In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[EX Q]: In this paper, we model the trajectory of sea vessels and provide a service that predicts in near-real time the position of any given vessel in 4’, 10’, 20’ and 40’ time intervals. We explore the necessary tradeoffs between accuracy, performance and resource utilization are explored given the large volume and update rates of input data. We start with building models based on well-established machine learning algorithms using static datasets and multi-scan training approaches and identify the best candidate to be used in implementing a single-pass predictive approach, under real-time constraints. The results are measured in terms of accuracy and performance and are compared against the baseline kinematic equations. Results show that it is possible to efficiently model the trajectory of multiple vessels using a single model, which is trained and evaluated using an adequately large, static dataset, thus achieving a significant gain in terms of resource usage while not compromising accuracy.
[EX A]: Employing traditional machine learning algorithms for big data streams analysis: the case of object trajectory prediction

[EX Q]: A new approach called SKYSET (Synthetic Knowledge Yield Social Entities Translation) is proposed to validate completeness and to reduce ambiguity from written instructional documentation. SKYSET utilizes a quintuple set of standardized categories, which differs from traditional approaches that typically use triples. The SKYSET System defines the categories required to form a standard template for representing information that is portable across different domains. It provides a standardized framework that enables sentences from written instructions to be translated into sets of category typed entities on a table or database. The SKYSET entities contain conceptual units or phrases that represent information from the original source documentation. SKYSET enables information concatenation where multiple documents from different domains can be translated and combined into a single common filterable and searchable table of entities. Index Terms – Cognitive Linguistics, Dual Intentional Semantic Decomposition and Reconstruction (DISDR), Frame Semantics, Information Concatenation, Information Integration, Information Summarization, Knowledge Architecture, Knowledge Management Internal External (KMIE), Metalinguistic Awareness, Synthetic Knowledge Yield Social Entities Translation (SKYSET).
[EX A]: Introducing SKYSET - a Quintuple Approach for Improving Instructions

[EX Q]: Deep learning models, which learn high-level feature representations from raw data, have become popular for machine learning and artificial intelligence tasks that involve images, audio, and other forms of complex data. A number of software “frameworks” have been developed to expedite the process of designing and training deep neural networks, such as Caffe [11], Torch [4], and Theano [1]. Currently, these frameworks can harness multiple GPUs on the same machine, but are unable to use GPUs that are distributed across multiple machines; because even average-sized deep networks can take days to train on a single GPU when faced with 100s of GBs to TBs of data, distributed GPUs present a prime opportunity for scaling up deep learning. However, the limited inter-machine bandwidth available on commodity Ethernet networks presents a bottleneck to distributed GPU training, and prevents its trivial realization. To investigate how existing software frameworks can be adapted to efficiently support distributed GPUs, we propose Poseidon, a scalable system architecture for distributed inter-machine communication in existing deep learning frameworks. In order to assess Poseidon’s effectiveness, we integrate Poseidon into the Caffe [11] framework and evaluate its performance at training convolutional neural networks for object recognition in images. Poseidon features three key contributions that improve the training speed of deep neural networks on clusters: (i) a three-level hybrid architecture that allows Poseidon to support both CPU-only clusters as well as GPU-equipped clusters, (ii) a distributed wait-free backpropagation (DWBP) algorithm to improve GPU utilization and to balance communication, and (iii) a dedicated structure-aware communication protocol (SACP) to minimize communication overheads. We empirically show that Poseidon converges to the same objective value as a single machine, and achieves state-of-the-art training speedup across multiple models and well-established datasets, using a commodity GPU cluster of 8 nodes (e.g. 4.5× speedup on AlexNet, 4× on GoogLeNet, 4× on CIFAR-10). On the much larger ImageNet 22K dataset, Poseidon with 8 nodes achieves better speedup and competitive accuracy to recent CPU-based distributed deep learning systems such as Adam [2] and Le et al. [16], which use 10s to 1000s of nodes.
[EX A]:
Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines