Inter-battery topic representation learning

In this paper, we present the Inter-Battery Topic Model (IBTM). Our approach extends traditional topic models by learning a factorized latent variable representation. The structured representation leads to a model that marries benefits traditionally associated with a discriminative approach, such as feature selection, with those of a generative model, such as principled regularization and ability to handle missing data. The factorization is provided by representing data in terms of aligned pairs of observations as different views. This provides means for selecting a representation that separately models topics that exist in both views from the topics that are unique to a single view. This structured consolidation allows for efficient and robust inference and provides a compact and efficient representation. Learning is performed in a Bayesian fashion by maximizing a rigorous bound on the log-likelihood. Firstly, we illustrate the benefits of the model on a synthetic dataset,. The model is then evaluated in both uni- and multi-modality settings on two different classification tasks with off-the-shelf convolutional neural network (CNN) features which generate state-of-the-art results with extremely compact representations

Global parameter identification of stochastic reaction networks from single trajectories

We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell--cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and efficient exact stochastic simulation algorithms that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.Comment: Article in print as a book chapter in Springer's "Advances in Systems Biology

Quality assessment of atmospheric surface fields over the Baltic Sea from an ensemble of regional climate model simulations with respect to ocean dynamics

Climate model results for the Baltic Sea region from an ensemble of eight simulations using the Rossby Centre Atmosphere model version 3 (RCA3) driven with lateral boundary data from global climate models (GCMs) are compared with results from a downscaled ERA40 simulation and gridded observations from 1980-2006. The results showed that data from RCA3 scenario simulations should not be used as forcing for Baltic Sea models in climate change impact studies because biases of the control climate significantly affect the simulated changes of future projections. For instance, biases of the sea ice cover in RCA3 in the present climate affect the sensitivity of the model's response to changing climate due to the ice-albedo feedback. From the large ensemble of available RCA3 scenario simulations two GCMs with good performance in downscaling experiments during the control period 1980-2006 were selected. In this study, only the quality of atmospheric surface fields over the Baltic Sea was chosen as a selection criterion. For the greenhouse gas emission scenario A1B two transient simulations for 1961-2100 driven by these two GCMs were performed using the regional, fully coupled atmosphere-ice-ocean model RCAO. It was shown that RCAO has the potential to improve the results in downscaling experiments driven by GCMs considerably, because sea surface temperatures and sea ice concentrations are calculated more realistically with RCAO than when RCA3 has been forced with surface boundary data from GCMs. For instance, the seasonal 2 m air temperature cycle is closer to observations in RCAO than in RCA3 downscaling simulations. However, the parameterizations of air-sea fluxes in RCAO need to be improved

Synergistic and antagonistic effects of land use and non‐native species on community responses to climate change

Climate change, land‐use change and introductions of non‐native species are key determinants of biodiversity change worldwide. However, the extent to which anthropogenic drivers of environmental change interact to affect biological communities is largely unknown, especially over longer time periods. Here, we show that plant community composition in 996 Swedish landscapes has consistently shifted to reflect the warmer and wetter climate that the region has experienced during the second half of the 20th century. Using community climatic indices, which reflect the average climatic associations of the species within each landscape at each time period, we found that species compositions in 74% of landscapes now have a higher representation of warm‐associated species than they did previously, while 84% of landscapes now host more species associated with higher levels of precipitation. In addition to a warmer and wetter climate, there have also been large shifts in land use across the region, while the fraction of non‐native species has increased in the majority of landscapes. Climatic warming at the landscape level appeared to favour the colonization of warm‐associated species, while also potentially driving losses in cool‐associated species. However, the resulting increases in community thermal means were apparently buffered by landscape simplification (reduction in habitat heterogeneity within landscapes) in the form of increased forest cover. Increases in non‐native species, which generally originate from warmer climates than Sweden, were a strong driver of community‐level warming. In terms of precipitation, both landscape simplification and increases in non‐natives appeared to favour species associated with drier climatic conditions, to some extent counteracting the climate‐driven shift towards wetter communities. Anthropogenic drivers can act both synergistically and antagonistically to determine trajectories of change in biological communities over time. Therefore, it is important to consider multiple drivers of global change when trying to understand, manage and predict biodiversity in the future

Determining Interacting Objects in Human-Centric Activities via Qualitative Spatio-Temporal Reasoning

Abstract. Understanding the activities taking place in a video is a chal-lenging problem in Artificial Intelligence. Complex video sequences con-tain many activities and involve a multitude of interacting objects. De-termining which objects are relevant to a particular activity is the first step in understanding the activity. Indeed many objects in the scene are irrelevant to the main activity taking place. In this work, we consider human-centric activities and look to identify which objects in the scene are involved in the activity. We take an activity-agnostic approach and rank every moving object in the scene with how likely it is to be involved in the activity. We use a comprehensive spatio-temporal representation that captures the joint movement between humans and each object. We then use supervised machine learning techniques to recognize relevant objects based on these features. Our approach is tested on the challeng-ing Mind’s Eye dataset.

Highly temporally resolved response to seasonal surface melt of the Zachariae and 79N outlet glaciers in northeast Greenland

The seasonal response to surface melting of the Northeast Greenland Ice Stream outlets, Zachariae and 79N, is investigated using new highly temporally resolved surface velocity maps for 2016 combined with numerical modeling. The seasonal speedup at 79N of 0.15 km/yr is suggested to be driven by a decrease in effective basal pressure induced by surface melting, whereas for Zachariae its 0.11 km/yr seasonal speedup correlates equally well with the breakup of its large ice mélange. We investigate the influence 76 km long floating tongue at 79N, finding it provides little resistance and that most of it could be lost without impacting the dynamics of the area. Furthermore, we show that reducing the slipperiness along the tongue-wall interfaces produces a velocity change spatially inconsistent with the observed seasonal speedup. Finally, we find that subglacial sticky spots such as bedrock bumps play a negligible role in the large-scale response to a seasonally enhanced basal slipperiness of the region

Integrating isotopes and documentary evidence : dietary patterns in a late medieval and early modern mining community, Sweden

We would like to thank the Archaeological Research Laboratory, Stockholm University, Sweden and the Tandem Laboratory (Ångström Laboratory), Uppsala University, Sweden, for undertaking the analyses of stable nitrogen and carbon isotopes in both human and animal collagen samples. Also, thanks to Elin Ahlin Sundman for providing the δ13C and δ15N values for animal references from Västerås. This research (Bäckström’s PhD employment at Lund University, Sweden) was supported by the Berit Wallenberg Foundation (BWS 2010.0176) and Jakob and Johan Söderberg’s foundation. The ‘Sala project’ (excavations and analyses) has been funded by Riksens Clenodium, Jernkontoret, Birgit and Gad Rausing’s Foundation, SAU’s Research Foundation, the Royal Physiographic Society of Lund, Berit Wallenbergs Foundation, Åke Wibergs Foundation, Lars Hiertas Memory, Helge Ax:son Johnson’s Foundation and The Royal Swedish Academy of Sciences.Peer reviewedPublisher PD

Use of expert elicitation to assign weights to climate and hydrological models in climate impact studies

Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analysed, and the result of a research experiment is presented using model weighting with the participation of six climate model experts and six hydrological model experts. For the experiment, seven climate models are a priori selected from a larger EURO-CORDEX (Coordinated Regional Downscaling Experiment - European Domain) ensemble of climate models, and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual rounds of elicitation of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological-impact modellers in general are more open for assigning weights to different models in a multi-model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only re-establish a uniform weight between climate models.This work was funded by the project AQUA-CLEW, which is part of ERA4CS (European Research Area for Climate Services), an ERANET (European Research Area Net-work) initiated by JPI Climate (Joint Programming Initiative) andfunded by Formas (Sweden); German Aerospace Center (DLR, Germany); Ministry of Education, Science and Research (BMBWF,Austria); Innovation Fund Denmark; Ministry of Economic Affairs and Digital Transformation (MINECO, Spain); and French National Research Agency with co-funding by the European Commission (grant no. 69046). The contribution of Philippe Lucas-Picher was supported by the French National Research Agency (future investment programme no. ANR-18-MPGA-0005). Rafael Pimentel acknowledges funding by the Modality 5.2 of the Programa Propio 2018 of the University of Córdoba and the Juan de la Cierva Incorporación programme of the Ministry of Science and Innovation (grant no. IJC2018-038093-I). Rafael Pimentel and María J. Polo are members of DAUCO (Unit of Excellence reference no. CEX2019-000968-M), with financial support from the Spanish Ministry of Science and Innovation and the Spanish State Research Agency, through the Severo Ochoa Centre of Excellence and María de Maeztu Unit of Excellence in research and development (R&D)

Adverse Effects of Methylmercury: Environmental Health Research Implications

Background: The scientific discoveries of health risks resulting from methylmercury exposure began in 1865 describing ataxia, dysarthria, constriction of visual fields, impaired hearing, and sensory disturbance as symptoms of fatal methylmercury poisoning. Objective: Our aim was to examine how knowledge and consensus on methylmercury toxicity have developed in order to identify problems of wider concern in research. Data sources and extraction: We tracked key publications that reflected new insights into human methylmercury toxicity. From this evidence, we identified possible caveats of potential significance for environmental health research in general. Synthesis: At first, methylmercury research was impaired by inappropriate attention to narrow case definitions and uncertain chemical speciation. It also ignored the link between ecotoxicity and human toxicity. As a result, serious delays affected the recognition of methylmercury as a cause of serious human poisonings in Minamata, Japan. Developmental neurotoxicity was first reported in 1952, but despite accumulating evidence, the vulnerability of the developing nervous system was not taken into account in risk assessment internationally until approximately 50 years later. Imprecision in exposure assessment and other forms of uncertainty tended to cause an underestimation of methylmercury toxicity and repeatedly led to calls for more research rather than prevention. Conclusions: Coupled with legal and political rigidity that demanded convincing documentation before considering prevention and compensation, types of uncertainty that are common in environmental research delayed the scientific consensus and were used as an excuse for deferring corrective action. Symptoms of methylmercury toxicity, such as tunnel vision, forgetfulness, and lack of coordination, also seemed to affect environmental health research and its interpretation