In general, the forces of competition are imposing a need for more effective decision making at all levels in organizations. Progressive Approach to Modeling: Modeling for decision making involves two distinct parties, one is the decision-maker and the other is the model-builder known as the analyst. Therefore, the analyst must be equipped with more than a set of analytical methods.
On the one hand, some people claim it can be accomplished safely; others dispute that. In an attempt to make progress, a National Academies study committee propounded a framework to use when analyzing proposed solutions. Robot Learning in Simulation for Grasping and Manipulation Beatrice Liang Teaching a robot to acquire complex motor skills in complicated environments is one of the most ambitious problems facing roboticists today.
Grasp planning is a subset of this problem which can be solved through complex geometric and physical analysis or computationally expensive data driven analysis.
As grasping problems become more difficult, building analytical models becomes challenging. Consequently, we aim to learn a grasping policy through a simulation-based data driven approach.
POS uses a novel priority-based scheduling algorithm that naturally considers partial order information dynamically, and guarantees that each partial order will be explored with significant probability. This probabilistic guarantee of error detection is exponentially better than state-of-the-art sampling approaches.
Besides theoretical guarantees, POS is extremely simple and lightweight to implement.
In our design, an elastic lens array is placed on top of a sparse, rigid array of pixels. This lens array is then stretched using a small mechanical motion in order to change the field of view of the system.
We present in this paper the characterization of such a system and simulations which demonstrate the capabilities of stretchcam. We follow this with the presentation of images captured from a prototype device of the proposed design.
Our prototype system is able to achieve 1. To manage an IoT device, the user first needs to join it to an existing network.
Then, the IoT device has to be authenticated by the user. The authentication process often requires a two-way communication between the new device and a trusted entity, which is typically a hand- held device owned by the user.
To ease and standardize this process, we present the Device Enrollment Protocol DEP as a solution to the enrollment problem described above. The application allows the user to authenticate IoT devices and join them to an existing protected network.
However, RNNs are still often used as a black box with limited understanding of the hidden representation that they learn. Existing approaches such as visualization are limited by the manual effort to examine the visualizations and require considerable expertise, while neural attention models change, rather than interpret, the model.
We propose a technique to search for neurons based on existing interpretable models, features, or programs. Reliable Synchronization in Multithreaded Servers Rui Gu State machine replication SMR leverages distributed consensus protocols such as PAXOS to keep multiple replicas of a program consistent in face of replica failures or network partitions.
This fault tolerance is enticing on implementing a principled SMR system that replicates general programs, especially server programs that demand high availability. Unfortunately, SMR assumes deterministic execution, but most server programs are multithreaded and thus non-deterministic.
Moreover, existing SMR systems provide narrow state machine interfaces to suit specific programs, and it can be quite strenuous and error-prone to orchestrate a general program into these interfaces This paper presents CRANE, an SMR system that trans- parently replicates general server programs.
It leverages deterministic multithreading specifically, our prior system PARROT to make multithreaded replicas deterministic.The average rental price in Ave E / 20th St is currently $1,, based on NeighborhoodScout's exclusive analysis.
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With a crime rate of 76 per one thousand residents, Memphis has one of the highest crime rates in America compared to all communities of all sizes - from the smallest towns to the very largest cities. These crime maps compare the neighborhood crime rate with the national median of the most dangerous cities in each U.S.
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