In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Q: Sometimes SRS (Stereotactic Radio Surgery) requires using sphere packing on a Region of Interest (ROI) such as cancer to determine a treatment plan.   We have developed a sphere packing algorithm which packs non-intersecting spheres inside the ROI.   The region of interest in our case are those voxels which are identified as cancer tissues.   In this paper, we analyze the rotational invariant properties of  our sphere-packing algorithm which is based on distance transformations. Epsilon-Rotation invariant means the ability to arbitrary rotate the 3D ROI while keeping the volume properties remaining (almost) same within some limit of epsilon. The applied rotations produce spherical packing which remains highly correlated as we analyze the geometrically properties of sphere packing before and after the rotation of the volume data for the ROI. Our novel sphere packing algorithm has high degree of rotation invariance within the range of +/- epsilon. Our method used a shape descriptor derived from the values of the disjoint set of spheres form the distance-based sphere packing algorithm to extract the invariant descriptor from the ROI. We demonstrated by implementing these ideas using Slicer3D platform available for our research.   The data is based on sing MRI Stereotactic images. We presented several performance results on different benchmarks data of over 30 patients in Slicer3D platform.

A: Packing region of Interest (ROI) such as cancerous regions identified in 3D Volume Data, Packing spheres inside the ROI, rotating the ROI , measures of difference in sphere packing before and after the rotation.
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Q: Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of transfer. To address this issue we introduce the routing network paradigm, a novel neural network and training algorithm. A routing network is a kind of self-organizing neural network consisting of two components: a router and a set of one or more function blocks. A function block may be any neural network – for example a fully-connected or a convolutional layer. Given an input the router makes a routing decision, choosing a function block to apply and passing the output back to the router recursively, terminating when a fixed recursion depth is reached. In this way the routing network dynamically composes different function blocks for each input. We employ a collaborative multi-agent reinforcement learning (MARL) approach to jointly train the router and function blocks. We evaluate our model against cross-stitch networks and shared-layer baselines on multi-task settings of the MNIST, mini-imagenet, and CIFAR-100 datasets. Our experiments demonstrate a significant improvement in accuracy, with sharper convergence. In addition, routing networks have nearly constant per-task training cost while cross-stitch networks scale linearly with the number of tasks. On CIFAR100 (20 tasks) we obtain cross-stitch performance levels with an 85% average reduction in training time.


A: routing networks: a new kind of neural network which learns to adaptively route its input for multi-task learning
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Q: Conventional deep learning classifiers are static in the sense that they are trained on
 a predefined set of classes and learning to classify a novel class typically requires
 re-training. In this work, we address the problem of Low-shot network-expansion
 learning. We introduce a learning framework which enables expanding a pre-trained
 (base) deep network to classify novel classes when the number of examples for the
 novel classes is particularly small. We present a simple yet powerful distillation
 method where the base network is augmented with additional weights to classify
 the novel classes, while keeping the weights of the base network unchanged. We
 term this learning hard distillation, since we preserve the response of the network
 on the old classes to be equal in both the base and the expanded network. We
 show that since only a small number of weights needs to be trained, the hard
 distillation excels for low-shot training scenarios. Furthermore, hard distillation
 avoids detriment to classification performance on the base classes. Finally, we
 show that low-shot network expansion can be done with a very small memory
 footprint by using a compact generative model of the base classes training data
 with only a negligible degradation relative to learning with the full training set.

A:
 In this paper, we address the problem of Low-shot network-expansion learning
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