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Approved by the FDA in 1991, endovascular treatment consists of guiding a catheter from the femoral artery to the cerebral vasculature via the ICA or vertebral artery, depending on the location of the aneurysm. The procedure is guided by fluoroscopy, and when the catheter has reached the aneurysm, several soft platinum coils are deployed in the lumen of the lesion. These coils completely fill the lumen and induce the formation of a thrombus to occlude the aneurysm, preventing future rupture (4). A wide neck and large size of the aneurysm make the procedure more difficult with poorer results. This procedure appears to be safe relative to surgical treatment, and it is able to treat lesions that are difficult to approach surgically, but there are questions regarding the durability of the endovascular technique. In most studies, complete occlusion is achieved in 80% to 90% of patients. At post-treatment follow-up, however, small neck remnants are common, and some degree of thrombus recanalization is observed in 50% of all patients and up to 90% of patients with giant aneurysms. Both neck remnants and recanalization are associated with a risk of rupture, and 20% of patients may require more than one coiling procedure (23). Contrast enhanced transcranial Doppler sonography has been shown by a small study to be sensitive and specific (100% and 97%, respectively) in the detection of clinically relevant residual flow in the follow-up of coil-embolized aneurysms (26). IADSA is effective in visualizing treatment failures with residual necks or complete recanalization, but 3D MR appears to be more effective in evaluating \"partial treatments\" (27), such as incomplete recanalization, which may prove to be useful in detecting treatment failures before they have the potential to hemorrhage.
This work illustrates how sequential images from historical designer data can be used to emulate designer. Specifically, an independent agent algorithm is developed that utilizes methods from unsupervised representation learning, imitation learning, and image processing to model the entire design process as shown in Fig. 1. The agent uses a convolutional autoencoder to map the design states to low-dimensional embeddings, then it uses a neural network-based transition function to predict a design embedding based on the current state and then maps the embedding back to the original image dimensions. This process produces an image of how the next design should look like. Finally, a control algorithm driven by image processing constructs outputs the parameters to execute the operation. This agent is trained offline, does not require carefully labeled state-action data and is agnostic to objectives and other parameters. Even though the current framework utilizes images (a two-dimensional array), the framework can be extended to other problems where the raw state is representable using an N-dimensional array. The contributions made by this research can be listed as follows: A generalized methodology is presented that can be used across various design problems to learn implicit design strategies organically from data, using a pixel-based representation.
The training was accomplished in a 2-step process where the first step pretrains the network and the second step fine-tunes to predict meaningful designs. In the first step, the network must learn to recreate the current design itself. This pretraining step is important since the weights of the network are initialized randomly and training may not lead to meaningful predictions [72]; pretraining helps in finding better weights by providing good initialization to the network weights. In this fine-tuning step, the learning rate is lowered, and the network is trained to recreate the next design in the sequence. Through experimentation, the mentioned architecture was finalized for the transition network, a final MSE of 0.0072 (or binary accuracy of 90.05%) is achieved for the test case and an R-squared value of 0.8105 was achieved by the final network. This indicates that the network was able to explain a large majority of the variance in the data. The final network was arrived at through an iterative hyper-parameter search, where numerous architectures were compared, preference was given to smaller networks as larger networks tend to overfit the data. A major boost in performance was seen when past design states were concatenated in the input, showing the importance of the relation between design trajectories and next state prediction.
Learning from historical human data helps the agent identify the important regions of a design space and implicitly learn to navigate it, helping reach good performing designs. This imitation learning, however, has its limitations since the agent can at best do as well as the teacher, i.e., the human in this case, and also it may only learn to work with designs similar to shown in the training data in case of overfitting. In order to overcome this, the networks are trained separately to first create design embeddings that can learn general features of a truss design and hence can represent even new unseen truss designs and then learn generic design operations on them. This can allow the agents to explore new designs by possible interpolations between design embeddings. The agent might still learn bad strategies in case the quality of the subject is bad. The aim for the current research was to illustrate an ability to extract design strategies from data however in future work knowledge about metrics could be infused to ignore low performing strategies along with a careful selection of training data. Further experimentation also needs to be carried out to test the novelty in designs and how different they are from the training data to evaluate the generality of the learnt operations. This motivates us to utilize and test other methods of learning in the agents as well as identify the effect of training data on the final performance. For the current study, the agents were only trained to imitate humans using metric agnostic design data. In the future, embedding these agents in a goal-driven environment where real-time rewards are provided for optimal designs can lead to interesting results. Also, creating hybrid approaches of imitation learning with other methods of active learning can also be explored since it can significantly enhance agent performance.
The results of 2 multicenter, randomized, double-blind, placebo-controlled, parallel-group clinical trials established the effectiveness of topiramate in the preventive treatment of migraine. The design of both trials (Study 11 was conducted in the U.S. and Study 12 was conducted in the U.S. and Canada) was identical, enrolling patients with a history of migraine, with or without aura, for at least 6 months, according to the International Headache Society (IHS) diagnostic criteria. Patients with a history of cluster headaches or basilar, ophthalmoplegic, hemiplegic, or transformed migraine headaches were excluded from the trials. Patients were required to have completed up to a 2-week washout of any prior migraine preventive medications before starting the baseline phase.
In half-duplex environments, it is possible for both the switch and the connected device to sense the wire and transmit at exactly the same time and result in a collision. Collisions can cause runts, FCS, and alignment errors due to the frame not completely copied to the wire, which results in fragmented frames.
This course covers the introduction of the tools and resources available to students in programming, mark-up language and services on the Internet. Topics include standard mark-up language Internet services, creating web pages, using search engines, file transfer programs; and database design and creation with DBMS products. Upon completion students should be able to demonstrate knowledge of programming tools, deploy a web-site with mark-up tools, and create a simple database table.
Prerequisites: Take MAT-121 This course is designed to cover concepts in algebra, function analysis, and trigonometry. Topics include exponential and logarithmic functions, transformations of functions, Law of Sines, Law of Cosines, vectors, and statistics. Upon completion, students should be able to demonstrate the ability to use mathematics and technology for problem-solving, analyzing and communicating results. 59ce067264