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. (a) For the purpose of this Article, “term workload” means the direct instructional component or non- instructional assignment.
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:
Regularization. Notwithstanding any other provisions of this Agreement, a faculty member will become regular when either:
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: ∑︁ 𝑘 𝐶(𝜃) = (𝑟𝜃(x𝑖) − y𝑖)2 , (4) 𝑖=0 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: ∑︁ 𝑘 𝐶(𝜃) = − log(𝑃 (y𝑖|x𝑖, 𝜃)) . (5) 𝑖=0
Regularization. Regularization involves introducing additional information in order to stabilize an ill-posed inverse problem in the presence of noise. This information is usually in the form of a penalty: restrictions on smoothness of the solution or bounds on the vector space norm. We begin by showing why regularization is needed, and how it can be done through spectral filtering. To simplify the discussion, we assume a linear ill-posed inverse problem of the form
Regularization. Conversion of Instructors from Term to Regular StatusA term employee will be eligible for regularization if they have worked a minimum of 633 hours in each of two consecutive appointment years. Regularization will be based on: the total hours worked in each of the two consecutive qualifying years, at the lesser of the two years, to a maximum of full-time; that number of hours will be converted to an FTE value; the department will determine the allocation of 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 12 months, with an annual scheduled break (lay-off notice not required, no provisions of lay-off apply). Conversion of Part-time Term to Increased Regular Increase to regular appointment will be based on: additional term hours worked will be converted to regular, based on the total hours worked in each of the two (2) consecutive qualifying years, at the lesser of the two (2) years, to a maximum of full-time; that number of hours will be converted to an FTE value; the department will determine the allocation of workload (number of hours per day and months per year) to achieve that FTE.Other ConditionsConversions will be carried out upon review on April 1st for implementation for any change required by August 1st of each year.An appointment year is August 1st to July 31st.The availability of such qualifying ongoing employment is confirmed no later than October 1st after completion of the two consecutive appointment xxxxx.Xx all cases, regularization or conversion is subject to satisfactory evaluation, seniority considerations if relevant, availability of ongoing work, and qualifications for the work available.Multiple DepartmentsIn cases where hours for regularization or conversion are accrued in more than one (1) department, the following will apply:
Regularization. Regularization is a tool for the numerical treatment of ill-posed inverse prob- lems. There are two main approaches for regularization: direct and iterative regularization. As mentioned in the introduction, iterative regularization approaches are generally preferred for large-scale problems, but they suffer from semi-convergence limitations. To better understand the need for regularization, we first present a the- oretical analysis based on the singular value decomposition, or SVD. Let A = UΣVT denote the SVD of A, where the columns ui of U and vi of V contain, respectively, the left and right singular vectors of A and Σ = diag(σ1, σ2, · · · , σn) is a diagonal matrix containing the singular val- ues of A, with σ1 ≥ σ2 ≥ · · · ≥ σn > 0. Using the singular value expansion of A, an inverse solution can be written as n T n T n T inv σi i x = A−1b = Σ ui bv i=1 = Σ ui b true v i=1 + Σ ui ε v i=1 σi . (2.2) σi i s x˛tr¸ue x s er˛r¸or x As indicated above, the inverse solution is comprised of two components: x true, which is the desired solution, and an error term. Before discussing algorithms to compute approximations of x true, it is useful to study the error term. Matrices arising from ill-posed inverse problems have the following proper- ties.
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) U where | | denotes the 2-norm.
