Automatic classification of animal vocalizations has great potential to enhance the monitoring of species movements and behaviors. This is particularly true for monitoring nocturnal bird migration, ...where automated classification of migrants' flight calls could yield new biological insights and conservation applications for birds that vocalize during migration. In this paper we investigate the automatic classification of bird species from flight calls, and in particular the relationship between two different problem formulations commonly found in the literature: classifying a short clip containing one of a fixed set of known species (N-class problem) and the continuous monitoring problem, the latter of which is relevant to migration monitoring. We implemented a state-of-the-art audio classification model based on unsupervised feature learning and evaluated it on three novel datasets, one for studying the N-class problem including over 5000 flight calls from 43 different species, and two realistic datasets for studying the monitoring scenario comprising hundreds of thousands of audio clips that were compiled by means of remote acoustic sensors deployed in the field during two migration seasons. We show that the model achieves high accuracy when classifying a clip to one of N known species, even for a large number of species. In contrast, the model does not perform as well in the continuous monitoring case. Through a detailed error analysis (that included full expert review of false positives and negatives) we show the model is confounded by varying background noise conditions and previously unseen vocalizations. We also show that the model needs to be parameterized and benchmarked differently for the continuous monitoring scenario. Finally, we show that despite the reduced performance, given the right conditions the model can still characterize the migration pattern of a specific species. The paper concludes with directions for future research.
Real estate and noncore assets can represent a significant - and often largely untapped - resource for health systems pursuing any number of financial or operational goals. Since the COVID-19 ...pandemic took hold in the United States in March 2020, health systems have had to balance ongoing operational disruptions and their resulting cash flow strains with balance sheet strength. ADDITIONAL CONSIDERATIONS Before pursuing a monetization transaction, the health system should check with its financial advisers, auditors and legal counsel to get their view of the transaction and its impact on the organization's financial statements. Health system leaders should also be aware that alternative capital sources accessed through real estate or noncore asset monetization transactions will generally come at a higher cost than traditional funding, such as tax-exempt debt.
In each of these areas, the catalogue should define not only the core treasury subfunctions (i.e., cash management or debt issuance), but also how core treasury interacts with other parts of the ...enterprise (i.e., how capital management interacts with capital allocation) and how it is supported by various external relationships (i.e., with commercial or investment banks or advisers). The treasury functions identified above-capital management, treasury operations, external financing, and invested assets-remain the main areas of consideration, and each contains functions that require focused attention to planning by the board and senior management as early in the consolidation process as possible. The objective is to establish the right cash targets and cash management processes and to put in place commercial banking relationships that achieve the right balance between product execution, fees, and access to bank credit.
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