1. A method for statistical mapping between genetic information and facial image data, the method comprising:
collecting a multiplicity of sets of genetic information and matching facial image data representing a multiplicity of individuals;
representing said genetic information of each of said multiplicity of individuals as a first multidimensional representation;
representing said facial image data of each of said multiplicity of individuals as a second multidimensional representation; and
inferring correlative, non-causal, statistical relationships between said first multidimensional representations and said second multidimensional representations.
2. A method for mapping between genetic information and facial image data according to claim 1 and wherein said inferring non-causal statistical relationships excludes inferring causal statistical relationships.
3. A method for mapping between genetic information and facial image data according to claim 1 and wherein said first representation employs at least one of the following methods:
representing nucleotide sequences of length n as vectors of length 4n wherein each element depicts the number of occurrences of each nucleotide at a specific sequence location in said genetic information;
representing STR data of n loci as vectors of length 2n wherein each element depicts either the lower or the higher repetition number at one locus in said genetic information;
representing allele information of n loci, each having a multiplicity of \u03b3i, i=1 . . . n alleles as vectors of length
\u2211
i
=
1
n
\u2062
\u2062
\u03b3
i
wherein each vector element denotes the number of times each allele is present in said genetic information; and
representing said genetic information implicitly by providing a similarity function between said genetic information arising from every two individuals.
4. A method for mapping between genetic information and facial image data according to claim 1 and wherein said second representation employs at least one of the following algorithms:
detection of a face region within said facial image data by using a learned template; and
alignment of said face region by the detection of a set of fiducial points.
5. A method for generating a facial image based on genetic information, the method comprising:
providing results of statistical mapping between genetic information and facial image data including face shape and facial texture information produced by inferring correlative, non-causal, statistical relationships between first multidimensional representations of said genetic information and second multidimensional representations of said facial image data for a multiplicity of persons;
applying said results of said statistical mapping to genetic information obtained from an individual not necessarily being an individual whose genetic information is used for inferring said statistical relationships, thereby to obtain facial image data including at least face shape information for said individual; and
employing said facial image data including face shape and facial texture information to generate said facial image of said individual.
6. A method for generating a facial image based on genetic information according to claim 5 and wherein said facial image data of each of said multiplicity of persons also includes facial texture information.
7. A method for generating a facial image based on genetic information according to claim 5 and wherein:
said second multidimensional representation employs at least one of the following algorithms:
detection of a face region within said facial image data by using a learned template; and
alignment of said face region by the detection of a set of fiducial points, and
said second multidimensional representation of said facial image data of each of said multiplicity of persons and a multidimensional representation of facial image data of said individual includes at least one of the following representations:
a shape vector comprising a multiplicity of elements each indicating a location of a feature point or a contour point in said facial image data; and
a texture vector comprising a multiplicity of elements each indicating an image measurement at one image location of said facial image data.
8. A method for generating a facial image according to claim 7 and wherein said statistical mapping employs at least one of the following algorithms:
regularized linear regression of said shape vector; and
regularized linear regression of said texture vector.
9. A method for generating a facial image according to claim 7 and wherein said applying the results of said mapping employs at least one of the following algorithms:
use of linear regression models for recovering said shape vector of said individual; and
use of linear regression models for recovering said texture vector of said individual.
10. A method for generating a facial image according to claim 7 and wherein said employing said facial image data including face shape and facial texture included in said facial image data of said individual employs thin plate spline warping of said texture vector of said individual with in accordance with said locations of said shape vector of said individual.
11. A method for generating a facial image according to claim 5 and wherein said statistical mapping employs at least one of the following algorithms:
regularized linear regression of said face shape information; and
regularized linear regression of said facial texture information.
12. A method for generating a facial image according to claim 5 and wherein said applying the results of said mapping employs at least one of the following algorithms:
use of linear regression models for at least one of recovering, generating and predicting said face shape information included in said facial image data of said individual; and
use of linear regression models for recovering said facial texture information included in said facial image data of said individual.
13. A method for generating a facial image according to claim 5 and wherein said employing said facial image data including face shape and facial texture included in said facial image data of said individual employs thin plate spline warping of said facial texture information of said individual in accordance with said face shape information of said individual.
14. A method for selecting a facial image corresponding to an individual from facial images of a plurality of persons based on genetic information of said individual, the method comprising:
providing results of statistical mapping between genetic information and facial image data produced by inferring non-causal (correlative) statistical relationships between first multidimensional representations of said genetic information and second multidimensional representations of said facial image data for a multiplicity of individuals, not necessarily including said individual and said plurality of persons; and
applying said results of said statistical mapping to genetic information obtained from said individual and to said facial images of said plurality of persons, thereby to select said facial image of said individual from among said facial images of said plurality of persons.
15. A method for selecting a facial image according to claim 14 and wherein said facial image data of each of said plurality of persons is represented as a multidimensional representation.
16. A method for selecting a facial image according to claim 15 and wherein:
said multidimensional representation of said facial image data of each of said plurality of persons employs at least one of the following algorithms:
detection of a face region within said facial image data by using a learned template; and
alignment of said face region by the detection of a set of fiducial points, and
said multidimensional representation of said facial image data of each of said plurality of persons includes at least one of the following representations:
a vector containing a multiplicity of histograms of image gradients at a multiplicity of image locations; and
a vector containing a multiplicity of histograms of local-binary-patterns obtained at a multiplicity of image locations.
17. A method for selecting a facial image according to claim 14 and wherein said inferring of statistical relationships employs at least one of the following algorithms:
Canonical Correlation Analysis;
Kernel Canonical Correlation Analysis;
Nearest Neighbor Transfer;
Generalized Nearest Neighbor Transfer;
Inverse Nearest Neighbor Transfer;
Inverse Generalized Nearest Neighbor Transfer;
identification based on the structure of recovered phylogenetic-trees;
statistical classifier learning of multidimensional representations each containing information from said first multidimensional representation and said second multidimensional representation;
Common Discriminant Feature Extraction;
Maximal Margin Robot; and
Preference Margin Optimization.
18. A method for selecting a facial image according to claim 14 and wherein selecting said facial image of said individual from among said facial images of said plurality of persons employs at least one of the following methods:
transformation of said facial image data and said genetic information using transformations obtained by employing Canonical Correlation Analysis;
transformation of said facial image data and said genetic information using transformations obtained by employing Kernel Canonical Correlation Analysis;
selection based on minimal DL1 distance;
selection based on minimal DCoL1 distance;
selection based on minimal DL2 distance;
selection based on minimal DCoL2 distance;
selection based on minimal DC distance;
selection based on minimal DCC distance;
selection based on the Nearest Neighbor Transfer decision rule;
selection based on the Generalized Nearest Neighbor Transfer decision rule;
selection based on the Inverse Nearest Neighbor Transfer decision rule;
selection based on the Inverse Generalized Nearest Neighbor Transfer decision rule;
selection based on examining said genetic information obtained from said individual and said facial images of said plurality of persons within the context of phylogenetic trees constructed from said first multidimensional representations of said genetic information and said second multidimensional representations of said facial image data for said multiplicity of individuals;
selection based on applying classification rules to multidimensional representations each containing information from said first multidimensional representation and said second multidimensional representation;
transformation of said facial image data and said genetic information using transformations obtained by employing Common Discriminant Feature Extraction;
selection based on the Maximal Margin Robot matching score; and
selection based on the Preference Margin Optimization matching score.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A chemical mechanical polishing slurry for polishing a copper layer without excessively or destructively polishing a barrier layer beneath the copper layer, comprising:
an acid, wherein said acid is selected from the group consisting of hexanedioic acid, itaconic acid, formic acid, and 1,2,3,4-butane tetracarboxylic acid;
a surfactant, wherein said surfactant is selected from the group consisting of an anionic surfactant and a nonionic surfactant;
a silica sol having silica polishing particles that are surface modified with a surface charge modifier and that have potassium ions attached thereto, wherein said potassium ions are in an amount greater than 100 ppm based on a total weight of said silica polishing particles;
wherein said slurry has a pH value ranging from 3 to 4.
2. The chemical mechanical polishing slurry as claimed in claim 1, wherein said silica sol is produced by decationizing a solution of a silicate through a cation exchanger to form silicic acid in the solution, alkalizing the silicic acid with potassium hydroxide or potassium water glass to form the silica polishing particles, surface-modifying the silica polishing particles with the surface charge modifier, and decationizing the solution through a cation exchanger so as to remove mobile cation from the solution after surface-modifying.
3. The chemical mechanical polishing slurry as claimed in claim 2, wherein said surface charge modifier is an aluminate.
4. The chemical mechanical polishing slurry as claimed in claim 3, wherein said surface charge modifier is selected from the group consisting of sodium aluminate and potassium aluminate.
5. The chemical mechanical polishing slurry as claimed in claim 1, wherein said surface charge modifier is an oxide of a metal having a valence number greater than +3 and selected from the group consisting of aluminum, chromium, gallium, titanium, and zirconium.
6. The chemical mechanical polishing slurry as claimed in claim 1, wherein said acid is hexanedioic acid.
7. The chemical mechanical polishing slurry as claimed in claim 1, further comprising an additive selected from the group consisting of a biocide, a corrosion inhibitor, an oxidant, and a stabilizer.
8. The chemical mechanical polishing slurry as claimed in claim 7, wherein said corrosion inhibitor is benzotriazole.
9. The chemical mechanical polishing slurry as claimed in claim 7, wherein said oxidant is selected from the group consisting of hydrogen peroxide, ferric nitrate, potassium iodate, peracetic acid, and potassium permanganate.
10. The chemical mechanical polishing slurry as claimed in claim 9, wherein said oxidant is hydrogen peroxide.