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.

[Q]: Despite being so vital to success of Support Vector Machines, the principle of separating margin maximisation is not used in deep learning. We show that minimisation of margin variance and not maximisation of the margin is more suitable for improving generalisation in deep architectures. We propose the Halfway loss function that minimises the Normalised Margin Variance (NMV) at the output of a deep learning models and evaluate its performance against the Softmax Cross-Entropy loss on the MNIST, smallNORB and CIFAR-10 datasets.
[A]: Effects of the optimisation of the margin distribution on generalisation in deep architectures


[Q]: We formalize synthesis of shared control protocols with correctness guarantees for temporal logic specifications. More specifically, we introduce a modeling formalism in which both a human and an autonomy protocol can issue commands to a robot towards performing a certain task. These commands are blended into a joint input to the robot. The autonomy protocol is synthesized using an abstraction of possible human commands accounting for randomness in decisions caused by factors such as fatigue or incomprehensibility of the problem at hand. The synthesis is designed to ensure that the resulting robot behavior satisfies given safety and performance specifications, e.g., in temporal logic. Our solution is based on nonlinear programming and we address the inherent scalability issue by presenting alternative methods. We assess the feasibility and the scalability of the approach by an experimental evaluation.
[A]: Synthesis of Shared Control Protocols with Provable Safety and Performance Guarantees


[Q]: Tang (618-907 AD) and Song (960-1279) dynasties are two very important periods in the development of Chinese literary. The majority forms of the poetry in Tang and Song were Shi (詩) and Ci (詞), respectively. Tang Shi and Song Ci established crucial foundations of the Chinese literature, and their influences in both literary works and daily lives of the Chinese communities last until today. Recognizing the importance of Tang Shi, a Chinese emperor of the Qing dynasty (1644-1912), Kangxi, ordered to compile a collection of Tang poems, Quan-Tang-Shi (QTS, 全唐詩). QTS contains nearly 50 thousand works of about 2200 poets. A similar effort for compiling a collection of Song Ci from the private sector began in the Ming dynasty (1368-1644), and achieved in a collection called Quan-Song-Ci (QSC, 全宋詞) in the early Republican period of China (ca. 1937). QSC contains around 20 thousand works of about 1330 poets. The exact statistics about QTS and QSC may vary slightly depending on the sources. In the past more than a thousand years, literary and linguistic researchers have had done a myriad of research about the poetry of the Tang and Song dynasties. Hence, it is beyond our capacity and not our objective to review the literature in this abstract. Traditional researchers studied and compared poetic works that were produced by different authors and in different time periods to produce insightful and invaluable analyses and commentaries. Most of the time, the researchers focused on the poems of selected poets. Even when computing supports become available, studying poems of specific poets is still an important and popular type of research in poetry. Software tools facilitate the analysis of poetry from a panoramic perspective, and may lead to applications that would be very challenging in the past. For instance, Zhou and his colleagues analyze the contents of collected couplets and Tang poems for creating couplets. Yan and his
[A]:
Quantitative Analyses of Chinese Poetry of Tang and Song Dynasties: Using Changing Colors and Innovative Terms as Examples