After recent school shootings in parts of the country and more than a dozen threats made against school districts here in Central Pennsylvania, many wonder how safety within schools could be improved. A professor from Harrisburg University says he knows something that could help: technology. Advanced technology is right at our fingertips, and Ron Jones, a cyber security professor at Harrisburg University says lawmakers should be pushing to have it within our schools. "I don't see anybody in any political spectrum standing up, making that kind of profound statement," he said. Jones says technology like facial recognition could save lives.

Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic efficiency, and environmental impact. Rather than installing additional, costly sensing and communication infrastructure to obtain additional real-time information, it may be possible to use existing sensing capabilities and leverage knowledge about the system to reduce the need for new infrastructure. In this paper, we disaggregate a distribution feeder's demand measurements into: 1) the demand of a population of air conditioners, and 2) the demand of the remaining loads connected to the feeder. We use an online learning algorithm, Dynamic Fixed Share (DFS), that uses the real-time distribution feeder measurements as well as models generated from historical building- and device-level data. We develop two implementations of the algorithm and conduct case studies using real demand data from households and commercial buildings to investigate the effectiveness of the algorithm. The case studies demonstrate that DFS can effectively perform online disaggregation and the choice and construction of models included in the algorithm affects its accuracy, which is comparable to that of a set of Kalman filters.

In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD). Unlike the choices of snapshot and starting points in SVRG and its proximal variant, Prox-SVRG, the two vectors of VR-SGD are set to the average and last iterate of the previous epoch, respectively. The settings allow us to use much larger learning rates, and also make our convergence analysis more challenging. We also design two different update rules for smooth and non-smooth objective functions, respectively, which means that VR-SGD can tackle non-smooth and/or non-strongly convex problems directly without any reduction techniques. Moreover, we analyze the convergence properties of VR-SGD for strongly convex problems, which show that VR-SGD attains linear convergence. Different from its counterparts that have no convergence guarantees for non-strongly convex problems, we also provide the convergence guarantees of VR-SGD for this case, and empirically verify that VR-SGD with varying learning rates achieves similar performance to its momentum accelerated variant that has the optimal convergence rate $\mathcal{O}(1/T^2)$. Finally, we apply VR-SGD to solve various machine learning problems, such as convex and non-convex empirical risk minimization, leading eigenvalue computation, and neural networks. Experimental results show that VR-SGD converges significantly faster than SVRG and Prox-SVRG, and usually outperforms state-of-the-art accelerated methods, e.g., Katyusha.

In this paper we answer the following question: what is the infinitesimal generator of the diffusion process defined by a kernel that is normalized such that it is bi-stochastic with respect to a specified measure? More precisely, under the assumption that data is sampled from a Riemannian manifold we determine how the resulting infinitesimal generator depends on the potentially nonuniform distribution of the sample points, and the specified measure for the bi-stochastic normalization. In a special case, we demonstrate a connection to the heat kernel. We consider both the case where only a single data set is given, and the case where a data set and a reference set are given. The spectral theory of the constructed operators is studied, and Nystr\"om extension formulas for the gradients of the eigenfunctions are computed. Applications to discrete point sets and manifold learning are discussed.

This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers' and practitioners' efforts in knowledge acquisition for more than a century. We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution.

I'm a High School student with a reasonably basic research project where I am to implement an AI Agent to learn and master games and graph a linear regression of its time to mastery versus the task complexity. My partner and I have decided task complexity is to be based on the number of state spaces (or different inputs) the AI can use. We would like to find a good primary AI and have been using public OpenAi templates. Do any of you guys have suggestions on an efficient and effective way to make a "cookie cutter" algorithm? We'd like for it to be as easy to understand as possible.

Somewhere, buried in one of tens of millions of cell samples, could lie the next great breakthrough in disease prevention or cure. But, one of the great barriers to finding it could be the need for human eyes to evaluate a corresponding mountain of cell images, one by one. In an era when terabytes of data can be analyzed in just a few days, the opportunity to enhance automation of biomedical analysis could help researchers achieve breakthroughs faster in the treatment of almost every disease--from cancer, diabetes and rare disorders to the common cold. To spur this automation, Booz Allen Hamilton (NYSE: BAH) and Kaggle launched the 2018 Data Science Bowl, a 90-day competition that calls on thousands of participants globally to train deep learning models to examine images of cells and identify nuclei, regardless of the experimental setup--and without human intervention. Creators of the top algorithms will split $170,000 in cash and prizes, including an NVIDIA DGX Station, a personal AI supercomputer that delivers the computing capacity of 400 CPUs in a desktop workstation.

If you want a talk on Python with the Theano library in under an hour, targeted towards beginners, then you can refer to this talk by Alec Radford. Unlike most other talks on this topic, this one compares the features of an'old' net versus a'modern' net, ie nets prior to 2000 versus nets post-2012.

Canada higher education prowess is long-established. But some of its top colleges have been threatening the top spots traditionally held by UK and US universities. Indeed, Canadian universities are a dynamic group and each university has its own unique experience to offer both undergraduates and postgraduates. Check out these top Canadian universities: 1. University of Toronto The University of Toronto is widely considered to be the best university in Canada and one of the best universities in the world. Founded in 1827, it's a public research university with a student body of 60,000 and a;

Today, organizations across the globe are faced with an alarming reality: in the very near future, a significant number of senior leaders will hit average retirement age. According to the Pew Research Center, 10,000 baby boomers per day are turning 65 in the US alone. A vice president of human resources, realizing that 75% of the senior leaders in his large global manufacturing company are less than five years away from the average retirement age, described the situation as watching "a train wreck about to happen." It is not unusual for organizational leaders to find themselves with no particular idea as to who the future leaders will be, what the criteria is to select them, and how to prepare them for their future roles. Part of the problem is the sheer number of leaders that must be groomed for leadership positions.