Improving Primate Sounds Classification using Binary Presorting for Deep Learning

In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular. Unfortunately, available datasets from this field of research are often not optimal learning material; Samples can be weakly labeled, of different lengths or come with a poor signal-to-noise ratio. In this work, we introduce a generalized approach that first relabels subsegments of MEL spectrogram representations, to achieve higher performances on the actual multi-class classification tasks. For both the binary pre-sorting and the classification, we make use of convolutional neural networks (CNN) and various data-augmentation techniques. We showcase the results of this approach on the challenging \textit{ComparE 2021} dataset, with the task of classifying between different primate species sounds, and report significantly higher Accuracy and UAR scores in contrast to comparatively equipped model baselines.Comment: DeLT

Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA

Quantum computing provides powerful algorithmic tools that have been shown to outperform established classical solvers in specific optimization tasks. A core step in solving optimization problems with known quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) is the problem formulation. While quantum optimization has historically centered around Quadratic Unconstrained Optimization (QUBO) problems, recent studies show, that many combinatorial problems such as the TSP can be solved more efficiently in their native Polynomial Unconstrained Optimization (PUBO) forms. As many optimization problems in practice also contain continuous variables, our contribution investigates the performance of the QAOA in solving continuous optimization problems when using PUBO and QUBO formulations. Our extensive evaluation on suitable benchmark functions, shows that PUBO formulations generally yield better results, while requiring less qubits. As the multi-qubit interactions needed for the PUBO variant have to be decomposed using the hardware gates available, i.e., currently single- and two-qubit gates, the circuit depth of the PUBO approach outscales its QUBO alternative roughly linearly in the order of the objective function. However, incorporating the planned addition of native multi-qubit gates such as the global Molmer-Sorenson gate, our experiments indicate that PUBO outperforms QUBO for higher order continuous optimization problems in general

Production of an Anise-and Woodruff-like Aroma by Monokaryotic Strains of Pleurotus sapidus Grown on Citrus Side Streams

The production of natural flavors by means of microorganisms is of great interest for the food and flavor industry, and by-products of the agro-industry are particularly suitable as substrates. In the present study, Citrus side streams were fermented using monokaryotic strains of the fungus Pleurotus sapidus. Some of the cultures exhibited a pleasant smell, reminiscent of woodruff and anise, as well as herbaceous notes. To evaluate the composition of the overall aroma, liquid/liquid extracts of submerged cultures of a selected monokaryon were prepared, and the volatiles were isolated via solvent-assisted flavor evaporation. Aroma extract dilution analyses revealed p-anisaldehyde (sweetish, anisic-and woodruff-like) with a flavor dilution factor of 218 as a character impact compound. The coconut-like, herbaceous, and sweetish smelling acyloin identified as (2S)-hydroxy-1-(4-methoxyphenyl)-1-propanone also contributed to the overall aroma and was described as an aroma-active substance with an odor threshold in air of 0.2 ng L−1 to 2.4 ng L−1 for the first time. Supplementation of the culture medium with isotopically substituted L-tyrosine elucidated this phenolic amino acid as precursor of p-anisaldehyde as well as of (2S)-hydroxy-1-(4-methoxyphenyl)-1-propanone. Chiral analysis via HPLC revealed an enantiomeric excess of 97% for the isolated product produced by P. sapidus

Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability

Stochastic partial observability poses a major challenge for decentralized coordination in multi-agent reinforcement learning but is largely neglected in state-of-the-art research due to a strong focus on state-based centralized training for decentralized execution (CTDE) and benchmarks that lack sufficient stochasticity like StarCraft Multi-Agent Challenge (SMAC). In this paper, we propose Attention-based Embeddings of Recurrence In multi-Agent Learning (AERIAL) to approximate value functions under stochastic partial observability. AERIAL replaces the true state with a learned representation of multi-agent recurrence, considering more accurate information about decentralized agent decisions than state-based CTDE. We then introduce MessySMAC, a modified version of SMAC with stochastic observations and higher variance in initial states, to provide a more general and configurable benchmark regarding stochastic partial observability. We evaluate AERIAL in Dec-Tiger as well as in a variety of SMAC and MessySMAC maps, and compare the results with state-based CTDE. Furthermore, we evaluate the robustness of AERIAL and state-based CTDE against various stochasticity configurations in MessySMAC.Comment: Accepted at ICML 202

SNAPSHOT USA 2019 : a coordinated national camera trap survey of the United States

This article is protected by copyright. All rights reserved.With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August - 24 November of 2019). We sampled wildlife at 1509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the USA. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as well as future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.Publisher PDFPeer reviewe

Mammal responses to global changes in human activity vary by trophic group and landscape

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.Peer reviewe

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The German \u27Social\u27 Revolution, the Murder of Walther Rathenau, and the Night of Broken Glass: The Association of Political Violence and Economics in Interwar Germany, 1918-1938

In the years leading up to the First World War, Germany\u27s socio-political character was defined by a nationalistic fervor that harkened back to Otto von Bismarck\u27s unification of the Prussian and Austrian nation-states in 1862. At the turn of the twentieth century, Germany was the backbone of European economics. The German economy dictated foreign capital investment, contributed to two-thirds of the continent\u27s output of steel and coal, and represented the emergence of a new European economy built on domestic production and foreign trade. From an imperialistic perspective, the First World War between 1914-1918, was an inevitable development when considering colonial Europe\u27s complex game of international geo-politics. The conflict exhausted Europe\u27s previously flourishing industrial market and redefined the social and political ideologies of the masses. For Germany in particular, defeat in the First World War was a disaster. It not only marked the emergence of a divided political spectrum, it also set the scene for an interwar period shaped by economic hardship and domestic insurrection. In the immediate aftermath of the war, Germany descended into chaos. The destruction of the Empire\u27s old monarchy resulted in a violent political vacuum that pitted Germany\u27s more ˜moderate\u27 liberal democrats against the emerging forces of Communism from the east. This was known as the German ˜Social Revolution and ultimately led to the rise of Germany\u27s first democratic state: the Weimar Republic. From its inception, the Weimar Republic was plagued by economic weakness. Hyperinflation, a depreciating currency, and depressed real money wages within the German labor market destabilized the country\u27s postwar economy. Economic weakness laid the groundwork for a crippled middle and lower class and made Germany a breeding ground for politically associated violence. With the onset of the Great Depression in 1929, Adolf Hitler and his anti-Semitic Nazi party appealed to a deeply troubled citizenry and steered Germany away from the recession by constructing a Third German Reich along Fascist lines. The Nazis created a marketplace that promoted rearmament, national autarky and the extermination of the Jewish domestic influence. The Nazis politicized anti-Semitism by coordinating nation-wide pogroms, organized mass killings, and the ˜Aryanization\u27 of Jewish property/business. In order to fully understand the association of economics and political violence in Germany between 1918-1938, this paper investigates three historical case studies. The German ˜Social\u27 Revolution of 1918-1919, the murder of Walther Rathenau in 1922, and Kristallnacht ( ˜The Night of Broken Glass\u27) in 1938 function as effective models demonstrating the economic origins of street violence during the interwar period. In turn, this study offers a unique examination of the close interdependence of politics and economics and presents a comparative analysis of Weimar Germany and Hitler\u27s Third Reich

Protein diffusion in crowded electrolyte solutions

We report on a combined cold neutron backscattering and spin-echo study of the short-range and long-range nanosecond diffusion of the model globular protein bovine serum albumin (BSA) in aqueous solution as a function of protein concentration and NaCl salt concentration. Complementary small angle X-ray scattering data are used to obtain information on the correlations of the proteins in solution. Particular emphasis is put on the effect of crowding, i.e. conditions under which the proteins cannot be considered as objects independent of each other. We thus address the question at which concentration this crowding starts to influence the static and in particular also the dynamical behaviour. We also briefly discuss qualitatively which charge effects, i.e. effects due to the interplay of charged molecules in an electrolyte solution, may be anticipated. Both the issue of crowding as well as that of charge effects are particularly relevant for proteins and their function under physiological conditions, where the protein volume fraction can be up to approximately 40% and salt ions are ubiquitous. The interpretation of the data is put in the context of existing studies on related systems and of existing theoretical models