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 Input:
The developpment of the Internet of Things (IoT) concept revives Responsive Environments (RE) technologies. Nowadays, the idea of a permanent connection between physical and digital world is technologically possible. The capillar Internet relates to the Internet extension into daily appliances such as they become actors of Internet like any hu-man. The parallel development of Machine-to-Machine communications and Arti cial Intelligence (AI) technics start a new area of cybernetic. This paper presents an approach for Cybernetic Organism (Cyborg) for RE based on Organic Computing (OC). In such approach, each appli-ance is a part of an autonomic system in order to control a physical environment. The underlying idea is that such systems must have self-x properties in order to adapt their behavior to external disturbances with a high-degree of autonomy.

Ex Output:
TOWARD ORGANIC COMPUTING APPROACH FOR CYBERNETIC RESPONSIVE ENVIRONMENT


Ex Input:
Independent Component Analysis (ICA) is the problem of learning a square matrix A, given samples of X = AS, where S is a random vector with independent coordinates. Most existing algorithms are provably efficient only when each Si has finite and moderately valued fourth moment. However, there are practical applications where this assumption need not be true, such as speech and finance. Algorithms have been proposed for heavy-tailed ICA, but they are not practical, using random walks and the full power of the ellipsoid algorithm multiple times. The main contributions of this paper are: (1) A practical algorithm for heavy-tailed ICA that we call HTICA. We provide theoretical guarantees and show that it outperforms other algorithms in some heavy-tailed regimes, both on real and synthetic data. Like the current state-of-the-art, the new algorithm is based on the centroid body (a first moment analogue of the covariance matrix). Unlike the state-of-the-art, our algorithm is practically efficient. To achieve this, we use explicit analytic representations of the centroid body, which bypasses the use of the ellipsoid method and random walks. (2) We study how heavy tails affect different ICA algorithms, including HTICA. Somewhat surprisingly, we show that some algorithms that use the covariance matrix or higher moments can successfully solve a range of ICA instances with infinite second moment. We study this theoretically and experimentally, with both synthetic and real-world heavy-tailed data.

Ex Output:
Heavy-Tailed Analogues of the Covariance Matrix for ICA


Ex Input:
<lb>In applications such as recommendation systems and revenue management, it is important to<lb>predict preferences on items that have not been seen by a user or predict outcomes of comparisons<lb>among those that have never been compared. A popular discrete choice model of multinomial<lb>logit model captures the structure of the hidden preferences with a low-rank matrix. In order to<lb>predict the preferences, we want to learn the underlying model from noisy observations of the<lb>low-rank matrix, collected as revealed preferences in various forms of ordinal data. A natural<lb>approach to learn such a model is to solve a convex relaxation of nuclear norm minimization.<lb>We present the convex relaxation approach in two contexts of interest: collaborative ranking<lb>and bundled choice modeling. In both cases, we show that the convex relaxation is minimax<lb>optimal. We prove an upper bound on the resulting error with finite samples, and provide a<lb>matching information-theoretic lower bound.

Ex Output:

Collaboratively Learning Preferences from Ordinal Data