Nabi Core
Elva Stuller
Earlier this year, Mattel significantly strengthened its digital reach with the purchase of bankrupt tablet-maker Fuhu, best known for its nabi learning systems. Now the toymaker is putting its acquisition to full use with the launch of three standalone Barbie, Hot Wheels and American Girl tablets powered by nabi.
The Barbie tablet features dozens of themed apps, games and videos that let users explore potential careers and create their own comic strips. There is also a video series called Barbie Spy Squad, which follows the iconic doll and her friends as they go on covert missions.
Hot Wheels Labs videos, which teach kids about the science of cars and tracks, will appear on the Hot Wheels tablet, along with a Tracks & Hacks section housing more than 25 apps. The American Girl tablet, meanwhile, includes more than 70 apps, videos and games featuring characters and creative craft ideas.
This is a prospective observational study in pediatric cardiac intensive care unit, at Hamad General Hospital, Doha Qatar. Patients who underwent biventricular repair on cardio-pulmonary bypass were included. At hour 1, 4, 8 and 12 post surgery, core to toe temperature gradient was recorded. At similar time intervals, Oxygen extraction ratio (OER) was calculated using formula SaO2 - SvO2/ SaO2 x 100, where SaO2 is systemic arterial saturations and SvO2 is mixed venous (central) saturations.
The core to toe temperature gradient does not correlate with the oxygen extraction ratio (surrogate marker of cardiac output) during the early post-operative phase in pediatric patients following cardiac surgery under cardiopulmonary bypass. There are multiple factors effecting this relationship and need further studies.
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Materials and methods: Between May 1997 and April 2000, 65 patients 5 to 62 years old underwent laser core-through urethrotomy for posttraumatic urethral stricture. Most patients had been involved in a motor vehicle accident but 8 and 3 sustained trauma after tractor injury and a railroad accident, respectively. All patients underwent suprapubic cystostomy formation and 18 had previously undergone anastomotic urethroplasty, railroad or attempted cold knife core-through urethrotomy. Mean stricture length was 2.2 cm. on bi-directional uroradiography and endoscopy. All strictures were in the bulbomembranous urethra except 3, which were prostatic-supraprostatic. The procedure was technically unsuccessful in 4 cases. Core-through urethrotomy was performed using Nd:YAG a 600 mu. contact bare fiber at 15 to 20 W. on an outpatient basis. Catheter removal and voiding cystourethrography were performed at 6 weeks. Uroflowmetry and urethroscopy were done 3 months after urethral catheter removal. Followup was 9 to 44 months.
Results: Nd:YAG laser core-through urethrotomy was performed on an outpatient basis successfully in all except 4 cases without any intraoperative or postoperative complications. Blood transfusion was not required. Although most patients were symptom-free, a few underwent initial optical internal urethrotomy and/or endoscopic dilation before the stricture became stable. The urethral lumen was obliterated again in 2 cases.
Quality first instruction. Our students receive high-quality, evidence-based, core grade-level instruction. To ensure quality first instruction, students are monitored and their progress evaluated. Based on data, identified students receive additional differentiated instruction providing extra support, acceleration, and extended learning opportunities to improve their academic performance.
Every Tuesday, our teaching staff participates in research-based professional development. Through reflection, our teachers are continuously improving their knowledge and skills. Key reflection questions teachers engage in:
Grade 2 Students in grade two explore the lives of actual people who make a difference in their everyday lives and learn the stories of extraordinary people from history whose achievements have touched them, directly or indirectly.
Our school is transitioning to the Next Generation Science Standards (NGSS), adopted by the State Board of Education in September, 2013. Our teachers are encouraged to attend professional development provided by the Local District and bring the materials to schools to discuss with colleagues and collaborate to explore the new standards.
Students demonstrate the physical, intellectual, social and emotional skills that promote healthful lifelong success in a global society. Students are empowered to think critically and use collaborative skills to solve problems. The comprehensive and culturally relevant physical education program in schools is aligned with the California Physical Education Content Standards and develops student competencies in the following:
This article introduces the basics of machine learning theory, laying down the common concepts and techniques involved. This post is intended for people starting with machine learning, making it easy to follow the core concepts and get comfortable with machine learning basics.
Machine learning is an application of artificial intelligence in which a machine learns from past experiences or input data to make future predictions. There are three common categories of machine learning: supervised learning, unsupervised learning and reinforcement learning.
In classification, the machine learning model takes in data and predicts the most likely category, class or label it belongs to based on its values. Some examples of classification include predicting stock prices and categorizing articles to news, politics or leisure based on its content.
When we have unclassified and unlabeled data, the system attempts to uncover patterns from the data . There is no label or target given for the examples. One common task is to group similar examples together called clustering.
Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best. This is known as the reinforcement signal. For example, maximizing the points won in a game over a lot of moves.
Most commonly used regressions techniques are linear regression and logistic regression. We will discuss the theory behind these two prominent techniques alongside explaining many other key concepts like gradient descent algorithm, overfit and underfit, error analysis, regularization, hyperparameters and cross validation techniques involved in machine learning.
In linear regression problems, the goal is to predict a real-value variable y from a given pattern X. In the case of linear regression the output is a linear function of the input. Let ŷ be the output our model predicts: ŷ = WX+b
Here X is a vector or features of an example, W are the weights or vector of parameters that determine how each feature affects the prediction, and b is a bias term. So, our task T is to predict y from X. Now ,we need to measure performance P to know how well the model performs.
We then take the absolute value of the error to take into account both positive and negative values of error. Finally, we calculate the mean for all recorded absolute errors or the average sum of all absolute errors.
To minimize the error, the model updates the model parameters W while experiencing the examples of the training set. These error calculations when plotted against the W is also called cost function J(w), since it determines the cost/penalty of the model. So, minimizing the error is also called as minimizing the cost function J.
In the gradient descent algorithm, we start with random model parameters and calculate the error for each learning iteration, keep updating the model parameters to move closer to the values that results in minimum cost.
The gradient of the cost function is calculated as a partial derivative of cost function J with respect to each model parameter wj, where j takes the value of number of features [1 to n]. α, alpha, is the learning rate, or how quickly we want to move towards the minimum. If α is too large, we can overshoot. If α is too small, it means small steps of learning, which increases the overall time it takes the model to observe all examples.
In logistic regression, the response variable describes the probability that the outcome is the positive case. If the response variable is equal to or exceeds a discrimination threshold, the positive class is predicted. Otherwise, the negative class is predicted.
We cannot use the same cost function that we used for linear regression because the sigmoid function will cause the output to be wavy, causing many local optima. In other words, it will not be a convex function.
In order to ensure the cost function is convex, and therefore, ensure convergence to the global minimum, the cost function is transformed using the logarithm of the sigmoid function. The cost function for logistic regression looks like:
Hyperparameters are higher-level parameters that describe structural information about a model that must be decided before fitting model parameters. Examples of hyperparameters we discussed so far include: Learning rate (alpha) and regularization (lambda).
The process to select the optimal values of hyperparameters is called model selection. if we reuse the same test data set over and over again during model selection, it will become part of our training data, and the model will be more likely to over fit.
In many applications, however, the supply of data for training and testing will be limited, and in order to build good models, we wish to use as much of the available data as possible for training. However, if the validation set is small, it will give a relatively noisy estimate of predictive performance. One solution to this dilemma is to use cross-validation.
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