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YES
2 1
 YES
3 1
 NO
 YES
3 2
 YES
1 1
 YES
1 1
 YES
1 2
 YES
3 5
 YES
1 3
 NO
 NO
 YES
2 3
 YES
1 3
 YES
1 1
 YES
1 3
 YES
1 1
 NO
 YES
1 1
 YES
1 1
 YES
1 1
 YES
1 1
 YES
1 1
 YES
1 1
 YES
1 1
 YES
2 4
 YES
1 2
 YES
2 3
 YES
1 2
 YES
9 10
 YES
1 2
 YES
1 2
 YES
1 2
 YES
1 3
 YES
4 1
 NO
 YES
1 3
 YES
1 4
 YES
9 10
 YES
5 4
 YES
3 1
 NO
 NO
 YES
2 3
 YES
1 1
 YES
1 10
 YES
3 1
 YES
1 4
 YES
1 3
 YES
3 3
 YES
3 10
 YES
1 1
 YES
1 4
 YES
1 1
 YES
1 1
 YES
6 5
 YES
1 1
 YES
1 3
 YES
1 2
 YES
3 3
 YES
1 5
 YES
1 2
 YES
4 4
 YES
1 4
 NO
 YES
3 2
 YES
5 2
 YES
1 1
 YES
2 3
 YES
1 4
 YES
3 3
 YES
1 1
 YES
1 1
 YES
1 2
 YES
9 10
 YES
2 2
 YES
2 2
 NO
 YES
1 3
 YES
1 3
 YES
1 1
 YES
3 3
 YES
2 2
 YES
4 2
 YES
1 2
 YES
2 2
 YES
1 2
 YES
2 1
 YES
3 2
 YES
2 2
 YES
2 3
 YES
6 3
 YES
1 5
 YES
1 1
 YES
1 1
 YES
2 2
 YES
1 4
 YES
2 2
 NO
 YES
2 2
 YES
1 1
 YES
6 1
 YES
3 3
 YES
1 1
 YES
2 2
 NO
 YES
2 2
 NO
 YES
4 4
 YES
4 1
 YES
2 3
 NO

#http://codeforces.com/problemset/problem/699/B

def filter_by_column(column, distribution):
	#return a modified dist with elemnts that don't have 
	#column == column ex:
	#filter_by_column(1, [(0,1),(0,0),(0,2)]) >> [(0,0),(0,2)]
	temp = []
	for cell in distribution:
		if cell[1] != column:
			temp.append(cell)
	return temp


def filter_by_row(row, distribution):
	#return a modified dist with elemnts that don't have 
	#row == row ex:
	#filter_by_row(0, [(0,1),(0,0),(0,2)]) >> []
	temp = []
	for cell in distribution:
		if cell[0] != row:
			temp.append(cell)
	return temp


def same_row(distribution):
	
	#if dist is not empty
	if distribution:
		length = len(distribution)
		#loop over dist and return False if dist[index][0] != dist[index+1][0]
		for index in range(length - 1):
			if distribution[index][0] != distribution[index+1][0]:
				return False
	
	#return True if dist is empty
	return True

def same_column(distribution):
	
	#if dist is not empty
	if distribution:
		length = len(distribution)
		#loop over dist and return False if dist[index][1] != dist[index+1][1]
		for index in range(length - 1):
			if distribution[index][1] != distribution[index+1][1]:
				return False
	
	#return True if dist is empty
	return True



def OneBomb():
	
	wall = '*'
	
	n_rows, n_columns = map(int, input().split())
	
	rows_density = [0]*n_rows
	columns_density = [0]*n_columns
	wall_distribution = []
	
	for row in range(n_rows):
		
		current_row = input().strip()
		rows_density[row] += current_row.count(wall)
		
		for column in range(n_columns):
			
			if current_row[column] == wall:
				columns_density[column] += 1
				wall_distribution.append((row, column))
	
	#print(wall_distribution, rows_density, columns_density, sep='\n')
	
	#the maximum number of walls in the same column, row
	max_column_density = columns_density.index(max(columns_density))
	max_row_density = rows_density.index(max(rows_density))
	#print(max_column_density, max_row_density, sep='\n')
	
	#if the map is empty, return NO
	#if max_column_density == max_row_density == 0:
	#	print('NO')
	#
	##if the map isn't empty
	#else:
	#check wall distribution (horizontal: walls are in the same row,
	# vertical: walls are in the same column)
	if max_column_density > max_row_density:
		#if walls are vertical, remove 
		mod_density = filter_by_column(max_column_density, wall_distribution)
		if same_row(mod_density):
			print('YES')
			if mod_density:
				print(mod_density[0][0]+1, max_column_density+1)
			else:
				print(1, max_column_density+1)
		else:
			print('NO')
	
	else:
		mod_density = filter_by_row(max_row_density, wall_distribution)
		if same_column(mod_density):
			print('YES')
			if mod_density:
				print(max_row_density+1, mod_density[0][1]+1)
			else:
				print(max_row_density+1, 1)
		else:
			print('NO')
	
	#print(filter_by_column(max_column_density, wall_distribution))


if __name__ == '__main__':
	OneBomb()
