6741849
Blind Separation of Delayed and Convolved Sources
We address the difficult problem of separating multiple speakers with multiple microphones in a real room. We combine the work of Torkkola and Amari, Cichocki and Yang, to give Natural Gradient information maximisation rules for recurrent (IIR) networks, blindly adjusting delays, separating and deconvolving mixed signals. While they work well on simulated data, these rules fail in real rooms which usually involve non-minimum phase transfer functions, not-invertible using stable IIR filters. An approach that sidesteps this problem is to perform infomax on a feedforward architecture in the frequency domain (Lambert 1996). We demonstrate real-room separation of two natural signals using this approach.
207739442
Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources
An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have sub- and supergaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub- and supergaussian regimes. We demonstrate that the extended infomax algorithm is able to separate 20 sources with a variety of source distributions easily. Applied to high-dimensional data from electroencephalographic recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.
8163714
Scaling Laws in Natural Scenes and the Inference of 3D Shape
This paper explores the statistical relationship between natural images and their underlying range (depth) images. We look at how this relationship changes over scale, and how this information can be used to enhance low resolution range data using a full resolution intensity image. Based on our findings, we propose an extension to an existing technique known as shape recipes [3], and the success of the two methods are compared using images and laser scans of real scenes. Our extension is shown to provide a two-fold improvement over the current method. Furthermore, we demonstrate that ideal linear shape-from-shading filters, when learned from natural scenes, may derive even more strength from shadow cues than from the traditional linear-Lambertian shading cues.
76211
Negative weights make adversaries stronger
The quantum adversary method is one of the most successful techniques for proving lower bounds on quantum query complexity. It gives optimal lower bounds for many problems, has application to classical complexity in formula size lower bounds, and is versatile with equivalent formulations interms of weight schemes, eigen values, and Kolmogorov complexity. All these formulations rely on the principlethat if an algorithm successfully computes a function then, in particular, itis able to distinguish between inputs which map to different values.
 We present a stronger version of the adversary method which goes beyond this principle to make explicit use of the stronger condition that the algorithm actually computes the function. This new method, which we call ADV+-, has all the advantages ofthe old: it is a lower bound on bounded-error quantum query complexity, its square is a lower bound on formula size, and it behaves well with respect tofunction composition. Moreover ADV+- is always at least as large as the adversary method ADV, and we show an example of a monotone function forwhich ADV+-(f)=Omega(ADV(f)1.098). We also give examples showing that ADV+- does not face limitations of ADV like the certificate complexity barrier and the property testing barrier.
6758538
Separable equilibrium state probabilities via time reversal in Markovian process algebra
The reversed compound agent theorem (RCAT) is a compositional result that uses Markovian process algebra (MPA) to derive the reversed process of certain interactions between two continuous time Markov chains at equilibrium. From this reversed process, together with the given, forward process, the joint state probabilities can be expressed as a product-form, although no general algorithm has previously been given. This paper first generalises RCAT to multiple (more than two) cooperating agents, which removes the need for multiple applications and inductive proofs in cooperations of an arbitrary number of processes. A new result shows a simple stochastic equivalence between cooperating, synchronised processes and corresponding parallel, asynchronous processes. This greatly simplifies the proof of the new, multi-agent theorem, which includes a statement of the desired product-form solution itself as a product of given state probabilities in the parallel components. The reversed process and product-form thus derived rely on a solution to certain rate equations and it is shown, for the first time, that a unique solution exists under mild conditions--certainly for queueing networks and G-networks.
40366616
Parallel routing algorithms in Benes-Clos networks
A new parallel algorithm for route assignment in Benes-Clos network is studied. In packet switching systems, switch fabrics must be able to provide internally conflict-free paths simultaneously and to accommodate packets requesting for connections in real-time as they arrive at the inputs. Most known sequential route assignment algorithms, such as the looping algorithm for Benes (1962) networks or Clos (1953) networks, are designed for circuit switching systems where the switching configuration can be rearranged at a relatively low speed. Most existing parallel routing algorithms are not practical for packet switching because they either assume the set of connection requests is a full permutation or fail to deal with output contentions among the set of input packets. We develop a parallel routing algorithm by solving a set of Boolean equations which are derived from the connection requests and the symmetric structure of the Benes network. Our approach can handle both the partial permutations and the output contention problem easily. The time complexity of our algorithm is O(log/sup 2/N), where N is the network size. Furthermore, we extend the algorithm and show that it can be applied to the Clos network if the number of central modules is a power of two.
44753947
Constraint-based wrapper specification and verification for cooperative information systems
In this paper, we propose the use of semistructured constraints in wrappers to mitigate the impact of poor extraction accuracy on Cooperative Information System (CIS) data quality. Wrappers are a critical element of CISs whenever the constituent information systems publish semistructured text such as forms, reports, and memos rather than structured databases. The accuracy of CIS data that stem from text depends upon the wrappers as well as the accuracy of the underlying sources. Wrapper specification is the process of defining patterns (i.e. regular expressions) to extract information from semistructured text. Wrapper verification is the process of ensuring extraction accuracy--that the extracted information faithfully reflects the underlying source. We focus on the problem of extraction accuracy. We use constraints on semistructured data for both wrapper specification and verification. Consequently, we perform extraction and verification simultaneously. We apply the concept to wrappers for a Uniform Domain Name Dispute Resolution Policy (UDRP) CIS of arbitration decisions. UDRP decisions are currently distributed across arbitration authorities on three continents. The accuracy of data extracted using constraint-based specification and verification is measured by Type I and Type II errors.