Influences on mutual fund performance: comparing US and Europe using qualitative comparative analysis

This study examines the conditions that lead mutual funds to underperform or outperform competitors. Using fuzzy-set qualitative comparative analysis (fsQCA), we draw upon extensive research on fund returns to affirm and extend earlier discoveries. Fund performance (Morningstar ratings), features of the funds themselves, and characteristics of the fund managers are considered. Positive Morningstar star and analyst ratings are necessary conditions for funds to generate value (measured by Jensen’s alpha). Funds with low management fees and low ongoing fees have attractive Sharpe ratios and high returns. Likewise, large funds with good Morningstar ratings have good Sharpe ratios and returns, often when fund managers have short tenures

Spatial variability of mangrove primary productivity in the neotropics

Mangroves are considered one of the most productive ecosystems in the world with significant contributions as carbon sinks in the biosphere. Yet few attempts have been made to assess global patterns in mangrove net primary productivity, except for a few assumptions relating litterfall rates to variation in latitude. We combined geophysical and climatic variables to predict mangrove litterfall rates at continental scale. On a per‐area basis, carbon flux in litterfall in the neotropics is estimated at 5 MgC·ha−1·yr−1, between 20% and 50% higher than previous estimates. Annual carbon fixed in mangrove litterfall in the neotropics is estimated at 11.5 TgC, which suggests that current global litterfall estimates extrapolated from mean reference values may have been underestimated by at least 5%. About 5.8 TgC of this total carbon fixed in the neotropics is exported to estuaries and coastal oceans, which is nearly 30% of global carbon export by tides. We provide the first attempt to quantify and map the spatial variability of carbon fixed in litterfall in mangrove forests at continental scale in response to geophysical and climatic environmental drivers. Our results strengthen the global carbon budget for coastal wetlands, providing blue carbon scientists and coastal policy makers with a more accurate representation of the potential of mangroves to offset carbon dioxide emissions

A new species of Brachycephalus (Anura Brachycephalidae) from Santa Catarina, southern Brazil

A new species of Brachycephalus (Anura: Brachycephalidae) is described from the Atlantic Forest of northeastern state of Santa Catarina, southern Brazil. Nine specimens (eight adults and a juvenile) were collected from the leaf litter of montane forests 790–835 m above sea level (a.s.l.). The new species is a member of the pernix group by its bufoniform shape and the absence of dermal co-ossification and is distinguished from all its congeners by a combination of its general coloration (dorsal region of head, dorsum, legs, arms, and flanks light, brownish green to dark, olive green, with darker region in the middle of the dorsum and a white line along the vertebral column in most specimens) and by its smooth dorsum. The geographical distribution of the new species is highly reduced (extent of occurrence estimated as 25.04 ha, or possibly 34.37 ha). In addition, its habitat has experienced some level of degradation, raising concerns about the future conservation of the species. Preliminary density estimates suggest one calling individual every 3–4 m2 at 815–835 m a.s.l. and every 100 m2 at 790 m a.s.l. Together with the recently described B. boticario and B. fuscolineatus, the new species is among the southernmost species of Brachycephalus known to date

Interpretable and Steerable Sequence Learning via Prototypes

One of the major challenges in machine learning nowadays is to provide predictions with not only high accuracy but also user-friendly explanations. Although in recent years we have witnessed increasingly popular use of deep neural networks for sequence modeling, it is still challenging to explain the rationales behind the model outputs, which is essential for building trust and supporting the domain experts to validate, critique and refine the model. We propose ProSeNet, an interpretable and steerable deep sequence model with natural explanations derived from case-based reasoning. The prediction is obtained by comparing the inputs to a few prototypes, which are exemplar cases in the problem domain. For better interpretability, we define several criteria for constructing the prototypes, including simplicity, diversity, and sparsity and propose the learning objective and the optimization procedure. ProSeNet also provides a user-friendly approach to model steering: domain experts without any knowledge on the underlying model or parameters can easily incorporate their intuition and experience by manually refining the prototypes. We conduct experiments on a wide range of real-world applications, including predictive diagnostics for automobiles, ECG, and protein sequence classification and sentiment analysis on texts. The result shows that ProSeNet can achieve accuracy on par with state-of-the-art deep learning models. We also evaluate the interpretability of the results with concrete case studies. Finally, through user study on Amazon Mechanical Turk (MTurk), we demonstrate that the model selects high-quality prototypes which align well with human knowledge and can be interactively refined for better interpretability without loss of performance.Comment: Accepted as a full paper at KDD 2019 on May 8, 201

Composition and temporal patterns of larval fish communities in Chesapeake and Delaware Bays, USA

Comparing larval fish assemblages in different estuaries provides insights about the coastal distribution of larval populations, larval transport, and adult spawning locations. We simultaneously compared the larval fish assemblages entering 2 Middle Atlantic Bight (MAB) estuaries(Delaware Bay and Chesapeake Bay, USA) through weekly sampling from 2007 to 2009. In total,43 taxa (32 families) and 36 taxa (24 families) were collected in Delaware and Chesapeake Bays,respectively. Mean taxonomic diversity, mean richness, and evenness were generally lower in Delaware Bay. Communities of both bays were dominated by Anchoaspp., Gobiosomaspp.,Micropogonias undulatus, and Brevoortia tyrannus; Paralichthys spp. was more abundant in Delaware Bay and Microgobius thalassinus was more abundant in Chesapeake Bay. Inter-annual variation in the larval fish communities was low at both sites, with a relatively consistent composition across years, but strong seasonal (intra-annual) variation in species composition occurred in both bays. Two groups were identified in Chesapeake Bay: a ‘winter’ group dominated by shelf-spawned species and a ‘summer’ group comprising obligate estuarine species and coastal species.In Delaware Bay, 4 groups were identified: a ‘summer’ group of mainly obligate estuarine fishes being replaced by a ‘fall’ group; ‘winter’ and ‘spring’ groups were dominated by shelf-spawned and obligate estuarine species, respectively. This study demonstrates that inexpensive and simultaneous sampling in different estuaries provides important insights into the variability in community structure of fish assemblages at large spatial scales

A distributed computation of Interpro Pfam, PROSITE and ProDom for protein annotation

Interpro is a widely used tool for protein annotation in genome sequencing projects, demanding a large amount of computation and representing a huge time-consuming step. We present a strategy to execute programs using databases Pfam, PROSITE and ProDom of Interpro in a distributed environment using a Java-based messaging system. We developed a two-layer scheduling architecture of the distributed infrastructure. Then, we made experiments and analyzed the results. Our distributed system gave much better results than Interpro Pfam, PROSITE and ProDom running in a centralized platform. This approach seems to be appropriate and promising for highly demanding computational tools used for biological applications