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]: Polynomial approximations to boolean functions have led to many positive results in computer science. In particular, polynomial approximations to the sign function underly algorithms for agnostically learning halfspaces, as well as pseudorandom generators for halfspaces. In this work, we investigate the limits of these techniques by proving inapproximability results for the sign function. Firstly, the “polynomial regression” algorithm of Kalai et al. (SIAM J. Comput. 2008) shows that halfspaces can be learned with respect to log-concave distributions on Rn in the challenging agnostic learning model. The power of this algorithm relies on the fact that under log-concave distributions, halfspaces can be approximated arbitrarily well by low-degree polynomials. We ask whether this technique can be extended beyond log-concave distributions, and establish a negative result. We show that polynomials of any degree cannot approximate the sign function to within arbitrarily low error for a large class of non-log-concave distributions on the real line, including those with densities proportional to exp(−|x|0.99). This impossibility result extends to multivariate distributions, and thus gives a strong limitation on the power of the polynomial regression algorithm for halfspaces. Secondly, we investigate the derandomization of Chernoff-type concentration inequalities. Chernoff-type tail bounds on sums of independent random variables have pervasive applications in theoretical computer science. Schmidt et al. (SIAM J. Discrete Math. 1995) showed that these inequalities can be established for sums of random variables with only O(log(1/δ))-wise independence, for a tail probability of δ. We show that their results are tight up to constant factors. These results rely on techniques from weighted approximation theory, which studies how well functions on the real line can be approximated by polynomials under various distributions. We believe that these techniques will have further applications in other areas of theoretical computer science. Harvard University, School of Engineering and Applied Sciences. Supported by an NDSEG Fellowship and NSF grant CNS-1237235. Harvard University, School of Engineering and Applied Sciences. Supported by NSF grant CCF-1116616 and the Lord Rutherford Memorial Research Fellowship.
[EX A]: Weighted Polynomial Approximations: Limits for Learning and Pseudorandomness

[EX Q]: Eliminating the negative effect of highly non-stationary environmental noise is a long-standing research topic for speech recognition but remains an important challenge nowadays. To address this issue, traditional unsupervised signal processing methods seem to have touched the ceiling. However, data-driven based supervised approaches, particularly the ones designed with deep learning, have recently emerged as potential alternatives. In this light, we are going to comprehensively summarise the recently developed and most representative deep learning approaches to deal with the raised problem in this article, with the aim of providing guidelines for those who are going deeply into the field of environmentally robust speech recognition. To better introduce these approaches, we categorise them into singleand multi-channel techniques, each of which is specifically described at the front-end, the back-end, and the joint framework of speech recognition systems. In the meanwhile, we describe the pros and cons of these approaches as well as the relationships among them, which can probably benefit future research.
[EX A]: Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

[EX Q]: We propose a methodology for designing dependable Artificial Neural Networks (ANN) by extending the concepts of understandability, correctness, and validity that are crucial ingredients in existing certification standards. We apply the concept in a concrete case study in designing a high-way ANNbased motion predictor to guarantee safety properties such as impossibility for the ego vehicle to suggest moving to the right lane if there exists another vehicle on its right.
[EX A]:
Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives