Identifying First-person Camera Wearers in Third-person Videos

We consider scenarios in which we wish to perform joint scene understanding, object tracking, activity recognition, and other tasks in environments in which multiple people are wearing body-worn cameras while a third-person static camera also captures the scene. To do this, we need to establish person-level correspondences across first- and third-person videos, which is challenging because the camera wearer is not visible from his/her own egocentric video, preventing the use of direct feature matching. In this paper, we propose a new semi-Siamese Convolutional Neural Network architecture to address this novel challenge. We formulate the problem as learning a joint embedding space for first- and third-person videos that considers both spatial- and motion-domain cues. A new triplet loss function is designed to minimize the distance between correct first- and third-person matches while maximizing the distance between incorrect ones. This end-to-end approach performs significantly better than several baselines, in part by learning the first- and third-person features optimized for matching jointly with the distance measure itself

An Explicit Method for Fast Monocular Depth Recovery in Corridor Environments

Monocular cameras are extensively employed in indoor robotics, but their performance is limited in visual odometry, depth estimation, and related applications due to the absence of scale information.Depth estimation refers to the process of estimating a dense depth map from the corresponding input image, existing researchers mostly address this issue through deep learning-based approaches, yet their inference speed is slow, leading to poor real-time capabilities. To tackle this challenge, we propose an explicit method for rapid monocular depth recovery specifically designed for corridor environments, leveraging the principles of nonlinear optimization. We adopt the virtual camera assumption to make full use of the prior geometric features of the scene. The depth estimation problem is transformed into an optimization problem by minimizing the geometric residual. Furthermore, a novel depth plane construction technique is introduced to categorize spatial points based on their possible depths, facilitating swift depth estimation in enclosed structural scenarios, such as corridors. We also propose a new corridor dataset, named Corr\_EH\_z, which contains images as captured by the UGV camera of a variety of corridors. An exhaustive set of experiments in different corridors reveal the efficacy of the proposed algorithm.Comment: 10 pages, 8 figures. arXiv admin note: text overlap with arXiv:2111.08600 by other author

Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction

Deep learning methods have surpassed the performance of traditional techniques on a wide range of problems in computer vision, but nearly all of this work has studied consumer photos, where precisely correct output is often not critical. It is less clear how well these techniques may apply on structured prediction problems where fine-grained output with high precision is required, such as in scientific imaging domains. Here we consider the problem of segmenting echogram radar data collected from the polar ice sheets, which is challenging because segmentation boundaries are often very weak and there is a high degree of noise. We propose a multi-task spatiotemporal neural network that combines 3D ConvNets and Recurrent Neural Networks (RNNs) to estimate ice surface boundaries from sequences of tomographic radar images. We show that our model outperforms the state-of-the-art on this problem by (1) avoiding the need for hand-tuned parameters, (2) extracting multiple surfaces (ice-air and ice-bed) simultaneously, (3) requiring less non-visual metadata, and (4) being about 6 times faster.Comment: 10 pages, 7 figures, published in WACV 201

Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis

BackgroundSepsis is a heterogeneous disease, therefore the single-gene-based biomarker is not sufficient to fully understand the disease. Higher-level biomarkers need to be explored to identify important pathways related to sepsis and evaluate their clinical significance.MethodsGene Set Enrichment Analysis (GSEA) was used to analyze the sepsis transcriptome to obtain the pathway-level expression. Limma was used to identify differentially expressed pathways. Tumor IMmune Estimation Resource (TIMER) was applied to estimate immune cell abundance. The Spearman correlation coefficient was used to find the relationships between pathways and immune cell abundance. Methylation and single-cell transcriptome data were also employed to identify important pathway genes. Log-rank test was performed to test the prognostic significance of pathways for patient survival probability. DSigDB was used to mine candidate drugs based on pathways. PyMol was used for 3-D structure visualization. LigPlot was used to plot the 2-D pose view for receptor-ligand interaction.ResultsEighty-four KEGG pathways were differentially expressed in sepsis patients compared to healthy controls. Of those, 10 pathways were associated with 28-day survival. Some pathways were significantly correlated with immune cell abundance and five pathways could be used to distinguish between systemic inflammatory response syndrome (SIRS), bacterial sepsis, and viral sepsis with Area Under the Curve (AUC) above 0.80. Seven related drugs were screened using survival-related pathways.ConclusionSepsis-related pathways can be utilized for disease subtyping, diagnosis, prognosis, and drug screening

Impacts of Change in Atmospheric CO₂ Concentration on <i>Larix gmelinii</i> Forest Growth in Northeast China from 1950 to 2010

Although CO2 fertilization on plant growth has been repeatedly modeled to be the main reason for the current changes in the terrestrial carbon sink at the global scale, there have been controversial findings on the CO2 fertilization effects on forests from tree-ring analyses. In this study, we employed conventional dendrochronological tree-ring datasets from Northeast China, to detect the effect of CO2 fertilization on Larix gmelinii growth from 1950 to 2010. Among four sites, there were two sites exhibiting a significant residual growth enhancement at a 90% confidence level after removing the size, age and climaterelated trends of tree-ring indices. In addition, we found consistency (R from 0.26 to 0.33, p &lt; 0.1) between the high frequency CO2 fluctuation and residual growth indices at two of the four sites during the common period. A biogeochemical model was used to quantitatively predict the contribution of elevated atmospheric CO2 on accumulated residual growth enhancement. As found in the tree-ring data, 14% of the residual growth was attributed to the CO2 fertilization effect, while climate was responsible for approximately the remainding 86%

Relationship between Net Primary Productivity and Forest Stand Age under Different Site Conditions and Its Implications for Regional Carbon Cycle Study

Net primary productivity (NPP) is a key component in the terrestrial ecosystem carbon cycle, and it varies according to stand age and site class index (SCI) for different forest types. Here we report an improved method for describing the relationships between NPP and stand age at various SCI values for the main forest types and groups in Heilongjiang Province, China, using existing yield tables, biomass equations, and forest inventory data. We calculated NPP as the sum of four components: Annual accumulation of live biomass, annual mortality of biomass, foliage turnover, and fine root turnover in soil. We also consider the NPP of understory vegetation or moss. These NPP-age relationships under different site conditions indicate that the NPP values of broadleaved and coniferous, as well as broadleaved mixed forests increase rapidly and reach a maximum when in young forests. However, for coniferous forest types, the maximum NPP generally occurs in mature forests. In addition, a higher SCI leads to a higher NPP value. Finally, we input these NPP-age relationships at various SCI values into the Integrated Terrestrial Ecosystem Carbon (InTEC) model to modify NPP modeling to estimate NPP in Heilongjiang Province in China from 2001 to 2010. All of the results showed that the methods reported in this study provide a reliable approach for estimating regional forest carbon budgets

Quantitative SERS Analysis by Employing Janus Nanoparticles with Internal Standards

Abstract Surface‐enhanced Raman scattering (SERS), a powerful analysis technique featuring ultrahigh sensitivity and the ability in chemically specific detection, has encountered intrinsic challenges in quantitative analysis due to the signal heterogeneity arising from sample preparation, molecular distribution, and experimental conditions. Herein, the plasmonic Janus nanoparticle, which is curved on one side and flat on the other, as a universal platform for quantitative analysis is proposed. The probe molecules are adsorbed on the curved side as internal standards to correct the signal fluctuation while the target molecules are adsorbed on the flat side for SERS measurement, and thus competitive adsorption between different molecules is prevented. Moreover, the Janus nanoparticles are partially embedded in a flexible and transparent membrane, enabling liquid‐state SERS measurement which is favorable to form a uniform self‐assembly molecular monolayer for quantitative SERS analysis. The quantitation of different biochemical molecules including Rhodamine 6G, crystal violet, and adenine are demonstrated, and an extension in linear response region for quantitative analysis is observed. The findings suggest a robust approach toward quantitative analysis