1. A computer implemented method of training a prediction model to estimate a learning curve indicative of training of a statistical machine translation (SMT), the computer implemented method comprising:
selecting a first set of anchor points, wherein each of the first set of anchor points corresponds to a different number of monolingual segments, wherein the first set of anchor points are selectable by a user;
computing a weight vector for each of the first set of anchor points based, at least in part, on a first set of parameters associated with each of the first set of anchor points; and
computing a first confidence score for each of the first set of anchor points based on the weight vector.
2. The computer implemented method of claim 1 further comprising computing a second set of parameters associated with the learning curve based, at least in part, on the weight vector and the first confidence score.
3. The computer implemented method of claim 1, wherein the computing of the first confidence score comprises:
fitting a first curve based on each of the first set of anchor points to determine a first score for each of the first set of anchor points;
computing the weight vector for each of the first set of anchor points based, at least in part, on a regression model and the first set of parameters, and the first score and
computing the first confidence score for each of the first set of anchor points based on the weight vector, the first set of parameters, and the first score.
4. The computer implemented method of claim 1, wherein the first set of parameters include average length of a segment, average length of tokens, lexical diversity, word-order divergence.
5. The computer implemented method of claim 1, wherein the second set of parameters includes a value at an origin, a slope at the origin, and an asymptotic value.
6. The computer implemented method of claim 1, wherein the first set of anchor points includes at least three anchor points.
7. The computer implemented method of claim 1 further comprising displaying the learning curve to the user.
8. A computer implemented method of estimating a learning curve indicative of training of a statistical machine translation (SMT), the computer implemented method comprising:
selecting a second set of anchor points, wherein each of the second set of anchor points corresponds to a different number of bi-lingual segments, wherein the second set of anchor points are selectable by a user;
fitting a second curve based on each of the second set of anchor points; and
estimating a second set of parameters of the second curve to obtain the learning curve, wherein the second set of parameters include a value at an origin, a slope at the origin, and an asymptotic value.
9. The computer implemented method of claim 8, wherein the estimating corresponds to at least one of extrapolating or interpolating.
10. The computer implemented method of claim 8 further comprising fitting a first curve based on the second set of anchor points.
11. The computer implemented method of claim 10 further comprising computing a root mean square error based on the learning curve and the first curve.
12. The computer implemented method of claim 8 further comprising displaying the learning curve to the user.
13. A computer implemented method of training a prediction model to estimate a learning curve associated with training of a statistical machine translation (SMT), the computer implemented method comprising:
selecting a first set of anchor points, wherein each of the first set of anchor points corresponds to a different number of monolingual segments, wherein the first set of anchor point are selectable by a user;
selecting a second set of anchor points, wherein each of the second set of anchor points corresponds to a different number of bi-lingual segments, wherein the second set of anchor point are selectable by the user;
fitting a second curve based on the second set of anchor points;
extending the second curve to the first set of anchor points;
computing a second confidence score for each of the first set of anchor points based, at least in part, on one or more parameters associated with the second curve;
computing a weight vector for each of the first set of anchor points based on a first set of parameters associated with each of the first set of anchor points; and
computing a first confidence score for each of the first set of anchor points based on the weight vector.
14. The computer implemented method of claim 13 further comprising computing a second set of parameters associated with the learning curve based, at least in part on, the first confidence score, the second confidence score, and the weight vector.
15. The computer implemented method of claim 13, wherein the computing the first confidence score further comprises:
fitting a first curve based on each of the first set of anchor points to determine a first score for each of the first set of anchor points;
computing the weight vector for each of the first set of anchor points based, at least in part, on a regression model, the first set of parameters, and the first score; and
computing the first confidence score for each of the first set of anchor points based on the weight vector, the first set of parameters and the first score.
16. The computer implemented method of claim 15, wherein the first curve corresponds to a benchmark curve.
17. The computer implemented method of claim 13, wherein the first set of parameters include average length of a segment, average length of tokens, lexical diversity, word-order divergence.
18. The computer implemented method of claim 13, wherein the second set of parameters includes a slope, asymptotic value and steady state point.
19. The computer implemented method of claim 13, wherein extending corresponds to at least one of extrapolating or interpolating.
20. The computer implemented method of claim 13 further comprising displaying the learning curve to the user.
21. A prediction model server for estimating a learning curve indicative of training of a SMT, the system comprising:
an anchor point selection module configured to select at least one of a first set of anchor points or a second set of anchor points;
a curve fitting module configured to fit a second curve based on each of the second set of anchor points;
an estimation module configured to extrapolate the second curve to the first set of anchor points;
a scoring module configured to:
compute a second confidence score for each of the first set of anchor points based, at least in part, on one or more parameters associated with the second curve; and
compute a weight vector for each of the first set of anchor points based on a first set of parameters associated with each of the first set of anchor points; and
wherein, the curve fitting module is further configured to determine a second set of parameters associated with the learning curve based, at least in part, on the second confidence score and the weight vector.
22. The prediction model server of claim 21, wherein the first set of parameters include average length of a segment, average length of tokens, lexical diversity, word-order divergence.
23. The prediction model server of claim 21 further comprising:
a benchmark module configured to generate a first curve to determine a first score for each of the first set of anchor points;
a regression module configured to generate a regression model, wherein the weight vector is assigned to each of the first set of anchor points using the regression model based on the first set of parameters, and the first score; and
the scoring module configured to compute a first confidence score based on the weight vector, the first set of parameters and the first score.
24. The prediction model server of claim 23, wherein the second set of parameters is determined based on the first confidence score.
25. The prediction model server of claim 21, wherein each of the first set of anchor points includes a different number of monolingual segments.
26. The prediction model server of claim 21, wherein each of the second set of anchor points includes a different number of bi-lingual segments.
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 firearm suppressor, comprising:
a hollow outer can defining an interior with a cross-sectional shape and an interior longitudinal length, the outer can having:
an interior-threaded proximal end; and
a radially closed distal end defining a longitudinal can bore opening the interior of the can to the environment, the longitudinal can bore having a diameter sufficient to permit a projectile to pass therethrough;
a barrel nut having an interior barrel bore shaped to receive at least a portion of a firearm barrel therethrough and an outer-threaded distal end corresponding to the interior-threaded proximal end of the outer can to removably secure the outer can thereto, the barrel nut being operable to secure the firearm barrel to a firearm; and
a monolithic baffle having:
at least one intermediate baffle wall disposed proximal of the distal end of the outer can, the at least one intermediate baffle wall:
having an intermediate longitudinal bore axially aligned with the longitudinal can bore; and
extending radially outward to a shape substantially equal to the cross-sectional shape of the outer can interior to substantially prevent fluid from passing through the at least one intermediate baffle wall other than through the intermediate longitudinal bore;
a proximal end portion having a proximal baffle wall defining a proximal longitudinal bore axially aligned with the intermediate longitudinal bore and the longitudinal can bore and shaped to receive at least a portion of the firearm barrel and removably secure thereto, the proximal baffle wall extending radially outward to a shape smaller than the cross-sectional shape of the outer can interior to permit fluid to pass from one side of the proximal baffle wall to the other in addition to through the proximal longitudinal bore; and
a longitudinal baffle extent shorter than the interior longitudinal length such that, when the baffle and the outer can are installed on the firearm, the outer can and the barrel nut seal the baffle therein and define an interior proximal plenum extending from the proximal end portion to the barrel nut, the proximal baffle wall shaped to direct fluid ejecting from the firearm barrel distally back in an opposite direction into the interior proximal plenum as a muzzle brake.