14,061 research outputs found
Thumb-bangers : exploring the cultural bond between video games and heavy metal
« Heavy Metal Generations » is the fourth volume in the series of papers drawn from the 2012 Music, Metal and Politics international conference (http://www.inter-disciplinary.net/publishing/product/heavy-metal-generations/).Heavy metal and video games share an almost simultaneous birth, with Black Sabbathâs debut album in 1970 and Nolan Bushnellâs Computer Space in 1971. From Judas Priestâs âFreewheel Burningâ music video in 1984 to Tim Schaferâs BrĂŒtal Legend in 2009, the exchanges between these two subcultures have been both reciprocal and exponential. This chapter will present a historical survey of the bond between video games and heavy metal cultures through its highest-profile examples. There are two underlying reasons for this symbiosis: 1) the historical development and popular dissemination of the video game came at an opportune time, first with the video game arcades in the 1970s and early 1980s, and then with the Nintendo Entertainment System, whose technical sound-channel limitations happened to fall in line with the typical structures of heavy metal; 2) heavy metal and video games, along with their creators and consumers, have faced similar sociocultural paths and challenges, notably through the policies set in place by the PMRC and the ESRB, and a flurry of lawsuits and attacks, especially from United States congressmen, that resulted in an overlapping of their respective spaces outside dominant culture. These reasons explain the natural bond between these cultural practices, and the more recent developments like Last Chance to Reasonâs Level 2 let us foresee a future where new hybrid creations could emerge
Dual-to-kernel learning with ideals
In this paper, we propose a theory which unifies kernel learning and symbolic
algebraic methods. We show that both worlds are inherently dual to each other,
and we use this duality to combine the structure-awareness of algebraic methods
with the efficiency and generality of kernels. The main idea lies in relating
polynomial rings to feature space, and ideals to manifolds, then exploiting
this generative-discriminative duality on kernel matrices. We illustrate this
by proposing two algorithms, IPCA and AVICA, for simultaneous manifold and
feature learning, and test their accuracy on synthetic and real world data.Comment: 15 pages, 1 figur
On Global Warming (Softening Global Constraints)
We describe soft versions of the global cardinality constraint and the
regular constraint, with efficient filtering algorithms maintaining domain
consistency. For both constraints, the softening is achieved by augmenting the
underlying graph. The softened constraints can be used to extend the
meta-constraint framework for over-constrained problems proposed by Petit,
Regin and Bessiere.Comment: 15 pages, 7 figures. Accepted at the 6th International Workshop on
Preferences and Soft Constraint
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning
Finding tight bounds on the optimal solution is a critical element of
practical solution methods for discrete optimization problems. In the last
decade, decision diagrams (DDs) have brought a new perspective on obtaining
upper and lower bounds that can be significantly better than classical bounding
mechanisms, such as linear relaxations. It is well known that the quality of
the bounds achieved through this flexible bounding method is highly reliant on
the ordering of variables chosen for building the diagram, and finding an
ordering that optimizes standard metrics is an NP-hard problem. In this paper,
we propose an innovative and generic approach based on deep reinforcement
learning for obtaining an ordering for tightening the bounds obtained with
relaxed and restricted DDs. We apply the approach to both the Maximum
Independent Set Problem and the Maximum Cut Problem. Experimental results on
synthetic instances show that the deep reinforcement learning approach, by
achieving tighter objective function bounds, generally outperforms ordering
methods commonly used in the literature when the distribution of instances is
known. To the best knowledge of the authors, this is the first paper to apply
machine learning to directly improve relaxation bounds obtained by
general-purpose bounding mechanisms for combinatorial optimization problems.Comment: Accepted and presented at AAAI'1
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