An integrated simulation model to evaluate national policies for the abatement of agricultural nutrients in the Baltic Sea

This study introduces a prototype model for evaluating policies to abate agricultural nutrients in the Baltic Sea from a Finnish national point of view. The stochastic simulation model integrates nutrient dynamics of nitrogen and phosphorus in the sea basins adjoining the Finnish coast, nutrient loads from land and other sources, benefits from nutrient abatement (in the form of recreation and other ecosystem services) and the costs of agricultural abatement activities. The aim of this study is to present the overall structure of the model and to demonstrate its potential using preliminary parameters. The model is made flexible for further improvements in all of its ecological and economic components. Results of a sensitivity analysis suggest that investments in reducing the nutrient runoff from arable land in Finland would become profitable only if Finland’s neighbors in the northern Baltic committed themselves to similar reductions. Environmental investments for improving water quality yield the highest returns for the Bothnian Bay and the Gulf of Finland, and smaller returns for the Bothnian Sea. In the Bothnian Bay, the abatement activities become profitable because the riverine loads from Finland represent a high proportion of the total nutrient loads. In the Gulf of Finland, this proportion is low, but the size of the coastal population benefiting from improved water quality is high.ecosystem services, nutrient abatement, Monte Carlo simulation, recreation, valuation, Environmental Economics and Policy, Research Methods/ Statistical Methods,

Partially calibrated semi-generalized pose from hybrid point correspondences

In this paper we study the problem of estimating the semi-generalized pose of a partially calibrated camera, i.e., the pose of a perspective camera with unknown focal length w.r.t. a generalized camera, from a hybrid set of 2D-2D and 2D-3D point correspondences. We study all possible camera configurations within the generalized camera system. To derive practical solvers to previously unsolved challenging configurations, we test different parameterizations as well as different solving strategies based on the state-of-the-art methods for generating efficient polynomial solvers. We evaluate the three most promising solvers, i.e., the H51f solver with five 2D-2D correspondences and one 2D-3D correspondence viewed by the same camera inside generalized camera, the H32f solver with three 2D-2D and two 2D-3D correspondences, and the H13f solver with one 2D-2D and three 2D-3D correspondences, on synthetic and real data. We show that in the presence of noise in the 3D points these solvers provide better estimates than the corresponding absolute pose solvers

Calibrated and Partially Calibrated Semi-Generalized Homographies

In this paper, we propose the first minimal solutions for estimating the semi-generalized homography given a perspective and a generalized camera. The proposed solvers use five 2D-2D image point correspondences induced by a scene plane. One of them assumes the perspective camera to be fully calibrated, while the other solver estimates the unknown focal length together with the absolute pose parameters. This setup is particularly important in structure-from-motion and image-based localization pipelines, where a new camera is localized in each step with respect to a set of known cameras and 2D-3D correspondences might not be available. As a consequence of a clever parametrization and the elimination ideal method, our approach only needs to solve a univariate polynomial of degree five or three. The proposed solvers are stable and efficient as demonstrated by a number of synthetic and real-world experiments

A Novel Application of Polynomial Solvers in mmWave Analog Radio Beamforming

Beamforming is a signal processing technique where an array of antenna elements can be steered to transmit and receive radio signals in a specific direction. The usage of millimeter wave (mmWave) frequencies and multiple input multiple output (MIMO) beamforming are considered as the key innovations of 5th Generation (5G) and beyond communication systems. The technique initially performs a beam alignment procedure, followed by data transfer in the aligned directions between the transmitter and the receiver. Traditionally, beam alignment involves periodical and exhaustive beam sweeping at both transmitter and the receiver, which is a slow process causing extra communication overhead with MIMO and massive MIMO radio units. In applications such as beam tracking, angular velocity, beam steering etc., the beam alignment procedure is optimized by estimating the beam directions using first order polynomial approximations. Recent learning-based SOTA strategies for fast mmWave beam alignment also require exploration over exhaustive beam pairs during the training procedure, causing overhead to learning strategies for higher antenna configurations. In this work, we first optimize the beam alignment cost functions e.g. the data rate, to reduce the beam sweeping overhead by applying polynomial approximations of its partial derivatives which can then be solved as a system of polynomial equations using well-known tools from algebraic geometry. At this point, a question arises: 'what is a good polynomial approximation?' In this work, we attempt to obtain a 'good polynomial approximation'. Preliminary experiments indicate that our estimated polynomial approximations attain a so-called sweet-spot in terms of the solver speed and accuracy, when evaluated on test beamforming problems.Comment: Accepted for publication in the SIGSAM's ACM Communications in Computer Algebra, as an extended abstrac

Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network

One of the precepts of food security is the proper functioning of the global food markets. This calls for open and timely intelligence on crop production on an agroclimatically meaningful territorial scale. We propose an operationally suitable method for large-scale in-season crop yield estimations from a satellite image time series (SITS) for statistical production. As an object-based method, it is spatially scalable from parcel to regional scale, making it useful for prediction tasks in which the reference data are available only at a coarser level, such as counties. We show that deep learning-based temporal convolutional network (TCN) outperforms the classical machine learning method random forests and produces more accurate results overall than published national crop forecasts. Our novel contribution is to show that mean-aggregated regional predictions with histogram-based features calculated from farm-level observations perform better than other tested approaches. In addition, TCN is robust to the presence of cloudy pixels, suggesting TCN can learn cloud masking from the data. The temporal compositing of information do not improve prediction performance. This indicates that with end-to-end learning less preprocessing in SITS tasks seems viable

Lightweight Monocular Depth with a Novel Neural Architecture Search Method

This paper presents a novel neural architecture search method, called LiDNAS, for generating lightweight monocular depth estimation models. Unlike previous neural architecture search (NAS) approaches, where finding optimized networks is computationally demanding, the introduced novel Assisted Tabu Search leads to efficient architecture exploration. Moreover, we construct the search space on a pre-defined backbone network to balance layer diversity and search space size. The LiDNAS method outperforms the state-of-the-art NAS approach, proposed for disparity and depth estimation, in terms of search efficiency and output model performance. The LiDNAS optimized models achieve result superior to compact depth estimation state-of-the-art on NYU-Depth-v2, KITTI, and ScanNet, while being 7%-500% more compact in size, i.e the number of model parameters.acceptedVersionPeer reviewe

Myofibroblastic cell activation and neovascularization predict native liver survival and development of esophageal varices in biliary atresia

Peer reviewe

Genome-wide time-to-event analysis on smoking progression stages in a family-based study

Background: Various pivotal stages in smoking behavior can be identified, including initiation, conversion from experimenting to established use, development of tolerance, and cessation. Previous studies have shown high heritability for age of smoking initiation and cessation; however, time-to-event genome-wide association studies aiming to identify underpinning genes that accelerate or delay these transitions are missing to date. Methods: We investigated which single nucleotide polymorphisms (SNPs) across the whole genome contribute to the hazard ratio of transition between different stages of smoking behavior by performing time-to-event analyses within a large Finnish twin family cohort (N = 1962), and further conducted mediation analyses of plausible intermediate traits for significant SNPs. Results: Genome-wide significant signals were detected for three of the four transitions: (1) for smoking cessation on 10p14 (P = 4.47e-08 for rs72779075 flanked by RP11-575N15 and GATA3), (2) for tolerance on 11p13 (P = 1.29e-08 for rs11031684 in RP1-65P5.1), mediated by smoking quantity, and on 9q34.12 (P = 3.81e-08 for rs2304808 in FUBP3), independent of smoking quantity, and (3) for smoking initiation on 19q13.33 (P = 3.37e-08 for rs73050610 flanked by TRPM4 and SLC6A16) in analysis adjusted for first time sensations. Although our top SNPs did not replicate, another SNP in the TRPM4-SLC6A16 gene region showed statistically significant association after region-based multiple testing correction in an independent Australian twin family sample. Conclusion: Our results suggest that the functional effect of the TRPM4-SLC6A16 gene region deserves further investigation, and that complex neurotransmitter networks including dopamine and glutamate may play a critical role in smoking initiation. Moreover, comparison of these results implies that genetic contributions to the complex smoking behavioral phenotypes vary among the transitions.Peer reviewe

No Serological Evidence of Influenza A H1N1pdm09 Virus Infection as a Contributing Factor in Childhood Narcolepsy after Pandemrix Vaccination Campaign in Finland

Peer reviewe