Towards Efficient Data Valuation Based on the Shapley Value

"How much is my data worth?" is an increasingly common question posed by organizations and individuals alike. An answer to this question could allow, for instance, fairly distributing profits among multiple data contributors and determining prospective compensation when data breaches happen. In this paper, we study the problem of data valuation by utilizing the Shapley value, a popular notion of value which originated in coopoerative game theory. The Shapley value defines a unique payoff scheme that satisfies many desiderata for the notion of data value. However, the Shapley value often requires exponential time to compute. To meet this challenge, we propose a repertoire of efficient algorithms for approximating the Shapley value. We also demonstrate the value of each training instance for various benchmark datasets

Towards More Accurate Radio Telescope Images

Radio interferometry usually compensates for high levels of noise in sensor/antenna electronics by throwing data and energy at the problem: observe longer, then store and process it all. We propose instead a method to remove the noise explicitly before imaging. To this end, we developed an algorithm that first decomposes the instances of antenna correlation matrix, the so-called visibility matrix, into additive components using Singular Spectrum Analysis and then cluster these components using graph Laplacian matrix. We show through simulation the potential for radio astronomy, in particular, illustrating the benefit for LOFAR, the low frequency array in Netherlands. Least-squares images are estimated with far higher accuracy with low computation cost without the need for long observation time

DETERMINING POSITIONS OF TRANSDUCERS FOR RECEIVING AND/OR TRANSMITTING WAVE SIGNALS

The invention is notably directed to a method for determining positions {pi}i=1,..., N of transducers {Ai}i=1,..., N of an apparatus. The transducers are assumed to be configured for receiving wave signals from and/or transmitting wave signals to one or more regions {Rm}m=1,..., M of interest in an n-dimensional space, with n = 2 or 3. The method first comprises determining an n-dimensional spatial filter function , which matches projections {Pm}m=1,..., M of the one or more regions {Rm}m=1,..., M of interest onto an n – 1-dimensional sphere centered on the apparatus. Then, a density function is obtained, based on a Fourier transform of the determined spatial filter function . Finally, a position pi is determined, within said n-dimensional space, for each of N transducers, based on the obtained density function and a prescribed number N of the transducers. The invention is further directed to related devices, apparatuses and systems, as well as computer program products

METHOD AND SYSTEM TO REDUCE NOISE IN PHASED-ARRAY SIGNALS FROM RECEIVERS LOCATED AT DIFFERENT LOCATIONS

The present invention is notably directed to a computerized method to reduce noise in phased- array signals from a set of receivers at different locations. Time-series are received from the receivers, which time-series form phased-array signals. The time-series are ordered based on the different locations of the receivers and spatially phased series are obtained from the ordered time- series. Each of the spatially phased series obtained comprises a series of signal values that are spatially phased. A noise component is identified in each of the spatially phased series obtained and removed from the spatially phased series to obtain denoised series. The invention is further directed to related receiver systems and computer program products

On Denoising Crosstalk in Radio Interferometry

Noise reduction in radio interferometers is a formidable task due to the relatively weak signals under observation. The usual strategy is to compensate with a large number of antennas and extend the observation time. We showed recently that one could denoise directly antenna time-series, under the assumption of uncorrelated noise. This work is a first step to extend it for when crosstalk is present. We first propose a subspace based algorithm to estimate noise covariance, and then demonstrate that the noise covariance can be accurately estimated, and the image denoised. We show sky images generated using a core station LOFAR, and that even with one tenth the observation time (and thus one tenth the data) the estimate can still be enhanced

Denoising radio interferometric images by subspace clustering

Radio interferometry usually compensates for high levels of noise in sensor/antenna electronics by throwing data and energy at the problem: observe longer, then store and process it all. Furthermore, only the end image is cleaned, reducing flexibility substantially. We pro- pose instead a method to remove the noise explicitly before imaging. To this end, we developed an algorithm that first decomposes the sensor signals into components using Singular Spectrum Analysis and then cluster these components using graph Laplacian matrix. We show through simulation the potential for radio astronomy, in particular, illustrating the benefit for LOFAR, the low frequency array in Netherlands. From telescopic data to least-squares image estimates, far higher accuracy with low computation cost results without the need for long observation time