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Richard Bernstein

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4 papers

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AAAI Conference 2019 Conference Paper

Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant

Johan Bjorck
Brendan H. Rappazzo
Di Chen
Richard Bernstein
Peter H. Wrege
Carla P. Gomes

In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa. Earth’s biodiversity is increasingly under threat by sources of anthropogenic change (e. g. resource extraction, land use change, and climate change) and surveying animal populations is critical for developing conservation strategies. However, manually monitoring tropical forests or deep oceans is intractable. For species that communicate acoustically, researchers have argued for placing audio recorders in the habitats as a costeffective and non-invasive method, a strategy known as passive acoustic monitoring (PAM). In collaboration with conservation efforts, we construct a large labeled dataset of passive acoustic recordings of the African Forest Elephant via crowdsourcing, compromising thousands of hours of recordings in the wild. Using state-of-the-art techniques in artificial intelligence we improve upon previously proposed methods for passive acoustic monitoring for classification and segmentation. In real-time detection of elephant calls, network bandwidth quickly becomes a bottleneck and efficient ways to compress the data are needed. Most audio compression schemes are aimed at human listeners and are unsuitable for low-frequency elephant calls. To remedy this, we provide a novel end-to-end differentiable method for compression of audio signals that can be adapted to acoustic monitoring of any species and dramatically improves over näive coding strategies.

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AAAI Conference 2015 Conference Paper

Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa

Stefano Ermon
Yexiang Xue
Russell Toth
Bistra Dilkina
Richard Bernstein
Theodoros Damoulas
Patrick Clark
Steve DeGloria

Understanding spatio-temporal resource preferences is paramount in the design of policies for sustainable development. Unfortunately, resource preferences are often unknown to policy-makers and have to be inferred from data. In this paper we consider the problem of inferring agents’ preferences from observed movement trajectories, and formulate it as an Inverse Reinforcement Learning (IRL) problem. With the goal of informing policy-making, we take a probabilistic approach and consider generative models that can be used to simulate behavior under new circumstances such as changes in resource availability, access policies, or climate. We study the Dynamic Discrete Choice (DDC) models from econometrics and prove that they generalize the Max-Entropy IRL model, a widely used probabilistic approach from the machine learning literature. Furthermore, we develop SPL-GD, a new learning algorithm for DDC models that is considerably faster than the state of the art and scales to very large datasets. We consider an application in the context of pastoralism in the arid and semi-arid regions of Africa, where migratory pastoralists face regular risks due to resource availability, droughts, and resource degradation from climate change and development. We show how our approach based on satellite and survey data can accurately model migratory pastoralism in East Africa and that it considerably outperforms other approaches on a largescale real-world dataset of pastoralists’ movements in Ethiopia collected over 3 years.

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AAAI Conference 2014 Conference Paper

Challenges in Materials Discovery – Synthetic Generator and Real Datasets

Ronan Le Bras
Richard Bernstein
John Gregoire
Santosh Suram
Carla Gomes
Bart Selman
R. Bruce van Dover

Newly-discovered materials have been central to recent technological advances. They have contributed significantly to breakthroughs in electronics, renewable energy and green buildings, and overall, have promoted the advancement of global human welfare. Yet, only a fraction of all possible materials have been explored. Accelerating the pace of discovery of materials would foster technological innovations, and would potentially address pressing issues in sustainability, such as energy production or consumption. The bottleneck of this discovery cycle lies, however, in the analysis of the materials data. As materials scientists have recently devised techniques to efficiently create thousands of materials and experimentalists have developed new methods and tools to characterize these materials, the limiting factor has become the data analysis itself. Hence, the goal of this paper is to stimulate the development of new computational techniques for the analysis of materials data, by bringing together the complimentary expertise of materials scientists and computer scientists. In collaboration with two major research laboratories in materials science, we provide the first publicly available dataset for the phase map identification problem. In addition, we provide a parameterized synthetic data generator to assess the quality of proposed approaches, as well as tools for data visualization and solution evaluation.

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IJCAI Conference 2013 Conference Paper

Crowdsourcing Backdoor Identification for Combinatorial Optimization

Ronan Le Bras
Richard Bernstein
Carla P. Gomes
Bart Selman
R. Bruce van Dover

We will show how human computation insights can be key to identifying so-called backdoor variables in combinatorial optimization problems. Backdoor variables can be used to obtain dramatic speedups in combinatorial search. Our approach leverages the complementary strength of human input, based on a visual identiﬁcation of problem structure, crowdsourcing, and the power of combinatorial solvers to exploit complex constraints. We describe our work in the context of the domain of materials discovery. The motivation for considering the materials discovery domain comes from the fact that new materials can provide solutions for key challenges in sustainability, e. g. , in energy, new catalysts for more efﬁcient fuel cell technology.

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