Geometric Sequences and Series by MATHguide. It may be necessary to calculate the number of terms in a certain geometric sequence. To do so, we would need to know two things.
MATHEMATICS 405 Notes MODULE - III Sequences And Series Arithmetic And Geometric Progressions 13 ARITHMETIC AND GEOMETRIC PROGRESSIONS Succession of numbers of. Geometric Mean Tutorial Geometric Mean Definition: Geometric Mean is a kind of average of a set of numbers that is different from the arithmetic average. An assortment of concepts in math that help us deal with sequences and proofs. Arithmetic sequence. The terms progress by a common difference. 1, 5, 9, 13, 17. This is arithmetic: a=1 (first term) d=+4 (the difference) 7, 2, -3, -8. For two numbers x and y, let x, a, y be a sequence of three numbers. If x, a, y is an arithmetic progression then 'a' is called arithmetic mean.The investment is actually worth $17,010 at the end of the five years. The arithmetic mean overstated the return by almost 4%. The correct mean to use in these.
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Lumia 920 is one of the popular smartphones Nokia developed to run on Windows 8. Nokia Lumia 920 features a large 4.5-inch screen. Where can I find the IMEI number for my phone? Where can I get support for my Lumia smartphone? This path requires that you currently own an older Nokia device. The second path is using just the two phones, no mediator needed; the data will be transferred directly from one phone to the other. We will explore both options, since these two paths are not mutually exclusive: You might want to do both to ensure that your personal data also has a home on the cloud, apart from being transferred to the Nokia Lumia. What you will need to get started. Apart from the phones in question, you will need the following: Step 0: Setting up your Microsoft account. Skip this step, if you already have a Microsoft account setup. Your Microsoft account (erstwhile Windows Live ID/Hotmail account) is a one- stop pass to access everything Microsoft. If you are an Xbox Live user, then you already have one of these; ditto if you use services such as Hotmail (now Outlook. For Windows Phone 8 devices, the Microsoft account is used to access the Store (where the apps reside). Setting up is simple, follow these steps on your Nokia Lumia phone: Unlock your phone to access the home screen. Flick left from the home screen to access the app screen. Scroll down to find the Settings app, it should have a gear icon next to it Find the . Once again, you can take both routes if you have a Nokia Symbian device. Let’s start with the cloud setup. Using Sky. Drive to backup and sync your contacts from an older Nokia phone. If you are coming to the Nokia Lumia from an older Nokia Symbian phone, then this is the way to go. First, if you don’t already have it, download the Nokia Suite (http: //www. You will need a Nokia account to do so. This is a two- stage process: first, you will copy your phone’s data to Sky. 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Once you have the Nokia Suite started, follow these steps: Click on Tools > Upload to Skydrive. Click on Start. In the next screen you can choose the data you wish to sync to the cloud. NOTE: you can only copy photos which are in these formats: jpg, gif, png. Before you can start the sync, the Nokia Suite will ask you to grant it permission to access your Sky. Drive account. This will require you to sign in to your Microsoft account. Please do so and then click on Yes in the next screen. That’s pretty much that. The Nokia Suite will now transfer your personal data to Sky. Drive. Once done, you have finished the first stage of this sync process. Step 1: Click here to start the process which will upload your Nokia phone’s data to Skydrive. Step 2: Click on Start to begin the upload process. Step 3: Here, you can choose which data you sync to the cloud. Step 4a: Login to your Sky. Drive account first. Step 4b: Grant the Nokia Suite permission to access your Sky. Drive account. Step 5a: Nokia Suite will now sync your phone’s data to the cloud. Step 5b: . Your Nokia phone’s data is now synced to your Sky. Drive account. Your contacts will now be stored in the Sky. Drive Phonebook. Your photos will be saved to an album named after your old phone (E5- 0. To complete the process, log in to your Microsoft account on your Nokia Lumia if you haven’t already (see Step 0). If you had logged in earlier, your data should start syncing with your Nokia Lumia phone through Sky. Drive, give it some time? Unfortunately, there isn’t a straightforward way to sync your messages to the Nokia Lumia using the Nokia Suite; at least as of this writing. But fret not, the second option will also allow you to upload your SMSes! Syncing data from i. Phone, Blackberry, or Android to the Nokia Lumia. You should consider this route if: You want to transfer your data from an i. Phone, an Android, or a Blackberry. You want to transfer your contacts and SMS messages. This method uses an app on the Nokia Lumia phone. The app wont’ transfer your calendar entries or your photos currently; although, the latter is hinted at in a future release of the app. Let’s get started, follow these steps: On your Nokia Lumia phone click on the Store icon to access the apps marketplace. Click on . Scroll down to find the Transfer my Data app. Tap to launch. To transfer SMS messages and contacts from your old phone to the Nokia Lumia, you will need to enable Bluetooth on both phones. Tap Continue in the Transfer my Data app, the next screen will prompt you to turn on your Bluetooth if it isn’t on. Meanwhile, turn on the Bluetooth on your other phone and make sure the phone is discoverable. Once Bluetooth is turned on, the Nokia Lumia will search for your other phone and will ask you to pair the two. Follow the on- screen prompts on both phones to set up the pairing. Tap on your older phone to start the transfer process. You can transfer your contacts and text messages. The next screen will show you the transfer progress. Depending on the data being transferred, this can take several minutes. Keep the phones close to each other and let them complete the process. All done! You will find your contacts under the Nokia Lumia phone’s People Hub and your text messages under the Messaging app. Step 2: On your Windows 8 Phone, head to Apps from Nokia to start the process. Step 3: Under the Apps from Nokia section, scroll down to find the Transfer my Data app. Step 1. 0: Once the Bluetooth is turned on for both your phones, pair the two and start the transfer process. Step 1. 2: You can transfer your contacts and text messages (SMS) between the two phones. Step 1. 3: Here, you will see the transfer progress. Step 1. 4: ! You have now transferred your contacts and SMS messages from your old phone to the Nokia Lumia. In Conclusion. It’s really strange that the Nokia Lumia doesn’t sync with the older Nokia Suite – that would’ve eliminated the need to juggle two different apps, across different platforms. Nevertheless, this is how things are, and all said and done, it’s not very difficult to get data from the older Nokias to the Nokia Lumia series of phone. To conclude: (1) you can transfer your contacts, calendar and photos using a combination of Nokia Suite and Sky. Drive (assuming you are coming to the Nokia Lumia from an older Nokia phone). Nokia X - Wikipedia. The Nokia X was a mid- tier smartphone announced as part of the Nokia X family in February 2. Nokia X platform. The device shipped on the same day as the unveiling, with Nokia targeting the product for emerging markets. It is currently sold and maintained by Microsoft Mobile. On 1. 7 July 2. 01. Microsoft announced that it would discontinue the line. Nokia Corporation. Retrieved 5 August 2. Archived from the original on 2. August 2. 01. 4. Retrieved 5 August 2. Phandroid. com. Retrieved 2. Retrieved 5 August 2. Retrieved 5 August 2. Retrieved 5 August 2. Retrieved 5 August 2. Retrieved 5 August 2. Retrieved 2. 01. 4- 0. Retrieved 2. 01. 4- 0. Retrieved 2. 01. 4- 0. Conversations. nokia. Retrieved 2. 01. 4- 0. Reinforcement Learning – Adit Deshpande – CS Undergrad at UCLA ('1. This is the 2nd installment of a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. This week focuses on Reinforcement Learning. Last time was Generative Adversarial Networks ICYMIIntroduction to Reinforcement Learning. Categories of Machine Learning Before getting into the papers, let’s first talk about what reinforcement learning is. The field of machine learning can be separated into 3 main categories. Supervised Learning. Unsupervised Learning. Reinforcement Learning. The first category, supervised learning, is the one you may be most familiar with. It relies on the idea of creating a function or model based on a set of training data, which contains inputs and their corresponding labels. Convolutional Neural Networks are a great example of this, as the images are the inputs and the outputs are the classifications of the images (dog, cat, etc). Unsupervised learning seeks to find some sort of structure within data through methods of cluster analysis. One of the most well- known ML clustering algorithms, K- Means, is an example of unsupervised learning. Reinforcement learning is the task of learning what actions to take, given a certain situation/environment, so as to maximize a reward signal. The interesting difference between supervised and reinforcement learning is that this reward signal simply tells you whether the action (or input) that the agent takes is good or bad. Learning can be exciting when playing these fun learning games. Membership is not required and the games are free. From classroom games and simulations to mobile gaming and reinforcement, our business solutions are uniquely customized and specifically engineered to meet your. Browse our library of learning games, guaranteed to bring classroom fun! It doesn’t tell you anything about what the best action is. Contrast this to CNNs where the corresponding label for each image input is a definite instruction of what the output should be for each input. For example, an agent’s action of moving left instead of right means that the agent will receive different input from the environment at the next time step. Let’s look at an example to start off. The RL Problem So, let’s first think about what have in a reinforcement learning problem. Let’s imagine a tiny robot in a small room. We haven’t programmed this robot to move or walk or take any action. It’s just standing there. This robot is our agent. Like we mentioned before, reinforcement learning is all about trying to understand the optimal way of making decisions/actions so that we maximize some reward. R. This reward is a feedback signal that just indicates how well the agent is doing at a given time step. The action A that an agent takes at every time step is a function of both the reward (signal telling the agent how well it’s currently doing) and the state S, which is a description of the environment the agent is in. The mapping from environment states to actions is called our policy P. The policy basically defines the agent’s way of behaving at a certain time, given a certain situation. Now, we also have a value function V which is a measure of how good each position is. This is different from the reward in that the reward signal indicates what is good in the immediate sense, while the value function is more indicative of how good it is to be in this state/position in the long run. Finally, we also have a model M which is the agent’s representation of the environment. This is the agent’s model of how it thinks that the environment is going to behave. Markov Decision Process So, let’s now think back to our robot (the agent) in the small room. Our reward function is dependent on what we want the agent to accomplish. Let’s say that we want it to move to one of the corners of the room where it will receive a reward. The robot will get a +2. We basically want the robot to get the corner as fast as possible. The actions the agent can take are moving north, south, east, or west. The agent’s policy can be a simple one, where the behavior is that the agent will always move to the location with the higher value function. Makes sense right? A position with a high value function = good to be in this position (with regards to long term reward). Now, this whole RL environment can be described with a Markov Decision Process. For those that haven’t heard the term before, an MDP is a framework for modeling an agent’s decision making. It contains a finite set of states (and value functions for those states), a finite set of actions, a policy, and a reward function. Our value function can be split into 2 terms. State- value function V: The expected return from being in a state S and following a policy . This return is calculated by looking at summation of the reward at each future time step (The gamma refers to a constant discount factor, which means that the reward at time step 1. Action- value function Q: The expected return from being in a state S, following a policy ? Well, we want to solve it, of course. By solving an MDP, you’ll be able to find the optimal behavior (policy) that maximizes the amount of reward the agent can expect to get from any state in the environment. Solving the MDP We can solve an MDP and get the optimum policy through the use of dynamic programming and specifically through the use of policy iteration (there is another technique called value iteration, but won’t go into that right now). The idea is that we take some initial policy . The way we do this is through the Bellman expectation equation. This equation basically says that our value function, given that we’re following policy . If you think about it closely, this is equivalent to the value function definition we used in the previous section. Using this equation is our policy evaluation component. In order to get a better policy, we use a policy improvement step where we simply act greedily with respect to the value function. In other words, the agent takes the action that maximizes value. Now, in order to get the optimal policy, we repeat these 2 steps, one after the other, until we converge to optimal policy . The MDP essentially tells you how the environment works, which realistically is not going to be given in real world scenarios. When not given an MDP, we use model free methods that go directly from the experience/interactions of the agents and the environment to the value functions and policies. We’re going to be doing the same steps of policy evaluation and policy improvement, just without the information given by the MDP. The way we do this is instead of improving our policy by optimizing over the state value function, we’re going to optimize over the action value function Q. Remember how we decomposed the state value function into the sum of immediate reward and value function of the successor state? Well, we can do the same with our Q function. Now, we’re going to go through the same process of policy evaluation and policy improvement, except we replace our state value function V with our action value function Q. Now, I’m going to skip over the details of what changes with the evaluation/improvement steps. To understand MDP free evaluation and improvement methods, topics such as Monte Carlo Learning, Temporal Difference Learning, and SARSA would require whole blogs just themselves (If you are interested, though, please take a listen to David Silver’s Lecture 4 and Lecture 5). Right now, however, I’m going to jump ahead to value function approximation and the methods discussed in the Alpha. Go and Atari Papers, and hopefully that should give a taste of modern RL techniques. All Educational Software.com: Buy discount educational software for kids and adults with free shipping. Learn to type, language software, and fun math games. KS2 science activities, tests and notes for primary school children studying living things, materials and physical processes. Create and print worksheets, chore charts, and arts and crafts ideas. Kids earn points from doing chores, worksheets, and arts and crafts to use to adopt a pet cat. Instructors have reported this as an area. How computers can learn to get better at playing games. A site for AI researchers and game programmers.The main takeaway is that we want to find the optimal policy . Look at the above Q equation. We’re taking in a specific state S and action A, and then computing a number that basically tells us what the expected return is. Now let’s imagine that our agent moves 1 millimeter to the right. This means we have a whole new state S’, and now we’re going to have to compute a Q value for that. In real world RL problems, there are millions and millions of states so it’s important that our value functions understand generalization in that we don’t have to store a completely separate value function for every possible state. The solution is to use a Q value function approximation that is able to generalize to unknown states. So, what we want is some function, let’s call is Qhat, that gives a rough approximation of the Q value given some state S and some action A. This function is going to take in S, A, and a good old weight vector W (Once you see that W, you already know we’re bringing in some gradient descent ). It is going to compute the dot product between x (which is just a feature vector that represents S and A) and W. The way we’re going to improve this function is by calculating the loss between the true Q value (let’s just assume that it’s given to us for now) and the output of the approximate function. After we compute the loss, we use gradient descent to find the minimum value, at which point we will have our optimal W vector. This idea of function approximation is going to be very key when taking a look at the papers a little later. Just One More Thing Before getting to the papers, just wanted to touch on one last thing. An interesting discussion with the topic of reinforcement learning is that of exploration vs exploitation. Exploitation is the agent’s process of taking what it already knows, and then making the actions that it knows will produce the maximum reward. This sounds great, right? The agent will always be making the best action based on its current knowledge. However, there is a key phrase in that statement. Current knowledge. If the agent hasn’t explored enough of the state space, it can’t possibly know whether it is really taking the best possible action. This idea of taking actions with the main purpose of exploring the state space is called exploration. This idea can be easily related to a real world example. Let’s say you have a choice of what restaurant to eat at tonight. REINFORCEMENT LEARNING AND POMDPs. REINFORCEMENT LEARNING IN PARTIALLY OBSERVABLE WORLDS. Realistic environments are not fully observable. The essential question. To address this issue, Schmidhuber. OOPS or other methods. Schmidhuber. Schmidhuber. Kompella, M. Stollenga, M. Schmidhuber. Continual curiosity- driven skill acquisition from high- dimensional video inputs for humanoid robots. Artificial Intelligence, 2. Doi: 1. 0. 1. 01. Schmidhuber, F. Srivastava, F. Schmidhuber. Coello Coello, V. Pavone, eds.. 1. 2th Int. Schmidhuber. Controzzi, C. Cipriani, A. Foerster, M. Carrozza, J. Schmidhuber. Schmidhuber. International Conference on Machine Learning ICML 2. Edinburgh. Schmidhuber. Kompella, M. Stollenga, L. Schmidhuber. IEEE Conference on Development and Learning / Epi. Rob 2. 01. 2. (ICDL- Epi. Rob'1. 2), San Diego, 2. Srivastava, B. Steunebrink, J. Schmidhuber. Continually Adding Self- Invented Problems to the Repertoire: First Experiments with Power. Play. IEEE Conference on Development and Learning / Epi. Rob 2. 01. 2. (ICDL- Epi. Rob'1. 2), San Diego, 2. Schmidhuber. International Conference on Artificial Neural Networks (ICANN 2. Lausanne, 2. 01. 2. Foerster, J. Schmidhuber. Schaul, J. Schmidhuber. Schmidhuber. Incremental Basis Construction from Temporal Difference Error. Proceedings of the 2. International Conference on Machine Learning (ICML- 1. Graziano, J. Schmidhuber. Schaul, Yi Sun, D. Wierstra, F. Schmidhuber. Curiosity- Driven Optimization. Schmidhuber, F. Schmidhuber. Graziano, M. Schmidhuber. Schmidhuber. Glasmachers, J. Schmidhuber. Graziano, J. Schmidhuber. Steunebrink, J. Schmidhuber. Schmidhuber. Formal Theory of Creativity, Fun, and Intrinsic Motivation (1. Schaul and J. Schmidhuber. Metalearning. Scholarpedia, 5(6): 4. Osendorfer, T. Schmidhuber. Neural Networks 2. Wierstra, S. Schmidhuber. Journal of Machine Learning Research (JMLR). Wierstra, S. Schmidhuber. Wierstra, A. Schmidhuber. Schmidhuber. Schmidhuber. Journal of SICE, 4. Extended version. Xiv: 0. 81. 2. 4. PDF (Dec 2. 00. 8). PDF (April 2. 00. Wierstra, A. Foerster, J. Schmidhuber. Wierstra, T. Schmidhuber. Togelius, T. Wierstra, C. Schmidhuber. Togelius, J. Schmidhuber. Schmidhuber, R. Miikkulainen. Schmidhuber. In. Knowledge- Based Intelligent Information and. Engineering Systems KES- 2. Lecture Notes in Computer Science LNCS 5. Springer, 2. 00. 8. Schmidhuber. Schaul and J. Schmidhuber. Osendorfer, T. Peters, and J. Schmidhuber. Neruda, editors. Proceedings of the. International Conference on Artificial Neural Networks ICANN- 2. ICANN 2. 00. 8, Prague, LNCS 5. Springer- Verlag Berlin Heidelberg, 2. Wierstra, T. Schmidhuber. Neruda, editors. Proceedings of the. International Conference on Artificial Neural Networks ICANN- 2. ICANN 2. 00. 8, Prague. Springer- Verlag Berlin Heidelberg, 2. Wierstra, T. Schmidhuber. Fitness Expectation Maximization. Wierstra, T. Schmidhuber. Wierstra, J. Schmidhuber. Wierstra, A. Foerster, J. Schmidhuber. Solving Deep Memory POMDPs. Recurrent Policy Gradients. Schmidhuber. Schmidhuber, and R. Miikkulainen (2. 00. Schmidhuber. 2. 23- 2. Springer- Verlag Berlin Heidelberg, 2. Gomez and J. Schmidhuber. Springer- Verlag Berlin Heidelberg, 2. Bakker and J. Schmidhuber. Bonarini, E. Yoshida, and B. Schmidhuber. Zhumatiy, G. Gruener, and J. Schmidhuber. Schmidhuber. Obermayer, eds.. Advances in Neural Information Processing Systems 1. NIPS'1. 5. MIT Press, Cambridge MA, p. Schmidhuber. Schmidhuber's CSEM grant 2. Giles, eds.. Sequence Learning: Paradigms, Algorithms, and Applications. Springer, 2. 00. 1. Schmidhuber. Dorffner, H. Hornik, eds.. Proceedings of Int. Wiering and J. Schmidhuber. Salustowicz and M. Wiering and J. Schmidhuber. Schmidhuber, J. Zhao, and M. Schraudolph. Thrun and L. Pratt, eds.. Learning to learn, Kluwer, pages 2. Salustowicz and J. Schmidhuber. Wiering and J. Schmidhuber. Saitta, ed.. Machine Learning. Proceedings of the 1. International Conference. Morgan Kaufmann Publishers, San Francisco, CA, 1. Wiering and J. Schmidhuber. Schmidhuber. Moody, and D. Touretzky, editors. Advances in Neural Information Processing Systems 3, NIPS'3, pages 5. San. Mateo, CA: Morgan Kaufmann, 1. Schmidhuber and R. Simula, and J. Kangas, editors. Artificial Neural Networks, pages 3. Elsevier Science Publishers. B. V., North- Holland, 1. Touretzky, J. Elman. T. Sejnowski, and G. Hinton. editors, Proc. San Mateo, CA: Morgan Kaufmann, 1. IEEE/INNS International Joint Conference on Neural. Networks, San Diego, volume 2, pages 2. INNC International Neural Network Conference, Paris. Eckmiller, G. Hartmann, and G. Hauske, editors, Parallel. Processing in Neural Systems and Computers, pages 2. North- Holland. 1. Kindermann and A. Linden, editors, Proceedings of. Distributed Adaptive Neural Information Processing', St. Augustin, 2. 4.- 2. Oldenbourg, 1. 99. Schmidhuber. Gruener, J. Schmidhuber. Wiering and J. Schmidhuber. Schmidhuber. Pfeiffer, B. Blumberg, J. Wilson, eds.. From Animals to Animats 5: Proceedings. Fifth International Conference on Simulation of Adaptive. Behavior, p. 2. 23- 2. MIT Press, 1. 99. Hochreiter, and J. 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Rev. 0. 4/1. 6. PDFEnglish. DWC0. 03. MESDeclaraci. PDFSpanish. DWC0. SDeclaraci. 1. 0/0. PDFSpanish. DWC0. SDEmployer's Wage Statement for School Districts. Rev. 1. 0/0. 5. PDFEnglish. DWC0. 03. SDSDeclaraci. PDFSpanish. DWC0. Employer's Contest of Compensability. Rev. 1. 1/0. 8. PDFEnglish. DWC0. 05. Employer Notice of No Coverage or Termination of Coverage. Rev. 0. 1/1. 3 - For help and an instructional video see “Electronic Filing - Online Forms” above. PDFEnglish. DWC0. Supplemental Report of Injury. Rev. 1. 0/0. 5. PDFEnglish. DWC0. 07. Employer’s Report of Non- covered Employee’s Occupational Injury or Disease. Rev. 0. 1/1. 3. PDFEnglish. DWC0. 08. Return- to- Work Reimbursement Program for Employers. Rev. 0. 4/1. 0. WORDEnglish. DWC0. 08. Return- to- Work Reimbursement Program for Employers. Rev. 0. 4/1. 0. PDFEnglish. DWC0. 20. Insurance Carrier's Notice of Coverage/Cancellation/Non- Renewal of Coverage. Rev. 1. 0/0. 5. PDFEnglish. DWC0. 20. ACorrection/Revision/Endorsement to Existing Policy. Rev. 1. 0/0. 5. PDFEnglish. DWC0. 20. SISelf- Insured Governmental Entity Coverage Information. Rev. 0. 8/1. 2 - Using Google Chrome or Mozilla Firefox to file electronically? PDFEnglish. DWC0. SExamen M. 0. 3/1. PDFEnglish. DWC0. Acuerdo para Disputa de Beneficios. Rev. 0. 3/1. 7. PDFSpanish. DWC0. 25. Benefit Dispute Settlement. Rev. 0. 2/1. 7. PDFEnglish. DWC0. 25s. Acuerdo por Disputa de Beneficios. Rev. 0. 2/1. 7. PDFSpanish. DWC0. 26. Request for Reimbursement of Payment Made by Health Care Insurer. Rev. 0. 1/1. 5. PDFEnglish. DWC0. 27. Designation of Insurance Carrier’s Austin Representative. Rev. 1. 2/1. 1. PDFEnglish. DWC0. 30. Austin Representative’s Authorized Designees. Rev. 1. 2/1. 1. PDFEnglish. DWC0. 31. Application for Division Approval of Change in the Payment Period and/or Purchase of an Annuity for Death Benefits. Rev. 0. 2/1. 7. PDFEnglish. DWC0. 31s. Solicitud para Obtener Aprobaci. PDFSpanish. DWC0. Request for Designated Doctor Examination. Rev. 1/1. 3. PDFEnglish. DWC0. 32. SSolicitud para Obtener un Examen por Parte de un M. PDFSpanish. DWC0. Carrier's Request for Reduction of Income Benefits Due to Contribution. Rev. 0. 2/1. 7. PDFEnglish. DWC0. 35. Application for Division Approval of the Purchase of an Annuity for Lifetime Income Benefits. Rev. 0. 2/1. 7. PDFEnglish. DWC0. 41. Employee's Claim for Compensation for a Work- Related Injury or Occupational Disease. Rev. 3/0. 7. PDFEnglish. DWC0. 41. Employee's Claim for Compensation for a Work- Related Injury or Occupational Disease. Rev. 3/0. 7. WORDEnglish. DWC0. 41. SReclamo del Empleado para Compensaci. PDFSpanish. DWC0. SReclamo del Empleado para Compensaci. WORDSpanish. DWC0. Claim for Workers’ Compensation Death Benefits. Rev. 0. 3/1. 6. WORDEnglish. DWC0. 42. Claim for Workers’ Compensation Death Benefits. Rev. 0. 3/1. 6. PDFEnglish. DWC0. 42. SReclamaci. WORDSpanish. DWC0. SReclamaci. 3/1. 6. PDFSpanish. DWC0. Election to Engage in Arbitration. Rev. 0. 6/1. 2. PDFEnglish. DWC0. 44. SElecci. PDFSpanish. DWC0. Request to Schedule, Reschedule, or Cancel a Benefit Review Conference (BRC)Rev. PDFEnglish. DWC0. ARequest for a Medical Contested Case or SOAH Hearing. Rev. 0. 9/0. 7, applicable only to medical disputes that were filed prior to June 1, 2. PDFEnglish. DWC0. ASSolicitud para una Audiencia para Disputar Beneficios M. PDFSpanish. DWC0. MRequest to Schedule, Reschedule, or Cancel a Benefit Review Conference to Appeal a Medical Fee Dispute Decision (BRC- MFD)Rev. PDFEnglish. DWC0. MSSolicitud para Programar, Reprogramar, o Cancelar una Conferencia para Revisi. PDFSpanish. DWC0. Employee's Request for Acceleration of Impairment Income Benefits. Rev. 0. 2/1. 7. PDFEnglish. DWC0. 46. SSolicitud del Trabajador Lesionado para Recibir un Pago Acelerado de Beneficios por Causa del Impedimento Corporal. Rev. 0. 2/1. 7. PDFSpanish. DWC0. 47. Employee’s Request for Advance of Benefits. Rev. 0. 2/1. 7. PDFEnglish. DWC0. 47. SSolicitud del Empleado para Obtener Beneficios por Adelantado. Rev. 0. 2/1. 7. PDFSpanish. DWC0. 48. Request to Get Reimbursed for Travel Costs. Rev. 0. 2/1. 7. PDFEnglish. DWC0. 48. SSolicitud para Obtener un Reembolso por Gastos de Viaje. Rev. 0. 2/1. 7. PDFSpanish. DWC0. 49. Request to Schedule a Medical Contested Case Hearing (MCCH)Rev. PDFEnglish. DWC0. SSolicitud para Programar una Audiencia para Disputar Beneficios M. PDFSpanish. DWC0. Employee's Election for Commuted (Lump Sum) Impairment Income Benefits. Rev. 0. 2/1. 7. PDFEnglish. DWC0. 51. SElecci. PDFSpanish. DWC0. Application for Supplemental Income Benefits. Rev. 0. 2/1. 7. PDFEnglish. DWC0. 52. SAplicaci. PDFSpanish. DWC0. Employee Request to Change Treating Doctor. Rev. 0. 3/1. 2. PDFEnglish. DWC0. 53. SSolicitud del Empleado para Cambiar de M. PDFSpanish. DWC0. Notice to Employee: Intention to Request Division Permission to Adjust Benefits. Rev. 0. 2/1. 7. PDFEnglish. DWC0. 54. SAviso al/a la Empleado/a: Intencion de Solicitar permiso a la Divisi. PDFSpanish. DWC0. Request to Adjust Average Weekly Wage for Seasonal Employee. Rev. 0. 2/1. 7. PDFEnglish. DWC0. 55. SSolicitud de Ajuste al Salario Medio Semanal de un(a) Empleado/a de Temporada. Rev. 0. 2/1. 7. PDFSpanish. DWC0. 56. Carrier's Request for Seasonal Employee Wage Information from Texas Workforce Commission Records. Rev. 0. 2/1. 7. PDFEnglish. DWC0. 57. Request for Extension of Maximum Medical Improvement Date for Spinal Surgery. Rev. 0. 2/1. 7. PDFEnglish. DWC0. 57. SSolicitud para Extensi. PDFSpanish. DWC0. Request for Interlocutory Order. Rev. 0. 9/0. 7. PDFEnglish. DWC0. 60. Medical Fee Dispute Resolution Request. Rev. 0. 6/1. 2. PDFEnglish. DWC0. 60. SSolicitud para Resoluci. PDFSpanish. DWC0. Medical Interlocutory Order Request - Continued Use of a Drug Previously Prescribed and Dispensed and Excluded from TDI- DWC’s Closed Formulary. Rev. 8/1. 1. PDFEnglish. DWC0. 65. Application for Inclusion on Registry of Private Providers of Vocational Rehabilitation Services. Rev. 1/1. 1. PDFEnglish. DWC0. 66. Statement of Pharmacy Services. Rev. 1. 2/1. 1. PDFEnglish. DWC0. 67. Designated Doctor Certification Application Rev. PDFEnglish. DWC0. Designated Doctor Examination Data Report. Rev. 2/1. 7. PDFEnglish. DWC0. 69. Report of Medical Evaluation. Rev. 1/1. 5 (for use on or after 1/1/1. PDFEnglish. DWC0. Instructions For Completing The ADA J5. Dental Claim Form For Texas Workers' Compensation Claims. Rev. 1. 0/0. 5. PDFEnglish. DWC0. 72. Medical Quality Review Panel Application. Rev. 0. 1/1. 3. PDFEnglish. DWC0. 73. Work Status Report. Rev. 0. 2/1. 1. PDFEnglish. DWC0. 74. Description of Injured Employee’s Employment. Rev. 9/0. 9. PDFEnglish. DWC0. 81. Agreement Between General Contractor and Sub- Contractor to Provide Worker's Compensation Insurance. Rev. 1. 0/0. 5. PDFEnglish. DWC0. 81. SAcuerdo Entre el Contratista General y el Sub Contratista. Rev. 0. 9/0. 7. PDFSpanish. DWC0. 82. Agreement for Motor Carriers and Owner Operators. Rev. 1. 0/0. 5. PDFEnglish. DWC0. 83. Agreement for Certain Building and Construction Workers. Rev. 1. 0/0. 5. PDFEnglish. DWC0. 83. SAcuerdo para Ciertos Trabajadores de Edificaci. PDFSpanish. DWC0. Exception to Application of Joint Agreement for Certain Building and Construction Workers. Rev. 1. 0/0. 5. PDFEnglish. DWC0. 85. Agreement Between General Contractor and Subcontractor to Establish Independent Relationship. Rev. 1. 0/0. 5. PDFEnglish. DWC0. 85. SAcuerdo Entre el Contratista General y el Sub Contratista Para Establecer una Relaci. PDFSpanish. DWC1. Program Review Report. Rev. 0. 8/0. 6. WORDEnglish. DWC1. 01. Program Review Report. Rev. 0. 8/0. 6. PDFEnglish. DWC1. 02. Accident Prevention Plan Cover Sheet. Rev. 0. 8/0. 6. WORDEnglish. DWC1. 02. Accident Prevention Plan Cover Sheet. Rev. 0. 8/0. 6. PDFEnglish. DWC1. 03. Approved Professional Source Safety Consultant Application. Rev. 1. 2/0. 6 - Note: The Approved Professional Source designation applies only to Loss Control Representatives of Texas Mutual Insurance Company as of September 1, 2. WORDEnglish. DWC1. Employer Request for DWC Safety Consultation. Rev. 0. 8/0. 6. PDFEnglish. DWC1. 05. Accident Prevention Services Worksheet. Rev. 1. 0/1. 3. WORDEnglish. DWC1. 05. Accident Prevention Services Worksheet. Rev. 1. 0/1. 3. PDFEnglish. DWC1. 09. Accident Prevention Services Annual Report. Rev. 1. 0/1. 3. WORDEnglish. DWC1. 09. Accident Prevention Services Annual Report. Rev. 1. 0/1. 3. PDFEnglish. DWC1. 50. Notice of Representation. Rev. 1. 2/1. 6. PDFEnglish. DWC1. 50. ANotice of Withdrawal of Representation. Rev. 1. 2/1. 6. PDFEnglish. DWC1. 50. ASAviso de Anulaci. PDFSpanish. DWC1. SAviso de Representaci. PDFSpanish. DWC1. Attorney Application for Web Access. Rev. 1. 2/1. 6. PDFEnglish. DWC1. 52. Application for Attorney Fees. Rev. 1. 2/1. 6. PDFEnglish. DWC1. 53. Request for Copies of Confidential Claimant Information. Rev. 1. 0/0. 6. PDFEnglish. DWC1. 53s. Solicitud para Obtener Copias de la Informaci. PDFEnglish. DWC1. Workers' Compensation Complaint Form. Rev. 0. 3/1. 6. PDFEnglish. DWC1. 54. SQuejas de Compensaci. PDFSpanish. DWC1. Request for Record Check. Rev. 1. 0/0. 5. PDFEnglish. DWC1. 56. Prospective Employment Authorization and Certification. Rev. 1. 0/0. 5. PDFEnglish. DWC1. 56. SCertificaci. PDFSpanish. DWC2. Locations of Employer’s Business(es)Addendum to DWC Form- 0. DWC Form- 0. 20 - Rev. PDFEnglish. DWC2. SLocaciones del Negocio(s) del Empleador. Suplemento para el Formulario DWC0. Formulario DWC0. 20 - Rev. PDFSpanish. DWC2. Surety Bond for Certified Self- Insurance Liabilities. Rev. 1/0. 6. WORDEnglish. DWC2. 10. Surety Bond for Certified Self- Insurance Liabilities. Web Site Test Tools and Site Management Tools More than 540 tools listed in 14 categories. Documentation. 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The amount of points is meant to be equitable across all activities with differences based on the number of teams and/or individuals participating. Almost every organization has a series of awards that are focused on the leadership and management in the organization. Department managers, region managers, division.Awarded to a second- third- or fourth-year Nursing student from Prince County, or who has received his/her pre-university education in Prince County, who has a. Financing your education can seem like a scary proposition, but there are numerous scholarships available to help students, both graduate and undergraduate. Teams that win a state championship will earn the same points regardless of the activity. Schools receive 2. Top 1. 0. Schools with teams in the Top 1. The overall winner in an activity receives 2. Schools with teams that finish atop a classification but not overall receive 1. Ties for the overall winner or first place in a classification are awarded the same points. All other ties are split between the places. Oregonian Cup standings. Sportsmanship. A sportsmanship component was added to the calculations in 2. Schools receive 2. Fall, Winter, Spring) for having no ejections. Relay teams in Swimming and Track & Field get 1. Individual participants in Golf and Cross Country can earn additional points for a high finish, (3. For example, an individual placing first in a golf championship receives 6. Leveling the Playing Field. Fall. Winter. Spring. A1. 02. 44. 25. A1. A9. 21. 36. 3A7. 15. A6. 12. 21. 1A4. 10. Similar to scoring systems in other states, we don't want the winning school to just be the school that offers the most activities. For this reason we limit the number of events for which a school can earn points each season (Fall, Winter, and Spring). 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West Point has participated in the Olmsted Scholar Program since its inception in October 1. To date, West Point has had 9. Click here to view a list of recent Olmsted Scholars. For more information on The George and Carol Olmsted Foundation and MG Olmsted, please visit the Foundation's website www. NSU Scholarships Information. Each year, Nova Southeastern University offers countless scholarship opportunities for undergraduate, graduate, and first professional students aimed at recognizing the academic achievement and leadership potential of future students. Thanks to generous donations from alumni, corporations, donors, and friends, NSU offers significant university and college scholarships each academic year, including awards for first- year and continuing students. Search and apply for as many scholarships as you qualify for and be sure to regularly visit this site for newly added opportunities which are highlighted below. Most institutional scholarships become available in March and have deadlines in May/June. If you are an NSU employee using a tuition waiver, you are ineligible for institutional scholarships. Select from the categories below or conduct your own search. FEATURED NEW SCHOLARSHIPSThis category includes institutional and external opportunities that have an upcoming deadline. Many provide only one or two awards and are not offered every year. This listing is updated often, so keep checking for new scholarship postings! Featured New Scholarships. UNDERGRADUATE INSTITUTIONAL SCHOLARSHIPSIn this category, you will find undergraduate scholarships offered by the various NSU colleges, centers, and departments. Undergraduate Institutional Scholarships. GRADUATE INSTITUTIONAL SCHOLARSHIPSIn this category, you will find graduate scholarships offered by the various NSU colleges, centers, and departments. Graduate Institutional Scholarships. EXTERNAL SCHOLARSHIPSIn this category, you will find scholarships offered by various entities outside of the university. This list is arranged in alphabetical order by name of the scholarship. These particular opportunities are normally offered every year, and most are well known. External Scholarships. INTERNATIONAL SCHOLARSHIPSNSU offers our international student population scholarship opportunities based on academics. International Scholarships. |
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November 2017
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