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.

Example Input: In this paper, we present a transfer learning approach for music classification and regression tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network. We show how this convnet feature can serve as general-purpose music representation. In the experiments, a convnet is trained for music tagging and then transferred to other music-related classification and regression tasks. The convnet feature outperforms the baseline MFCC feature in all the considered tasks and several previous approaches that are aggregating MFCCs as well as lowand high-level music features.
Example Output: TRANSFER LEARNING FOR MUSIC CLASSIFICATION AND REGRESSION TASKS

Example Input: Most Software Defined Networks (SDN) traffic engineering applications use excessive and frequent global monitoring in order to find the optimal Quality-of-Service (QoS) paths for the current state of the network. In this work, we present the motivations, architecture and initial evaluation of a SDN application called Cognitive Routing Engine (CRE) which is able to find near-optimal paths for a user-specified QoS while using a very small monitoring overhead compared to global monitoring which is required to guarantee that optimal paths are found. Smaller monitoring overheads bring the advantage of smaller response time for the SDN controllers and switches. The initial evaluation of CRE on a SDN representation of the GEANT academic network shows that it is possible to find near-optimal paths with a small optimality gap of 1.65% while using 9.5 times less monitoring.
Example Output: Towards a Cognitive Routing Engine for Software Defined Networks

Example Input: The assignment of unique IDs to n variables is an instance of the well-known renaming problem, for which multiple algorithms have been proposed in the literature on distributed algorithms. However, to our knowledge, all these algorithms focus on robustness to failures, and ignore the issue of privacy. On the contrary, in this paper we do not consider agent failures, and we rather need an algorithm that protects agent and topology privacy. To this purpose, we propose Algorithm 1, which is a modification of the pseudo-tree generation algorithm in Online Appendix 2, and is an improved version of the algorithm proposed by Léauté and Faltings (2009). Each variable x is assigned a unique number idx that corresponds to the order in which it is first visited during the distributed traversal of the constraint graph (or, more precisely, an upper bound thereon). This is done by appending to each CHILD message the number id of variables visited so far (lines 8, 29 and 31). Each variable adds a random number to id so as not to leak any useful upper bound on its number of neighbors (lines 5 and 15). At the end of this algorithm, the root variable discovers an upper bound n+ on the total number of variables, and reveals it to everyone (lines 35 and 22 to 24).
Example Output:
Protecting Privacy through Distributed Computation in Multi-agent Decision Making Online Appendix 3: Unique ID Generation Algorithm