18,821 research outputs found
Pyroomacoustics: A Python package for audio room simulations and array processing algorithms
We present pyroomacoustics, a software package aimed at the rapid development
and testing of audio array processing algorithms. The content of the package
can be divided into three main components: an intuitive Python object-oriented
interface to quickly construct different simulation scenarios involving
multiple sound sources and microphones in 2D and 3D rooms; a fast C
implementation of the image source model for general polyhedral rooms to
efficiently generate room impulse responses and simulate the propagation
between sources and receivers; and finally, reference implementations of
popular algorithms for beamforming, direction finding, and adaptive filtering.
Together, they form a package with the potential to speed up the time to market
of new algorithms by significantly reducing the implementation overhead in the
performance evaluation step.Comment: 5 pages, 5 figures, describes a software packag
LibKGE – A knowledge graph embedding library for reproducible research
LibKGE (https://github.com/uma-pi1/kge) is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction. The key goals of LibKGE are to enable reproducible research, to provide a framework for comprehensive experimental studies, and to facilitate analyzing the contributions of individual components of training methods, model architectures, and evaluation methods. LibKGE is highly configurable and every experiment can be fully reproduced with a single configuration file. Individual components are decoupled to the extent possible so that they can be mixed and matched with each other. Implementations in LibKGE aim to be as efficient as possible without leaving the scope of Python/Numpy/PyTorch. A comprehensive logging mechanism and tooling facilitates in-depth analysis. LibKGE provides implementations of common knowledge graph embedding models and training methods, and new ones can be easily added. A comparative study (Ruffinelli et al., 2020) showed that LibKGE reaches competitive to state-of-the-art performance for many models with a modest amount of automatic hyperparameter tuning
Forecasting of commercial sales with large scale Gaussian Processes
This paper argues that there has not been enough discussion in the field of
applications of Gaussian Process for the fast moving consumer goods industry.
Yet, this technique can be important as it e.g., can provide automatic feature
relevance determination and the posterior mean can unlock insights on the data.
Significant challenges are the large size and high dimensionality of commercial
data at a point of sale. The study reviews approaches in the Gaussian Processes
modeling for large data sets, evaluates their performance on commercial sales
and shows value of this type of models as a decision-making tool for
management.Comment: 1o pages, 5 figure
Programming support for an integrated multi-party computation and MapReduce infrastructure
We describe and present a prototype of a distributed computational infrastructure and associated high-level programming language that allow multiple parties to leverage their own computational resources capable of supporting MapReduce [1] operations in combination with multi-party computation (MPC). Our architecture allows a programmer to author and compile a protocol using a uniform collection of standard constructs, even when that protocol involves computations that take place locally within each participant’s MapReduce cluster as well as across all the participants using an MPC protocol. The highlevel programming language provided to the user is accompanied by static analysis algorithms that allow the programmer to reason about the efficiency of the protocol before compiling and running it. We present two example applications demonstrating how such an infrastructure can be employed.This work was supported in part
by NSF Grants: #1430145, #1414119, #1347522, and #1012798
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