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.

[EX 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.
[EX 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.

[EX Q]: Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents.
 Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-cooccurrence networks.
 Furthermore, social networks can be extracted from email corpora, tweets, or social media. 
 When it comes to visualising these large corpora, either the textual content or the network graph are used.

 In this paper, we propose to incorporate both, text and graph, to not only visualise the semantic information encoded in the documents' content but also the relationships expressed by the inherent network structure.
 To this end, we introduce a novel algorithm based on multi-objective optimisation to jointly position embedded documents and graph nodes in a two-dimensional landscape.
 We illustrate the effectiveness of our approach with real-world datasets and show that we can capture the semantics of large document collections better than other visualisations based on either the content or the network information.
[EX A]: Dimensionality reduction algorithm to visualise text with network information, for example an email corpus or co-authorships.

[EX Q]: Low bit-width integer weights and activations are very important for efficient inference, especially with respect to lower power consumption. We propose to apply Monte Carlo methods and importance sampling to sparsify and quantize pre-trained neural networks without any retraining. We obtain sparse, low bit-width integer representations that approximate the full precision weights and activations. The precision, sparsity, and complexity are easily configurable by the amount of sampling performed. Our approach, called Monte Carlo Quantization (MCQ), is linear in both time and space, while the resulting quantized sparse networks show minimal accuracy loss compared to the original full-precision networks. Our method either outperforms or achieves results competitive with methods that do require additional training on a variety of challenging tasks.
[EX A]:
Monte Carlo methods for quantizing pre-trained models without any additional training.

