All The Claims

1. An apparatus comprising:
an asynchronous constraint satisfaction problem solving module, the asynchronous constraint satisfaction problem solving module executable by one or more processors, the asynchronous constraint problem solving module configured to:
assign a priority level and a rank to each variable of a plurality of variables such that no two variables include both a same priority level and a same rank;
propagate at least one constraint to the plurality of variables in a first thread by reducing a speculative propagation range of a first variable of the plurality of variables when a first value in the speculative propagation range of the first variable is in conflict with the constraint; and
in a second thread, (1) update an explanation for the reduction in the speculative propagation range of the first variable, and (2) backtrack when a choice of a second value for a second variable of the plurality of variables would result in the speculative propagation range of the first variable becoming the empty set, wherein backtracking includes taking back a choice associated with a variable with a lowest priority level and if two choices are associated with respective variables assigned the same priority level then backtracking includes taking back the choice associated with the variable of the respective variables assigned a lower rank.
2. The apparatus of claim 1, wherein the asynchronous constraint satisfaction problem solving module is configured to:
choose the second value for the second variable in a third thread, the value chosen based on a heuristic.
3. The apparatus of claim 1, wherein the asynchronous constraint satisfaction problem solving module is configured to:
receive, during runtime, a new constraint; and
propagate, during runtime, the new constraint in the first thread.
4. The apparatus of claim 3, wherein the asynchronous constraint satisfaction problem solving module is configured to:
receive the plurality of variables and the at least one constraint;
choose the second value for the second variable, the choice made by selecting a value from a search range of the second variable; and
produce output including a solution value for the plurality of variables that satisfies one or more of; the at least one constraints and the new constraint.
5. The apparatus of claim 2, wherein each variable of the plurality of variables includes:
a choice status indicating if the variable is the subject of the choice based on the heuristic;
an initial range including all possible values of the variable;
a search range that is a subset of the initial range of the variable, the search range reduced from the initial range through a backtracking process; and
the speculative propagation range, the speculative propagation range a subset of the search range, the speculative propagation range including an explanation, the explanation including a history of the choices and constraints that reduced the speculative propagation range of the variable.
6. The apparatus of claim 5, wherein;
the search range of the first variable is initialized to the initial range of the first variable;
the speculative propagation range of the first variable is initialized to the initial range of the first variable;
the explanation of the speculative propagation range of the first variable is initialized to the empty set; and
the choice status of the first variable is initialized to indicate that the first variable is not currently the subject of a choice.
7. The apparatus of claim 5, wherein backtracking comprises:
when the explanation indicates that the choice affects only the speculative propagation range of the first variable, reduce the search range of the second variable by removing the second value from the search range and undo all other reductions based on the choice of the second value for the second variable.
8. A computer implemented method comprising:
assigning a priority level and rank to each variable of a plurality of variables such that no two variables include both a same priority level and a same rank;
propagating, using a first processor, at least one constraint to the plurality of variables by reducing a speculative propagation range of a first variable of the plurality of variables when a first value in the speculative propagation range is in conflict with the constraint; and
updating, using a second processor, an explanation for the reduction in the speculative propagation range of the first variable; and
backtracking when a choice of a second value for a second variable of the plurality of variables would result in the speculative propagation range of the first variable becoming the empty set, wherein backtracking includes taking back a choice associated with a variable with a lowest priority level and if two choices are associated with respective variables assigned the same priority level then taking back the choice associated with a variable of the respective variables assigned a lower rank.
9. The method of claim 8, comprising:
choosing, using a third processor, the second value for the second variable based on a heuristic.
10. The method of claim 8, comprising:
receiving, during runtime, a new constraint; and
propagating, during runtime the new constraint in the first thread.
11. The method of claim 10, comprising:
receiving the plurality of variables and the at least one constraint;
choosing a value for the second variable, the choice made by selecting a value from a search range of the second variable; and
producing an output including a solution value for the plurality of variables that satisfies one or more of: the at least one constraint and the new constraint.
12. The method of claim 11, comprising
receiving an initial range for a variable of the plurality of variables, the initial range including all possible values for the variable;
creating a choice status for the variable, the choice status indicating if the variable is the subject of the choice based on the heuristic;
creating a search range for the variable, the search range a subset of the initial range of the variable, the search range reduced from the initial range through the backtracking process; and
creating the speculative propagation range for the variable, the speculative propagation range a subset of the search range of the variable, the speculative propagation range including an explanation, the explanation including a history of the choices and constraints that reduced the initial range of the variable to the speculative propagation range of the variable.
13. The method of claim 12, comprising:
initializing the search range of the first variable to the initial range of the first variable;
initializing the speculative propagation range of the first variable to the initial range of the first variable;
initializing the explanation of the speculative propagation range of the first variable to the empty set; and
initializing the choice status of the first variable to indicate that the first variable is not currently the subject of a choice.
14. The method of claim 13, wherein backtracking when a choice of a second value for a second variable of the plurality of variables would result in the speculative propagation range of the first variable becoming the empty set includes:
when the explanation indicates that the choice affects only the speculative propagation range of the first variable, reducing the search range of the second variable by removing the second value from the search range and undo all other reductions based on the choice of the second value for the second variable.
15. A non-transitory machine readable storage device that stores instructions, the instructions, which when performed by a machine, cause the machine to perform operations for asynchronous constraint satisfaction problem solving, comprising:
assigning a priority level and a rank to each variable of a plurality of variables such that no two variables include both a same priority level and a same rank;
propagating, using a first processor, at least one constraint to the plurality of variables by reducing a speculative propagation range of a first variable of the plurality of variables when a first value in the speculative propagation range is in conflict with the constraint; and
updating, using a second processor, an explanation for the reduction in the speculative propagation range of the first variable; and
backtracking when a choice of a second value for a second variable of the plurality of variables would result in the speculative propagation range of the first variable becoming the empty set, wherein backtracking includes taking back a choice associated with a variable with a lowest priority level and if two choices are associated with respective variables assigned the same priority level then taking back the choice associated with a variable of the respective variables assigned a lower rank.
16. The machine readable storage device of claim 15, wherein the instructions include instructions which when performed by the machine, cause the machine to perform operations comprising:
choosing, using a third processor, the second value for the second variable based on a heuristic.
17. The machine readable storage device of claim 15, wherein the instructions include instructions which when performed by the machine, cause the machine to perform operations comprising:
receiving, during runtime, a new constraint; and
propagating, during runtime, the new constraint in the first thread.
18. The machine readable storage device of claim 15, wherein the instructions include instructions which when performed by the machine, cause the machine to perform operations comprising:
receiving the plurality of variables and the at least one constraint;
choosing a value for the second variable, the choice made by selecting a value from a search range of the second variable; and

producing output including a solution value for the plurality of variables that satisfies one or more of: the at least one constraint and the new constraint.
19. The machine readable storage device of claim 15, wherein the instructions for backtracking when a choice of a second value for a second variable of the plurality of variables would result in the speculative propagation range of the first variable becoming the empty set include instructions which when performed by the machine, cause the machine to perform operations comprising:
when the explanation indicates that the choice affects only the speculative propagation range of the first variable, reducing the search range of the second variable by removing the second value from the search range and undo all other reductions based on the choice of the second value for the second variable.
20. The machine readable storage device of claim 18, wherein the instructions include instructions which when performed by the machine, cause the machine to perform operations comprising:
receiving an initial range for a variable of the plurality of variables, the initial range including all possible values for the variable;
creating a choice status for the variable, the choice status indicating if the variable is the subject of the choice based on the heuristic;
creating a search range for the variable, the search range a subset of the initial range of the variable, the search range reduced from the initial range through the backtracking process; and
creating the speculative propagation range for the variable, the speculative propagation range a subset of the search range of the variable, the speculative propagation range including an explanation, the explanation including a history of the choices and constraints that reduced the initial range of the variable to the speculative propagation range of the variable.
The claims below are in addition to those above.
All refrences to claims which appear below refer to the numbering after this setence.

1. A method comprising:
receiving a detected image of at least one face;
generating a plurality of candidate face images based on the detected image, wherein a candidate face image is generated based on one or more of row or column shifts of pixels of the detected image;
analyzing the candidate face images based on data of at least one model identifying one or more poses related in part to at least one of a position or an orientation of respective candidate face images; and
determining that the image of at least one face corresponds to one of the poses based in part on one or more items of data of the candidate face images passing criteria identified by the model as corresponding to the pose.
2. The method of claim 1
wherein the at least one model comprises a plurality of models, each of the models corresponding to a respective pose, of a plurality of poses;
wherein analyzing the candidate face images further comprises analyzing each of the candidate face images to determine whether the candidate face images passes criterion established for each of the models; and
wherein determining that the image of at least one face corresponds to one of the poses further comprises determining, for each of the models, a total number of the candidate face images that pass the criterion.
3. The method of claim 2,
wherein determining that the image of at least one face corresponds to one of the poses comprises determining a pose of the at least one face of the detected image that corresponds to a respective pose of a corresponding model in which a highest number of the candidate face images passed the criterion.
4. The method of claim 1, wherein the at least one model comprises a plurality of models, each of the models corresponding to a respective pose of a plurality of poses, wherein analyzing the image of the at least one face based on data of at least one model comprises analyzing the detected image based in part on data of each of the models, the method further comprising:
determining, for each of the models, whether the detected image passes at least one condition for a respective pose of the poses; and
calculating one or more confidence scores associated with each of the models in response to determining that the detected image passed the condition for the respective pose.
5. The method of claim 1, wherein the at least one model comprises a plurality of models, each of the models corresponding to a respective pose of a plurality of poses, wherein analyzing the candidate face images based on data of at least one model comprises analyzing each of the candidate face images to determine whether each candidate face images passes criterion established for each of the models, the method further comprising:
determining, for each of the models, a confidence score for each of the candidate face images that passed the criterion.
6. The method of claim 5, further comprising:
adding each of the confidence scores corresponding to the models to obtain a plurality of total confidence scores, each of the total confidence scores corresponding to a respective model of the models; wherein determining that the image of at least one face corresponds to one of the poses further comprises determining that a pose of the image of at least one face corresponds to a respective pose of a corresponding model that is determined to comprise a highest total confidence score of the total confidence scores.
7. The method of claim 1, wherein the at least one model comprises a canonical correlation analysis model corresponding to a plurality of poses, each of the poses assigned a corresponding label, wherein analyzing the candidate face images based on data of at least one model comprises analyzing data of the detected image and data associated with the poses to determine whether the detected image corresponds to one of the poses, the method further comprising:
assigning a label of a corresponding pose to the detected image in response to determining that the detected image is associated with the corresponding pose;
wherein determining that the image of at least one face corresponds to one of the poses is based in part on determining that a pose of the face relates to the corresponding pose based at least in part on a value of the assigned label.
8. The method of claim 1, wherein the at least one model comprises a canonical correlation analysis model corresponding to a plurality of poses, each of the poses assigned a corresponding label of a plurality of labels, the method further comprising:
assigning a respective label of the labels to each of the candidate face images that are determined to be related in part to at least one of the poses.
9. An apparatus comprising:
at least one processor; and
at least one memory including computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following:
receive a detected image of at least one face;
generate a plurality of candidate face images based on the detected image, wherein a candidate face image is generated based on one or more of row or column shifts of pixels of the detected image;
analyze the candidate face images based on data of at one model identifying one or more poses related in part to at least one of a position or an orientation of respective candidate face images; and
determine that the image of at least one face corresponds to one of the poses based in part on one or more items of data of the candidate face images passing criteria identified by the model as corresponding to the pose.
10. The apparatus of claim 9, wherein the at least one model comprises a plurality of models, each of the models corresponding to a respective pose, of a plurality of poses;
wherein causing the apparatus to analyze the candidate face images further comprises causing the apparatus to analyze each of the candidate face images to determine whether the candidate face images passes criterion established for each of the models; and
wherein causing the apparatus to determine that the image of at least one face corresponds to one of the poses further comprises causing the apparatus to determine, for each of the models, a total number of the candidate face images that pass the criterion.
11. The apparatus of claim 10,
wherein causing the apparatus to determine that the image of at least one face corresponds to one of the poses comprises causing the apparatus to determine a pose of the at least one face of the detected image that corresponds to a respective pose of a corresponding model in which a highest number of the candidate face images passed the criterion.
12. The apparatus of claim 10, wherein the plurality of poses comprises at least one of a frontal pose, a tilt pose, a right half profile pose, a left half profile pose, a right full profile pose or a left full profile pose.
13. The apparatus of claim 9, wherein the at least one model comprises a plurality of models, each of the models corresponding to a respective pose of a plurality of poses, and
wherein causing the apparatus to analyze the image of the at least one face based on data of at least one model comprises causing the apparatus to analyze the detected image based in part on data of each of the models, wherein the memory and computer program code are further configured to, with the processor, cause the apparatus to:
determine, for each of the models, whether the detected image passes at least one condition for a respective pose of the poses; and
calculate one or more confidence scores associated with each of the models in response to determining that the detected image passed the condition for the respective pose.
14. The apparatus of claim 13, wherein the memory and computer program code are configured to, with the processor, cause the apparatus to:
determine that a pose of the face corresponds to a respective pose of a corresponding model assigned a highest confidence score of the confidence scores.
15. The apparatus of claim 9,
wherein the at least one model comprises a plurality of models, each of the models corresponding to a respective pose of a plurality of poses, wherein causing the apparatus to analyze the candidate face images based on data of at least one model comprises causing the apparatus to analyze each of the candidate face images to determine whether each candidate face images passes criterion established for each of the models, wherein the apparatus is further caused to:
determine, for each of the models, a confidence score for each of the candidate face images that passed the criterion.
16. The apparatus of claim 15, wherein the memory and computer program code are configured to, with the processor, cause the apparatus to:
add each of the confidence scores corresponding to the models to obtain a plurality of total confidence scores, each of the total confidence scores corresponding to a respective model of the models, wherein causing the apparatus to determine that a pose of the image of at least one face corresponds to a respective pose of a corresponding model that is determined to comprise a highest total confidence score among the total confidence scores.
17. The apparatus of claim 9, wherein the at least one model comprises a canonical correlation analysis model corresponding to a plurality of poses, each of the poses assigned a corresponding label, wherein causing the apparatus to analyze the candidate face images based on data of at least one model comprises causing the apparatus to analyze data of the detected image and data associated with the poses to determine whether the detected image corresponds to one of the poses, wherein the apparatus is further caused to:
assign a label of a corresponding pose to the detected image in response to determining that the detected image is associated with the corresponding pose;
wherein causing the apparatus to determine that the image of at least one face corresponds to one of the poses is based in part on causing the apparatus to determine that a pose of the face relates to the corresponding pose based at least in part on a value of the assigned label.
18. The apparatus of claim 9, wherein the at least one model comprises a canonical correlation analysis model corresponding to a plurality of poses, each of the poses assigned a corresponding label of a plurality of labels, and wherein the memory and computer program code are configured to, with the processor, cause the apparatus to:
assign a respective label of the labels to each of the candidate face images that are determined to be related in part to at least one of the poses.
19. The apparatus of claim 18, wherein a value of the assigned label denotes that the candidate face images correspond to a type of pose and wherein the memory and computer program code are configured to, with the processor, cause the apparatus to:
determine that a pose of the face comprises a pose that is determined to correspond to a highest number of the candidate face images.
20. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:
program code instructions configured to facilitate receipt of a detected image of at least one face;
program code instructions to generate a plurality of candidate face images based on the detected image, wherein a candidate face image is generated based on one or more of row or column shifts of pixels of the detected image;
program code instructions configured to analyze the candidate face images based on data of at least one model identifying one or more poses related in part to at least one of a position or an orientation of respective candidate face images; and
program code instructions configured to determine that the image of at least one face corresponds to one of the poses based in part on one or more items of data of the candidate face images passing criteria identified by the model as corresponding to the pose.

1. A method for analyzing a geophysical data volume obtained or derived from a geophysical survey of a subsurface region to determine physical structure of the subsurface region, comprising:
(a) dividing the data volume into a plurality N of initial segments, at least one segment being greater than one voxel in size;
(b) successively combining pairs of segments until the number of segments is reduced to a selected number M, where M<N; and
(c) analyzing some or all of either the M segments, or segments from an intermediate stage of combination, to interpret subsurface physical structure.
2. The method of claim 1, wherein M=1, and a combination record is kept showing remaining segments after each segment pair combination.
3. The method of claim 1, wherein the initial segments are topologically consistent.
4. The method of claim 3, wherein segments throughout the combining are topologically consistent, meaning that combinations that would violate topological consistency are not performed.
5. The method of claim 1, wherein combining is performed by successive combination of a smallest segment with its smallest neighbor.
6. The method of claim 5, wherein segment size and neighborhoods are determined only once and recorded in tables that are updated after each stage of the successive combining.
7. The method of claim 5, wherein neighbor is restricted to lateral neighbor at one or more selected stages of combining, or neighbor is restricted to vertical neighbor at one or more selected stages of combining.
8. The method of claim 1, wherein combining pairs of segments comprises:
sequencing the initial segments by a selected sequence rule;
graphing sequential sequence number vs. segment size;
smoothing the graph a plurality of times and tracking segment number boundaries that survive and those that vanish at each smoothing stage; and
using order of vanishing of boundaries to determine order of combining corresponding pairs of segments.
9. The method of claim 1, wherein each segment pair that is combined are most similar neighbors, and similarity is based on value for each segment of a selected attribute of the geophysical data.
10. The method of claim 9, wherein each segment pair that is combined also contains the currently smallest segment.
11. The method of claim 1, wherein distance between two segments is used as a factor to favor or disfavor combination of the two segments.
12. The method of claim 1, wherein connectivity between two segments is used as a factor to favor or disfavor combination of the two segments.
13. The method of claim 1, wherein sequence of combination of segment pairs is based on concurrence, meaning extent of each pair’s common boundary.
14. The method of claim 13, wherein concurrence values are weighted by a selected weighting function.
15. The method of claim 1, wherein at least two different methods for selecting pairs of segments to combine are used in reducing from N segments to M segments.
16. The method of claim 15, wherein a first method is used for combining pairs until a selected stage of combination is reached, then the segments defined at that stage of combination are used as masks to constrain a successive combining of the N initial segments using a second method for combining.
17. The method of claim 1, wherein analyzing the M segments comprises highgrading, ranking, or prioritizing the M segments based on one or more measures selected from a group consisting of segment geometrical properties, direct hydrocarbon indicators or DHI’s, and other secondary data collocated with the M segments.
18. The method of claim 17, further comprising using the one or more measures after a plurality of stages of combination to determine an optimum stage of combination for analysis.
19. The method of claim 18, wherein determining an optimum stage of combination for analysis comprises using said one or more measures to compute an entropy value for each of the plurality of stages, and then using the entropy values to determine an optimum stage of combination for analysis.
20. The method of claim 1, wherein analyzing the M segments comprises analyzing small segments combined to form larger ones, and combining, correlating or contrasting results.
21. The method of claim 1, wherein in the combining pairs of segments, each segment can be combined only with one of a prescribed list of neighbors.
22. The method of claim 1, wherein the method is computer implemented, and at least the successively combining pairs of segments is performed using the computer.
23. The method of claim 1, wherein interpreting the subsurface physical structure comprises searching a segment display for indication of one or more geobodies that potentially represent hydrocarbon accumulations.
24. A method for producing hydrocarbons from a subsurface region, comprising:
(a) obtaining a geophysical data volume from a survey of the subsurface region;
(b) analyzing the geophysical data volume to determine physical structure of the subsurface region, using a method as described in claim 1, which is incorporated herein by reference; and
(c) drilling a well into the subsurface region based at least partly on the preceding analysis, and producing hydrocarbons from the well.
The claims below are in addition to those above.
All refrences to claims which appear below refer to the numbering after this setence.

1. An optical-fiber cable, comprising:
one or more optical fibers;
an enclosing tape at least partially enclosing said one or more optical fibers;
a plurality of discrete deposits of adhesive material coupling said enclosing tape to at least one said optical fiber; and
a buffer tube substantially enclosing said one or more optical fibers, said enclosing tape, and said discrete deposits of adhesive material;
wherein said buffer tube has a buffer-tube adhesive filling coefficient of between about 0.001 and 0.05 measured over a buffer-tube length of 100 meters.
2. An optical-fiber cable according to claim 1, wherein:
said one or more optical fibers comprise a ribbon stack; and
the optical-fiber cable has an optical-fiber pullout force of least about 0.1625 Nfiber in accordance with the Ribbon Pullout Test Procedure as set forth in the Verizon Technical Purchasing Requirements VZ.TPR.9430 (Issue 4, April 2010).
3. An optical-fiber cable according to claim 1, wherein said enclosing tape is perforated.
4. An optical-fiber cable according to claim 1, wherein said enclosing tape comprises a water-swellable tape.
5. An optical-fiber cable according to claim 1, wherein at least one of said discrete deposits of adhesive material couples said enclosing tape to said buffer tube.
6. An optical-fiber cable according to claim 1, wherein said discrete deposits of adhesive material couple said enclosing tape to at least one said optical fiber without requiring frictional coupling.
7. An optical-fiber cable according to claim 1, wherein said discrete deposits of adhesive material resist movement of said one or more optical fibers relative to said enclosing tape without requiring the application of an external compressive force upon said enclosing tape.
8. An optical-fiber cable according to claim 1, wherein said discrete deposits of adhesive material comprise a two-part silicone.
9. An optical-fiber cable according to claim 1, wherein:
said buffer tube defines free space therein; and
at a cross section of said buffer tube, at least one of said discrete deposits of adhesive material substantially fills the free space within said buffer tube.
10. An optical-fiber cable according to claim 1, wherein said buffer tube has a buffer-tube adhesive filling coefficient of less than about 0.01 measured over a buffer-tube length of 100 meters.
11. An optical-fiber cable according to claim 1, wherein said buffer tube has a buffer-tube adhesive filling coefficient of between about 0.0015 and 0.005 measured over a buffer-tube length of 100 meters.
12. An optical-fiber cable according to claim 1, wherein said buffer tube has a buffer-tube adhesive filling coefficient of between about 0.002 and 0.003 measured over a buffer-tube length of 100 meters.
13. An optical-fiber cable according to claim 1, wherein said buffer tube is substantially free of thixotropic filling greases.
14. An optical-fiber cable, comprising:
one or more optical fibers;
an enclosing tape at least partially enclosing said one or more optical fibers;
a plurality of discrete deposits of adhesive material coupling said enclosing tape to at least one said optical fiber; and
a cable jacket substantially enclosing said one or more optical fibers, said enclosing tape, and said discrete deposits of adhesive material;
wherein the optical-fiber cable has a cable adhesive filling coefficient of between about 0.0005 and 0.05 measured over a cable length of 100 meters.
15. An optical-fiber cable according to claim 14, wherein:
said one or more optical fibers comprise a ribbon stack; and
the optical-fiber cable has an optical-fiber pullout force of least about 0.1625 Nfiber in accordance with the Ribbon Pullout Test Procedure as set forth in the Verizon Technical Purchasing Requirements VZ.TPR.9430 (Issue 4, April 2010).
16. An optical-fiber cable according to claim 14, wherein said enclosing tape is perforated.
17. An optical-fiber cable according to claim 14, wherein said enclosing tape comprises a water-swellable tape.
18. An optical-fiber cable according to claim 14, wherein at least one of said discrete deposits of adhesive material couples said enclosing tape to said cable jacket.
19. An optical-fiber cable according to claim 14, wherein said discrete deposits of adhesive material couple said enclosing tape to at least one said optical fiber without requiring frictional coupling.
20. An optical-fiber cable according to claim 14, wherein said discrete deposits of adhesive material resist movement of said one or more optical fibers relative to said enclosing tape without requiring the application of an external compressive force upon said enclosing tape.
21. An optical-fiber cable according to claim 14, wherein said discrete deposits of adhesive material comprise a two-part silicone.
22. An optical-fiber cable according to claim 14, wherein the optical-fiber cable has a cable adhesive filling coefficient of less than about 0.005 measured over a cable length of 100 meters.
23. An optical-fiber cable according to claim 14, wherein the optical-fiber cable has a cable adhesive filling coefficient of between about 0.0015 and 0.0025 measured over a cable length of 100 meters.
24. An optical-fiber cable according to claim 14, comprising a buffer tube positioned within said cable jacket, said buffer tube substantially enclosing said one or more optical fibers, said enclosing tape, and said discrete deposits of adhesive material.
25. A method for manufacturing an optical-fiber cable, comprising
applying a substantially uncured adhesive to (i) one or more optical fibers andor (ii) an enclosing tape, the substantially uncured adhesive having a viscosity at application of between about 500 centipoise and 5000 centipoise;
at least partially enclosing the optical fibers with the enclosing tape; and
curing the substantially uncured adhesive.
26. A method according to claim 25, comprising
extruding a molten polymeric tube around the optical fibers and the enclosing tape; and
cooling the molten polymeric tube to form a buffer tube or cable jacket;
wherein the substantially uncured adhesive does not finish curing until the molten polymeric tube solidifies.
27. A method according to claim 25, comprising seeping at least a portion of the substantially uncured adhesive entirely through the enclosing tape after the substantially uncured adhesive has been applied and before the substantially uncured adhesive finishes curing.
28. A method according to claim 25, wherein the viscosity at application of the substantially uncured adhesive is between about 1000 centipoise and 4000 centipoise.
29. A method according to claim 25, wherein the viscosity at application of the substantially uncured adhesive is between about 2000 centipoise and 3000 centipoise.