I
AE
S In
t
er
na
t
io
na
l
J
o
urna
l o
f
Art
if
icia
l In
t
ellig
ence
(
I
J
-
AI)
Vo
l.
9
,
No
.
2
,
J
u
n
e
2020
,
p
p
.
336
~
3
4
8
I
SS
N:
2
2
5
2
-
8938
,
DOI
: 1
0
.
1
1
5
9
1
/i
j
ai.
v
9
.i
2
.
p
p
336
-
3
4
8
336
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
a
i
.
ia
esco
r
e.
co
m
Co
nflict
ing
opin
i
o
ns in conn
ec
tion
w
ith
dig
ital super
intelligence
Ah
m
ed
Al
-
I
m
a
m
1
,
M
a
re
k
A.
M
o
t
y
k
a
2
,
M
a
rius
z
Z
.
J
ędrz
e
j
k
o
3
1
CERV
O Brain
Re
se
a
rc
h
Ce
n
tre,
F
a
c
u
lt
y
o
f
M
e
d
icin
e
,
Un
iv
e
rsity
o
f
L
a
v
a
l,
Ca
n
a
d
a
2
In
stit
u
te
o
f
S
o
c
io
lo
g
y
,
F
a
c
u
lt
y
o
f
S
o
c
io
lo
g
y
a
n
d
Histo
ry
,
Un
iv
e
rsity
o
f
Rz
e
s
z
o
w
,
P
o
lan
d
3
F
a
c
u
lt
y
o
f
M
e
d
ica
l
S
c
ien
c
e
s a
n
d
He
a
lt
h
S
c
ien
c
e
s,
Un
iv
e
rsity
o
f
R
a
d
o
m
,
P
o
lan
d
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Feb
2
0
,
2
0
20
R
ev
i
s
ed
A
p
r
7
,
2
0
20
A
cc
ep
ted
A
p
r
2
1
,
2
0
20
In
1
9
6
4
,
Nik
o
lai
Ka
rd
a
sh
e
v
p
r
o
p
o
se
d
th
e
Ka
rd
a
sh
e
v
sc
a
le,
a
s
y
ste
m
f
o
r
m
e
a
su
rin
g
th
e
e
x
ten
t
o
f
te
c
h
n
o
l
o
g
ica
l
a
d
v
a
n
c
e
m
e
n
t
o
f
a
c
iv
il
iza
ti
o
n
b
a
se
d
o
n
th
e
m
a
g
n
it
u
d
e
o
f
e
n
e
rg
y
c
o
n
su
m
p
ti
o
n
.
W
e
a
r
e
a
p
p
ro
a
c
h
in
g
a
n
in
e
v
it
a
b
le
ty
p
e
-
1
c
iv
il
iza
ti
o
n
,
a
n
d
a
rti
f
icia
l
su
p
e
rin
tell
ig
e
n
c
e
su
p
e
rio
r
t
o
t
h
a
t
o
f
h
u
m
a
n
s
c
a
n
c
o
n
c
u
r
w
it
h
a
h
ig
h
e
r
-
h
iera
rc
h
y
K
a
rd
a
sh
e
v
c
iv
il
iza
ti
o
n
.
W
e
a
im
to
su
rv
e
y
p
u
b
li
c
o
p
i
n
io
n
s,
sp
e
c
if
ica
ll
y
v
id
e
o
g
a
m
e
rs,
w
o
rld
w
id
e
c
o
m
p
a
re
d
t
o
th
o
se
in
P
o
lan
d
,
c
o
n
c
e
rn
i
n
g
a
rti
f
icia
l
g
e
n
e
ra
l
in
telli
g
e
n
c
e
a
n
d
su
p
e
rin
telli
g
e
n
c
e
.
W
e
i
m
p
le
m
e
n
ted
a
n
a
m
a
l
g
a
m
o
f
c
ro
ss
-
se
c
ti
o
n
a
l
a
n
d
lo
n
g
it
u
d
in
a
l
a
n
a
ly
s
e
s
o
f
th
e
d
a
tab
a
se
o
f
li
tera
tu
re
a
n
d
G
o
o
g
le
se
a
rc
h
e
n
g
in
e
.
T
h
e
g
e
o
g
ra
p
h
ic
m
a
p
p
in
g
o
f
su
rf
a
c
e
we
b
u
se
rs
w
h
o
a
re
in
tere
ste
d
in
a
rti
f
icia
l
su
p
e
rin
telli
g
e
n
c
e
re
v
e
a
led
th
e
to
p
ten
c
o
n
tri
b
u
ti
n
g
c
o
u
n
tri
e
s:
Ira
n
,
M
e
x
ico
,
Co
l
o
m
b
ia,
Bra
z
il
,
In
d
ia,
P
e
r
u
,
S
o
u
t
h
A
f
rica
,
Ro
m
a
n
ia,
S
w
it
z
e
rlan
d
,
a
n
d
C
h
il
e
.
D
e
v
e
lo
p
in
g
c
o
u
n
tr
ies
a
c
c
o
u
n
ted
f
o
r
5
4
.
8
4
%
o
f
th
e
to
tal
m
a
p
.
P
o
li
s
h
p
e
o
p
le
we
re
les
s
e
n
th
u
sia
stic
a
b
o
u
t
a
rti
f
icia
l
g
e
n
e
ra
l
in
telli
g
e
n
c
e
a
n
d
s
u
p
e
rin
telli
g
e
n
c
e
c
o
m
p
a
re
d
w
it
h
th
e
re
st
o
f
th
e
w
o
rld
.
F
u
t
u
risti
c
tec
h
n
o
l
o
g
ica
l
in
n
o
v
a
ti
o
n
s
im
p
l
y
a
n
a
c
c
e
lera
ti
o
n
in
a
rti
f
icia
l
in
telli
g
e
n
c
e
a
n
d
su
p
e
rin
telli
g
e
n
c
e
.
T
h
is
sc
e
n
a
rio
c
a
n
b
e
p
e
ss
im
isti
c
,
a
s
su
p
e
rin
telli
g
e
n
c
e
c
a
n
re
n
d
e
r
h
u
m
a
n
-
b
a
se
d
a
c
ti
v
it
ies
o
b
s
o
lete
.
Ho
w
e
v
e
r,
in
teg
ra
ti
n
g
a
rti
f
ici
a
l
in
telli
g
e
n
c
e
w
it
h
h
u
m
a
n
s,
v
ia
b
ra
in
-
c
o
m
p
u
ter
in
terf
a
c
e
tec
h
n
o
lo
g
ies
,
c
a
n
b
e
p
ro
tec
ti
v
e
.
No
n
e
th
e
les
s,
leg
islatio
n
i
n
c
o
n
n
e
c
ti
o
n
w
it
h
in
f
o
rm
a
ti
o
n
tec
h
n
o
lo
g
ies
is
m
a
n
d
a
to
ry
to
re
g
u
late
u
p
c
o
m
in
g
d
ig
it
a
l
k
n
o
w
led
g
e
a
n
d
su
p
e
ri
n
telli
g
e
n
c
e
.
K
ey
w
o
r
d
s
:
A
r
ti
f
icial
i
n
tel
lig
e
n
ce
C
o
n
s
c
io
u
s
n
es
s
Kar
d
ash
ev
s
ca
le
Su
p
er
in
te
l lig
e
n
ce
T
ec
h
n
o
lo
g
ical
s
i
n
g
u
lar
it
y
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Ah
m
ed
Al
-
I
m
a
m
,
C
E
R
VO
B
r
ain
R
esear
c
h
C
e
n
tr
e,
Facu
lt
y
o
f
Me
d
ici
n
e,
Un
i
v
er
s
i
t
y
o
f
L
av
a
l,
2
6
0
1
C
h
e
m
in
d
e
la
C
a
n
ar
d
ièr
e,
Qu
éb
ec
,
QC
G1
J
2
G3
,
C
an
ad
a.
E
m
ail: a
h
m
ed
.
al
-
i
m
a
m
.
1
@
u
la
v
al.
ca
1.
I
NT
RO
D
UCT
I
O
N
T
h
er
e
h
av
e
b
ee
n
s
ev
er
al
tech
n
o
lo
g
ical
ad
v
an
ce
m
e
n
ts
in
m
o
d
er
n
h
i
s
to
r
y
,
in
c
lu
d
in
g
t
h
e
ag
r
icu
l
tu
r
al
r
ev
o
l
u
tio
n
i
n
th
e
1
7
th
an
d
1
8
th
ce
n
tu
r
ies
t
h
a
t
h
as
its
r
ad
ix
s
i
n
ce
1
0
,
0
0
0
B
C
,
th
e
in
d
u
s
tr
ial
r
ev
o
lu
tio
n
in
th
e
1
8
th
an
d
1
9
th
ce
n
t
u
r
ies,
th
e
s
ec
o
n
d
i
n
d
u
s
t
r
ial
r
ev
o
lu
tio
n
i
n
th
e
1
9
th
an
d
2
0
th
ce
n
tu
r
ies,
th
e
s
cien
t
if
ic
-
tec
h
n
ical
r
ev
o
l
u
tio
n
in
t
h
e
late
2
0
th
ce
n
t
u
r
y
,
a
n
d
th
e
d
ig
ital r
ev
o
l
u
tio
n
i
n
t
h
e
s
ec
o
n
d
h
al
f
o
f
t
h
e
2
0
th
ce
n
tu
r
y
[
1
-
2
]
.
Si
m
ilar
l
y
,
m
il
it
ar
y
p
r
o
g
r
es
s
h
a
s
w
it
n
es
s
ed
a
co
llater
al
b
o
o
m
i
n
g
o
f
tech
n
o
l
o
g
ie
s
,
t
h
e
latest
o
f
w
h
ic
h
is
f
i
f
t
h
-
g
e
n
er
atio
n
w
ar
f
ar
e,
also
k
n
o
w
n
a
s
n
o
n
-
co
n
t
ac
t
w
ar
f
ar
e
[
3
-
4
]
.
T
ec
h
n
o
lo
g
ical
p
r
o
g
r
ess
w
o
u
ld
n
o
t
h
a
v
e
b
ee
n
p
o
s
s
ib
le
w
it
h
o
u
t
th
e
i
n
n
o
v
atio
n
o
f
s
il
ico
n
-
b
ased
en
titi
e
s
k
n
o
w
n
as
co
m
p
u
ter
s
[
5
-
6]
,
an
d
t
h
er
e
is
m
u
c
h
d
eb
ate
o
v
er
w
h
o
in
v
e
n
ted
th
e
f
ir
s
t
ca
lcu
la
tin
g
m
ac
h
in
e
[
6
-
7
]
.
I
t
is
p
o
s
s
ib
le
t
h
at
th
e
t
w
o
-
m
ille
n
n
ia
o
l
d
m
ec
h
a
n
i
s
m
f
r
o
m
a
n
cien
t
Gr
ee
ce
,
ar
o
u
n
d
t
h
e
ti
m
e
o
f
t
h
e
g
r
ea
t
A
r
c
h
i
m
ed
es,
k
n
o
w
n
a
s
th
e
An
ti
k
y
t
h
er
a,
r
ep
r
esen
t
s
t
h
e
o
ld
est
co
m
p
u
t
i
n
g
m
ec
h
an
is
m
[
8
-
1
0
]
.
I
n
R
en
aiss
a
n
ce
I
tal
y
,
t
h
e
I
talia
n
p
o
ly
m
a
th
L
eo
n
ar
d
o
d
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
r
ti
f
I
n
tell
I
SS
N:
2252
-
8938
C
o
n
flictin
g
o
p
in
io
n
s
in
co
n
n
e
ctio
n
w
ith
d
ig
ita
l su
p
erin
tellig
en
ce
(
A
h
med
A
l
-
I
ma
m
)
337
Vin
ci
in
v
e
n
ted
a
m
ec
h
an
ical
ca
lcu
lato
r
,
an
i
n
v
e
n
tio
n
t
h
at
I
B
M
co
m
p
an
y
r
ep
licated
i
n
t
h
e
2
0
th
ce
n
tu
r
y
[
1
1
-
1
2
]
.
L
ater
,
i
n
th
e
ea
r
l
y
1
7
t
h
ce
n
tu
r
y
,
th
e
p
r
o
d
ig
y
B
lais
e
P
ascal
d
ev
elo
p
ed
a
m
o
r
e
s
o
p
h
is
ticated
m
ec
h
a
n
ica
l
ca
lcu
lato
r
[
1
1
,
1
3
]
.
B
ef
o
r
e
t
h
e
s
ec
o
n
d
w
o
r
ld
w
ar
,
Alan
T
u
r
in
g
cr
ea
ted
th
e
T
u
r
in
g
m
ac
h
in
e
in
1
9
3
6
,
a
m
at
h
e
m
a
tical
m
o
d
el
o
f
co
m
p
u
tatio
n
t
h
at
d
e
f
i
n
es
an
ab
s
tr
a
ct
m
ac
h
i
n
e
[
1
4
]
.
A
l
m
o
s
t
th
r
e
e
d
ec
ad
es
later
,
t
h
e
Un
ited
Sta
tes
Dep
ar
t
m
en
t
o
f
Def
e
n
s
e
p
ar
en
t
ed
t
h
e
d
ev
e
lo
p
m
en
t
o
f
a
d
ec
e
n
tr
alize
d
co
m
p
u
ter
n
et
w
o
r
k
th
a
t
f
o
r
m
ed
th
e
ar
c
h
et
y
p
e
o
f
w
h
at
later
b
ec
am
e
t
h
e
i
n
ter
n
et,
i
n
cl
u
d
in
g
in
f
a
m
o
u
s
d
ee
p
w
eb
[
5
,
1
5
]
.
A
r
ti
f
icial
in
te
llig
e
n
ce
,
ab
b
r
ev
iated
as
A
I
,
is
t
h
e
s
i
m
u
latio
n
o
f
h
u
m
an
in
tel
li
g
en
ce
b
y
a
co
m
p
u
ter
s
y
s
te
m
[
1
6
-
1
7
]
.
Ar
tif
icial
n
e
u
r
al
n
et
w
o
r
k
s
ar
e
n
o
n
-
b
io
lo
g
ic
m
i
m
ic
s
o
f
t
h
e
n
e
u
r
al
n
et
w
o
r
k
s
o
f
t
h
e
b
r
ain
,
an
d
t
h
ese
s
y
s
te
m
s
ca
n
lear
n
to
ca
r
r
y
o
u
t
an
al
y
s
es
w
i
th
o
u
t
b
ein
g
p
r
e
-
p
r
o
g
r
a
m
m
ed
to
b
e
task
-
s
p
ec
i
f
ic
[
1
8
]
.
T
h
ese
t
a
s
k
s
ar
e
n
o
t
li
m
it
ed
to
p
atter
n
r
ec
o
g
n
it
io
n
,
co
m
p
u
ter
v
is
io
n
,
an
d
r
ea
l
-
t
i
m
e
p
r
o
ce
s
s
i
n
g
,
as
w
ell
as
f
o
r
ec
asti
n
g
a
n
al
y
t
ics
f
o
r
b
ig
d
ata
[
1
9
-
2
2
]
.
I
n
[
2
3
]
,
W
ar
r
e
n
Mc
C
u
llo
ch
a
n
d
W
alter
P
itt
s
p
r
o
p
o
s
ed
a
b
asic
co
m
p
u
ter
m
o
d
el
m
i
m
ick
i
n
g
,
to
s
o
m
e
d
eg
r
ee
,
th
e
n
e
u
r
al
n
et
w
o
r
k
s
o
f
t
h
e
h
u
m
an
b
r
ain
.
C
u
r
r
en
tl
y
,
a
r
ti
f
icia
l
in
telli
g
e
n
ce
i
n
cl
u
d
es
a
s
p
ec
tr
u
m
o
f
th
r
ee
o
v
er
lap
p
i
n
g
ca
te
g
o
r
ies:
n
ar
r
o
w
ar
ti
f
icia
l
i
n
tell
ig
en
ce
(
n
ar
r
o
w
A
I
)
,
ar
tif
icial
g
en
er
al
i
n
telli
g
e
n
ce
(
A
GI
)
,
an
d
ar
tific
ial
s
u
p
er
in
tel
lig
e
n
ce
[
2
4
]
.
R
esear
ch
er
s
also
d
ef
in
ed
f
o
u
r
m
ai
n
ca
teg
o
r
ies
o
f
A
I
m
ac
h
i
n
es:
r
ea
ctiv
e
m
ac
h
i
n
es,
t
h
o
s
e
w
it
h
li
m
ited
m
e
m
o
r
y
,
t
h
eo
r
y
-
of
-
m
i
n
d
co
m
p
lian
t
A
I
m
ac
h
in
e
s
,
an
d
th
e
a
n
ticip
ate
d
s
elf
-
a
w
ar
e
m
ac
h
i
n
es
[
2
5
-
2
6
]
.
Ho
w
e
v
er
,
alb
eit
th
e
ad
v
a
n
ce
s
i
n
n
e
u
r
o
lo
g
ica
l
s
cien
ce
s
,
t
h
er
e
is
n
o
u
n
a
n
i
m
o
u
s
a
g
r
ee
m
en
t
o
n
t
h
e
d
e
f
in
itio
n
o
f
h
u
m
an
co
n
s
cio
u
s
n
es
s
[
2
7
]
.
P
er
h
ap
s
it
ca
n
b
e
“
s
i
m
p
l
y
”
d
ef
i
n
ed
as
“
t
h
e
q
u
ali
t
y
o
r
s
tate
o
f
b
ein
g
a
w
ar
e,
esp
ec
iall
y
o
f
s
o
m
et
h
i
n
g
w
i
th
in
o
n
eself
”
[
2
8
]
.
No
n
eth
ele
s
s
,
A
la
n
T
u
r
in
g
es
p
o
u
s
ed
th
e
r
en
o
w
n
ed
T
u
r
in
g
test
,
w
h
ic
h
is
a
test
o
f
a
m
ac
h
in
e
'
s
ab
ilit
y
to
ex
h
ib
it
i
n
tel
lig
e
n
t
b
eh
av
io
r
i
n
d
is
ti
n
g
u
i
s
h
ab
le
f
r
o
m
t
h
at
o
f
a
h
u
m
a
n
,
i
n
cl
u
d
in
g
th
e
ch
ar
ac
ter
i
s
tics
o
f
s
elf
-
a
w
ar
e
n
es
s
[
2
9
]
.
T
h
e
e
x
p
o
n
en
tial
g
r
o
w
t
h
o
f
tec
h
n
o
lo
g
i
es
in
th
e
d
ig
ital
ep
o
ch
,
b
ac
k
ed
b
y
s
u
b
s
tan
t
iati
n
g
ev
id
en
ce
o
f
Mo
o
r
e's
la
w
a
n
d
th
e
r
ec
en
t
e
m
er
g
en
ce
o
f
q
u
an
t
u
m
co
m
p
u
ter
s
,
i
n
d
icate
t
h
at
a
h
u
m
a
n
-
l
e
v
el
m
ac
h
in
e
i
n
telli
g
e
n
ce
(
HL
MI
)
,
as
w
ell
as
a
s
u
b
s
eq
u
e
n
t
s
u
p
er
in
telli
g
e
n
ce
,
co
u
ld
b
e
im
m
i
n
en
t,
as
E
lo
n
Mu
s
k
d
is
clo
s
ed
r
ec
en
tl
y
[
3
0
-
3
2
]
.
Mu
s
k
p
r
ed
icts
th
at
A
I
w
ill
tak
e
o
v
er
m
o
s
t
o
f
,
i
f
n
o
t
al
l,
h
u
m
a
n
ac
tiv
it
ies,
in
cl
u
d
in
g
ad
v
a
n
ce
d
r
esear
ch
[
3
2
]
.
Oth
er
s
ar
g
u
e
t
h
at
h
u
m
a
n
it
y
is
m
ar
ch
i
n
g
to
w
ar
d
s
a
n
in
e
v
itab
le
tech
n
o
lo
g
ical
s
in
g
u
lar
it
y
[
3
3
-
3
5
]
.
T
h
an
k
s
to
t
h
e
p
io
n
ee
r
i
n
g
e
f
f
o
r
ts
o
f
W
er
n
er
Heise
n
b
er
g
an
d
R
ic
h
ar
d
Fe
y
n
m
a
n
p
io
n
ee
r
i
n
g
e
f
f
o
r
ts
i
n
q
u
an
t
u
m
m
ec
h
a
n
ics,
it
ap
p
ea
r
s
th
at
th
e
i
n
v
e
n
tio
n
o
f
q
u
a
n
t
u
m
co
m
p
u
ti
n
g
w
ill
f
u
r
t
h
er
ac
ce
ler
ate,
au
g
m
e
n
ti
n
g
A
I
p
o
ten
tial
s
,
as r
ep
o
r
ted
b
y
Go
o
g
le
in
2
0
1
9
[
3
6
-
3
8
]
.
I
n
[
3
9
]
,
th
e
So
v
iet
astro
n
o
m
er
Nik
o
lai
Kar
d
ash
e
v
p
r
o
p
o
s
ed
th
e
Kar
d
ash
e
v
s
ca
le,
a
s
y
s
te
m
f
o
r
m
ea
s
u
r
in
g
th
e
le
v
el
o
f
tec
h
n
o
lo
g
ical
ad
v
an
ce
m
e
n
t
b
a
s
e
d
o
n
t
h
e
m
ag
n
it
u
d
e
a
n
d
m
o
d
alities
o
f
en
er
g
y
co
n
s
u
m
p
tio
n
.
T
h
e
cu
r
r
en
t
h
u
m
a
n
ci
v
ilizatio
n
s
till
d
o
es
n
o
t
q
u
ali
f
y
as
t
y
p
e
-
1
,
d
esp
ite
th
e
tr
e
m
e
n
d
o
u
s
p
r
o
g
r
ess
in
co
n
n
ec
tio
n
w
ith
e
n
er
g
y
e
x
p
lo
itatio
n
s
in
ce
th
e
e
r
a
o
f
th
e
p
r
o
li
f
ic
elec
tr
o
m
a
g
n
etic
in
n
o
v
at
io
n
s
o
f
Nico
la
T
esla
[
3
9
-
4
0
]
.
Kar
d
ash
ev
i
n
tr
o
d
u
ce
d
f
i
v
e
t
y
p
es
o
f
ci
v
ilizatio
n
s
,
f
o
u
r
o
f
w
h
ic
h
m
an
d
ate
th
e
co
n
ce
p
t
o
f
a
tech
n
o
lo
g
ical
s
i
n
g
u
lar
it
y
,
e
n
co
m
p
a
s
s
i
n
g
an
ar
tific
ial
s
u
p
er
i
n
telli
g
e
n
ce
[
3
3
,
3
9
]
.
T
h
er
e
is
m
u
c
h
e
v
i
d
e
n
ce
t
h
at
w
e
ar
e
ap
p
r
o
ac
h
in
g
a
Kar
d
as
h
ev
t
y
p
e
-
1
civ
il
izatio
n
,
at
w
h
ich
w
e
ca
n
h
ar
n
es
s
all
t
h
e
e
n
er
g
y
r
eso
u
r
ce
s
o
n
ea
r
th
in
ad
d
itio
n
to
u
tili
z
in
g
a
ll
th
e
s
o
lar
e
n
er
g
y
w
it
h
t
h
e
h
i
g
h
e
s
t
p
o
s
s
ib
le
e
f
f
icien
c
y
[
3
9
]
.
A
cc
o
r
d
in
g
to
C
ar
l
Sag
a
n
,
h
u
m
a
n
it
y
is
c
u
r
r
en
tl
y
g
o
in
g
th
r
o
u
g
h
a
tr
an
s
it
io
n
al
p
h
ase
th
at
i
s
“
t
y
p
ical
o
f
a
c
iv
ilizat
io
n
ab
o
u
t
to
in
te
g
r
ate
th
e
t
y
p
e
I
Kar
d
ash
e
v
s
ca
le.
”
[
4
1
]
.
Sig
n
s
o
f
a
n
i
m
m
in
e
n
t
t
y
p
e
-
1
civ
ilizatio
n
ar
e
n
o
t
li
m
ited
to
th
e
in
n
o
v
atio
n
s
i
n
ar
tific
ial
i
n
tell
ig
en
ce
,
a
s
w
ell
a
s
r
ea
lis
tic
p
l
an
s
f
o
r
co
lo
n
izin
g
Ma
r
s
a
n
d
s
ee
k
i
n
g
alter
n
at
iv
e
r
eso
u
r
ce
s
to
f
o
s
s
il
-
b
ased
f
u
el
o
n
E
ar
th
,
i
n
clu
d
i
n
g
n
u
clea
r
f
u
s
io
n
r
ea
cto
r
s
an
d
r
e
n
e
w
ab
le
e
n
er
g
y
r
eso
u
r
ce
s
[
4
1
-
4
5
]
.
P
o
ten
tial
ev
id
e
n
ce
o
f
a
m
o
r
e
ad
v
an
ce
d
Kar
d
as
h
ev
ci
v
ilizatio
n
is
th
e
p
u
zz
li
n
g
B
o
ö
tes
v
o
id
,
w
h
ic
h
m
a
y
r
ep
r
esen
t a
n
ea
r
-
o
m
n
ip
o
ten
t t
y
p
e
-
3
ex
tr
ater
r
estrial
Kar
d
ash
e
v
civ
i
lizatio
n
[
4
6
-
4
7
]
.
T
h
e
cr
ea
tio
n
o
f
th
e
u
n
iv
er
s
e
t
o
o
k
p
lace
at
1
4
b
illi
o
n
y
ea
r
s
ag
o
,
w
h
ile
t
h
e
ea
r
lies
t
s
i
g
n
o
f
a
ca
r
b
o
n
-
b
ased
b
io
lo
g
ical
li
f
e
f
o
r
m
o
n
ea
r
th
e
x
i
s
ted
at
ar
o
u
n
d
f
o
u
r
b
illi
o
n
y
ea
r
s
a
g
o
[
4
8
-
4
9
]
.
Du
r
in
g
th
e
Mio
ce
n
e
ep
o
ch
,
Ho
m
i
n
i
n
s
tr
an
s
m
o
g
r
if
i
ed
o
v
er
n
o
les
s
t
h
a
n
s
e
v
e
n
m
i
llio
n
y
ea
r
s
[
5
0
-
5
1
]
.
I
n
co
m
p
ar
is
o
n
,
i
t
to
o
k
h
o
m
o
s
ap
ien
s
f
o
u
r
h
u
n
d
r
ed
th
o
u
s
a
n
d
y
ea
r
s
to
e
m
er
g
e
in
Af
r
ica,
w
h
ile
it
is
p
lau
s
ib
le
t
h
at
s
u
p
e
r
in
telli
g
e
n
t
s
i
lico
n
-
b
as
ed
en
titi
e
s
w
i
ll
co
ex
is
t
w
it
h
h
u
m
a
n
s
w
it
h
i
n
t
h
e
n
ex
t
f
e
w
ce
n
t
u
r
ies
[
5
0
,
5
2
]
.
It
is
r
em
ar
k
ab
le,
g
i
v
en
th
a
t
ev
en
i
f
a
d
ig
ital
s
u
p
er
in
telli
g
en
ce
e
m
er
g
e
s
o
v
er
th
e
n
e
x
t
t
en
m
il
len
n
ia,
it
w
ill
s
til
l
r
ep
r
esen
t
les
s
th
a
n
o
n
e
-
m
illi
o
n
t
h
o
f
t
h
e
ag
e
o
f
o
u
r
u
n
i
v
er
s
e.
Ou
r
p
r
i
m
ar
y
o
b
j
ec
tiv
e
is
to
s
u
r
v
e
y
t
h
e
o
p
in
io
n
s
,
s
p
ec
i
f
icall
y
v
id
eo
g
a
m
er
s
,
co
n
ce
r
n
in
g
t
h
e
ar
tif
icial
g
en
er
al
in
telli
g
en
ce
an
d
s
u
p
er
in
telli
g
e
n
ce
.
We
,
b
ased
o
n
o
u
r
ar
ea
s
o
f
e
x
p
er
tis
e,
s
h
all
d
is
c
u
s
s
t
h
e
li
ter
atu
r
e
o
n
th
e
i
m
p
le
m
en
tatio
n
o
f
ar
ti
f
ici
al
in
te
lli
g
en
ce
in
m
ed
icin
e
an
d
s
o
ci
o
lo
g
y
.
W
e
c
h
o
s
e
v
id
eo
g
a
m
er
s
b
ec
au
s
e
we
o
p
in
e
th
at
th
e
y
ar
e
m
o
ti
v
ated
t
o
co
m
p
lete
s
u
r
v
e
y
s
i
n
co
n
n
ec
t
io
n
w
it
h
m
o
d
er
n
tec
h
n
o
lo
g
y
.
A
d
d
itio
n
al
l
y
,
v
id
eo
g
a
m
e
s
n
o
w
ad
a
y
s
in
cl
u
d
e
a
lo
t
o
f
tech
n
o
lo
g
ical
i
n
n
o
v
atio
n
s
,
in
cl
u
d
i
n
g
t
h
o
s
e
o
n
ar
tif
ic
ial
in
telli
g
e
n
ce
.
W
e
r
ea
s
o
n
th
at
r
ea
d
er
s
s
h
o
u
ld
ca
r
e
ab
o
u
t
v
id
eo
g
a
m
er
s
’
o
p
in
io
n
s
as
th
e
y
r
ep
r
esen
t
a
s
u
b
s
tan
tial
s
e
g
m
e
n
t
o
f
th
e
y
o
u
t
h
,
i
n
clu
d
i
n
g
th
o
s
e
f
r
o
m
t
h
e
d
ev
elo
p
in
g
w
o
r
ld
.
B
esid
es
,
w
e
o
p
in
e
t
h
at
t
h
e
y
m
a
y
h
a
v
e
d
if
f
er
e
n
t
o
p
in
io
n
s
b
ec
au
s
e
t
h
e
y
p
o
s
s
es
s
d
is
ti
n
ct
d
e
m
o
g
r
ap
h
ic
s
,
i
n
clu
d
i
n
g
ag
e,
d
is
tr
ib
u
t
io
n
o
f
g
e
n
d
er
,
an
d
et
h
n
o
g
r
ap
h
ic
p
ar
am
eter
s
.
T
o
th
e
b
est
o
f
o
u
r
k
n
o
w
led
g
e,
o
u
r
s
t
u
d
y
i
s
t
h
e
f
i
r
s
t
to
ex
p
lo
r
e
p
u
b
lic
o
p
i
n
io
n
s
,
u
s
in
g
s
u
r
v
e
y
t
o
o
ls
an
d
b
ig
d
ata,
in
co
n
n
ec
tio
n
w
i
th
d
ig
ita
l s
u
p
er
in
telli
g
e
n
ce
,
an
d
w
it
h
in
t
h
e
co
n
te
x
t o
f
th
e
Kar
d
ash
ev
s
ca
le.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
I
n
t J
A
r
ti
f
I
n
tell
,
Vo
l.
9
,
No
.
2
,
J
u
n
e
20
20
:
336
–
3
4
8
338
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
T
he
s
y
s
t
e
m
a
t
ic
re
v
iew
o
f
t
he
lite
ra
t
ure
Du
r
in
g
Sep
te
m
b
er
an
d
Octo
b
er
2
0
1
9
,
w
e
co
n
d
u
cted
a
p
r
ag
m
atic
r
ev
ie
w
o
f
th
e
d
atab
ase
s
o
f
th
e
p
ee
r
-
r
ev
ie
w
ed
p
u
b
lis
h
ed
liter
atu
r
e
in
an
ai
m
to
ass
es
s
th
e
e
x
ten
t
o
f
ar
tif
icial
i
n
telli
g
e
n
ce
an
d
m
ac
h
in
e
lear
n
in
g
-
b
ased
r
esear
ch
.
W
e
s
ea
r
ch
ed
E
m
b
ase
[
E
ls
ev
ier
Data
b
ase
|
Sco
p
u
s
]
,
P
u
b
Me
d
[
th
e
Un
i
t
ed
States
Natio
n
al
L
ib
r
ar
y
o
f
Me
d
ici
n
e]
,
an
d
t
h
e
C
o
c
h
r
an
e
L
ib
r
ar
y
[
t
h
e
C
o
ch
r
an
e
Data
b
a
s
e
o
f
S
y
s
te
m
atic
R
e
v
ie
w
s
|
t
h
e
C
o
ch
r
an
e
C
o
llab
o
r
atio
n
]
.
W
e
d
ep
lo
y
ed
an
ex
h
a
u
s
ti
v
e
c
o
m
b
i
n
atio
n
o
f
g
e
n
er
ic
ter
m
s
an
d
Me
SH
-
b
ased
k
e
y
w
o
r
d
s
,
as
w
ell
a
s
tr
u
n
ca
tio
n
s
,
s
h
u
f
f
led
w
it
h
B
o
o
lean
o
p
er
ato
r
s
.
2
.
2
.
T
he
s
urv
ey
Qu
a
n
tic
Dr
ea
m
,
a
v
id
eo
g
a
m
e
d
ev
elo
p
er
,
cr
ea
ted
th
e
titl
e
“De
tr
o
it:
B
ec
o
m
e
H
u
m
an
”,
in
w
h
ic
h
t
h
e
y
in
co
r
p
o
r
ated
an
elec
tr
o
n
ic
p
o
ll
[
5
3
]
.
T
h
e
w
o
r
ld
w
id
e
s
ales
o
f
th
e
v
id
eo
g
a
m
e
r
ea
ch
ed
a
p
p
r
o
x
im
a
tel
y
t
h
r
ee
m
illi
o
n
co
p
ies
,
m
o
s
t
o
f
w
h
ic
h
ar
e
in
A
s
ian
co
u
n
tr
ie
s
,
w
h
ich
in
d
ica
tes
th
at
th
e
p
o
ll
i
s
v
al
id
an
d
in
f
er
en
tial,
a
s
th
o
u
s
an
d
s
o
f
v
id
eo
g
a
m
in
g
en
t
h
u
s
ia
s
ts
co
m
p
leted
t
h
e
s
u
r
v
e
y
[
5
4
]
.
T
h
e
s
u
r
v
e
y
h
ad
te
n
ite
m
s
as
m
u
ltip
le
-
ch
o
ice
q
u
esti
o
n
s
[
5
5
]
.
B
ased
o
n
th
e
m
atic
a
n
al
y
s
i
s
,
s
ev
e
n
ar
ticle
s
o
f
th
e
p
o
ll
ass
o
ciate
w
it
h
ar
ti
f
icial
in
telli
g
e
n
ce
an
d
t
h
e
n
e
u
r
o
lo
g
y
co
n
ce
p
t
o
f
co
n
s
c
io
u
s
n
es
s
[
5
6
]
.
I
n
Au
g
u
s
t
2
0
1
9
,
w
e
co
m
m
u
n
icate
d
w
it
h
Qu
a
n
tic
Dr
ea
m
to
r
ep
licate
th
eir
s
u
r
v
e
y
f
o
r
d
is
tr
ib
u
tio
n
a
m
o
n
g
th
e
y
o
u
t
h
an
d
v
id
eo
g
a
m
er
s
in
P
o
lan
d
.
Du
r
in
g
t
h
e
4
th
q
u
ar
ter
o
f
2
0
1
9
,
w
e
d
e
v
elo
p
ed
an
d
d
is
tr
ib
u
t
ed
th
e
s
u
r
v
e
y
e
lectr
o
n
icall
y
v
ia
Go
o
g
le
Fo
r
m
s
.
Fo
r
ex
ter
n
a
l
v
alid
it
y
,
t
h
e
s
u
r
v
e
y
i
n
clu
d
ed
a
r
ep
lica
o
f
th
e
o
r
ig
in
a
l
te
n
ite
m
s
,
tr
a
n
s
lat
ed
in
to
t
h
e
P
o
lis
h
lan
g
u
a
g
e,
to
m
ap
o
p
in
io
n
s
o
n
ar
tif
ic
ial
g
e
n
er
al
i
n
tell
ig
en
ce
an
d
h
u
m
a
n
-
lev
el
m
ac
h
in
e
in
telli
g
en
ce
.
T
h
e
s
u
r
v
e
y
i
n
cl
u
d
ed
a
q
u
es
t
io
n
co
n
ce
r
n
in
g
t
h
e
b
elie
f
i
n
Go
d
(
Qu
esti
o
n
4
)
,
an
d
w
e
a
r
g
u
e
t
h
at
r
elig
io
u
s
b
ac
k
g
r
o
u
n
d
s
an
d
co
n
v
ict
io
n
s
ca
n
p
r
o
v
id
e
a
g
o
o
d
h
in
t
o
n
th
e
p
o
ten
tial
co
r
r
elatio
n
w
it
h
o
p
in
io
n
s
o
n
ar
tific
ia
l
in
telli
g
e
n
ce
a
n
d
ex
p
o
n
e
n
tial
tech
n
o
lo
g
ical
ad
v
a
n
ce
s
.
R
eli
g
io
u
s
i
n
d
iv
id
u
als
m
a
y
o
p
p
o
s
e
o
r
b
e
less
lib
er
al
co
n
ce
r
n
i
n
g
a
d
ig
ital s
u
p
er
in
tel
lig
e
n
ce
th
at
ca
n
b
e
s
el
f
-
a
w
ar
e
an
d
s
u
p
er
io
r
to
h
u
m
a
n
s
.
W
e
ch
o
s
e
to
d
is
tr
ib
u
te
o
u
r
s
u
r
v
e
y
i
n
P
o
lan
d
f
o
r
f
o
u
r
m
ai
n
r
ea
s
o
n
s
:
1
)
P
o
lan
d
is
a
f
o
r
m
er
l
y
o
cc
u
p
ied
n
atio
n
b
y
t
h
e
U
n
io
n
o
f
So
v
iet
So
cialist
R
ep
u
b
lics
(
US
S
R
)
[
in
v
ad
ed
b
y
t
h
e
So
v
iet
s
in
1
9
3
9
]
,
an
d
th
e
d
em
o
g
r
ap
h
ics
o
f
t
h
e
P
o
lis
h
p
o
p
u
latio
n
s
h
ar
e
s
s
i
m
ilar
itie
s
w
it
h
E
aster
n
E
u
r
o
p
e
an
co
u
n
tr
ies
.
2
)
B
esid
es,
th
e
astro
p
h
y
s
icis
t
Ni
k
o
lai
Kar
d
as
h
ev
w
h
o
p
io
n
ee
r
ed
th
e
Kar
d
ash
e
v
s
ca
le
w
as
a
So
v
iet
n
a
tio
n
al
.
3
)
W
e
h
ad
r
eliab
le
ac
ce
s
s
to
s
u
r
v
e
y
t
h
e
P
o
lis
h
p
o
p
u
latio
n
a
s
t
h
e
2
n
d
,
an
d
th
e
t
h
ir
d
au
th
o
r
o
f
t
h
is
s
tu
d
y
ar
e
P
o
lis
h
n
atio
n
al
s
an
d
ac
ad
e
m
ic
s
w
h
o
w
o
r
k
at
ter
tiar
y
ed
u
ca
t
io
n
al
i
n
s
tit
u
tes
i
n
P
o
lan
d
[
th
e
Un
iv
er
s
it
y
o
f
R
ze
s
zo
w
a
n
d
th
e
U
n
iv
er
s
it
y
o
f
R
ad
o
m
]
.
4
)
Ou
r
s
u
r
v
e
y
o
f
th
e
P
o
lis
h
p
o
p
u
latio
n
ca
n
b
e
a
g
o
o
d
r
ep
r
e
s
en
tat
iv
e
o
f
E
a
s
ter
n
E
u
r
o
p
e
an
d
th
e
v
a
s
t
Asi
atic
t
e
r
r
ito
r
ies
p
r
ev
io
u
s
l
y
o
cc
u
p
ied
b
y
t
h
e
f
o
r
m
er
So
v
iet
Un
io
n
,
th
er
eb
y
p
r
o
v
id
i
n
g
s
o
m
e
i
n
f
er
en
ce
w
h
e
n
co
m
p
a
r
in
g
w
ith
th
e
r
e
s
t
o
f
th
e
w
o
r
ld
.
4
)
Ou
r
p
r
elim
i
n
ar
y
d
ata
an
al
y
s
i
s
,
b
ased
o
n
Go
o
g
le
T
r
en
d
s
an
al
y
s
es,
co
n
f
i
r
m
ed
th
at
t
h
e
Kar
d
as
h
ev
s
ca
le
is
m
o
s
t
p
o
p
u
lar
a
m
o
n
g
n
atio
n
als
o
f
t
h
e
U
k
r
ain
e,
R
u
s
s
ia,
a
n
d
P
o
lan
d
,
as
w
ell
as
s
ev
er
al
o
th
er
co
u
n
tr
ies o
f
th
e
f
o
r
m
er
US
SR
.
2
.
3
.
G
o
o
g
le
-
ba
s
ed
a
na
ly
t
ics
T
o
m
ap
th
e
g
eo
g
r
ap
h
ic
d
is
tr
ib
u
tio
n
o
f
t
h
e
v
id
eo
g
a
m
er
s
w
h
o
r
esp
o
n
d
ed
to
th
e
s
u
r
v
e
y
,
w
e
u
s
ed
Go
o
g
le
T
r
en
d
s
f
o
r
a
r
etr
o
s
p
ec
tiv
e
lo
n
g
it
u
d
in
al
an
al
y
s
is
o
f
d
ata
[
5
7
]
.
A
s
n
ap
s
h
o
t
w
as
ta
k
en
o
n
th
e
2
6
th
o
f
Au
g
u
s
t
2
0
1
9
,
u
s
in
g
t
h
e
k
e
y
wo
r
d
“
Detr
o
it:
B
ec
o
m
e
Hu
m
a
n
”,
to
m
ap
th
e
s
ea
r
ch
v
o
lu
m
e
o
v
er
th
e
s
u
r
f
ac
e
w
eb
f
o
r
th
e
p
er
io
d
f
r
o
m
th
e
b
eg
i
n
n
in
g
o
f
2
0
1
7
to
t
h
e
c
u
r
r
en
t
d
ate.
W
e
also
i
m
p
le
m
en
t
ed
k
e
y
w
o
r
d
s
to
as
s
es
s
t
h
e
g
eo
g
r
ap
h
ic
m
ap
p
in
g
o
f
s
u
r
f
ac
e
w
eb
u
s
er
s
w
h
o
s
ea
r
c
h
ed
f
o
r
ar
tif
icial
g
e
n
er
al
in
telli
g
e
n
ce
an
d
r
elate
d
t
o
p
ics
s
in
ce
2
0
0
4
.
T
h
e
k
e
y
w
o
r
d
s
in
c
lu
d
ed
“A
r
ti
f
icial
g
en
er
al
i
n
tel
lig
e
n
ce
”,
“
E
x
is
te
n
tia
l
r
is
k
f
r
o
m
ar
tif
ic
ial
g
e
n
er
al
in
telli
g
e
n
c
e”
,
“
S
u
p
er
in
tel
lig
e
n
ce
”,
“
T
ec
h
n
o
lo
g
ical
s
i
n
g
u
la
r
it
y
”,
an
d
“Ka
r
d
ash
e
v
s
ca
le”
.
W
e
ex
p
lo
r
ed
th
e
s
ea
r
ch
v
o
lu
m
e
o
n
th
e
s
u
r
f
ac
e
w
eb
f
o
r
th
e
s
a
m
e
k
e
y
w
o
r
d
s
b
y
u
s
i
n
g
th
e
Ke
y
w
o
r
d
s
E
v
er
y
wh
er
e
A
d
d
-
o
n
f
o
r
th
e
Go
o
g
le
C
h
r
o
m
e
w
eb
b
r
o
w
s
er
[
v
er
s
io
n
7
7
.
0
.
3
8
6
5
.
9
0
(
Of
f
ic
ial
B
u
ild
)
(
6
4
-
b
it)]
[
5
8
]
.
Key
w
o
r
d
s
E
v
er
y
w
h
er
e
p
r
o
v
id
ed
d
ata
o
n
th
e
m
o
n
t
h
l
y
s
ea
r
ch
v
o
lu
m
e
a
s
w
e
ll a
s
t
h
e
c
o
s
t p
er
click
(
C
P
C
)
i
n
U
n
ited
States
d
o
llar
s
.
C
P
C
r
ep
r
esen
ts
t
h
e
a
m
o
u
n
t
t
h
at
ad
v
er
tis
er
s
ar
e
p
a
y
in
g
f
o
r
a
s
i
n
g
le
click
f
o
r
a
k
e
y
w
o
r
d
in
Go
o
g
le
A
d
W
o
r
d
s
[
5
8
].
T
h
e
s
ea
r
ch
ter
m
v
o
l
u
m
e
ca
n
p
r
o
v
id
e
an
esti
m
ate
o
f
t
h
e
in
ter
est
o
f
in
d
i
v
id
u
a
ls
i
n
s
p
ec
if
ic
t
o
p
ics.
C
P
C
m
etr
ic
s
also
r
ef
lect
t
h
e
in
ter
es
t o
f
t
h
e
p
o
p
u
latio
n
in
s
p
ec
i
f
ic
t
h
e
m
es.
T
h
e
h
ig
h
er
th
e
C
P
C
,
t
h
e
h
i
g
h
e
r
th
e
in
ter
es
t.
2
.
4
.
St
a
t
is
t
ica
l a
na
ly
s
is
,
et
hics
,
a
nd
t
he
lev
el
-
of
-
ev
idence
W
e
i
m
p
le
m
e
n
ted
t
h
e
Sta
tis
tic
al
P
ac
k
ag
e
f
o
r
th
e
So
cial
Scie
n
ce
s
[
I
B
M
-
SP
S
S
v
er
s
io
n
2
4
]
an
d
E
x
ce
l
[
Mic
r
o
s
o
f
t
Of
f
ice
2
0
1
6
]
w
it
h
in
te
g
r
ated
Data
An
al
y
s
i
s
T
o
o
lP
ak
ad
d
-
in
.
W
e
f
o
llo
w
ed
th
e
C
o
d
e
o
f
E
th
ics
o
f
th
e
W
o
r
ld
Me
d
ical
Ass
o
ciatio
n
(
Dec
lar
atio
n
o
f
Hel
s
in
k
i)
o
n
m
ed
ical
r
esear
c
h
in
v
o
l
v
in
g
h
u
m
a
n
s
u
b
j
ec
ts
,
E
U
Dir
ec
tiv
e
(
2
1
0
/6
3
/EU
)
o
n
th
e
p
r
o
tectio
n
o
f
an
i
m
als
u
s
ed
f
o
r
s
cien
tific
p
u
r
p
o
s
es,
Un
i
f
o
r
m
R
eq
u
ir
e
m
e
n
ts
f
o
r
m
an
u
s
cr
ip
ts
s
u
b
m
itted
to
b
io
m
ed
ical
j
o
u
r
n
al
s
a
n
d
t
h
e
et
h
ic
al
p
r
in
cip
les
d
e
f
i
n
ed
i
n
th
e
Fa
r
m
in
g
to
n
C
o
n
s
e
n
s
u
s
o
f
1
9
9
7
.
W
e
ev
alu
ated
th
e
le
v
el
-
of
-
ev
id
en
ce
ac
co
r
d
in
g
to
th
e
Ox
f
o
r
d
C
en
tr
e
f
o
r
E
v
id
en
ce
-
b
ased
Me
d
icin
e
[
Un
iv
er
s
it
y
o
f
O
x
f
o
r
d
,
2
0
1
6
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
r
ti
f
I
n
tell
I
SS
N:
2252
-
8938
C
o
n
flictin
g
o
p
in
io
n
s
in
co
n
n
e
ctio
n
w
ith
d
ig
ita
l su
p
erin
tellig
en
ce
(
A
h
med
A
l
-
I
ma
m
)
339
3.
RE
SU
L
T
S
3
.
1
.
T
he
l
it
er
a
t
ure
W
e
co
n
d
u
cted
a
s
y
s
te
m
atic
r
e
v
ie
w
o
f
th
e
liter
at
u
r
e
v
ia
C
o
ch
r
an
e
L
ib
r
ar
y
,
P
u
b
Me
d
,
an
d
E
m
b
a
s
e.
W
e
s
ea
r
ch
ed
f
o
r
in
d
ex
ed
p
u
b
licat
io
n
s
i
n
co
n
n
ec
t
io
n
w
it
h
m
ac
h
in
e
lear
n
i
n
g
an
d
ar
tific
ial
in
te
llig
e
n
ce
in
m
ed
ical
an
d
p
ar
am
ed
ical
r
esear
ch
F
ig
u
r
e
1
.
T
h
e
r
ev
ie
w
s
tr
ate
g
y
g
en
er
ated
an
i
m
p
r
ess
iv
e
to
tal
co
u
n
t
o
f
1
,
3
6
0
,
7
3
7
p
ap
er
s
d
is
tr
ib
u
ted
to
th
e
C
o
ch
r
an
e
L
ib
r
ar
y
o
f
S
y
s
te
m
atic
R
e
v
ie
w
s
[
6
,
3
8
2
(
0
.
4
7
%)]
,
th
e
Un
i
ted
States
Natio
n
al
L
ib
r
ar
y
o
f
Me
d
icin
e
[
1
,
1
9
3
,
0
4
2
(
8
7
.
6
8
%)]
,
an
d
E
m
b
ase
[
1
6
1
,
3
1
3
(
1
1
.
8
6
%)]
.
B
ased
o
n
th
e
co
m
b
i
n
atio
n
o
f
Me
SH
an
d
g
e
n
er
ic
ter
m
s
o
f
i
n
ter
est,
t
h
e
to
tal
n
u
m
b
er
o
f
p
u
b
licatio
n
s
w
as
2
0
9
,
9
5
0
allo
ca
ted
to
th
e
U
n
ited
States
N
L
M
[
1
5
3
,
7
6
0
(
7
3
.
2
4
%)]
,
an
d
E
m
b
ase
[
5
6
,
1
9
0
(
2
6
.
7
6
%)]
Fig
u
r
e
1
.
W
e
r
etr
iev
ed
a
v
ast
n
u
m
b
er
o
f
p
u
b
licatio
n
s
.
Hen
ce
,
w
e
a
n
tic
ip
ated
a
lar
g
e
p
er
ce
n
tag
e
o
f
f
alse
-
p
o
s
it
iv
e
d
ata
s
i
g
n
als,
i.e
.
,
p
u
b
licatio
n
s
t
h
a
t
w
er
e
n
o
t
r
elev
a
n
t
to
o
u
r
k
e
y
wo
r
d
s
-
b
ased
liter
atu
r
e
s
ea
r
ch
s
t
r
ateg
y
.
A
cc
o
r
d
in
g
l
y
,
w
e
f
ilter
ed
th
r
o
u
g
h
t
h
e
f
ir
s
t
t
w
o
h
u
n
d
r
ed
p
u
b
licatio
n
s
i
n
d
ex
ed
in
t
h
e
N
L
M
P
u
b
m
ed
d
atab
ases
.
Fig
u
r
e
1
.
E
x
p
lo
r
atio
n
o
f
m
ed
i
ca
l d
atab
ases
o
f
liter
atu
r
e
Fo
llo
w
i
n
g
a
f
u
ll
-
te
x
t
r
etr
iev
a
l
o
f
p
ap
er
s
o
f
in
ter
est,
o
n
l
y
f
i
f
teen
p
u
b
licat
io
n
s
(
7
.
5
%),
in
d
ex
ed
in
P
u
b
Me
d
,
im
p
le
m
e
n
ted
A
I
an
d
m
ac
h
i
n
e
lear
n
in
g
.
Mo
r
e
th
a
n
th
r
ee
q
u
ar
ter
s
(
8
6
.
6
7
%)
o
f
th
e
r
esear
ch
o
u
tp
u
t
w
er
e
o
r
ig
in
al
s
t
u
d
ies,
w
h
i
le
t
h
e
r
est
in
c
lu
d
ed
a
r
e
v
ie
w
ar
t
icle
an
d
a
n
ed
ito
r
ial
p
ap
er
.
E
ac
h
o
r
ig
i
n
al
s
t
u
d
y
d
ep
lo
y
ed
r
eg
r
es
s
io
n
m
o
d
els.
A
l
m
o
s
t
h
al
f
o
f
t
h
e
r
esear
c
h
o
u
tp
u
t
w
as
in
co
n
n
ec
tio
n
w
it
h
ca
r
d
io
v
a
s
cu
lar
d
is
ea
s
es
(
4
6
.
6
7
%),
in
ad
d
iti
o
n
to
p
r
ev
ale
n
t
r
is
k
y
m
ed
ic
al
co
n
d
itio
n
s
a
n
d
ag
r
ic
u
lt
u
r
e
-
r
elate
d
r
esear
ch
,
in
cl
u
d
in
g
ca
n
ce
r
o
u
s
co
n
d
itio
n
s
(
2
0
%)
an
d
v
eter
in
ar
y
ap
p
licatio
n
s
i
n
th
e
i
n
d
u
s
tr
y
(
1
3
.
3
3
%).
Oth
er
s
tu
d
ie
s
w
er
e
r
elate
d
to
ep
id
em
io
lo
g
ical
s
t
u
d
ies
an
d
ch
r
o
n
ic
i
n
f
ec
tio
n
s
,
i
n
cl
u
d
in
g
tu
b
er
cu
lo
s
is
(
2
0
%).
T
h
e
p
u
b
licatio
n
s
in
cl
u
d
ed
p
r
o
s
p
ec
tiv
e
co
h
o
r
ts
(
6
0
%),
an
ec
d
o
tal
r
ep
o
r
ts
(
1
3
.
3
3
%),
r
etr
o
s
p
ec
tiv
e
s
tu
d
ies
(
1
3
.
3
3
%),
cr
o
s
s
-
s
ec
tio
n
al
(
6
.
6
7
%)
an
d
r
ea
l
-
ti
m
e
an
a
l
y
s
es
(
6
.
6
7
%).
T
h
e
r
esear
ch
er
s
p
u
b
li
s
h
ed
m
o
s
t
o
f
th
ese
p
ap
er
s
in
2
0
1
8
(
2
6
.
6
7
%),
an
d
th
e
m
aj
o
r
it
y
o
f
t
h
e
r
esear
ch
o
r
i
g
i
n
ated
f
r
o
m
t
h
e
U
n
ited
State
s
(
4
6
.
6
7
%),
C
h
i
n
a
(
2
0
%),
an
d
Sp
ain
(
1
3
.
3
3
%),
in
ad
d
itio
n
to
Ger
m
a
n
y
,
t
h
e
Un
ited
Kin
g
d
o
m
,
an
d
th
e
Neth
er
la
n
d
s
.
3
.
2
.
T
he
s
urv
ey
W
o
r
ld
w
id
e
(
n
=3
,
5
7
1
)
,
6
8
%
o
f
in
d
iv
id
u
al
s
w
o
u
ld
co
n
s
id
er
h
av
in
g
a
r
elat
io
n
s
h
ip
w
it
h
a
n
a
n
d
r
o
id
th
at
lo
o
k
s
lik
e
a
h
u
m
a
n
,
6
2
%
th
i
n
k
t
h
at
tec
h
n
o
lo
g
y
co
u
ld
b
e
co
m
e
a
t
h
r
ea
t
to
h
u
m
a
n
it
y
,
7
0
%
f
i
n
d
th
e
m
s
el
v
es
d
ep
en
d
en
t
o
n
tec
h
n
o
lo
g
y
,
4
1
%
b
eliev
ed
i
n
Go
d
,
4
9
%
w
o
u
ld
let
an
a
n
d
r
o
id
tak
e
ca
r
e
o
f
th
e
ir
ch
ild
r
en
,
7
2
%
w
o
u
ld
ag
r
ee
to
b
e
o
p
er
a
ted
o
n
b
y
a
m
ac
h
i
n
e
in
ca
s
e
o
f
u
r
g
e
n
t
s
u
r
g
er
y
,
a
n
d
6
8
%
th
in
k
t
h
at
co
m
p
u
ter
s
co
u
ld
b
ec
o
m
e
s
el
f
-
a
w
ar
e
a
n
d
co
n
s
cio
u
s
as
s
h
o
w
n
in
T
ab
le
1
.
P
ar
ticip
an
ts
w
h
o
r
esp
o
n
d
ed
to
th
e
s
u
r
v
e
y
w
er
e
f
r
o
m
1
3
9
co
u
n
tr
ies.
T
h
e
to
p
t
en
co
n
tr
ib
u
ti
n
g
co
u
n
tr
ies
w
er
e
m
ai
n
l
y
f
r
o
m
t
h
e
f
ar
ea
s
t
A
u
s
tr
alasia,
i
n
cl
u
d
in
g
B
r
u
n
ei,
J
ap
an
,
Gu
a
m
,
So
u
t
h
Ko
r
ea
,
Ho
n
g
Ko
n
g
,
G
u
e
r
n
s
e
y
,
C
ze
c
h
ia,
Ma
ca
o
,
C
h
i
n
a,
an
d
Au
s
tr
alia.
C
o
u
n
tr
ies
f
r
o
m
t
h
e
Mid
d
le
E
ast
ac
co
u
n
ted
f
o
r
6
.
8
0
%,
an
d
th
o
s
e
f
r
o
m
t
h
e
n
o
r
t
h
o
f
Af
r
ica
an
d
th
e
Mid
d
le
E
as
t
s
u
m
m
ed
to
7
.
9
2
%
o
f
th
e
h
o
l
is
tic
m
ap
.
C
o
u
n
tr
ies
f
r
o
m
t
h
e
Mid
d
le
E
ast
an
d
th
e
No
r
t
h
o
f
A
f
r
ica
i
n
cl
u
d
ed
Ba
h
r
ain
,
Ku
w
ait,
th
e
Un
ited
A
r
ab
E
m
ir
ate
s
,
Qata
r
,
Sau
d
i
A
r
ab
ia,
T
u
r
k
e
y
,
L
eb
an
o
n
,
I
s
r
ae
l,
E
g
y
p
t,
L
ib
y
a,
I
r
an
,
T
u
n
is
ia,
J
o
r
d
an
,
L
ib
y
a,
O
m
a
n
,
I
r
aq
,
P
alestin
e,
Mo
r
o
cc
o
,
an
d
S
y
r
ia.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
I
n
t J
A
r
ti
f
I
n
tell
,
Vo
l.
9
,
No
.
2
,
J
u
n
e
20
20
:
336
–
3
4
8
340
T
ab
le
1
.
T
h
e
g
lo
b
al
s
u
r
v
e
y
Y
e
s
No
D
o
n
o
t
K
n
o
w
1
.
W
o
u
l
d
y
o
u
c
o
n
s
i
d
e
r
h
a
v
i
n
g
a
r
e
l
a
t
i
o
n
sh
i
p
w
i
t
h
a
n
a
n
d
r
o
i
d
t
h
a
t
l
o
o
k
s l
i
k
e
a
h
u
m
a
n
?
68
16
16
2
.
D
o
y
o
u
t
h
i
n
k
t
h
a
t
t
e
c
h
n
o
l
o
g
y
c
o
u
l
d
b
e
c
o
me
a
t
h
r
e
a
t
t
o
m
a
n
k
i
n
d
?
62
18
20
3
.
D
o
y
o
u
c
o
n
s
i
d
e
r
y
o
u
r
se
l
f
d
e
p
e
n
d
e
n
t
o
n
t
e
c
h
n
o
l
o
g
y
?
70
19
11
4
.
D
o
y
o
u
b
e
l
i
e
v
e
i
n
G
o
d
?
41
41
18
5
.
W
o
u
l
d
y
o
u
l
e
t
a
n
a
n
d
r
o
i
d
t
a
k
e
c
a
r
e
o
f
y
o
u
r
c
h
i
l
d
r
e
n
?
49
24
27
6
.
I
f
y
o
u
n
e
e
d
e
d
e
me
r
g
e
n
c
y
su
r
g
e
r
y
,
w
o
u
l
d
y
o
u
a
g
r
e
e
t
o
b
e
o
p
e
r
a
t
e
d
o
n
b
y
a
mac
h
i
n
e
?
72
12
16
7
.
D
o
y
o
u
t
h
i
n
k
o
n
e
d
a
y
mac
h
i
n
e
s c
o
u
l
d
d
e
v
e
l
o
p
c
o
n
s
c
i
o
u
s
n
e
ss?
68
16
16
A
n
d
r
o
i
d
s
F
l
y
i
n
g
C
a
r
s
S
p
a
c
e
T
o
u
r
i
sm
B
r
a
i
n
C
o
n
n
e
c
t
e
d
D
e
v
i
c
e
s
8
.
W
h
a
t
t
e
c
h
n
o
l
o
g
y
d
o
y
o
u
mo
st
a
n
t
i
c
i
p
a
t
e
?
35
13
21
31
1
H
o
u
r
2
H
o
u
r
s
4
H
o
u
r
s
M
o
r
e
D
o
n
o
t
K
n
o
w
9
.
H
o
w
mu
c
h
t
i
me
p
e
r
d
a
y
w
o
u
l
d
y
o
u
say
y
o
u
sp
e
n
d
o
n
a
n
e
l
e
c
t
r
o
n
i
c
d
e
v
i
c
e
?
1
9
22
61
7
A
B
o
o
k
A
C
e
l
l
p
h
o
n
e
P
e
n
a
n
d
P
a
p
e
r
A
C
o
n
so
l
e
A
n
I
n
st
r
u
me
n
t
1
0
.
I
f
y
o
u
h
a
d
t
o
l
i
v
e
o
n
a
d
e
se
r
t
e
d
i
sl
a
n
d
a
n
d
c
o
u
l
d
o
n
l
y
b
r
i
n
g
o
n
e
o
b
j
e
c
t
,
w
h
a
t
w
o
u
l
d
i
t
b
e
?
15
23
21
11
30
T
i
me
st
a
mp
:
A
u
g
u
st
2
6
th
,
2
0
1
9
.
N
u
me
r
i
c
a
l
v
a
l
u
e
s a
r
e
i
n
p
e
r
c
e
n
t
i
l
e
s.
I
n
P
o
lan
d
(
n
=4
5
5
)
,
1
5
%
o
f
i
n
d
iv
id
u
als
w
o
u
ld
co
n
s
id
er
h
a
v
in
g
a
r
elatio
n
s
h
ip
w
it
h
a
n
a
n
d
r
o
id
th
at
lo
o
k
s
lik
e
a
h
u
m
a
n
,
7
1
%
th
i
n
k
th
at
tec
h
n
o
lo
g
y
co
u
ld
b
ec
o
m
e
a
th
r
ea
t
to
h
u
m
an
k
i
n
d
,
5
5
%
f
ee
l
tec
h
n
o
lo
g
y
-
d
ep
en
d
en
t,
al
m
o
s
t t
w
o
-
t
h
ir
d
s
b
eliev
ed
in
Go
d
(
6
8
%),
2
1
% w
o
u
ld
allo
w
a
n
a
n
d
r
o
id
tak
e
c
ar
e
o
f
t
h
eir
ch
ild
r
en
,
6
2
%
w
o
u
ld
a
g
r
ee
to
b
e
o
p
er
ated
o
n
b
y
a
m
ac
h
in
e
i
n
ca
s
e
o
f
u
r
g
e
n
t
s
u
r
g
er
y
,
a
n
d
5
2
%
t
h
in
k
th
a
t
m
ac
h
in
e
s
co
u
ld
d
ev
elo
p
co
n
s
cio
u
s
n
es
s
as
s
h
o
w
n
i
n
T
ab
le
2
.
T
h
er
e
w
as
a
s
tatis
ticall
y
s
i
g
n
if
ica
n
t
d
i
f
f
er
en
ce
b
et
w
ee
n
th
e
in
ter
n
a
tio
n
al
s
u
r
v
e
y
an
d
t
h
e
P
o
lis
h
s
u
r
v
e
y
f
o
r
f
i
v
e
ite
m
s
o
u
t
o
f
te
n
as
s
h
o
w
n
in
T
ab
le
3
,
in
cl
u
d
in
g
h
av
in
g
a
r
elatio
n
s
h
ip
w
it
h
a
n
a
n
d
r
o
id
(
X
2
=2
3
.
0
3
;
d
f
=2
;
p
-
v
alu
e<
0
.
0
0
1
)
,
b
elief
in
Go
d
(
X
2
=
1
8
.
4
2
;
d
f
=
2
;
p
<0
.
0
0
1
)
,
allo
w
i
n
g
an
a
n
d
r
o
id
tak
e
ca
r
e
o
f
ch
ild
r
en
(
X
2
=
1
7
.
0
3
;
d
f
=2
;
p
<0
.
0
0
1
)
,
an
d
th
e
m
o
s
t
an
t
i
cip
ated
tech
n
o
lo
g
y
(X
2
=
1
2
.
8
4
;
d
f
=3
;
p
=0
.
0
0
5
)
.
I
t
ap
p
ea
r
s
th
at
P
o
lis
h
p
eo
p
le
ar
e
less
p
r
o
-
A
I
co
m
p
ar
ed
to
o
th
er
s
w
o
r
ld
w
id
e.
R
eli
g
io
u
s
,
e
th
n
o
g
r
ap
h
ic,
a
n
d
s
o
cio
cu
lt
u
r
al
asp
ec
ts
o
f
P
o
lis
h
s
o
ciet
y
m
a
y
co
n
tr
i
b
u
te
to
t
h
ese
d
is
cr
ep
a
n
cies,
th
at
r
esear
ch
er
s
ca
n
e
x
p
lo
r
e
in
f
u
t
u
r
e
s
t
u
d
ies
u
s
i
n
g
m
u
lti
v
ar
i
ate
an
al
y
tic
m
o
d
els.
T
ab
le
2
.
T
h
e
p
o
lis
h
s
u
r
v
e
y
Y
e
s
No
D
o
n
o
t
K
n
o
w
1
.
W
o
u
l
d
y
o
u
c
o
n
s
i
d
e
r
h
a
v
i
n
g
a
r
e
l
a
t
i
o
n
sh
i
p
w
i
t
h
a
n
a
n
d
r
o
i
d
t
h
a
t
l
o
o
k
s l
i
k
e
a
h
u
ma
n
?
15
72
13
2
.
D
o
y
o
u
t
h
i
n
k
t
h
a
t
t
e
c
h
n
o
l
o
g
y
c
o
u
l
d
b
e
c
o
me
a
t
h
r
e
a
t
t
o
m
a
n
k
i
n
d
?
71
14
15
3
.
D
o
y
o
u
c
o
n
s
i
d
e
r
y
o
u
r
se
l
f
d
e
p
e
n
d
e
n
t
o
n
t
e
c
h
n
o
l
o
g
y
?
55
31
14
4
.
D
o
y
o
u
b
e
l
i
e
v
e
i
n
G
o
d
?
68
16
16
5
.
W
o
u
l
d
y
o
u
l
e
t
a
n
a
n
d
r
o
i
d
t
a
k
e
c
a
r
e
o
f
y
o
u
r
c
h
i
l
d
r
e
n
?
21
59
20
6
.
I
f
y
o
u
n
e
e
d
e
d
e
me
r
g
e
n
c
y
su
r
g
e
r
y
,
w
o
u
l
d
y
o
u
a
g
r
e
e
t
o
b
e
o
p
e
r
a
t
e
d
o
n
b
y
a
ma
c
h
i
n
e
?
62
16
22
7
.
D
o
y
o
u
t
h
i
n
k
o
n
e
d
a
y
mac
h
i
n
e
s c
o
u
l
d
d
e
v
e
l
o
p
c
o
n
s
c
i
o
u
s
n
e
ss?
52
25
23
A
n
d
r
o
i
d
s
F
l
y
i
n
g
C
a
r
s
S
p
a
c
e
T
o
u
r
i
sm
B
r
a
i
n
C
o
n
n
e
c
t
e
d
D
e
v
i
c
e
s
8
.
W
h
a
t
t
e
c
h
n
o
l
o
g
y
d
o
y
o
u
mo
st
a
n
t
i
c
i
p
a
t
e
?
14
20
32
34
1
H
o
u
r
2
H
o
u
r
s
4
H
o
u
r
s
M
o
r
e
D
o
n
o
t
K
n
o
w
9
.
H
o
w
mu
c
h
t
i
me
p
e
r
d
a
y
w
o
u
l
d
y
o
u
say
y
o
u
sp
e
n
d
o
n
a
n
e
l
e
c
t
r
o
n
i
c
d
e
v
i
c
e
?
3
11
26
54
6
A
B
o
o
k
A
C
e
l
l
p
h
o
n
e
P
e
n
a
n
d
P
a
p
e
r
A
C
o
n
so
l
e
A
n
I
n
st
r
u
me
n
t
1
0
.
I
f
y
o
u
h
a
d
t
o
l
i
v
e
o
n
a
d
e
se
r
t
e
d
i
sl
a
n
d
a
n
d
c
o
u
l
d
o
n
l
y
b
r
i
n
g
o
n
e
o
b
j
e
c
t
,
w
h
a
t
w
o
u
l
d
i
t
b
e
?
16
45
15
6
18
D
a
t
e
o
f
S
u
r
v
e
y
D
i
st
r
i
b
u
t
i
o
n
:
f
r
o
m
2
5
th
S
e
p
t
e
mb
e
r
2
0
1
9
t
o
N
o
v
e
mb
e
r
6
th
,
2
0
1
9
.
N
u
me
r
i
c
a
l
v
a
l
u
e
s a
r
e
i
n
p
e
r
c
e
n
t
i
l
e
s.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
r
ti
f
I
n
tell
I
SS
N:
2252
-
8938
C
o
n
flictin
g
o
p
in
io
n
s
in
co
n
n
e
ctio
n
w
ith
d
ig
ita
l su
p
erin
tellig
en
ce
(
A
h
med
A
l
-
I
ma
m
)
341
T
ab
le
3
.
Glo
b
al
v
er
s
u
s
p
o
lis
h
s
u
r
v
e
y
: i
n
f
er
en
tia
l a
n
al
y
s
is
I
t
e
m
o
f
t
h
e
S
u
r
v
e
y
X
2
df
p
-
v
a
l
u
e
1
.
W
o
u
l
d
y
o
u
c
o
n
s
i
d
e
r
h
a
v
i
n
g
a
r
e
l
a
t
i
o
n
sh
i
p
w
i
t
h
a
n
a
n
d
r
o
i
d
t
h
a
t
l
o
o
k
s l
i
k
e
a
h
u
ma
n
?
2
3
.
0
3
2
<0
.
0
0
1
2
.
D
o
y
o
u
t
h
i
n
k
t
h
a
t
t
e
c
h
n
o
l
o
g
y
c
o
u
l
d
b
e
c
o
me
a
t
h
r
e
a
t
t
o
m
a
n
k
i
n
d
?
1
.
8
2
2
0
.
4
0
2
3
.
D
o
y
o
u
c
o
n
s
i
d
e
r
y
o
u
r
se
l
f
d
e
p
e
n
d
e
n
t
o
n
t
e
c
h
n
o
l
o
g
y
?
5
.
0
5
2
0
.
0
8
0
4
.
D
o
y
o
u
b
e
l
i
e
v
e
i
n
G
o
d
?
1
8
.
4
2
2
<0
.
0
0
1
5
.
W
o
u
l
d
y
o
u
l
e
t
a
n
a
n
d
r
o
i
d
t
a
k
e
c
a
r
e
o
f
y
o
u
r
c
h
i
l
d
r
e
n
?
1
7
.
0
3
2
<0
.
0
0
1
6
.
I
f
y
o
u
n
e
e
d
e
d
e
me
r
g
e
n
c
y
su
r
g
e
r
y
,
w
o
u
l
d
y
o
u
a
g
r
e
e
t
o
b
e
o
p
e
r
a
t
e
d
o
n
b
y
a
ma
c
h
i
n
e
?
2
.
2
7
2
0
.
3
2
2
7
.
D
o
y
o
u
t
h
i
n
k
o
n
e
d
a
y
mac
h
i
n
e
s c
o
u
l
d
d
e
v
e
l
o
p
c
o
n
s
c
i
o
u
s
n
e
ss?
5
.
3
8
2
0
.
0
6
8
8
.
W
h
a
t
t
e
c
h
n
o
l
o
g
y
d
o
y
o
u
mo
st
a
n
t
i
c
i
p
a
t
e
?
1
2
.
8
4
3
0
.
0
0
5
9
.
H
o
w
mu
c
h
t
i
me
p
e
r
d
a
y
w
o
u
l
d
y
o
u
say
y
o
u
sp
e
n
d
o
n
a
n
e
l
e
c
t
r
o
n
i
c
d
e
v
i
c
e
?
2
.
0
4
4
0
.
7
2
9
1
0
I
f
y
o
u
h
a
d
t
o
l
i
v
e
o
n
a
d
e
se
r
t
e
d
i
sl
a
n
d
a
n
d
c
o
u
l
d
o
n
l
y
b
r
i
n
g
o
n
e
o
b
j
e
c
t
,
w
h
a
t
w
o
u
l
d
i
t
b
e
?
1
2
.
6
7
4
0
.
0
1
3
S
i
g
n
i
f
i
c
a
n
t
p
-
v
a
l
u
e
s
a
r
e
i
n
b
o
l
d
.
3
.
3
.
G
o
o
g
le
-
ba
s
ed
a
na
ly
t
ics
W
e
ex
p
lo
r
ed
th
e
s
u
r
f
ac
e
w
e
b
v
ia
Ke
y
w
o
r
d
s
E
v
er
y
w
h
er
e
u
s
i
n
g
n
in
e
k
e
y
w
o
r
d
s
.
T
h
e
cu
m
u
lat
iv
e
m
o
n
t
h
l
y
s
ea
r
c
h
v
o
lu
m
e
w
as
1
,
4
9
2
,
7
7
0
,
allo
ca
ted
to
ar
tif
icial
g
e
n
er
al
i
n
telli
g
e
n
ce
[
8
,
1
0
0
(
0
.
5
4
%)]
,
ar
tif
icial
in
telli
g
e
n
ce
[
5
5
0
,
0
0
0
(
3
6
.
8
4
)
%],
co
n
s
cio
u
s
n
es
s
[
5
5
0
0
0
0
(
3
6
.
8
4
)
%],
ex
is
ten
tial
r
is
k
f
r
o
m
ar
ti
f
icia
l
g
e
n
er
al
in
telli
g
e
n
ce
[
4
0
(
0
.
0
0
%)]
,
h
u
m
an
-
le
v
el
m
ac
h
in
e
in
te
lli
g
en
ce
[
2
0
(
0
.
0
0
%)]
,
Kar
d
ash
ev
s
ca
le
[
1
1
0
(
0
.
0
1
%)]
,
m
ac
h
in
e
lear
n
i
n
g
[
3
6
8
,
0
0
0
(
2
4
.
6
5
%)
]
,
s
u
p
er
in
tell
ig
e
n
ce
[
9
,
9
0
0
(
0
.
6
6
%)]
,
an
d
tech
n
o
l
o
g
ical
s
in
g
u
lar
it
y
[
6
,
6
0
0
(
0
.
4
4
%)]
.
Su
r
f
ac
e
w
eb
u
s
er
s
s
ea
r
c
h
ed
m
o
s
tl
y
f
o
r
ar
ti
f
icial
in
te
lli
g
en
ce
,
co
n
s
cio
u
s
n
e
s
s
,
a
n
d
m
ac
h
in
e
lear
n
in
g
.
T
h
e
h
ig
h
es
t
co
s
t
p
er
click
(
C
P
C
)
w
a
s
f
o
r
ar
tif
icial
g
e
n
er
al
in
telli
g
en
ce
,
m
ac
h
i
n
e
lear
n
i
n
g
,
an
d
ar
tif
icial
i
n
tel
lig
e
n
ce
.
W
e
also
to
o
k
a
s
n
ap
s
h
o
t
o
f
Go
o
g
le
T
r
en
d
s
to
ass
e
s
s
th
e
s
p
atia
l
an
d
te
m
p
o
r
al
m
ap
p
in
g
o
f
s
u
r
f
ac
e
w
eb
u
s
er
s
w
h
o
ar
e
in
t
er
ested
in
th
e
co
n
ce
p
ts
o
f
ar
ti
f
icial
s
u
p
er
in
telli
g
en
ce
a
n
d
th
e
Kar
d
ash
ev
s
ca
le
Fig
u
r
es
2
an
d
3
.
C
o
n
ce
r
n
i
n
g
ar
tif
icial
s
u
p
er
in
te
lli
g
en
ce
,
t
h
e
to
p
co
n
tr
ib
u
t
in
g
co
u
n
tr
ie
s
w
er
e
I
r
an
,
Me
x
ico
,
C
o
lo
m
b
ia,
B
r
az
il,
I
n
d
ia,
P
er
u
,
So
u
th
Af
r
ica,
R
o
m
an
ia,
S
w
it
ze
r
lan
d
,
C
h
ile,
A
r
g
en
t
in
a,
t
h
e
Un
ited
Kin
g
d
o
m
,
Fin
la
n
d
,
Ger
m
an
y
,
Sp
ain
,
th
e
Un
i
ted
State
s
,
C
an
ad
a,
Au
s
t
r
alia,
P
h
ilip
p
in
es,
Ma
la
y
s
ia,
C
ze
ch
ia,
Den
m
ar
k
,
Vietn
a
m
,
No
r
w
a
y
,
S
w
ed
en
,
th
e
Net
h
er
lan
d
s
,
So
u
t
h
Ko
r
ea
,
Po
lan
d
,
Fra
n
ce
,
I
tal
y
,
an
d
T
u
r
k
e
y
.
Dev
elo
p
in
g
co
u
n
tr
ies
ac
co
u
n
t
ed
f
o
r
5
4
.
8
4
%
o
f
th
e
g
eo
g
r
ap
h
ic
m
ap
.
Su
r
p
r
is
in
g
l
y
,
I
r
an
r
an
k
ed
f
ir
s
t,
w
h
ile
o
th
er
co
u
n
tr
ie
s
f
r
o
m
th
e
Mi
d
d
le
E
ast
an
d
No
r
th
Af
r
ica
w
er
e
ab
s
e
n
t,
r
ef
lect
i
n
g
a
lac
k
o
f
a
w
ar
e
n
ess
o
n
ar
tif
icial
s
u
p
er
in
te
lli
g
en
ce
Fi
g
u
r
e
2
.
R
e
g
ar
d
in
g
t
h
e
Kar
d
as
h
ev
s
ca
le,
th
e
to
p
co
n
tr
ib
u
to
r
s
in
cl
u
d
ed
f
if
t
y
-
f
o
u
r
co
u
n
tr
ies.
T
h
e
f
ir
s
t
t
w
en
t
y
o
f
w
h
ic
h
i
n
cl
u
d
ed
Uk
r
ai
n
e,
R
u
s
s
ia,
P
o
lan
d
,
B
u
lg
ar
ia,
C
ze
c
h
ia,
Me
x
ico
,
C
o
lo
m
b
ia,
P
er
u
,
R
o
m
a
n
ia,
C
h
ile,
Sp
ain
,
C
h
in
a,
Fra
n
ce
,
I
tal
y
,
T
u
r
k
e
y
,
Gr
ee
ce
,
Ve
n
ez
u
ela,
B
r
az
il
,
S
w
itzer
lan
d
,
a
n
d
A
r
g
en
t
in
a.
De
v
elo
p
in
g
n
atio
n
s
ac
co
u
n
ted
f
o
r
5
9
.
2
6
%
o
f
th
e
m
ap
,
w
h
ile
I
r
an
w
a
s
ab
s
e
n
t,
as
w
ell
a
s
o
th
er
Mid
d
le
E
aster
n
an
d
No
r
th
Af
r
ican
co
u
n
tr
ie
s
ex
ce
p
t
th
e
Un
ited
A
r
ab
E
m
ir
ate
s
.
T
h
e
Kar
d
ash
ev
s
ca
le,
esp
o
u
s
ed
b
y
t
h
e
So
v
iet
astro
n
o
m
er
Nik
o
lai
Kar
d
as
h
ev
,
ap
p
ea
r
s
to
b
e
m
o
s
t
p
o
p
u
lar
am
o
n
g
n
atio
n
als
o
f
t
h
e
Uk
r
ain
e
a
n
d
R
u
s
s
ia,
as
w
ell
as
o
th
er
co
u
n
tr
ies o
f
th
e
f
o
r
m
er
USSR
.
Fig
u
r
e
2
.
Geo
-
Ma
p
p
in
g
v
ia
G
o
o
g
le
T
r
en
d
s
[
T
im
esta
m
p
: O
c
to
b
er
1
2
th
,
2
0
1
9
]
(
co
n
tin
u
e)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
I
n
t J
A
r
ti
f
I
n
tell
,
Vo
l.
9
,
No
.
2
,
J
u
n
e
20
20
:
336
–
3
4
8
342
Fig
u
r
e
2
.
Geo
-
Ma
p
p
in
g
v
ia
G
o
o
g
le
T
r
en
d
s
[
T
im
esta
m
p
: O
c
to
b
er
1
2
th
,
2
0
1
9
]
U
p
p
e
r
M
a
p
:
K
e
y
w
o
r
d
“
D
e
t
r
o
i
t
:
B
e
c
o
me
H
u
ma
n
”
[
T
i
me
st
a
mp
:
A
u
g
u
st
2
6
th
,
2
0
1
9
]
.
L
o
w
e
r
M
a
p
:
K
e
y
w
o
r
d
s “
A
r
t
i
f
i
c
i
a
l
g
e
n
e
r
a
l
i
n
t
e
l
l
i
g
e
n
c
e
”
,
“
Ex
i
st
e
n
t
i
a
l
r
i
s
k
f
r
o
m a
r
t
i
f
i
c
i
a
l
g
e
n
e
r
a
l
i
n
t
e
l
l
i
g
e
n
c
e
”
,
“
S
u
p
e
r
i
n
t
e
l
l
i
g
e
n
c
e
”
,
“
Te
c
h
n
o
l
o
g
i
c
a
l
si
n
g
u
l
a
r
i
t
y
”
,
a
n
d
“
K
a
r
d
a
s
h
e
v
sca
l
e
”
L
o
w
e
r
M
a
p
C
o
l
o
r
-
c
o
d
i
n
g
:
G
r
e
e
n
[
T
e
c
h
n
o
l
o
g
i
c
a
l
si
n
g
u
l
a
r
i
t
y
]
,
O
r
a
n
g
e
[
S
u
p
e
r
i
n
t
e
l
l
i
g
e
n
c
e
]
,
a
n
d
B
l
u
e
[
A
r
t
i
f
i
c
i
a
l
g
e
n
e
r
a
l
i
n
t
e
l
l
i
g
e
n
c
e
]
.
Go
o
g
le
T
r
en
d
s
g
en
er
ated
r
etr
o
s
p
ec
tiv
e
d
ata
o
n
f
iv
e
to
p
ics,
in
cl
u
d
in
g
ar
ti
f
icial
g
en
er
al
i
n
tellig
e
n
ce
,
th
e
e
x
is
te
n
tia
l
r
is
k
f
r
o
m
ar
t
if
i
cial
g
e
n
er
al
i
n
tell
ig
e
n
ce
,
s
u
p
er
in
telli
g
e
n
ce
,
tech
n
o
lo
g
ica
l
s
in
g
u
lar
it
y
,
a
n
d
t
h
e
Kar
d
ash
ev
s
ca
le.
T
h
e
m
o
s
t
p
o
p
u
lar
w
a
s
t
h
e
tech
n
o
lo
g
ical
s
i
n
g
u
lar
it
y
,
t
h
e
s
i
g
n
al
o
f
w
h
ich
p
ea
k
ed
in
J
u
l
y
2
0
1
0
,
o
r
ig
in
ati
n
g
p
r
in
cip
all
y
f
r
o
m
I
r
a
n
Fi
g
u
r
e
3
.
I
n
ter
e
s
ti
n
g
l
y
,
i
n
J
u
n
e
2
0
1
0
,
s
ev
er
al
r
e
s
ea
r
ch
er
s
i
n
f
o
r
m
ed
th
at
a
c
y
b
er
w
o
r
m
n
ic
k
n
a
m
ed
“
St
u
x
n
e
t”
s
tr
u
ck
t
h
e
I
r
an
ia
n
n
u
clea
r
f
ac
ili
t
y
[
5
9
-
6
0
]
.
A
b
l
o
g
w
eb
s
ite,
b
y
t
h
e
j
o
u
r
n
alis
t
B
r
ian
Kr
eb
s
,
r
ep
o
r
ted
th
e
in
c
id
en
t
o
n
th
e
1
5
th
o
f
J
u
l
y
2
0
1
0
,
w
h
ich
w
a
s
a
p
o
s
s
ib
le
j
o
in
t
o
p
er
atio
n
b
et
w
ee
n
t
h
e
U
n
ited
State
s
a
n
d
th
e
I
s
r
ae
li
i
n
tel
lig
e
n
ce
s
er
v
i
ce
s
[
6
1
]
.
Kasp
er
s
k
y
L
ab
o
r
ato
r
y
e
x
p
er
ts
esti
m
ated
th
at
St
u
x
n
e
t
p
r
o
p
ag
ated
o
v
er
elec
tr
o
n
ic
d
ev
ices
a
s
ea
r
l
y
as
Ma
r
ch
2
0
1
0
,
w
h
ile
t
h
e
f
ir
s
t
v
ar
ian
t
o
f
it
ap
p
ea
r
ed
in
J
u
n
e
2
0
0
9
[
6
2
-
6
3
]
.
T
h
is
ty
p
o
lo
g
y
o
f
c
y
b
er
attac
k
b
es
t
f
its
t
h
e
ca
te
g
o
r
y
o
f
n
o
n
-
co
n
tact
f
i
f
t
h
-
g
e
n
er
atio
n
w
ar
f
ar
e
[
3
-
4
]
.
Fig
u
r
e
3
.
T
h
e
in
ter
est o
f
s
u
r
f
a
ce
w
eb
u
s
er
s
[
Go
o
g
le
T
r
en
d
s
: 0
1
.
0
1
.
2
0
0
4
-
2
5
.
0
9
.
2
0
1
9
]
3
.
4
.
T
he
lev
el
-
of
-
ev
idence
A
cc
o
r
d
in
g
to
th
e
Ox
f
o
r
d
C
en
tr
e
f
o
r
E
v
id
en
ce
-
b
as
ed
Me
d
icin
e
(
OC
E
B
M)
,
o
u
r
s
tu
d
y
r
ep
r
esen
ts
an
a
m
alg
a
m
o
f
o
b
s
er
v
atio
n
al
cr
o
s
s
-
s
ec
tio
n
al
an
al
y
s
is
an
d
i
n
ter
n
et
s
n
ap
s
h
o
ts
(
Gr
ad
e
C
)
,
as
w
ell
as
lo
n
g
i
tu
d
i
n
al
an
a
l
y
s
es
v
ia
t
h
e
d
atab
ase
o
f
liter
at
u
r
e,
tr
en
d
s
d
atab
ases
,
an
d
th
e
G
o
o
g
le
s
ea
r
ch
en
g
i
n
e
0
20
40
60
80
100
120
2004
-
01
2004
-
07
2005
-
01
2005
-
07
2006
-
01
2006
-
07
2007
-
01
2007
-
07
2008
-
01
2008
-
07
2009
-
01
2009
-
07
2010
-
01
2010
-
07
2011
-
01
2011
-
07
2012
-
01
2012
-
07
2013
-
01
2013
-
07
2014
-
01
2014
-
07
2015
-
01
2015
-
07
2016
-
01
2016
-
07
2017
-
01
2017
-
07
2018
-
01
2018
-
07
2019
-
01
2019
-
07
A
r
t
i
f
i
c
i
a
l
g
e
n
e
r
a
l
i
n
t
e
l
l
i
g
e
n
c
e
Ex
i
st
e
n
t
i
a
l
r
i
sk
f
r
o
m a
r
t
i
f
i
c
i
a
l
g
e
n
e
r
a
l
i
n
t
e
l
l
i
g
e
n
c
e
S
u
p
e
r
i
n
t
e
l
l
i
g
e
n
c
e
Te
c
h
n
o
l
o
g
i
c
a
l
si
n
g
u
l
a
r
i
t
y
K
a
r
d
a
sh
e
v
sc
a
l
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
r
ti
f
I
n
tell
I
SS
N:
2252
-
8938
C
o
n
flictin
g
o
p
in
io
n
s
in
co
n
n
e
ctio
n
w
ith
d
ig
ita
l su
p
erin
tellig
en
ce
(
A
h
med
A
l
-
I
ma
m
)
343
(
Gr
ad
e
D)
[
6
4
]
.
A
cc
o
r
d
in
g
l
y
,
th
e
lev
el
-
of
-
e
v
id
en
ce
f
o
r
th
is
s
t
u
d
y
d
o
es
n
o
t
ap
p
l
y
to
t
h
e
ex
i
s
ti
n
g
s
ch
e
m
e
i
m
p
o
s
ed
b
y
th
e
O
C
E
B
M
in
2
0
1
6
.
4.
DIS
CU
SS
I
O
N
AND
CO
NC
L
US
I
O
N
A
r
ti
f
icial
i
n
telli
g
e
n
ce
,
a
n
e
x
p
o
n
en
tial
l
y
g
r
o
w
i
n
g
i
n
ter
d
is
ci
p
lin
ar
y
b
r
a
n
ch
o
f
co
m
p
u
ter
s
cien
ce
a
n
d
in
f
o
r
m
atio
n
tec
h
n
o
lo
g
ies,
r
eli
es
o
n
th
e
a
n
al
y
s
e
s
o
f
b
ig
d
ata
u
s
in
g
a
m
u
lti
tu
d
e
o
f
estab
li
s
h
ed
m
at
h
e
m
a
tica
l
m
o
d
el
s
,
in
cl
u
d
in
g
n
e
u
r
al
n
et
w
o
r
k
s
,
r
eg
r
ess
io
n
an
al
y
s
i
s
,
an
d
class
i
f
icatio
n
tr
ee
s
[
6
5
]
.
A
r
ti
f
icial
i
n
tell
ig
e
n
ce
atte
m
p
ts
to
m
an
e
u
v
er
to
w
ar
d
s
th
e
lo
w
es
t
er
r
o
r
o
f
p
r
ed
icti
o
n
s
b
y
i
m
p
le
m
en
ti
n
g
g
r
ad
ie
n
t
d
escen
t
alg
o
r
it
h
m
s
an
d
o
th
er
p
r
in
cip
les
o
f
m
ac
h
in
e
lear
n
in
g
[
6
5
-
6
8
]
.
AI
is
m
an
d
a
to
r
y
f
o
r
ap
p
licatio
n
s
r
elate
d
to
th
e
s
p
atio
-
te
m
p
o
r
al
d
escr
ip
tio
n
a
n
d
p
r
ed
ictio
n
o
f
p
h
en
o
m
en
a
o
f
i
n
ter
est,
i
n
cl
u
d
in
g
ep
id
e
m
io
lo
g
ical
an
d
d
i
g
ita
l
ep
id
em
io
lo
g
ical
in
v
est
ig
at
io
n
s
i
n
m
ed
ici
n
e
[
2
1
,
6
6
-
7
1
]
.
T
h
e
in
f
r
astru
c
tu
r
e
o
f
b
i
g
d
a
ta
u
p
o
n
w
h
ich
AI
alg
o
r
ith
m
s
o
p
er
ate
is
th
e
s
a
m
e
as
t
h
o
s
e
d
esi
g
n
ated
f
o
r
cla
s
s
ical
ep
id
e
m
io
lo
g
y
a
n
d
d
i
g
it
al
ep
id
e
m
io
lo
g
ica
l
r
esear
ch
[
7
2
]
.
R
esear
ch
er
s
ca
n
r
etr
iev
e
d
ata
u
s
i
n
g
s
u
r
v
e
y
to
o
ls
an
d
i
n
ter
n
et
s
n
ap
s
h
o
ts
,
lo
n
g
itu
d
in
a
l
an
d
cr
o
s
s
-
s
ec
tio
n
al
s
tu
d
ies,
a
n
al
y
s
es
o
f
w
eb
-
b
ased
s
o
cial
n
e
t
w
o
r
k
s
,
an
d
elec
tr
o
n
ic
co
m
m
er
ce
w
eb
s
ites
an
al
y
tic
s
o
f
th
e
s
u
r
f
ac
e
as
w
ell
as t
h
e
d
ee
p
w
e
b
,
in
clu
d
in
g
t
h
e
in
f
a
m
o
u
s
D
ar
k
n
et
h
y
p
er
m
ar
k
et
[
7
2
-
7
5
]
.
Mo
r
e
th
an
s
i
x
t
y
y
ea
r
s
a
g
o
,
J
o
h
n
Mc
C
ar
t
h
y
,
a
p
io
n
ee
r
o
f
t
h
e
co
n
ce
p
t
o
f
cr
ea
tin
g
i
n
tel
lig
e
n
t
co
m
p
u
ter
p
r
o
g
r
am
s
,
i
n
i
tia
ted
r
esear
ch
o
n
h
is
p
r
o
j
ec
t
an
d
p
r
esen
ted
th
e
p
r
o
ce
s
s
o
f
i
m
p
le
m
en
tin
g
h
is
id
ea
i
n
th
e
f
o
llo
w
in
g
w
a
y
:
“
Fo
r
th
e
p
r
esen
t
p
u
r
p
o
s
e
th
e
ar
tific
ial
in
tel
lig
e
n
ce
p
r
o
b
lem
is
ta
k
e
n
to
b
e
th
at
o
f
m
a
k
i
n
g
a
m
ac
h
in
e
b
eh
a
v
e
in
w
a
y
s
th
a
t
w
o
u
ld
b
e
ca
lled
in
tellig
en
t
if
a
h
u
m
an
w
er
e
s
o
b
eh
av
i
n
g
.
”
[
7
6
]
.
A
cc
o
r
d
in
g
to
T
o
b
y
W
al
s
h
(
2
0
1
7
)
,
Mc
C
ar
th
y
an
d
h
i
s
co
llea
g
u
es
in
t
h
e
f
a
m
o
u
s
Dar
t
m
o
u
th
S
u
m
m
er
R
esear
c
h
P
r
o
j
ec
t
d
r
ea
m
ed
o
f
d
escr
ib
in
g
t
h
e
p
r
o
ce
s
s
o
f
lear
n
i
n
g
a
n
d
o
th
er
asp
ec
ts
o
f
h
u
m
an
in
te
lli
g
en
ce
w
it
h
e
n
o
u
g
h
p
r
ec
is
io
n
to
b
e
s
im
u
lated
b
y
a
co
m
p
u
t
er
,
ex
p
licitl
y
,
tr
an
s
lati
n
g
t
h
i
n
k
in
g
i
n
to
co
m
p
u
ti
n
g
[
3
4
-
3
5
]
.
Six
d
ec
ad
es
later
,
r
esear
ch
er
s
h
a
v
e
m
ad
e
s
ig
n
i
f
ica
n
t
p
r
o
g
r
ess
in
th
is
f
ie
ld
o
f
s
cien
ce
.
T
h
ese
in
clu
d
e,
a
m
o
n
g
o
th
er
s
,
th
e
co
n
s
tr
u
ct
io
n
o
f
t
h
e
i
n
tel
lig
e
n
t
r
o
b
o
t
Sh
a
k
e
y
[
7
7
]
,
th
e
Den
d
r
al
p
r
o
j
ec
t,
w
h
ic
h
a
i
m
e
d
to
en
co
d
e
ex
p
er
t
k
n
o
w
led
g
e
i
n
co
m
p
u
ter
s
y
s
te
m
s
,
a
n
d
t
h
e
cr
ea
tio
n
o
f
t
h
e
c
h
ess
-
p
la
y
i
n
g
co
m
p
u
ter
p
r
o
g
r
a
m
Dee
p
B
lu
e
b
y
I
B
M,
w
h
ic
h
d
ef
ea
ted
Gar
r
y
Ka
s
p
ar
o
v
in
1
9
9
7
[
7
8
-
7
9
]
.
T
h
e
d
y
n
a
m
ic
d
ev
elo
p
m
e
n
t
o
f
t
h
e
s
cien
ce
o
f
ar
ti
f
icia
l
in
te
lli
g
e
n
ce
g
av
e
r
is
e
to
s
e
v
er
al
co
n
ce
r
n
s
t
h
at
p
o
in
t
o
u
t
t
h
e
r
is
k
s
as
s
o
ciate
d
w
it
h
i
ts
e
x
p
o
n
en
tial
p
r
o
g
r
es
s
[
8
0
-
8
2
]
.
T
h
e
in
d
icate
d
A
I
le
v
els
o
f
d
ev
elo
p
m
en
t
f
o
r
t
h
e
i
n
cr
ea
s
in
g
ca
p
ab
ilit
ie
s
o
f
th
in
k
i
n
g
m
ac
h
in
e
s
ar
e
as
f
o
llo
w
s
:
w
ea
k
A
I
(
i.e
.
,
ar
ti
f
ici
al
in
telli
g
en
ce
t
h
at
i
s
i
n
f
er
io
r
to
a
h
u
m
a
n
)
;
g
en
er
al
ar
tific
ial
in
te
lli
g
en
ce
(
i.e
.
,
i.e
.
,
ar
tif
icial
i
n
tel
lig
e
n
ce
t
h
at
is
eq
u
iv
ale
n
t
to
a
h
u
m
an
)
;
th
e
s
tr
o
n
g
A
I
a
n
d
s
u
p
er
in
telli
g
e
n
ce
(
i.e
.
,
m
ac
h
i
n
e
s
w
it
h
s
el
f
-
a
w
ar
en
e
s
s
a
n
d
s
u
p
er
io
r
in
telli
g
e
n
ce
to
a
h
u
m
an
)
[
3
5
,
8
3
]
.
T
h
e
r
ea
s
o
n
f
o
r
t
h
ese
d
an
g
er
s
is
p
r
ec
is
e
l
y
th
e
f
ea
r
o
f
i
n
telli
g
e
n
t
an
d
co
n
s
cio
u
s
m
ac
h
i
n
es,
w
h
ic
h
,
b
y
ac
h
iev
in
g
t
h
e
p
o
ten
tial
o
f
s
u
p
er
i
n
telli
g
e
n
ce
,
w
ill
b
e
ab
le
to
im
p
r
o
v
e
t
h
e
m
s
el
v
es
w
i
th
o
u
t
a
n
y
h
u
m
a
n
i
n
ter
v
e
n
tio
n
[
3
5
]
.
Nev
er
th
e
less
,
a
cc
o
r
d
in
g
to
Ka
p
lan
[
8
3
]
,
o
n
l
y
en
g
in
ee
r
i
n
g
er
r
o
r
s
o
r
p
o
o
r
ly
d
esig
n
ed
p
r
o
j
e
cts
r
elate
d
to
th
e
d
ev
elo
p
m
e
n
t o
f
A
I
ca
n
l
ea
d
to
u
n
in
ten
d
ed
o
r
u
n
k
n
o
w
n
co
n
s
eq
u
e
n
ce
s
.
Ma
c
h
in
e
s
m
a
y
h
av
e
t
h
e
ab
ilit
y
to
s
et
a
n
d
m
o
d
i
f
y
p
lan
n
ed
o
b
j
ec
tiv
es,
b
u
t
th
e
s
co
p
e
o
f
t
h
is
ab
ili
t
y
w
i
ll a
l
w
a
y
s
b
e
l
i
m
ited
to
t
h
e
m
ai
n
g
o
als
o
f
t
h
e
d
esig
n
p
h
ase
[
8
3
]
.
T
h
e
m
is
ta
k
e
th
at
Kap
la
n
p
o
in
ted
o
u
t
w
o
u
ld
b
e
to
d
esig
n
a
m
ac
h
in
e
w
h
o
s
e
p
r
o
g
r
am
w
o
u
ld
co
n
s
is
t
o
f
m
ai
n
tai
n
i
n
g
its
ex
is
ten
ce
at
all
co
s
ts
,
f
o
r
ex
a
m
p
le,
i
n
clu
d
i
n
g
t
h
e
an
n
i
h
ilat
io
n
o
f
h
u
m
a
n
k
i
n
d
.
He
co
n
s
id
er
s
s
u
ch
a
s
ce
n
ar
io
p
o
s
s
ib
le,
b
u
t
as
h
e
p
o
in
ts
o
u
t,
it
w
il
l
n
o
t
b
e
an
u
n
f
o
r
eseen
ev
o
lu
tio
n
r
es
u
lti
n
g
f
r
o
m
th
e
s
el
f
-
d
ev
elo
p
m
en
t
o
f
th
i
n
k
i
n
g
m
ac
h
i
n
es,
b
u
t
o
n
l
y
a
h
u
m
a
n
-
m
ad
e
er
r
o
r
[
8
3
]
.
A
cc
o
r
d
in
g
to
W
an
g
an
d
Go
er
tzel
(
2
0
0
7
)
,
t
h
e
ter
m
“
ar
ti
f
icial
g
e
n
er
al
i
n
telli
g
en
ce
”
d
o
es
n
o
t
h
av
e
a
p
r
ec
is
e
d
ef
in
i
tio
n
.
Ho
w
e
v
er
,
it
e
m
p
h
a
s
izes
th
e
g
e
n
er
ic
n
a
tu
r
e
o
f
t
h
e
d
esi
r
ed
ca
p
ab
ilit
ies
o
f
t
h
e
s
y
s
t
e
m
s
u
n
d
er
s
t
u
d
y
,
co
m
p
ar
ed
to
n
ar
r
o
w
A
I
,
w
h
i
ch
f
o
cu
s
e
s
o
n
s
y
s
te
m
s
w
it
h
h
ig
h
l
y
s
p
ec
ialized
i
n
telli
g
e
n
ce
an
d
s
k
ill
s
[
8
4
-
8
5
]
.
T
h
e
id
ea
is
to
co
n
s
tr
u
ct
m
ac
h
in
e
s
t
h
at
ar
e
ca
p
ab
le
o
f
w
o
r
k
in
g
o
n
a
n
y
p
r
o
b
le
m
at
a
h
u
m
an
-
l
e
v
el
o
r
ev
en
a
s
u
p
r
e
m
e
le
v
el
o
f
i
n
telli
g
e
n
ce
.
Ho
w
e
v
er
,
A
GI
d
o
es
n
o
t
a
s
s
u
m
e
t
h
at
th
in
k
i
n
g
m
ac
h
in
e
s
w
i
ll
b
e
s
el
f
-
a
w
ar
e
a
n
d
b
ec
o
m
e
a
t
h
r
ea
t
to
p
eo
p
le
[
3
5
]
.
A
GI
is
a
f
o
r
esee
n
p
r
o
j
ec
t
t
h
at
in
v
o
l
v
es
t
h
e
cr
ea
tio
n
o
f
a
m
ac
h
in
e
t
h
at
h
as
a
v
ir
tu
a
l
p
er
s
o
n
al
it
y
,
au
to
n
o
m
y
,
an
d
t
h
e
ab
ilit
y
to
m
ai
n
tai
n
a
n
d
i
m
p
r
o
v
e
its
el
f
,
ab
o
v
e
all,
to
lear
n
a
n
d
i
n
teg
r
ate
k
n
o
w
led
g
e
f
r
o
m
all
t
h
e
f
o
r
m
s
o
f
d
ata
th
e
y
an
al
y
ze
[
8
6
]
.
C
u
r
r
en
tl
y
,
p
r
o
g
r
a
m
m
er
s
u
s
e
n
ar
r
o
w
A
I
in
t
h
e
cr
ea
tio
n
o
f
ch
a
tb
o
ts
a
n
d
o
th
er
to
o
ls
f
o
r
co
m
m
u
n
icati
n
g
w
i
th
p
eo
p
le,
w
h
er
e
th
e
y
a
n
al
y
ze
th
o
u
s
an
d
s
o
f
te
x
ts
to
lear
n
ab
o
u
t
h
u
m
a
n
e
m
o
tio
n
s
an
d
b
eh
a
v
io
r
s
,
w
h
ich
th
e
y
ar
e
th
e
n
ab
le
to
r
ep
licate,
ad
j
u
s
tin
g
t
h
e
a
n
s
w
er
s
to
s
p
ec
if
ic
s
itu
a
tio
n
s
[
8
7
]
.
T
h
e
cu
r
r
en
t
r
ate
o
f
d
ev
elo
p
m
e
n
t
o
f
ar
ti
f
icial
i
n
telli
g
e
n
ce
s
y
s
te
m
s
i
s
s
o
d
y
n
a
m
ic
th
at
a
n
y
atte
m
p
t
to
s
u
m
m
ar
ize
p
r
o
g
r
ess
in
t
h
i
s
f
ield
is
o
u
t
o
f
d
ate
af
ter
a
f
e
w
m
o
n
t
h
s
.
I
n
t
h
eir
v
ie
w
p
o
i
n
t
ar
ticle
,
Katj
a
Gr
ac
e
et
al.
[
8
8
]
b
eliev
e
th
at
th
er
e
i
s
a
5
0
%
c
h
an
ce
th
at
A
I
w
i
ll
i
n
e
v
itab
l
y
o
u
tp
er
f
o
r
m
h
u
m
a
n
s
in
all
task
s
w
it
h
in
4
5
y
ea
r
s
,
a
n
d
i
t
w
il
l
au
to
m
at
e
all
h
u
m
an
j
o
b
s
w
it
h
i
n
1
2
0
y
ea
r
s
f
r
o
m
n
o
w
.
I
n
J
u
l
y
2
0
1
9
,
Mic
r
o
s
o
f
t
an
n
o
u
n
ce
d
th
at,
to
g
eth
er
w
i
th
Op
e
n
A
I
,
it
in
ten
d
s
to
w
o
r
k
o
n
t
h
e
d
ev
elo
p
m
en
t
o
f
A
I
,
i
n
v
e
s
ti
n
g
m
o
r
e
t
h
an
o
n
e
b
illi
o
n
d
o
llar
s
in
th
i
s
p
r
o
j
ec
t.
A
cc
o
r
d
in
g
to
th
e
C
E
O
o
f
Op
en
A
I
,
Sa
m
Alt
m
an
,
t
h
e
cr
ea
tio
n
o
f
A
GI
co
u
ld
b
e
th
e
m
o
s
t
p
iv
o
tal
ev
en
t
in
h
u
m
a
n
h
i
s
to
r
y
[
8
9
]
.
Oth
er
in
d
icato
r
s
o
f
a
tr
an
s
itio
n
to
w
ar
d
s
a
g
en
er
al
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
I
n
t J
A
r
ti
f
I
n
tell
,
Vo
l.
9
,
No
.
2
,
J
u
n
e
20
20
:
336
–
3
4
8
344
f
o
r
m
o
f
ar
ti
f
icial
i
n
telli
g
en
ce
,
in
cl
u
d
e
t
h
e
e
m
er
g
e
n
ce
o
f
“
a
u
to
m
a
tic
m
ac
h
in
e
le
ar
n
in
g
”
(
a
u
to
-
M
L
)
,
an
e
x
a
m
p
le
o
f
w
h
ic
h
is
th
e
G
o
o
g
le’
s
A
I
s
u
cc
es
s
f
u
ll
y
co
d
in
g
it
s
o
w
n
“A
I
C
h
ild
”
v
ia
a
r
ei
n
f
o
r
ce
m
en
t
lear
n
i
n
g
m
o
d
ali
t
y
[
9
0
-
9
1
]
.
Oth
er
atte
m
p
ts
ar
e
th
o
s
e
o
f
B
o
s
to
n
D
y
n
a
m
ics,
ac
q
u
ir
ed
b
y
Go
o
g
le
X
in
2
0
1
3
,
to
b
u
ild
r
o
b
o
ts
,
in
clu
d
in
g
a
p
leth
o
r
a
o
f
ad
v
an
ce
d
h
u
m
a
n
o
id
r
o
b
o
tics
[
9
2
]
.
B
y
t
h
e
en
d
o
f
t
h
e
las
t
Mille
n
iu
m
a
n
d
in
co
n
n
ec
tio
n
w
it
h
th
e
ap
p
licatio
n
o
f
A
I
in
m
e
d
ical
an
d
p
ar
am
ed
ical
r
esear
c
h
,
Fab
ia
n
et
al.
[
9
3
]
d
ev
elo
p
ed
a
p
r
e
d
ictio
n
m
o
d
el
f
o
r
b
r
ea
s
t
ca
n
ce
r
u
s
i
n
g
r
an
d
o
m
p
er
iar
eo
lar
f
in
e
-
n
ee
d
le
asp
ir
atio
n
c
y
to
lo
g
y
a
n
d
th
e
Gail
r
is
k
m
o
d
el.
I
n
2
0
0
2
,
Mo
o
n
s
an
d
c
o
lleag
u
e
s
w
er
e
ab
le
to
p
r
ed
ict
s
tr
o
k
e
in
th
e
g
e
n
er
al
E
u
r
o
p
ea
n
p
o
p
u
latio
n
ac
co
r
d
in
g
to
s
p
ec
if
ic
p
ar
a
m
e
ter
s
,
in
clu
d
i
n
g
th
e
f
ib
r
in
o
g
e
n
le
v
el
an
d
E
C
G
ch
ar
ac
ter
is
tic
s
[
9
4
]
.
L
u
q
u
er
o
et
al.
[
9
5
]
w
er
e
ab
le
to
an
aly
ze
th
e
tr
e
n
d
s
an
d
s
ea
s
o
n
ali
t
y
o
f
t
u
b
er
cu
lo
s
is
i
n
Sp
ain
f
o
r
t
h
e
p
er
io
d
1
9
9
6
-
2
0
0
4
b
y
u
s
i
n
g
d
ata
f
r
o
m
t
h
e
n
atio
n
al
s
u
r
v
eilla
n
ce
n
et
w
o
r
k
.
I
n
2
0
1
1
,
I
to
an
d
ass
o
ciate
s
p
io
n
ee
r
ed
a
ca
r
d
io
v
asc
u
lar
r
is
k
m
o
d
el
f
o
r
as
y
m
p
to
m
a
t
ic
in
d
i
v
id
u
al
s
w
it
h
ath
er
o
s
cler
o
s
is
i
n
co
n
n
ec
tio
n
w
it
h
c
y
s
tati
n
-
C
an
d
cr
ea
tin
in
e
[
9
6
]
.
Xu
et
al.
[
9
7
]
cr
ea
te
d
a
n
d
ep
id
em
io
lo
g
ical
-
m
o
lecu
lar
s
y
s
te
m
f
o
r
an
al
y
zi
n
g
th
e
r
elatio
n
s
h
ip
b
et
w
ee
n
s
m
o
k
in
g
,
E
p
s
tein
-
B
ar
r
v
ir
u
s
ac
ti
v
atio
n
,
an
d
th
e
r
is
k
o
f
n
a
s
o
p
h
ar
y
n
g
ea
l
ca
r
cin
o
m
a.
I
n
2
0
1
5
,
th
e
Sp
an
is
h
w
o
r
k
g
r
o
u
p
lead
b
y
T
o
r
r
es
m
an
ag
ed
to
g
e
n
er
ate
a
s
y
n
d
r
o
m
ic
s
u
r
v
e
illa
n
ce
s
y
s
te
m
b
ased
o
n
n
ea
r
r
ea
l
-
ti
m
e
ca
t
tle
m
o
r
talit
y
m
o
n
i
to
r
in
g
[
9
8
]
.
I
n
th
e
s
a
m
e
y
ea
r
i
n
C
h
i
n
a,
Ho
u
an
d
co
lleag
u
es
e
x
p
lo
r
ed
th
e
p
r
ed
ictiv
e
v
alu
e
o
f
h
i
g
h
-
s
e
n
s
iti
v
it
y
C
-
r
ea
ctiv
e
p
r
o
tein
in
m
id
d
le
-
ag
ed
in
d
i
v
id
u
a
ls
w
i
th
p
er
ip
h
e
r
al
ar
ter
io
s
cler
o
s
is
[
9
9
]
.
On
e
y
ea
r
later
,
H
w
a
n
g
a
n
d
co
lleag
u
es
in
v
est
ig
ated
th
e
ef
f
ec
t
o
f
i
n
cr
ea
s
ed
v
i
s
ce
r
al
ad
ip
o
s
e
tis
s
u
e
a
s
an
i
n
d
ep
en
d
en
t
p
r
ed
icto
r
f
o
r
th
e
ath
er
o
g
e
n
esi
s
[
1
0
0
]
.
Do
n
g
e
t
al.
[
1
0
1
]
w
er
e
s
u
cc
ess
f
u
l
i
n
ex
tr
ap
o
lati
n
g
a
r
is
k
p
r
ed
ictio
n
m
o
d
el
f
o
r
B
ar
r
et
t's
eso
p
h
ag
u
s
a
n
d
eso
p
h
ag
ea
l
ad
en
o
ca
r
ci
n
o
m
a
b
ased
o
n
g
en
etic,
cli
n
ical,
a
n
d
d
e
m
o
g
r
ap
h
ic
s
,
as
w
ell
a
s
lif
e
s
t
y
le
f
ac
to
r
s
.
I
n
th
e
s
a
m
e
y
ea
r
,
Steele
a
n
d
et
al.
[
1
0
2
]
co
n
clu
d
ed
th
at
m
ac
h
in
e
lear
n
i
n
g
m
o
d
els
d
ep
lo
y
ed
f
o
r
elec
tr
o
n
ic
h
ea
lt
h
r
ec
o
r
d
s
co
u
ld
o
u
tp
er
f
o
r
m
co
n
v
en
t
io
n
al
s
u
r
v
iv
a
l
m
o
d
els
f
o
r
p
r
ed
ictin
g
p
atien
t
m
o
r
talit
y
in
co
r
o
n
ar
y
ar
ter
y
d
is
ea
s
e
s
.
L
a
ter
th
at
y
ea
r
,
a
r
esear
ch
te
a
m
led
b
y
Yan
g
d
ev
elo
p
ed
a
r
is
k
p
r
ed
ictio
n
m
o
d
el
o
f
d
y
s
l
ip
id
e
m
ia
[
1
0
3
]
.
I
n
2
0
1
9
,
T
r
em
b
la
y
a
n
d
co
ll
ab
o
r
ato
r
s
s
u
cc
ee
d
ed
in
o
p
ti
m
izin
g
a
p
r
ed
ictio
n
m
o
d
el
b
y
u
til
izin
g
r
eg
r
e
s
s
io
n
t
r
ee
s
in
co
n
n
ec
tio
n
w
i
th
t
h
e
b
o
v
in
e
-
r
elate
d
i
n
d
u
s
tr
y
[
1
0
4
]
.
T
o
c
o
n
clu
d
e,
h
u
m
a
n
it
y
i
s
m
ar
ch
in
g
to
w
ar
d
s
a
f
ated
Kar
d
as
h
ev
t
y
p
e
-
1
civ
ili
za
tio
n
.
T
h
e
e
x
p
o
n
en
t
ial
g
r
o
w
t
h
o
f
tec
h
n
o
lo
g
ie
s
i
n
t
h
e
d
i
g
ital
ep
o
ch
,
b
ac
k
ed
b
y
s
u
b
s
ta
n
tia
tin
g
e
v
id
e
n
ce
o
f
Mo
o
r
e'
s
la
w
a
n
d
th
e
r
is
e
o
f
th
e
q
u
an
t
u
m
co
m
p
u
tin
g
er
a,
in
d
icate
s
th
a
t
h
u
m
an
-
le
v
el
m
ac
h
in
e
in
te
lli
g
en
ce
an
d
a
s
u
b
s
eq
u
en
t
s
u
p
er
in
te
lli
g
en
ce
ar
e
f
o
r
th
c
o
m
in
g
[
3
2
-
3
5
]
.
E
lo
n
Mu
s
k
,
a
tech
n
o
lo
g
y
e
n
tr
ep
r
en
eu
r
an
d
co
-
f
o
u
n
d
er
o
f
Neu
r
ali
n
k
an
d
Sp
ac
eX,
p
r
ed
icts
th
at
A
I
w
ill
ta
k
e
o
v
er
m
o
s
t
o
f
t
h
e
h
u
m
an
-
b
ased
a
ctiv
itie
s
,
r
ep
lacin
g
h
u
m
a
n
s
li
ter
all
y
[
3
2
-
3
5
]
.
Ma
n
y
ar
g
u
e
t
h
at
h
u
m
a
n
it
y
is
m
ar
ch
i
n
g
to
w
ar
d
s
an
i
n
ev
i
tab
le
tech
n
o
lo
g
ical
s
in
g
u
lar
it
y
.
Ho
w
e
v
er
,
in
te
g
r
atin
g
ar
ti
f
icial
i
n
telli
g
e
n
ce
w
it
h
h
u
m
an
s
,
v
ia
b
r
ain
-
co
m
p
u
ter
in
ter
f
ac
e
tech
n
o
lo
g
ies,
ca
n
b
e
p
r
o
tectiv
e.
No
n
eth
e
less
,
le
g
is
latio
n
i
n
co
n
n
ec
t
io
n
w
it
h
in
f
o
r
m
at
i
o
n
tech
n
o
lo
g
ies
is
m
an
d
ato
r
y
to
r
eg
u
late
t
h
e
u
p
c
o
m
in
g
s
u
p
er
in
tel
lig
e
n
ce
o
f
t
h
e
d
ig
ital e
p
o
ch
[
1
0
5
-
1
0
7
]
.
ACK
NO
WL
E
D
G
M
E
NT
S
T
h
e
au
th
o
r
s
w
o
u
ld
li
k
e
to
th
an
k
all
t
h
e
r
esp
o
n
d
en
ts
,
i
n
clu
d
in
g
t
h
e
p
ass
io
n
ate
v
id
eo
g
a
m
er
s
,
w
h
o
d
ed
icate
d
th
eir
ti
m
e
an
d
atte
n
t
io
n
to
co
m
p
lete
o
u
r
s
u
r
v
e
y
.
AVAI
L
AB
I
L
I
T
Y
O
F
DATA
Ou
r
d
ata
ar
e
av
ailab
le
u
p
o
n
r
e
q
u
est
f
r
o
m
th
e
co
r
r
esp
o
n
d
in
g
au
th
o
r
CO
NT
RIB
U
T
I
O
N
O
F
AU
T
H
O
RS:
C
o
n
ce
p
tu
a
lizatio
n
,
Ah
m
ed
A
l
-
I
m
a
m
,
a
n
d
Ma
r
ek
Mo
t
y
k
a
;
Fo
r
m
al
a
n
al
y
s
i
s
,
Ah
m
ed
A
l
-
I
m
a
m
;
I
n
v
e
s
tig
a
tio
n
,
Ah
m
ed
A
l
-
I
m
a
m
,
a
n
d
Ma
r
ek
Mo
t
y
k
a;
Me
t
h
o
d
o
lo
g
y
,
A
h
m
ed
A
l
-
I
m
a
m
;
P
r
o
j
ec
t
ad
m
i
n
is
tr
atio
n
,
Ah
m
ed
A
l
-
I
m
a
m
;
W
r
iti
n
g
–
o
r
ig
in
al
d
r
a
f
t,
Ah
m
ed
Al
-
I
m
a
m
,
Ma
r
ek
Mo
t
y
k
a
an
d
Ma
r
iu
s
z
J
ęd
r
ze
j
k
o
;
W
r
itin
g
–
r
ev
ie
w
&
ed
iti
n
g
,
M
ar
iu
s
z
J
ęd
r
ze
j
k
o
.
RE
F
E
R
E
NC
E
S
[1
]
C.
Co
rra
d
o
a
n
d
C.
Hu
lt
e
n
,
"
Ho
w
Do
Yo
u
M
e
a
su
re
a
“
T
e
c
h
n
o
lo
g
ica
l
Re
v
o
lu
ti
o
n
”
?
"
,
Ame
r
ica
n
Eco
n
o
mic
Rev
iew
,
v
o
l.
1
0
0
,
n
o
.
2
,
p
p
.
9
9
-
1
0
4
,
2
0
1
0
.
[2
]
R.
F
ra
n
k
e
,
"
T
e
c
h
n
o
l
o
g
ica
l
re
v
o
lu
ti
o
n
a
n
d
p
r
o
d
u
c
ti
v
it
y
d
e
c
li
n
e
:
Co
m
p
u
ter
in
tr
o
d
u
c
t
io
n
in
th
e
f
in
a
n
c
ial
in
d
u
stry
"
,
T
e
c
h
n
o
l
o
g
ica
l
F
o
re
c
a
stin
g
a
n
d
S
o
c
ia
l
C
h
a
n
g
e
,
v
o
l.
3
1
,
n
o
.
2
,
p
p
.
1
4
3
-
1
5
4
,
1
9
8
7
.
[3
]
T
.
Ha
m
m
e
s,
"
W
a
r
e
v
o
lv
e
s
in
to
th
e
f
o
u
rth
g
e
n
e
ra
ti
o
n
"
,
Co
n
tem
p
o
ra
ry
S
e
c
u
rity
Po
li
c
y
,
v
o
l.
2
6
,
n
o
.
2
,
p
p
.
1
8
9
-
2
2
1
,
2
0
0
5
.
[4
]
D.
Re
e
d
,
"
Be
y
o
n
d
th
e
W
a
r
o
n
T
e
rro
r:
In
to
th
e
F
if
th
G
e
n
e
ra
ti
o
n
o
f
W
a
r
a
n
d
C
o
n
f
li
c
t"
,
S
t
u
d
ies
in
C
o
n
fl
ict
&
T
e
rr
o
rism
,
v
o
l.
3
1
,
n
o
.
8
,
p
p
.
6
8
4
-
7
2
2
,
2
0
0
8
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
A
r
ti
f
I
n
tell
I
SS
N:
2252
-
8938
C
o
n
flictin
g
o
p
in
io
n
s
in
co
n
n
e
ctio
n
w
ith
d
ig
ita
l su
p
erin
tellig
en
ce
(
A
h
med
A
l
-
I
ma
m
)
345
[5
]
B.
L
e
in
e
r,
V
.
Ce
rf
,
D.
Clark
,
R
.
Ka
h
n
,
L
.
Kle
i
n
ro
c
k
,
D.
Ly
n
c
h
,
J.
P
o
ste
l,
L
.
Ro
b
e
rts
a
n
d
S
.
W
o
lf
f
,
"
A
b
rief
h
isto
ry
o
f
th
e
in
tern
e
t"
,
ACM
S
IGCO
M
M
Co
mp
u
ter
Co
mm
u
n
ica
ti
o
n
Rev
iew
,
v
o
l.
3
9
,
n
o
.
5
,
p
p
.
2
2
-
3
1
,
2
0
0
9
.
[6
]
M
.
Zelk
o
w
it
z
,
Ad
v
a
n
c
e
s in
c
o
m
p
u
ter
s
, 7
3
rd
e
d
.
L
o
n
d
o
n
:
A
c
a
d
e
m
i
c
,
2
0
0
8
,
p
p
.
1
-
5
5
.
[7
]
I.
A
n
d
ro
w
ich
,
Cli
n
ica
l
i
n
fo
rm
a
ti
o
n
sy
ste
ms
.
W
a
sh
in
g
to
n
,
D.C.
:
Am
e
rica
n
Nu
rse
s P
u
b
.
,
2
0
0
3
,
p
p
.
3
-
3
2
.
.
[8
]
F
re
e
th
,
Y.
Bit
sa
k
is,
X
.
M
o
u
ss
a
s,
J.
S
e
irad
a
k
is,
A
.
T
s
e
li
k
a
s,
H.
M
a
n
g
o
u
,
M
.
Zaf
e
iro
p
o
u
lo
u
,
R.
Ha
d
l
a
n
d
,
D.
Ba
te,
A
.
Ra
m
se
y
,
M
.
A
ll
e
n
,
A
.
Cra
w
l
e
y
,
P
.
Ho
c
k
le
y
,
T
.
M
a
lzb
e
n
d
e
r,
D.
G
e
lb
,
W
.
Am
b
risc
o
a
n
d
M
.
Ed
m
u
n
d
s
,
"
De
c
o
d
in
g
th
e
a
n
c
ien
t
G
re
e
k
a
stro
n
o
m
ica
l
c
a
lcu
la
to
r
k
n
o
w
n
a
s
th
e
A
n
ti
k
y
th
e
ra
M
e
c
h
a
n
is
m
"
,
Na
tu
re
,
v
o
l.
4
4
4
,
n
o
.
7
1
1
9
,
p
p
.
587
-
5
9
1
,
2
0
0
6
.
[9
]
T
.
F
re
e
th
,
A
.
Jo
n
e
s,
J.
S
tee
le
a
n
d
Y.
Bit
sa
k
is,
"
Ca
len
d
a
rs
w
it
h
Ol
y
m
p
iad
d
isp
lay
a
n
d
e
c
li
p
se
p
re
d
ictio
n
o
n
t
h
e
A
n
ti
k
y
th
e
ra
M
e
c
h
a
n
is
m
"
,
Na
tu
re
,
v
o
l.
4
5
4
,
n
o
.
7
2
0
4
,
p
p
.
6
14
-
6
1
7
,
2
0
0
8
.
[1
0
]
D.
S
p
in
e
ll
is,
"
T
h
e
A
n
ti
k
y
th
e
ra
M
e
c
h
a
n
ism
:
A
Co
m
p
u
ter
S
c
ien
c
e
P
e
rsp
e
c
ti
v
e
"
,
Co
mp
u
ter
,
v
o
l.
4
1
,
n
o
.
5
,
p
p
.
2
2
-
2
7
,
2
0
0
8
.
[1
1
]
H.
Bru
d
e
re
r,
"
M
o
re
Re
p
li
c
a
s
o
f
Histo
rica
l
Ca
lcu
latin
g
M
a
c
h
in
e
s
F
o
u
n
d
"
,
Ca
c
m.a
c
m.
o
rg
,
2
0
2
0
.
[
On
li
n
e
].
Av
a
il
a
b
le:
h
tt
p
s://
c
a
c
m
.
a
c
m
.
o
rg
/b
lo
g
s/b
l
o
g
-
c
a
c
m
/2
3
4
0
0
5
-
m
o
re
-
re
p
li
c
a
s
-
of
-
h
isto
rica
l
-
c
a
lcu
latin
g
-
m
a
c
h
in
e
s
-
f
o
u
n
d
/f
u
ll
tex
t.
[
A
c
c
e
ss
e
d
:
1
5
-
Oc
t
-
2
0
1
9
]
[1
2
]
F
.
M
o
o
n
,
"
F
ra
n
z
Re
u
lea
u
x
:
Co
n
t
rib
u
ti
o
n
s
t
o
1
9
t
h
c
e
n
tu
ry
k
in
e
m
a
ti
c
s
a
n
d
th
e
o
ry
o
f
m
a
c
h
in
e
s"
,
Ap
p
li
e
d
M
e
c
h
a
n
ics
Rev
iews
,
v
o
l.
5
6
,
n
o
.
2
,
p
p
.
2
6
1
-
2
8
5
,
2
0
0
3
[1
3
]
F
.
Kiste
rm
a
n
n
,
"
Blaise
P
a
sc
a
l'
s
a
d
d
in
g
m
a
c
h
in
e
:
n
e
w
f
in
d
in
g
s
a
n
d
c
o
n
c
lu
sio
n
s"
,
IEE
E
An
n
a
ls
o
f
th
e
Histo
ry
o
f
Co
mp
u
t
in
g
,
v
o
l
.
2
0
,
n
o
.
1
,
p
p
.
6
9
-
7
6
,
1
9
9
8
.
.
[1
4
]
L
.
Ho
m
e
m
,
"
T
h
e
G
e
n
iu
s
o
f
A
la
n
T
u
rin
g
:
T
h
e
Co
m
p
u
ti
n
g
Clas
sic
a
l
M
o
d
e
l"
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
M
a
c
h
in
e
L
e
a
rn
in
g
a
n
d
Co
m
p
u
t
in
g
,
v
o
l.
3
,
n
o
.
6
,
p
p
.
4
7
9
-
4
8
5
,
2
0
1
3
.
[1
5
]
A
.
A
l
-
I
m
a
m
a
n
d
B.
A
b
d
u
lM
a
jee
d
,
"
T
h
e
NP
S
P
h
e
n
o
m
e
n
o
n
a
n
d
t
h
e
De
e
p
W
e
b
:
In
tern
e
t
S
n
a
p
sh
o
t
s
o
f
th
e
Da
rk
n
e
t
a
n
d
P
o
ten
ti
a
ls
o
f
Da
ta M
in
in
g
"
,
Glo
b
a
l
J
o
u
rn
a
l
o
f
He
a
lt
h
S
c
ien
c
e
,
v
o
l.
9
,
n
o
.
1
1
,
p
.
8
6
-
1
0
1
,
2
0
1
7
.
[1
6
]
A
.
A
l
-
I
m
a
m
,
"
Op
ti
m
i
z
in
g
L
in
e
a
r
M
o
d
e
ls
v
ia
S
i
n
u
s
o
id
a
l
T
ra
n
sf
o
r
m
a
ti
o
n
f
o
r
Bo
o
ste
d
M
a
c
h
in
e
L
e
a
rn
in
g
in
M
e
d
icin
e
"
,
J
o
u
r
n
a
l
o
f
th
e
F
a
c
u
lt
y
o
f
M
e
d
icin
e
Ba
g
h
d
a
d
,
v
o
l.
6
1
,
n
o
.
3
,
4
,
p
.
1
-
9
,
2
0
1
9
.
[1
7
]
A
.
A
l
-
I
m
a
m
,
"
A
No
v
e
l
M
e
th
o
d
f
o
r
Co
m
p
u
tatio
n
a
ll
y
Eff
ic
a
c
io
u
s
L
in
e
a
r
a
n
d
P
o
ly
n
o
m
ial
Re
g
re
ss
i
o
n
A
n
a
ly
ti
c
s
o
f
Big
Da
ta i
n
M
e
d
icin
e
"
,
M
o
d
e
rn
A
p
p
li
e
d
S
c
ien
c
e
,
v
o
l
.
1
4
,
n
o
.
5
,
p
p
.
1
-
1
0
,
2
0
2
0
.
[1
8
]
J.
S
ietsm
a
a
n
d
R.
Do
w
,
"
Cre
a
ti
n
g
a
rti
f
icia
l
n
e
u
ra
l
n
e
tw
o
rk
s
th
a
t
g
e
n
e
ra
li
z
e
"
,
Ne
u
ra
l
Ne
two
rk
s
,
v
o
l.
4
,
n
o
.
1
,
p
p
.
67
-
7
9
,
1
9
9
1
.
[1
9
]
A
.
A
l
-
I
m
a
m
,
U.
Kh
a
li
d
,
N.
A
l
-
Ha
d
it
h
i
a
n
d
D
.
Ka
o
u
c
h
e
,
"
Re
a
l
-
ti
m
e
In
f
e
re
n
ti
a
l
A
n
a
l
y
ti
c
s
Ba
se
d
o
n
On
li
n
e
Da
tab
a
se
s
o
f
T
re
n
d
s:
A
Bre
a
k
th
ro
u
g
h
W
it
h
in
th
e
Disc
ip
li
n
e
o
f
Dig
it
a
l
Ep
id
e
m
io
lo
g
y
o
f
De
n
ti
stry
a
n
d
Ora
l
-
M
a
x
il
lo
f
a
c
ial
S
u
rg
e
r
y
"
,
M
o
d
e
rn
Ap
p
li
e
d
S
c
ie
n
c
e
,
v
o
l.
1
3
,
n
o
.
2
,
p
p
.
8
1
-
9
4
,
2
0
1
9
.
[2
0
]
A
.
A
l
-
I
m
a
m
a
n
d
F
.
A
l
-
L
a
m
i,
"
M
a
c
h
in
e
lea
rn
in
g
f
o
r
p
o
ten
t
d
e
r
m
a
to
lo
g
y
re
se
a
rc
h
a
n
d
p
ra
c
ti
c
e
"
,
J
o
u
rn
a
l
o
f
De
rm
a
to
lo
g
y
a
n
d
De
rm
a
t
o
lo
g
ic S
u
rg
e
ry
,
v
o
l.
2
4
,
n
o
.
1
,
p
p
.
1
-
4
,
2
0
2
0
.
[2
1
]
A
.
A
l
-
I
m
a
m
,
"
No
v
e
l
P
sy
c
h
o
a
c
ti
v
e
S
u
b
sta
n
c
e
s
Re
se
a
rc
h
:
On
th
e
Ne
c
e
ss
it
y
o
f
Re
a
l
-
ti
m
e
A
n
a
l
y
ti
c
s
a
n
d
P
re
d
ictiv
e
M
o
d
e
ll
i
n
g
"
,
Res
e
a
rc
h
a
n
d
A
d
v
a
n
c
e
s in
Psy
c
h
ia
try
,
v
o
l
.
7
,
n
o
.
1
,
2
0
2
0
,
[in
p
re
ss
].
[2
2
]
A
.
A
l
-
I
m
a
m
,
"
In
f
e
r
e
n
ti
a
l
A
n
a
l
y
sis
o
f
Big
Da
ta
in
Re
a
l
-
T
i
m
e
:
On
e
G
i
a
n
t
L
e
a
p
f
o
r
S
p
a
ti
o
tem
p
o
ra
l
Dig
it
a
l
Ep
id
e
m
io
lo
g
y
in
De
n
ti
str
y
"
.
Od
o
n
to
st
o
ma
t
o
lo
g
y
Res
e
a
rc
h
An
a
t
o
m
y
L
e
a
rn
in
g
&
Imp
la
n
to
l
o
g
y
,
v
o
l.
1
2
,
n
o
.
1
,
p
p
.
1
-
1
4
,
2
0
1
9
.
[2
3
]
W
.
M
c
c
u
ll
o
c
h
a
n
d
W
.
P
i
tt
s,
"
A
lo
g
ica
l
c
a
lcu
lu
s
o
f
th
e
id
e
a
s
imm
a
n
e
n
t
in
n
e
rv
o
u
s
a
c
ti
v
it
y
"
,
Bu
ll
e
ti
n
o
f
M
a
th
e
ma
ti
c
a
l
Bi
o
lo
g
y
,
v
o
l.
5
2
,
n
o
.
1
-
2
,
p
p
.
9
9
-
1
1
5
,
1
9
9
0
.
[2
4
]
H.
L
u
,
Y.
L
i,
M
.
Ch
e
n
,
H.
Kim
a
n
d
S
.
S
e
rik
a
wa
,
"
Bra
in
In
telli
g
e
n
c
e
:
G
o
b
e
y
o
n
d
A
rti
f
icia
l
In
telli
g
e
n
c
e
"
,
M
o
b
il
e
Ne
two
rk
s a
n
d
Ap
p
li
c
a
t
io
n
s
,
v
o
l.
2
3
,
n
o
.
2
,
p
p
.
3
6
8
-
3
7
5
,
2
0
1
7
.
[2
5
]
N.
Jo
sh
i,
"
7
Ty
p
e
s
o
f
A
rti
f
i
c
ial
In
telli
g
e
n
c
e
"
,
Fo
rb
e
s
,
2
0
2
0
.
[
On
li
n
e
]
.
A
v
a
il
a
b
le:
h
tt
p
s:/
/www
.
f
o
rb
e
s.c
o
m
/sit
e
s/c
o
g
n
it
iv
e
w
o
rld
/2
0
1
9
/0
6
/1
9
/7
-
ty
p
e
s
-
of
-
a
rti
f
icia
l
-
in
telli
g
e
n
c
e
/.
[
A
c
c
e
ss
e
d
:
1
5
-
Oc
t
-
2
0
1
9
]
[2
6
]
L
.
Wei,
"
L
e
g
a
l
risk
a
n
d
c
rim
in
a
l
im
p
u
tatio
n
o
f
w
e
a
k
a
rti
f
icia
l
i
n
telli
g
e
n
c
e
"
,
IOP
Co
n
fer
e
n
c
e
S
e
rie
s:
M
a
ter
ia
ls
S
c
ien
c
e
a
n
d
E
n
g
i
n
e
e
rin
g
,
v
o
l.
4
9
0
,
n
o
.
6
,
2
0
1
9
.
[2
7
]
S
.
De
h
a
e
n
e
,
H.
L
a
u
a
n
d
S
.
Ko
u
id
e
r,
"
W
h
a
t
is
c
o
n
sc
io
u
sn
e
ss
,
a
n
d
c
o
u
l
d
m
a
c
h
in
e
s
h
a
v
e
it
?
"
.
S
c
ien
c
e
,
v
o
l.
3
5
8
,
n
o
.
6
3
6
2
,
p
p
.
4
8
6
-
4
9
2
,
2
0
1
7
.
[2
8
]
"
De
f
in
it
io
n
o
f
CON
S
CIOU
S
NE
S
S
"
,
M
e
rr
ia
m
-
we
b
ste
r.c
o
m
,
2
0
2
0
.
[
On
li
n
e
]
.
A
v
a
il
a
b
le:
h
tt
p
s://
ww
w
.
m
e
rria
m
-
w
e
b
ste
r.
c
o
m
/d
ictio
n
a
ry
/co
n
sc
io
u
sn
e
ss
.
[
A
c
c
e
ss
e
d
:
1
5
-
Oc
t
-
2
0
1
9
]
[2
9
]
G
.
M
a
rc
u
s,
F
.
Ro
ss
i
a
n
d
M
.
V
e
lo
so
,
"
Be
y
o
n
d
t
h
e
T
u
rin
g
T
e
st"
,
AI
M
a
g
a
zin
e
,
v
o
l.
3
7
,
n
o
.
1
,
p
p
.
3
-
4
,
2
0
1
6
.
[3
0
]
T
.
Bo
lan
d
e
r,
"
Hu
m
a
n
v
s.
m
a
c
h
in
e
in
telli
g
e
n
c
e
"
.
Pro
c
e
e
d
in
g
s
o
f
Pr
a
g
ma
t
ic
Co
n
stru
c
ti
v
ism
,
v
o
l.
9
,
n
o
.
1
,
p
p
.
1
7
-
2
4
,
2
0
1
9
.
[3
1
]
M
.
Bu
c
h
a
n
a
n
,
"
G
e
n
e
ra
li
z
in
g
M
o
o
re
"
,
Na
tu
re
Ph
y
sic
s
,
v
o
l.
1
2
,
n
o
.
3
,
p
.
2
0
0
,
2
0
1
6
.
[3
2
]
"
Op
in
io
n
|
A
rti
f
i
c
ial
In
telli
g
e
n
c
e
’s
W
h
it
e
G
u
y
P
ro
b
lem
"
,
Ny
ti
me
s.c
o
m
,
2
0
2
0
.
[
On
li
n
e
]
.
A
v
a
il
a
b
le:
h
tt
p
s:/
/www
.
n
y
ti
m
e
s.c
o
m
/2
0
1
6
/
0
6
/2
6
/o
p
in
io
n
/su
n
d
a
y
/artif
i
c
ial
-
in
telli
g
e
n
c
e
s
-
w
h
it
e
-
g
u
y
-
p
ro
b
lem
.
h
tm
l.
[
A
c
c
e
ss
e
d
:
15
-
Oc
t
-
2
0
1
9
]
.
[3
3
]
A
.
P
o
tap
o
v
,
"
T
e
c
h
n
o
lo
g
ica
l
S
i
n
g
u
larity
:
W
h
a
t
Do
W
e
Re
a
ll
y
Kn
o
w
?
"
,
In
fo
rm
a
ti
o
n
,
v
o
l.
9
,
n
o
.
4
,
p
.
8
2
,
2
0
1
8
.
[3
4
]
J.
Hin
z
p
e
ter,
"
A
b
o
u
t
A
rti
f
icia
l
In
t
e
ll
ig
e
n
c
e
:
Ro
b
o
ts
a
n
d
P
h
il
o
so
p
h
y
"
,
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Bi
o
c
h
e
mistry
&
Ph
y
sio
l
o
g
y
,
v
o
l
.
4
,
n
o
.
2
,
p
p
.
1
-
2
,
2
0
1
9
.
[3
5
]
T
.
W
a
lsh
,
"
T
h
e
S
in
g
u
larity
M
a
y
Ne
v
e
r
Be
N
e
a
r"
,
AI
M
a
g
a
zin
e
,
v
o
l.
3
8
,
n
o
.
3
,
p
p
.
5
8
-
6
2
,
2
0
1
7
.
[3
6
]
F
.
A
ru
te,
K.
A
r
y
a
,
R.
Ba
b
b
u
s
h
,
e
t
a
l,
"
Qu
a
n
tu
m
su
p
re
m
a
c
y
u
sin
g
a
p
ro
g
ra
m
m
a
b
le
su
p
e
rc
o
n
d
u
c
ti
n
g
p
ro
c
e
ss
o
r"
.
Na
tu
re
,
v
o
l
.
5
7
4
,
p
p
.
5
0
5
–
5
1
0
,
2
0
1
9
.
[3
7
]
R.
P
.
F
e
y
n
m
a
n
,
"
Qu
a
n
tu
m
m
e
c
h
a
n
ica
l
c
o
m
p
u
ters
"
,
Fo
u
n
d
a
ti
o
n
s o
f
Ph
y
sic
s
,
v
o
l.
1
6
,
n
o
.
6
,
p
p
.
5
0
7
-
5
3
1
,
1
9
8
6
.
Evaluation Warning : The document was created with Spire.PDF for Python.