w
As
s
h
o
wn
in
Fig
u
r
e
1
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b
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Djan
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o
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r
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o
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s
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ip
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atio
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es a
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etr
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e
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Fig
u
r
e
1
.
AI
-
d
r
iv
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s
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s
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ch
itectu
r
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
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&
C
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n
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I
SS
N:
2088
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ated
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als s
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m
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ly
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o
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lin
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lik
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en
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atio
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v
ia
a
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ar
allel
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tio
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to
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C
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.
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p
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t
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g
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icien
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I
t
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cial
th
at,
in
th
e
b
a
ck
g
r
o
u
n
d
,
we
m
ai
n
tain
o
v
e
r
all
r
esp
o
n
s
iv
en
ess
with
in
th
e
s
y
s
tem
f
o
r
u
s
er
ex
p
er
ien
ce
wh
ile
h
an
d
lin
g
m
a
n
y
g
en
er
atio
n
o
p
er
atio
n
s
.
On
c
e
t
h
e
c
o
n
te
n
t
is
g
e
n
er
ate
d
a
n
d
s
a
v
e
d
i
n
th
e
d
ata
b
ase
,
D
jan
g
o
p
ac
k
a
g
es
u
p
t
h
e
s
e
lec
te
d
s
u
b
j
ec
t
lin
es
a
n
d
v
is
u
a
ls
f
o
r
s
e
n
d
in
g
.
T
h
e
s
y
s
t
em
is
in
te
g
r
at
ed
w
it
h
s
e
v
e
r
a
l
E
SP
s
,
i
n
cl
u
d
i
n
g
S
e
n
d
g
r
id
,
M
ail
g
u
n
,
a
n
d
Ma
il
ch
im
p
,
t
h
a
t
s
u
p
p
o
r
t
d
i
r
e
c
t
s
e
n
d
in
g
o
f
ca
m
p
ai
g
n
s
f
r
o
m
Dja
n
g
o
t
o
t
h
e
t
ar
g
e
t
a
u
d
i
en
c
e.
E
a
ch
o
n
e
o
f
t
h
e
in
t
eg
r
ati
o
n
s
wit
h
t
h
e
d
i
f
f
e
r
e
n
t
E
SP
s
a
r
e
a
lr
ea
d
y
p
r
e
-
s
et,
h
e
n
c
e
s
ea
m
l
ess
d
e
p
l
o
y
m
e
n
t
o
f
em
a
il
c
am
p
ai
g
n
s
is
p
o
s
s
ib
le
wit
h
o
u
t
n
ee
d
i
n
g
t
o
d
ea
l
w
it
h
a
n
y
a
p
p
li
ca
ti
o
n
p
r
o
g
r
a
m
m
in
g
i
n
t
e
r
f
ac
e
(
API
)
m
a
n
ag
em
en
t
f
r
o
m
t
h
e
o
u
ts
i
d
e
.
4.
M
E
T
H
O
DO
L
O
G
Y
4
.
1
.
Da
t
a
c
o
llect
io
n a
nd
prepro
ce
s
s
ing
T
h
is
s
tu
d
y
ad
o
p
ts
an
ap
p
lie
d
ex
p
er
im
e
n
tal
m
eth
o
d
o
lo
g
y
,
co
m
b
in
in
g
f
in
e
-
tu
n
in
g
o
f
L
L
Ms
an
d
s
tab
le
d
if
f
u
s
io
n
mo
d
els
with
co
m
p
a
r
ativ
e
e
v
alu
atio
n
b
ase
d
o
n
r
ea
l
-
wo
r
ld
ca
m
p
ai
g
n
d
a
ta.
Fin
e
-
tu
n
in
g
t
h
e
L
L
Ms
an
d
th
e
s
tab
le
d
if
f
u
s
io
n
m
o
d
els
r
eq
u
i
r
ed
h
ig
h
l
y
cu
r
ate
d
d
atasets
,
th
at
wen
t
th
r
o
u
g
h
s
tr
ict
p
r
ep
r
o
ce
s
s
in
g
wo
r
k
f
lo
ws,
ea
c
h
d
esig
n
e
d
f
o
r
th
e
p
ar
ticu
lar
n
ee
d
s
o
f
ea
ch
ar
ch
itectu
r
e.
T
h
e
d
ataset
f
o
r
t
h
e
L
L
M
co
n
s
is
ted
o
f
6
4
4
,
6
0
0
em
ail
s
u
b
ject
lin
es
s
o
u
r
ce
d
f
r
o
m
a
p
a
r
tn
er
m
a
r
k
etin
g
co
m
p
an
y
;
th
is
d
ataset
in
clu
d
ed
r
ele
v
an
t
m
etad
ata
s
u
ch
as
p
r
o
d
u
ct
id
e
n
tifie
r
s
,
d
eliv
er
y
s
tatis
tics
,
an
d
o
p
en
r
ates,
th
u
s
co
v
e
r
in
g
a
b
r
o
ad
v
iew
o
f
t
h
e
ef
f
ec
tiv
en
ess
o
f
th
e
ca
m
p
aig
n
s
.
T
h
e
lin
g
u
is
tic
d
iv
er
s
ity
o
f
th
e
s
u
b
ject
lin
es
r
ep
r
esen
te
d
s
ev
er
al
to
n
es
an
d
s
ty
les
th
at
co
n
s
titu
te
a
s
tr
o
n
g
f
o
u
n
d
atio
n
f
o
r
f
in
e
-
t
u
n
in
g
m
o
d
els
lik
e
GPT
-
3
.
5
,
PaL
M
2
,
an
d
B
E
R
T
f
o
r
g
en
er
atin
g
h
ig
h
-
q
u
ality
a
n
d
co
n
tex
t
-
s
en
s
itiv
e
s
u
b
ject
lin
es.
P
r
e
p
r
o
c
e
s
s
i
n
g
o
f
t
h
e
L
L
M
d
a
t
a
s
et
b
e
g
a
n
w
i
t
h
t
h
e
m
o
s
t
t
h
o
r
o
u
g
h
c
l
e
a
n
i
n
g
p
r
o
c
e
s
s
.
I
n
a
n
e
f
f
o
r
t
t
o
p
r
e
v
e
n
t
r
e
d
u
n
d
a
n
c
y
,
w
e
m
a
d
e
s
u
r
e
t
o
el
i
m
i
n
at
e
d
u
p
l
i
c
at
e
s
a
n
d
m
i
s
s
i
n
g
v
a
l
u
es
t
h
at
w
e
r
e
not
e
s
s
e
n
t
i
a
l
f
o
r
f
u
r
t
h
e
r
a
n
a
l
y
s
is
.
T
h
e
n
e
x
t
s
t
e
p
w
as
t
ex
t
n
o
r
m
a
l
i
z
a
t
i
o
n
,
w
h
i
c
h
e
n
s
u
r
es
t
h
a
t
p
u
n
c
t
u
a
t
i
o
n
a
n
d
c
a
p
i
t
al
iz
a
t
i
o
n
a
r
e
c
o
n
s
is
t
e
n
t
u
n
l
e
s
s
t
h
e
y
h
a
v
e
s
e
-
m
a
n
t
i
c
m
e
a
n
i
n
g
,
l
i
k
e
w
h
e
n
u
s
i
n
g
wo
r
d
s
l
i
k
e
“
FR
E
E
”
t
o
c
o
n
v
e
y
u
r
g
e
n
c
y
.
T
h
e
m
o
s
t
i
m
p
o
r
t
a
n
t
p
r
e
p
r
o
c
e
s
s
i
n
g
s
t
e
p
s
i
n
L
L
M
s
w
as
t
o
k
e
n
i
z
at
i
o
n
,
s
i
n
c
e
l
o
n
g
t
e
x
t
s
t
r
e
a
m
s
h
a
d
t
o
b
e
r
e
d
u
c
e
d
t
o
s
m
a
l
l
er
u
n
i
t
s
-
w
o
r
d
s
o
r
s
u
b
w
o
r
d
s
-
t
o
e
n
s
u
r
e
t
h
e
m
o
d
e
l
s
c
o
u
l
d
d
e
a
l
w
i
t
h
t
h
e
i
n
p
u
t
.
T
h
a
t
w
a
y
,
i
t
a
l
lo
w
e
d
t
h
e
m
o
d
e
l
s
t
o
t
a
k
e
i
n
t
h
e
s
y
n
t
a
c
t
i
c
s
t
r
u
c
t
u
r
e
a
n
d
s
e
m
a
n
t
i
c
n
u
a
n
c
e
s
n
e
c
e
s
s
a
r
y
i
n
m
a
r
k
e
t
i
n
g
l
a
n
g
u
a
g
e
.
F
i
n
a
l
l
y
,
i
n
o
r
d
e
r
t
o
c
o
n
v
e
r
t
w
o
r
d
s
t
o
c
o
n
t
i
n
u
o
u
s
v
e
c
t
o
r
r
e
p
r
e
s
e
n
t
a
t
i
o
n
s
,
we
u
s
e
d
p
r
e
-
t
r
a
i
n
e
d
e
m
b
e
d
d
i
n
g
s
-
W
o
r
d
2
V
e
c
-
.
T
h
is
e
n
a
b
l
e
d
t
h
e
m
o
d
e
l
s
t
o
u
n
d
e
r
s
t
a
n
d
t
h
e
f
in
e
-
g
r
a
i
n
e
d
r
e
l
a
t
i
o
n
s
h
i
p
s
b
e
tw
ee
n
w
o
r
d
s
l
i
k
e
“
d
is
c
o
u
n
t
”
a
n
d
“
p
r
o
m
o
t
i
o
n
”
.
T
h
e
s
ta
b
l
e
d
if
f
u
s
i
o
n
m
o
d
el
w
a
s
t
r
a
in
ed
o
n
a
d
a
tase
t
o
f
4
6
,
3
2
6
m
ar
k
eti
n
g
c
r
ea
ti
v
es
.
T
h
es
e
a
r
e
i
m
a
g
es
o
f
p
r
o
d
u
cts
w
it
h
m
eta
d
ata
:
f
o
r
ev
er
y
p
r
o
d
u
c
t,
th
e
r
e
is
a
p
r
o
d
u
ct
d
esc
r
i
p
ti
o
n
a
n
d
p
e
r
f
o
r
m
a
n
ce
i
n
d
ic
at
o
r
s
,
s
u
c
h
as
cli
c
k
-
t
h
r
o
u
g
h
r
at
es.
T
h
is
d
at
aset
ali
g
n
e
d
t
h
e
v
is
u
a
l
a
n
d
th
e
tex
tu
al
d
a
ta
i
n
a
wa
y
t
h
at
it
a
ll
o
we
d
t
h
e
m
o
d
el
to
g
e
n
e
r
a
te
c
o
n
te
x
t
u
a
ll
y
r
el
e
v
a
n
t
im
ag
es
g
i
v
en
te
x
t
u
al
p
r
o
m
p
ts
.
T
o
s
ta
n
d
a
r
d
iz
e
th
e
in
p
u
t
d
i
m
en
s
i
o
n
s
,
a
ll
im
ag
es
wer
e
r
es
iz
ed
t
o
a
9
:1
6
as
p
e
ct
r
ati
o
.
N
o
t
o
n
l
y
is
t
h
is
s
i
ze
t
h
e
b
ett
e
r
f
it
ti
n
g
f
o
r
e
m
ai
l
m
a
r
k
eti
n
g
,
b
u
t
it
als
o
r
et
ai
n
e
d
cr
iti
ca
l
v
is
u
al
d
etai
ls
f
r
o
m
d
i
f
f
er
e
n
t
s
i
ze
s
.
W
e
als
o
n
o
r
m
al
iz
ed
p
ix
el
v
al
u
es
to
a
m
ea
n
o
f
0
.
5
a
n
d
a
s
tan
d
ar
d
d
e
v
i
ati
o
n
o
f
0
.
5
ac
r
o
s
s
a
ll
c
o
lo
r
c
h
an
n
els.
T
h
is
s
t
ep
m
i
n
im
iz
ed
b
i
ases
ca
u
s
e
d
b
y
v
a
r
ia
ti
o
n
s
in
li
g
h
ti
n
g
,
co
l
o
r
i
n
te
n
s
it
y
,
a
n
d
c
o
n
t
r
as
t,
cr
ea
ti
n
g
a
u
n
i
f
o
r
m
d
at
aset
f
o
r
t
r
ai
n
i
n
g
.
D
ata
a
u
g
m
e
n
ta
ti
o
n
tec
h
n
i
q
u
es,
s
u
c
h
as
r
a
n
d
o
m
c
r
o
p
p
i
n
g
,
f
li
p
p
i
n
g
,
r
o
t
ati
o
n
,
a
n
d
c
o
l
o
r
ad
ju
s
tm
en
ts
,
wer
e
a
p
p
lie
d
t
o
i
n
t
r
o
d
u
c
e
v
ar
i
ab
i
lit
y
a
n
d
im
p
r
o
v
e
th
e
m
o
d
el
’
s
a
b
il
it
y
t
o
g
e
n
e
r
al
ize
.
T
h
es
e
a
u
g
m
en
tat
io
n
s
e
n
h
an
ce
d
t
h
e
r
o
b
u
s
t
n
ess
o
f
t
h
e
m
o
d
el
b
y
s
i
m
u
la
ti
n
g
d
i
v
e
r
s
e
r
ea
l
-
wo
r
l
d
s
ce
n
ar
i
o
s
a
n
d
v
is
u
a
l st
y
l
es
c
o
m
m
o
n
l
y
f
o
u
n
d
i
n
m
ar
k
eti
n
g
m
at
e
r
ials
.
Pre
p
r
o
ce
s
s
in
g
p
ip
elin
es
f
o
r
d
atasets
f
o
r
th
e
L
L
M
a
n
d
s
tab
le
d
if
f
u
s
io
n
tr
ain
in
g
wer
e
ca
r
ef
u
lly
d
esig
n
ed
s
o
th
at
th
ey
c
o
r
r
esp
o
n
d
with
th
e
r
esp
ec
tiv
e
d
ata
in
p
u
ts
.
On
o
n
e
h
an
d
,
L
L
Ms
r
ely
o
n
to
k
e
n
izatio
n
an
d
em
b
ed
d
in
g
s
in
o
r
d
er
to
e
x
tr
ac
t
s
em
an
tic
d
ep
th
in
tex
tu
al
d
ata,
an
d
o
n
th
e
o
th
er
,
s
tab
l
e
d
if
f
u
s
io
n
r
eq
u
i
r
es
n
o
r
m
aliza
tio
n
an
d
au
g
m
en
tati
o
n
tech
n
iq
u
es
to
m
ain
tain
v
is
u
al
in
teg
r
ity
.
T
h
is
g
iv
es
all
m
o
d
els
th
e
ab
ilit
y
to
b
etter
g
r
asp
th
e
p
s
y
ch
o
lo
g
y
o
f
th
e
u
s
er
,
a
n
d
u
n
d
er
s
tan
d
wh
ich
wo
r
d
s
o
r
wr
itin
g
s
ty
le
is
m
o
s
t
co
m
p
ellin
g
to
th
em
.
T
h
ese
ex
ten
s
iv
e
p
r
e
p
r
o
ce
s
s
in
g
s
tep
s
en
h
an
ce
d
th
e
m
o
d
els’
p
e
r
f
o
r
m
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ce
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d
also
p
r
ep
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ed
a
r
o
b
u
s
t
f
o
u
n
d
atio
n
f
o
r
f
u
r
th
er
tr
ain
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g
an
d
in
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e
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en
ce
o
p
er
atio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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I
n
t J E
lec
&
C
o
m
p
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n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
8
7
5
-
4
8
8
8
4882
4
.
2
.
M
o
del
f
ine
-
t
un
ing
I
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f
in
e
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tu
n
i
n
g
f
o
r
tex
tu
al
g
e
n
e
r
atio
n
,
th
e
g
o
al
was
to
ad
ap
t
l
ar
g
e
lan
g
u
ag
e
m
o
d
els
s
u
ch
as
GPT
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3
.
5
,
PaL
M
2
,
an
d
B
E
R
T
to
g
en
er
ate
em
ail
s
u
b
ject
lin
es
f
o
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etter
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p
er
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o
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m
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g
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ar
k
etin
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ca
m
p
aig
n
s
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Su
ch
f
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e
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tu
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v
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id
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tific
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im
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y
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o
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el
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er
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m
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ce
o
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tim
izatio
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.
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h
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ir
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t
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ted
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y
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er
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ar
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et
er
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th
e
lea
r
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g
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ate,
wh
ich
was
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et
to
0
.
0
0
1
,
m
in
im
izin
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ab
r
u
p
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s
tm
en
ts
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m
o
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lear
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ajec
to
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y
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d
en
s
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r
i
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g
a
s
m
o
o
th
g
r
ad
u
al
co
n
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er
g
en
ce
.
Up
n
e
x
t,
we
h
av
e
th
e
b
atch
s
ize,
s
elec
ted
to
b
e
1
6
.
T
h
is
m
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est
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ize
allo
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p
r
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ata
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m
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tatio
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estrictio
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ter
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f
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e
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o
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th
r
ee
e
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o
ch
s
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a
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eter
m
in
ed
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ased
o
n
th
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d
ata
s
et
'
s
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m
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lex
ity
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th
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s
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win
g
th
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m
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ai
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s
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ic
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atter
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ile
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o
id
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m
e
m
o
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izatio
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o
f
th
e
tr
ain
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g
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ata.
All
th
r
ee
m
o
d
els
wer
e
o
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tim
ize
d
u
s
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g
th
e
A
d
am
o
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tim
izer
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ic
h
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tr
e
m
ely
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icien
t
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lin
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ar
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ata.
T
h
is
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tim
izer
im
p
r
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v
es
th
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n
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g
e
n
ce
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ates
o
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e
m
o
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els
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y
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a
p
tiv
ely
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h
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g
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g
th
e
lea
r
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g
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ates
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r
in
g
tr
ain
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g
.
Gr
ad
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clip
p
in
g
f
u
r
t
h
er
s
tab
ilized
th
e
lear
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g
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r
o
ce
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s
f
o
r
GPT
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3
.
5
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s
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ically
to
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o
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lo
d
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g
g
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d
ien
ts
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at
m
ay
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e
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o
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o
m
e
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tr
ain
in
g
m
o
d
els o
n
l
o
n
g
e
r
s
eq
u
en
ce
s
,
as in
th
e
ca
s
e
o
f
em
ail
s
u
b
ject
g
en
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atio
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.
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r
th
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ev
al
u
atio
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th
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e
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ed
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o
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els,
a
d
v
an
ce
d
m
etr
ics
s
u
ch
as
b
ilin
g
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al
ev
alu
atio
n
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n
d
er
s
tu
d
y
(
B
L
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U)
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r
ec
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l
-
o
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ien
ted
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n
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er
s
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d
y
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r
Gis
tin
g
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alu
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(
R
OUGE
)
wer
e
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s
ed
.
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L
E
U
wa
s
s
elec
ted
b
ec
au
s
e
o
f
its
ap
p
r
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p
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iaten
ess
f
o
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m
ea
s
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r
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g
t
h
e
p
r
ec
is
io
n
o
f
g
en
e
r
ated
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ail
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b
ject
lin
es
co
m
p
ar
ed
with
h
u
m
an
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wr
itten
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ef
e
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ce
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es.
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L
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U
d
id
a
v
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y
g
o
o
d
j
o
b
in
ca
p
tu
r
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th
e
ac
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r
ac
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f
th
e
s
u
b
ject
lin
es
in
m
ain
tain
in
g
g
r
am
m
atica
l
s
tr
u
ctu
r
e
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d
co
h
er
e
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ce
.
I
n
ad
d
itio
n
,
th
e
R
OUGE
-
L
Su
m
m
etr
ic
was
u
s
ed
to
ju
d
g
e
t
h
e
u
n
ig
r
am
o
v
er
lap
b
e
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n
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en
e
r
ated
tex
t
an
d
th
e
r
ef
er
en
ce
s
u
b
ject
lin
es,
with
k
ey
p
h
r
ase
r
ec
all
in
f
o
cu
s
.
T
h
ese
m
etr
ics
allo
wed
f
o
r
a
s
tr
o
n
g
o
u
tp
u
t
c
o
m
p
ar
is
o
n
o
f
th
e
m
o
d
els
a
g
ain
s
t
h
u
m
a
n
-
g
en
e
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ated
c
o
n
ten
t
,
th
er
ef
o
r
e
q
u
an
tif
y
in
g
im
p
r
o
v
e
m
en
ts
in
s
u
b
ject
lin
e
f
lu
en
cy
a
n
d
r
elev
a
n
ce
.
T
h
e
f
in
e
-
tu
n
in
g
f
o
r
p
e
r
s
o
n
alize
d
m
ar
k
etin
g
i
m
ag
es
with
th
e
s
tab
le
d
if
f
u
s
io
n
m
o
d
el
ca
p
italized
o
n
k
n
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wled
g
e
g
r
ap
h
s
an
d
v
ec
to
r
em
b
ed
d
in
g
s
to
en
h
an
ce
t
h
e
c
o
n
tex
tu
al
r
elev
a
n
ce
o
f
g
en
er
a
ted
im
ag
es.
I
t
u
s
es
p
r
o
d
u
ct
m
etad
ata
in
f
o
r
m
atio
n
,
in
clu
d
in
g
b
u
t
n
o
t
lim
ited
to
p
r
o
d
u
ct
n
am
es,
p
r
o
d
u
ct
d
escr
ip
tio
n
s
,
an
d
ca
m
p
aig
n
p
er
f
o
r
m
a
n
ce
m
etr
ic
s
lik
e
click
-
th
r
o
u
g
h
r
ates,
to
c
r
ea
te
th
ese
k
n
o
wled
g
e
g
r
a
p
h
s
.
Kn
o
wled
g
e
g
r
a
p
h
s
allo
wed
th
e
m
o
d
el
to
lear
n
th
e
r
elatio
n
s
h
ip
s
b
etwe
en
m
ar
k
eti
n
g
elem
e
n
ts
an
d
h
e
n
ce
e
n
ab
le
d
th
e
g
en
e
r
atio
n
o
f
im
ag
es
th
at
co
u
ld
m
ee
t
th
e
r
eq
u
ir
em
e
n
ts
o
f
th
e
m
ar
k
etin
g
o
b
jectiv
es.
Als
o
,
v
ec
to
r
em
b
ed
d
in
g
s
allo
wed
r
ep
r
esen
tatio
n
s
o
f
tex
tu
al
d
ata
in
a
co
n
tin
u
o
u
s
v
ec
to
r
s
p
ac
e.
T
h
ese
em
b
ed
d
in
g
s
en
ab
led
th
e
s
em
an
tic
r
ich
n
ess
o
f
p
r
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d
u
ct
d
escr
ip
tio
n
s
to
b
e
c
ap
tu
r
ed
b
y
th
e
m
o
d
el
f
o
r
b
etter
an
d
c
o
h
er
e
n
t v
is
u
als.
Per
ce
p
tu
al
lo
s
s
was
u
s
ed
d
u
r
in
g
f
in
e
-
tu
n
in
g
to
e
n
h
an
ce
th
e
v
is
u
al
f
id
elity
o
f
th
e
g
en
e
r
ated
im
ag
es.
As
o
p
p
o
s
ed
to
tr
ad
itio
n
al
p
ix
e
l
-
wis
e
lo
s
s
f
u
n
ctio
n
s
,
p
er
ce
p
tu
al
lo
s
s
co
m
p
ar
es
h
ig
h
-
lev
el
f
e
atu
r
es
b
etwe
en
th
e
g
en
er
ated
an
d
r
ef
er
e
n
ce
im
ag
e
s
.
W
ith
th
e
u
s
e
o
f
th
is
ap
p
r
o
ac
h
,
th
e
m
o
d
el
p
r
o
d
u
ce
d
im
a
g
es th
at
wer
e
n
o
t o
n
l
y
r
ea
lis
tic
-
lo
o
k
in
g
b
u
t
also
s
em
an
tically
v
er
y
ac
c
u
r
ate,
m
a
k
in
g
th
em
m
o
r
e
r
elev
a
n
t
to
th
e
m
ar
k
etin
g
ca
m
p
aig
n
o
b
jectiv
es.
B
esid
es
th
e
d
escr
ib
ed
m
eth
o
d
s
,
ad
v
a
n
ce
d
n
eu
r
al
n
etwo
r
k
ar
ch
itectu
r
e
was
in
tr
o
d
u
ce
d
to
f
u
r
th
e
r
im
p
r
o
v
e
th
e
m
o
d
el'
s
ca
p
ab
ilit
y
.
A
g
r
a
p
h
c
o
n
v
o
lu
tio
n
al
n
etwo
r
k
(
GC
N)
was
u
s
ed
f
o
r
k
n
o
wled
g
e
g
r
a
p
h
p
r
o
ce
s
s
in
g
,
ab
le
to
ca
p
tu
r
e
c
o
m
p
licated
r
elatio
n
-
s
h
ip
s
am
o
n
g
en
titi
es
in
m
ar
k
etin
g
d
at
a.
T
h
is
allo
wed
th
e
m
o
d
el
to
g
en
er
ate
c
o
n
tex
tu
a
lly
en
r
ich
ed
v
is
u
als
th
at
ex
ac
tly
m
atch
th
e
ass
o
ciate
d
p
r
o
d
u
ct
in
f
o
r
m
atio
n
.
I
n
teg
r
atio
n
o
f
th
e
C
L
I
PTe
x
tMo
d
el
-
f
r
o
m
Op
en
AI
’
s
co
n
tr
asti
v
e
lan
g
u
ag
e
–
im
ag
e
p
r
etr
a
in
i
ng
(
C
L
I
P)
-
was
ap
p
lied
-
a
d
u
al
en
co
d
er
ar
ch
itectu
r
e
-
,
th
at
allo
wed
f
o
r
alig
n
m
en
t
b
etwe
en
tex
tu
al
an
d
v
is
u
al
asp
ec
ts
o
f
m
ar
k
etin
g
cr
ea
tiv
es.
I
t a
lig
n
e
d
laten
t r
ep
r
esen
tatio
n
s
o
f
tex
t
an
d
im
ag
es,
wh
ich
h
elp
e
d
C
L
I
P e
n
ab
le
th
e
m
o
d
el
to
g
en
e
r
ate
m
ar
k
etin
g
v
is
u
als
ap
p
ea
lin
g
a
n
d
co
n
tex
t
u
ally
co
r
r
ec
t,
clo
s
ely
r
e
f
lectin
g
p
r
o
d
u
ct
d
escr
ip
tio
n
s
g
iv
en
in
t
h
e
d
ataset
T
h
is
f
in
e
-
tu
n
in
g
p
r
o
ce
s
s
f
o
r
b
o
th
L
L
Ms
an
d
th
e
s
tab
le
d
i
f
f
u
s
io
n
m
o
d
el
d
em
o
n
s
tr
ated
s
ig
n
if
ican
t
im
p
r
o
v
em
e
n
t
in
th
eir
g
en
er
a
tio
n
ca
p
ab
ilit
ies.
T
h
is
is
esp
ec
ially
tr
u
e
f
o
r
th
e
p
r
o
d
u
cti
o
n
o
f
p
er
s
o
n
al
an
d
co
n
tex
tu
ally
r
elev
an
t
c
o
n
ten
t
r
elate
d
to
em
ail
m
ar
k
etin
g
ca
m
p
aig
n
s
.
B
y
in
c
o
r
p
o
r
atin
g
ad
v
an
ce
d
e
v
alu
atio
n
m
etr
ics,
k
n
o
wled
g
e
g
r
ap
h
s
,
v
e
cto
r
em
b
ed
d
in
g
s
,
an
d
p
er
ce
p
t
u
al
lo
s
s
,
h
ig
h
-
q
u
ality
o
u
tp
u
ts
ar
e
g
en
er
ated
b
y
th
e
m
o
d
els th
at
alig
n
ed
with
te
x
tu
al
an
d
v
is
u
al
d
em
a
n
d
s
o
f
m
o
d
er
n
m
ar
k
etin
g
s
tr
ateg
ies.
4
.
3
.
Sy
s
t
e
m
i
nte
g
ra
t
io
n
T
h
e
ar
ch
itectu
r
e
r
elies
o
n
Dja
n
g
o
as
th
e
co
r
e
f
r
am
ewo
r
k
,
i
n
teg
r
atin
g
t
h
e
L
L
M
an
d
s
tab
l
e
d
if
f
u
s
io
n
m
o
d
u
les.
Djan
g
o
au
to
m
atica
ll
y
f
ir
es
u
p
b
ac
k
g
r
o
u
n
d
Py
th
o
n
s
cr
ip
ts
,
wh
ich
g
en
er
ate
s
u
b
jec
t
lin
es
an
d
im
ag
es.
Py
th
o
n
s
cr
ip
ts
also
in
ter
f
ac
e
with
L
L
Ms
an
d
s
tab
le
d
if
f
u
s
io
n
m
o
d
els
th
at
ar
e
r
esp
o
n
s
i
b
le
f
o
r
th
e
c
o
n
ten
t
cr
ea
tio
n
,
p
u
llin
g
it
in
th
r
o
u
g
h
Djan
g
o
'
s
O
R
M
in
to
a
ce
n
tr
al
d
atab
ase.
T
h
is
m
ea
n
s
g
o
o
d
s
y
n
ch
r
o
n
izatio
n
b
etwe
en
g
en
er
ate
d
co
n
te
n
t a
n
d
th
e
m
ar
k
etin
g
ca
m
p
aig
n
b
eh
in
d
it.
I
t
p
r
o
v
id
es
th
is
t
h
r
o
u
g
h
C
eler
y
,
wh
ich
in
teg
r
ates
with
r
eso
u
r
ce
-
in
ten
s
iv
e
task
s
,
s
u
ch
as
g
en
er
atin
g
im
ag
es
th
at
ca
n
r
u
n
asy
n
ch
r
o
n
o
u
s
ly
,
k
ee
p
in
g
th
e
s
y
s
tem
r
ea
d
y
to
tak
e
o
n
m
an
y
r
eq
u
e
s
ts
b
ein
g
s
er
v
iced
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
AI
-
d
r
iven
in
teg
r
a
ted
s
ystem
fo
r
co
mp
r
eh
en
s
ive
ema
il ma
r
ke
tin
g
a
u
to
m
a
tio
n
(
S
o
u
ma
y
a
L
o
u
kili
)
4883
C
eler
y
f
ac
ilit
ates
asy
n
ch
r
o
n
o
u
s
ex
ec
u
tio
n
o
f
task
s
-
q
u
eu
in
g
th
em
in
a
m
ess
ag
e
b
r
o
k
er
ca
lled
R
ab
b
itMQ
f
o
r
b
ac
k
g
r
o
u
n
d
task
s
lik
e
i
m
ag
e
cr
ea
tio
n
to
b
e
d
is
tr
ib
u
ted
f
o
r
p
e
r
f
o
r
m
an
ce
an
d
h
o
r
izo
n
t
al
s
ca
lin
g
.
Fu
r
th
e
r
r
ed
u
ctio
n
in
laten
cy
is
ac
co
m
p
lis
h
ed
b
y
R
ed
is
-
b
ased
ca
ch
in
g
t
h
at
m
in
im
izes
r
ed
u
n
d
an
t
co
m
p
u
tatio
n
b
y
s
to
r
in
g
p
r
ev
io
u
s
ly
g
en
er
ated
co
n
ten
t.
W
e
u
s
e
Djan
g
o
's
O
R
M
f
o
r
ef
f
icien
t
h
an
d
lin
g
o
f
d
atab
ase
in
ter
ac
tio
n
s
an
d
h
a
n
d
lin
g
s
to
r
in
g
all
th
e
c
o
n
ten
t
g
e
n
er
ated
,
r
elatin
g
th
e
m
b
ac
k
t
o
th
eir
r
esp
ec
tiv
e
ca
m
p
aig
n
s
.
T
h
e
s
y
s
tem
ar
ch
itectu
r
e
is
m
o
d
u
lar
b
y
n
a
tu
r
e,
h
e
n
ce
s
ca
lab
le,
b
ec
au
s
e
ea
ch
co
m
p
o
n
e
n
t
ca
n
b
e
s
ca
led
in
d
e
p
en
d
e
n
tly
to
m
an
ag
e
th
e
i
n
cr
ea
s
ed
lo
a
d
.
At
d
ep
lo
y
m
en
t,
Djan
g
o
is
in
teg
r
ated
with
E
SP
s
lik
e
Sen
d
g
r
id
an
d
Ma
ilch
im
p
f
o
r
ef
f
icien
t a
n
d
d
ir
ec
t d
is
tr
ib
u
tio
n
o
f
e
-
m
ail
ca
m
p
aig
n
s
.
5.
E
XP
E
R
I
M
E
N
T
A
L
DE
SI
G
N
AND
E
VA
L
UA
T
I
O
N
5
.
1
.
E
x
perim
ent
a
l
s
et
up
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
p
r
o
p
o
s
ed
s
y
s
tem
f
o
r
th
e
g
e
n
er
atio
n
o
f
em
ail
s
u
b
ject
lin
es
an
d
v
is
u
al
co
n
ten
t
was
ev
alu
ated
u
s
in
g
a
co
n
tr
o
l
led
ex
p
e
r
im
en
tal
f
r
am
ewo
r
k
t
h
at
was
d
esig
n
ed
to
b
e
f
air
,
r
e
liab
le,
an
d
r
elev
a
n
t
f
o
r
r
ea
lis
tic
co
n
d
itio
n
s
o
f
m
ar
k
etin
g
.
Su
ch
an
ex
p
er
im
en
t
was
d
esig
n
ed
to
co
m
p
r
eh
en
s
iv
ely
test
th
e
ca
p
ab
ilit
ies
o
f
th
e
s
y
s
tem
in
cr
ea
tin
g
co
n
tex
tu
ally
ap
p
r
o
p
r
iate
co
n
ten
t
ac
r
o
s
s
a
wid
e
v
ar
iety
o
f
p
r
o
d
u
ct
ca
teg
o
r
ies
an
d
au
d
ien
ce
p
r
o
f
i
les,
with
s
tr
ict
co
n
tr
o
l
o
v
er
v
ar
iab
les
af
f
ec
tin
g
p
e
r
f
o
r
m
an
c
e
m
etr
ics.
T
h
e
aim
was to
co
m
p
ar
e
it to
c
o
n
ten
t
p
r
o
d
u
ce
d
b
y
h
u
m
a
n
s
,
m
o
r
e
s
p
ec
if
ically
em
ail
m
ar
k
etin
g
ex
p
e
r
ts
.
Fo
r
th
is
ex
p
e
r
im
en
t,
we
p
ick
e
d
5
d
if
f
e
r
en
t
p
r
o
d
u
cts
to
p
r
o
m
o
te,
ea
ch
f
r
o
m
a
d
is
tin
ct
ca
t
eg
o
r
y
.
T
h
e
g
o
al
was
to
test
o
u
t
th
e
s
u
b
jects
an
d
v
is
u
al
cr
ea
tiv
es
g
e
n
er
ated
b
y
th
e
s
y
s
tem
ac
r
o
s
s
s
ev
er
al
d
if
f
er
en
t
ca
teg
o
r
ies,
s
o
th
e
s
y
s
tem
’
s
ad
ap
tab
ilit
y
to
d
if
f
e
r
en
t
d
o
m
ain
s
is
ev
alu
ated
.
Su
ch
d
i
v
er
s
e
s
elec
tio
n
wo
u
ld
m
a
k
e
s
u
r
e
th
at
th
e
f
lex
ib
ilit
y
an
d
th
e
co
n
tex
tu
al
r
elev
an
ce
o
f
th
e
s
y
s
tem
co
u
ld
b
e
th
o
r
o
u
g
h
ly
test
ed
.
T
h
ese
ca
teg
o
r
ies
in
clu
d
e
d
co
n
s
u
m
e
r
elec
tr
o
n
ics,
f
ash
io
n
,
s
k
in
ca
r
e,
h
o
u
s
eh
o
ld
ap
p
lian
ce
s
an
d
in
s
u
r
an
ce
.
E
ac
h
p
r
o
d
u
ct
h
ad
d
if
f
e
r
en
t
r
eq
u
ir
e
m
en
ts
r
elate
d
to
m
ar
k
etin
g
,
in
clu
d
in
g
s
p
ec
if
ic
to
n
e
p
r
ef
e
r
en
ce
s
an
d
v
is
u
al
ae
s
th
etic
co
n
s
id
er
atio
n
s
.
Fo
r
i
n
s
tan
ce
,
co
n
s
u
m
er
elec
tr
o
n
ics
r
eq
u
i
r
ed
a
p
r
o
f
ess
io
n
al
a
n
d
tech
n
ical
to
n
e
,
wh
il
e
f
ash
io
n
p
r
o
d
u
cts n
ee
d
ed
m
o
r
e
cr
ea
tiv
e
an
d
v
is
u
ally
d
y
n
am
ic
co
n
ten
t.
Fo
r
ea
ch
p
r
o
d
u
ct,
th
e
s
y
s
tem
s
u
g
g
ested
th
r
ee
d
if
f
e
r
en
t
s
u
b
je
ct
lin
es
—
o
n
e
b
y
ea
c
h
L
L
M
—
an
d
m
an
y
v
is
u
al
d
esig
n
s
.
A
p
r
o
f
ess
io
n
al
em
ail
m
ar
k
eter
was
r
esp
o
n
s
i
b
le
o
f
d
escr
ib
in
g
th
e
to
n
e
an
d
im
p
r
ess
io
n
o
f
th
e
ca
m
p
aig
n
,
an
d
th
en
r
ev
iewe
d
t
h
e
g
en
er
ated
co
n
ten
t a
n
d
s
elec
ted
a
s
u
b
ject
lin
e
an
d
v
is
u
al
cr
ea
tiv
e
to
u
s
e.
T
h
is
s
tep
en
s
u
r
ed
th
at
th
e
co
n
ten
t c
o
m
p
lies
with
m
ar
k
etin
g
s
tan
d
ar
d
s
wh
ile
s
h
o
wca
s
in
g
th
e
s
y
s
tem
'
s
ca
p
ab
ilit
ies in
g
en
er
atin
g
h
ig
h
-
q
u
ality
,
c
o
m
p
ellin
g
o
u
tp
u
ts
.
W
h
en
it
co
m
es
to
th
e
r
ec
ip
ien
ts
o
f
th
e
p
r
o
m
o
tio
n
al
em
ails
,
a
u
d
ien
ce
s
eg
m
e
n
ts
wer
e
f
o
r
m
e
d
f
o
r
ea
c
h
p
r
o
d
u
ct
b
ased
o
n
d
em
o
g
r
ap
h
ic
an
d
b
eh
av
io
r
al
d
ata,
as
is
co
m
m
o
n
p
r
ac
tice
in
m
ar
k
et
in
g
.
Dem
o
g
r
a
p
h
ic
v
ar
iab
les
in
clu
d
ed
ag
e,
g
en
d
er
,
an
d
lo
ca
tio
n
,
wh
ile
b
eh
a
v
io
r
al
in
f
o
r
m
atio
n
co
n
s
is
ted
o
f
p
ast
en
g
ag
em
e
n
t
r
ates,
p
u
r
c
h
asin
g
h
is
to
r
y
,
an
d
b
r
o
wsi
n
g
ac
tiv
ities
.
Su
ch
s
eg
m
en
tatio
n
cr
iter
ia
allo
wed
f
o
r
th
e
g
e
n
er
atio
n
o
f
v
er
y
s
p
ec
if
ic
au
d
ien
ce
p
r
o
f
il
es
to
s
im
u
late
r
ea
l
-
w
o
r
ld
co
n
d
itio
n
s
f
o
r
th
e
m
o
s
t
ac
c
u
r
ate
m
ar
k
etin
g
e
f
f
o
r
ts
.
Sy
s
tem
-
g
en
er
ated
co
n
te
n
t
was
s
en
t
to
th
e
s
e
s
eg
m
en
ts
,
m
ir
r
o
r
in
g
th
e
s
eg
m
en
ts
tar
g
eted
b
y
h
u
m
an
-
g
en
er
ate
d
co
n
ten
t,
th
u
s
allo
win
g
a
f
air
c
o
m
p
ar
is
o
n
.
Fo
r
ev
en
m
o
r
e
f
air
n
ess
,
o
th
er
ca
m
p
aig
n
p
ar
a
m
eter
s
wer
e
s
et
to
b
e
th
e
s
am
e
b
e
twee
n
th
e
s
y
s
tem
-
g
en
er
ated
an
d
h
u
m
an
-
g
en
er
ated
co
n
te
n
t.
All
em
ails
wer
e
s
en
t
at
th
e
s
am
e
t
im
e
o
f
d
ay
an
d
o
v
er
a
co
n
s
is
ten
t
p
er
io
d
to
m
in
im
ize
th
e
u
n
d
er
ly
in
g
in
f
lu
e
n
ce
b
r
o
u
g
h
t
ab
o
u
t
b
y
tim
in
g
o
n
o
p
e
n
an
d
click
-
th
r
o
u
g
h
r
ates.
T
h
e
em
ail
tem
p
lates,
d
eliv
er
y
m
eth
o
d
s
,
an
d
s
u
p
p
lem
en
tar
y
d
esig
n
elem
en
ts
r
em
ain
e
d
id
en
tical
f
o
r
b
o
th
test
g
r
o
u
p
s
.
Fu
r
t
h
er
m
o
r
e,
ex
t
er
n
al
f
ac
to
r
s
s
u
ch
as
p
r
o
m
o
t
io
n
s
wer
e
av
o
i
d
ed
d
u
r
in
g
th
e
test
in
g
p
h
ase
to
m
in
im
ize
th
eir
im
p
ac
t
o
n
r
esu
l
ts
,
en
s
u
r
in
g
a
f
air
ev
alu
atio
n
.
T
h
is
ex
p
er
im
e
n
tal
s
etu
p
en
s
u
r
ed
a
r
ig
o
r
o
u
s
a
n
d
u
n
b
iased
ev
alu
atio
n
o
f
th
e
s
y
s
tem
’
s
ca
p
a
b
ilit
ies.
B
y
test
in
g
f
iv
e
d
if
f
er
en
t
ca
teg
o
r
i
es
o
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
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8
8
-
8
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I
n
t J E
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&
C
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m
p
E
n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
8
7
5
-
4
8
8
8
4884
−
C
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ates:
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ar
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r
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life
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t in
d
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b.
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test
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6.
RE
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Resul
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