Regularization. In preparation for the review set out below, the University will provide the Union with a copy of all temporary staffing activities summarized by classification and work area. This information will be provided to the Union by January 15 of each year covering the previous calendar year. The list will contain the following information: position, position number, incumbent, employee category, start date and end date. The University will provide a complete listing annually by January 15th of each year.
Regularization. Regularization is the process whereby an employee's term of appointment shall be revised from short-term to regular, continuous (full-time or proportional). To be eligible for regularization a short-term employee must have worked four (4) consecutive semesters in a two (2) year period, excluding spring/summer semesters, and have filled a position directly funded by the College base profile budget, and have received satisfactory comprehensive evaluations. Where further regularizable work is available in the third year, employees will be offered a regular continuous appointment as defined in Article 4.1. The appointment will be based on the average of the regularized work performed during the regularization period.
Regularization. Nothing in Article 4 prohibits the College’s right to regularize any position as it deems necessary. Regularization is the process whereby an employee's term of appointment shall be revised from short-term to regular, continuous (full-time or proportional). To be eligible for regularization:
a) a short-term employee must have worked two consecutive academic years immediately preceding regularization with an annual workload of fifty percent (50%) or greater in each of those years; and,
b) There is a reasonable expectation of ongoing employment for which the faculty member is deemed qualified, at a workload of fifty percent (50%) or greater of an annual full-time workload in the next academic; and,
c) The evaluations, if any, of the faculty member during the two consecutive academic years immediately preceding regularization have all been deemed satisfactory. A short-term employee who is eligible for regularization will be offered a regular appointment for the following academic year. The appointment proportion (of not less than fifty percent (50%)) will be based on the amount of work available in that year.
Regularization. (a) For the purpose of this Article, “term workload” means the direct instructional component or non- instructional assignment.
(b) Conversion of Instructors from Term to Regular Status A term employee will be eligible for regularization if he/she has worked a minimum of 0.4348 FTE term workload in each of two (2) consecutive appointment years (see Appendix I). Regularization will be based on:
(1) the average term workload of the two (2) consecutive qualifying years, to a maximum of full-time, will be converted to an FTE value (see Appendix I); and
(2) through an annual review, the department will determine the allocation of annual workload (number of hours per day and months per year) to achieve that FTE. Note: this could result in a regular appointment of less than twelve (12) months, with an annual scheduled break (lay-off notice not required, no provisions of lay-off apply).
(c) Conversion of Part-time Term Appointments to Increased Regular Status Increase to regular appointment will be based on:
(1) additional term workload will be converted to regular, based on the average term workload of the two (2) consecutive qualifying years, to a maximum of full-time, which will be converted to an FTE value (see Appendix I); and,
(2) through an annual review, the department will determine the allocation of annual workload (number of hours per day and months per year) to achieve that FTE.
(d) Other Conditions
(1) Conversions will be carried out upon review on April 1 for implementation for any change required by August 1 of each year.
(2) An appointment year is August 1 to July 31.
(3) In all cases, regularization or conversion is subject to satisfactory evaluation, seniority considerations if relevant, availability of ongoing work, and qualifications for the work available.
(4) Provided that all other conditions are met, the absence of an evaluation having been done shall not be a bar to regularization.
(5) The availability of such qualifying ongoing employment that is not for the purpose of leave replacement, such as under Articles 2.9, 11, 17, 18, 19, 22.6, 24.3, is confirmed no later than October 1 after completion of the two consecutive appointment years.
Regularization. Notwithstanding any other provisions of this Agreement, a faculty member will become regular when either:
(i) The faculty member has occupied a full-time position in the same discipline/program for 24 consecutive months, including non-instructional time, where that position has not been posted and the faculty member has received only satisfactory evaluations; or
(ii) A full-time regular position is advertised and the position has been filled by the faculty member on a full-time basis for at least 18 consecutive months, including non- instructional time, provided that the qualifications, abilities and experience of the faculty member are equal to those of the other applicants. The temporary faculty member will be granted an interview and upon written request, will be given reasons if unsuccessful.
Regularization. (a) For the purpose of this Article, "term workload" means the direct instructional component or non-instructional assignment.
Regularization. In our pre-trained experiments with SDA, we did not apply extra regularization during fine-tuning because the pre-training acts as a regularizer. For the DNN experiments we used L2-regularization and dropout. We ap- plied dropout to both the input and hidden layers, as adding dropout to the input layers has reduced error rates in some studies [18]. For dropout, we used 10% and 20% for the input layer, and 40% and 50% for the hidden layers, which follows the research of Xxxxxxxxxx et al. [40]. In addition, a factor of 0.0001 was used for L2 weight decay regularization, that adds a term to the cost function to penalize large weights. Stop criterion - We stopped training after 200 epochs for XXXx pre-trained by SDA, and after 300 epochs for XXXx without pre-training; alternatively, we stopped the training if within 10 epochs after a new low in validation error, no new low below current low multiplied by a threshold (0.995) was reached. This decision was motivated by the desire to continue training after attaining a new low to search for another new low. However, this was limited to prevent overfitting. Cost function - For SDA pre-training, we used the squared error. If we have 𝑘 training examples this can be calculated as follows: ∑︁ 𝐶(𝜃) = (𝑟𝜃(x𝑖) − y𝑖)2 , (4) where 𝜃 represents the parameters (weights of the neural network), 𝑟𝜃 represents the reconstruction vector (using 𝜃). The negative log-likelihood function was minimized for DNN: ∑︁ 𝐶(𝜃) = − log(𝑃 (y𝑖|x𝑖, 𝜃)) . (5)
Regularization. In this section we use a toy, k = 1, smoothing problem to motivate an approach to regularization which is adopted in what follows. We assume that the cluster centers are periodic with equally spaced observations so we may use a Fourier argument. In particular we work on the space of 1-periodic functions in H2, Y = .µ : [0, 1] → R s.t. µ(0) = µ(1) and µ ∈ H2Σ . (3.9) For arbitrary sequences (an), (bn) and data Ψn = {(tj, zj)}n ⊂ [0, 1] × Rd we define n−1 the functional f (ω)(µ) = an Σ |µ(tj) − zj|2 + bnǁ∇2µǁ2 2 . (3.10) Data are points in space-time: [0, 1] × R. The regularization is chosen so that it penalizes the L2 norm of the second derivative. For simplicity, we employ deterministic measurement times tj in the following proposition although this lies outside the formal framework which we consider subsequently. Another simplification we make is to use convergence in expectation rather than almost sure convergence. This simplifies our arguments. We stress that this section is the motivation for the problem studied in Section 3.3.2. We will give conditions on the scaling of an and bn that determine whether E min f (ω) and Eµ(n) stay bounded where µ(n) is the minimizer of f (ω).
Proposition 3.3.1. Let data be given by Ψn = {(tj, zj)}n with tj = j under the assumption zj = µ†(tj) + sj for sj iid noise with finite variance and µ† ∈ L2 and define Y by (3.9). Then inf µ∈Y f (ω)(µ) defined by (3.10) stays bounded (in expectation) if an = O( 1 ) for any positive sequence bn.
Regularization. The University of Maine System and the University of Maine Part-time Faculty Association recommend that, in order to recognize the work patterns of a class of part-time faculty, such faculty may be classified part-time regular faculty and their employment status be converted from temporary positions of part-time regular faculty with pay based upon workload and longevity of service rather than credit hour. The process for creating such positions would be negotiated in the next round of negotiations.
Regularization. When facing non-linear minimization problems, a question that naturally arises is whether or not the functional has a minimum. Even when minimizing a smooth function in Rn, this can be an issue. In fact, if the region of the admissible solutions is not bounded, the functional may not be bounded from below or, if bounded, may not achieve the minimum at any point. The same is true in the infinite dimensional Xxxxxxx spaces setting. Furthermore, in the context of DA, the data that we are trying to match are usu- ally affected by noise, due for instance to measurement errors. We can write the data as d dtrue ν (2.42) where ν is a white noise. In general, ν does not lie in the space spanned by all the possible solutions to the constraint equations. Nevertheless, the properties of the minimization problem (2.24) deteriorate in the presence of noise, which may impact the convergence of the minimization routine towards the optimum (if any). A common way to deal with this issue is to modify the functional, adding a term that penalizes admissible solutions with non-desired features. This technique is called variational regularization. The analysis of regularization techniques is be- yond the scope of this work. Here we introduce only the concept of regularization and we refer to [23, 100] for more details. The new functional to minimize can be written as J px, uq F px, uq αRpuq (2.43) where u is the control variable and α 0 is the regularization parameter, which determines how much the regularization term should affect the minimization pro- cess. To calibrate this parameter is not an easy task, and several methods have been proposed, such as Generalized Cross Validation, L-curve or the Discrepancy Prin- ciple (see, e.g., [23, 100]). The choice of R may change depending on the application. A popular choice is given by Tikhonov regularization. In this case, the expression for R is R }Lpu uref q} (2.44) where uref is a reference value for u, and L is a semi-definite operator. The most frequent choices for L are the identity operator, which penalizes admissible solu- tions with large norm, hence enhancing the convexity of the functional, and the gradient operator, which penalizes highly oscillating solutions. Another frequently used regularization is the Total Variation, given by Rpuq |∇u|dx, (2.45) where | | denotes the 2-norm.
