BEA: Revisiting anchor-based object detection DNN using Budding Ensemble Architecture

This paper introduces the Budding Ensemble Architecture (BEA), a novel reduced ensemble architecture for anchor-based object detection models. Object detection models are crucial in vision-based tasks, particularly in autonomous systems. They should provide precise bounding box detections while also calibrating their predicted confidence scores, leading to higher-quality uncertainty estimates. However, current models may make erroneous decisions due to false positives receiving high scores or true positives being discarded due to low scores. BEA aims to address these issues. The proposed loss functions in BEA improve the confidence score calibration and lower the uncertainty error, which results in a better distinction of true and false positives and, eventually, higher accuracy of the object detection models. Both Base-YOLOv3 and SSD models were enhanced using the BEA method and its proposed loss functions. The BEA on Base-YOLOv3 trained on the KITTI dataset results in a 6% and 3.7% increase in mAP and AP50, respectively. Utilizing a well-balanced uncertainty estimation threshold to discard samples in real-time even leads to a 9.6% higher AP50 than its base model. This is attributed to a 40% increase in the area under the AP50-based retention curve used to measure the quality of calibration of confidence scores. Furthermore, BEA-YOLOV3 trained on KITTI provides superior out-of-distribution detection on Citypersons, BDD100K, and COCO datasets compared to the ensembles and vanilla models of YOLOv3 and Gaussian-YOLOv3.Comment: 14 pages, 5 pages supplementary material. Accepted at BMVC-202

Piecewise Linear Transformation − Propagating Aleatoric Uncertainty in Neural Networks

Real-world data typically exhibit aleatoric uncertainty which has to be considered during data-driven decision-making to assess the confidence of the decision provided by machine learning models. To propagate aleatoric uncertainty repre-sented by probability distributions (PDs) through neural net-works (NNs), both sampling-based and function approxima-tion-based methods have been proposed. However, these methods suffer from significant approximation errors and are not able to accurately represent predictive uncertainty in the NN output. In this paper, we present a novel method, Piece-wise Linear Transformation (PLT), for propagating PDs through NNs with piecewise linear activation functions (e.g., ReLU NNs). PLT does not require sampling or specific as-sumptions about the PDs. Instead, it harnesses the piecewise linear structure of such NNs to determine the propagated PD in the output space. In this way, PLT supports the accurate quantification of predictive uncertainty based on the criterion exactness of the propagated PD. We assess this exactness in theory by showing error bounds for our propagated PD. Fur-ther, our experimental evaluation validates that PLT outper-forms competing methods on publicly available real-world classification and regression datasets regarding exactness. Thus, the PDs propagated by PLT allow to assess the uncer-tainty of the provided decisions, offering valuable support

Perdeuteration of cholesterol for neutron scattering applications using recombinant Pichia pastoris

Deuteration of biomolecules has a great impact on both quality and scope of neutron scattering experiments. Cholesterol is a major component of mammalian cells, where it plays a critical role in membrane permeability, rigidity and dynamics, and contributes to specific membrane structures such as lipid rafts. Cholesterol is the main cargo in low and high-density lipoprotein complexes (i.e. LDL, HDL) and is directly implicated in several pathogenic conditions such as coronary artery disease which leads to 17 million deaths annually. Neutron scattering studies on membranes or lipid-protein complexes exploiting contrast variation have been limited by the lack of availability of fully deuterated biomolecules and especially perdeuterated cholesterol. The availability of perdeuterated cholesterol provides a unique way of probing the structural and dynamical properties of the lipoprotein complexes that underly many of these disease conditions. Here we describe a procedure for in vivo production of perdeuterated recombinant cholesterol in lipid-engineered Pichia pastoris. Using flask and fed-batch fermenter cultures in deuterated minimal medium perdeuteration of the purified cholesterol was verified by mass spectrometry and its use in a neutron scattering study was demonstrated using neutron reflectometry

Abundance and diversity of CO2-fixing bacteria in grassland soils close to natural carbon dioxide springs

9 pages, 4 figures, 3 tables, 27 references.Gaseous conditions at natural CO2 springs (mofettes) affect many processes in these unique ecosystems. While the response of plants to extreme and fluctuating CO2 concentrations ([CO2 ]) is relatively well documented, little is known on microbial life in mofette soil. Therefore, it was the aim of this study to investigate the abundance and diversity of CO2 -fixing bacteria in grassland soils in different distances to a natural carbon dioxide spring. Samples of the same soil type were collected from the Stavešinci mofette, a natural CO2 spring which is known for very pure CO2 emissions, at different distances from the CO2 releasing vents, at locations that clearly differed in soil CO2 efflux (from 12.5 to over 200 μmol CO2 m −2 s −1 yearly average). Bulk and rhizospheric soil samples were included into analyses. The microbial response was followed by a molecular analysis of cbbL genes, encoding for the large subunit of RubisCO, a carboxylase which is of crucial importance for C assimilation in chemolitoautotrophic microbes. In all samples analyzed, the “red-like” type of cbbL genes could be detected. In contrast, the “green-like” type of cbbL could not be measured by the applied technique. Surprisingly, a reduction of “red-like” cbbL genes copies was observed in bulk soil and rhizosphere samples from the sites with the highest CO2 concentrations. Furthermore, the diversity pattern of “red-like” cbbL genes changed depending on the CO2 regime. This indicates that only a part of the autotrophic CO2 -fixing microbes could adapt to the very high CO2 concentrations and adverse life conditions that are governed by mofette gaseous regime.This research was supported by the grant P4-0085 (ARRS, Republic of Slovenia), Scientific and Educational Foundation of the Republic of Slovenia, Public Fund and UL 327/45,24.112006 (SOCRATES/ERASMUS scholarship, U.V.).Peer reviewe

Suppressive effects of yard waste composts on Pythium spp. and Phytophthora spp. damping-off - a review about several research studies

In three long-term studies Yard Waste Composts (YWC) turned out to be sustainable suppressive to Pythium and Phytophthora root rot reducing the disease up to 98%. This was shown under lab and commercial conditions with composts from model and pilot-scale systems. Though suppression was related to microbial activity of the media we could not find a single microbial indicator for suppression over a wide range of samples. High quality YWC of different age showed distinct DNA fingerprints but similar suppressive effects. Thus, still it remains unclear what kind of microbial populations and functions are affecting disease suppression. However, non-destructive near-infrared spectroscopy (NIRS) turned out to predict suppression at least satisfactorily

Comparison of Barley Succession and Take-All Disease as Environmental Factors Shaping the Rhizobacterial Community during Take-All Decline▿

The root disease take-all, caused by Gaeumannomyces graminis var. tritici, can be managed by monoculture-induced take-all decline (TAD). This natural biocontrol mechanism typically occurs after a take-all outbreak and is believed to arise from an enrichment of antagonistic populations in the rhizosphere. However, it is not known whether these changes are induced by the monoculture or by ecological rhizosphere conditions due to a disease outbreak and subsequent attenuation. This question was addressed by comparing the rhizosphere microflora of barley, either inoculated with the pathogen or noninoculated, in a microcosm experiment in five consecutive vegetation cycles. TAD occurred in soil inoculated with the pathogen but not in noninoculated soil. Bacterial community analysis using terminal restriction fragment length polymorphism of 16S rRNA showed pronounced population shifts in the successive vegetation cycles, but pathogen inoculation had little effect. To elucidate rhizobacterial dynamics during TAD development, a 16S rRNA-based taxonomic microarray was used. Actinobacteria were the prevailing indicators in the first vegetation cycle, whereas the third cycle—affected most severely by take-all—was characterized by Proteobacteria, Bacteroidetes, Chloroflexi, Planctomycetes, and Acidobacteria. Indicator taxa for the last cycle (TAD) belonged exclusively to Proteobacteria, including several genera with known biocontrol traits. Our results suggest that TAD involves monoculture-induced enrichment of plant-beneficial taxa

Response of soil microbial communities to different management practices in surface soils of a soybean agroecosystem in Argentina

Argentina is the world’s third most important soybean producer; hence, there is an urgent need to preserve soil health by applying appropriate agricultural practices to maintain sustainable production in the upcoming years. Because productivity of agricultural systems largely depends on soil microbial processes, the influence of different management strategies on soil microbial community structure was analyzed in a long-term field trial started in 1992. The experimental design was a split-plot arrangement of treatments, consisting in two tillage treatments: zero tillage (ZT) and reduced tillage (RT), in combination with two crop rotation treatments: soybean monoculture (SS) and corn-soybean (CS). Phospholipid fatty acid (PLFA) profiles were used to assess total microbial community structure. Denaturing gradient gel electrophoresis (DGGE) profiles of 18S rRNA were generated to describe the influence of crop practices on fungal communities. Total PLFA content was lowest in soil under reduced tillage and soybean monoculture; therefore the use of reduced tillage-soybean monoculture in agroecosystems might produce important reductions in total microbial biomass. The structure of total microbial communities, as estimated by PLFA, was affected by crop rotation. Moreover, the fungal communities, as estimated by DGGE analysis, were influenced by combined effects of crop rotation and tillage system.Fil: Vargas Gil, Silvina. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias. Centro de Investigaciones de Ciencias Veterinarias y Agronómicas. Instituto de Fitopatologia y Fisiologia Vegetal; ArgentinaFil: Meriles, Jose Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); ArgentinaFil: Conforto, C.. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias. Centro de Investigaciones de Ciencias Veterinarias y Agronómicas. Instituto de Fitopatologia y Fisiologia Vegetal; ArgentinaFil: Basanta, María del Valle. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Córdoba. Estación Experimental Agropecuaria Manfredi; ArgentinaFil: Radl, Vivianne. Helmholtz Zentrum Munich. Institute of Soil Ecology; AlemaniaFil: Hagn, Alexandra. Helmholtz Zentrum Munich. Institute of Soil Ecology; AlemaniaFil: Schloter, Michael. Helmholtz Zentrum Munich. Institute of Soil Ecology; AlemaniaFil: March, Guillermo. Instituto Nacional de Tecnología Agropecuaria. Centro Nacional de Investigaciones Agropecuarias. Centro de Investigaciones de Ciencias Veterinarias y Agronómicas. Instituto de Fitopatologia y Fisiologia Vegetal; Argentin