Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g. security surveillance and autonomous driving). In this paper, we demonstrate illumination information encoded in multispectral images can be utilized to significantly boost performance of pedestrian detection. A novel illumination-aware weighting mechanism is present to accurately depict illumination condition of a scene. Such illumination information is incorporated into two-stream deep convolutional neural networks to learn multispectral human-related features under different illumination conditions (daytime and nighttime). Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy. Putting all of the pieces together, we present a powerful framework for multispectral pedestrian detection based on multi-task learning of illumination-aware pedestrian detection and semantic segmentation. Our proposed method is trained end-to-end using a well-designed multi-task loss function and outperforms state-of-the-art approaches on KAIST multispectral pedestrian dataset

Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g. daytime and nighttime). In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared channels. Specifically, our method takes pairs of aligned visible and infrared images with easily obtained bounding box annotations as input and estimates accurate prediction maps to highlight the existence of pedestrians. It offers two major advantages over the existing anchor box based multispectral detection methods. Firstly, it overcomes the hyperparameter setting problem occurred during the training phase of anchor box based detectors and can obtain more accurate detection results, especially for small and occluded pedestrian instances. Secondly, it is capable of generating accurate detection results using small-size input images, leading to improvement of computational efficiency for real-time autonomous driving applications. Experimental results on KAIST multispectral dataset show that our proposed method outperforms state-of-the-art approaches in terms of both accuracy and speed

Unsupervised Domain Adaptation for Multispectral Pedestrian Detection

Multimodal information (e.g., visible and thermal) can generate robust pedestrian detections to facilitate around-the-clock computer vision applications, such as autonomous driving and video surveillance. However, it still remains a crucial challenge to train a reliable detector working well in different multispectral pedestrian datasets without manual annotations. In this paper, we propose a novel unsupervised domain adaptation framework for multispectral pedestrian detection, by iteratively generating pseudo annotations and updating the parameters of our designed multispectral pedestrian detector on target domain. Pseudo annotations are generated using the detector trained on source domain, and then updated by fixing the parameters of detector and minimizing the cross entropy loss without back-propagation. Training labels are generated using the pseudo annotations by considering the characteristics of similarity and complementarity between well-aligned visible and infrared image pairs. The parameters of detector are updated using the generated labels by minimizing our defined multi-detection loss function with back-propagation. The optimal parameters of detector can be obtained after iteratively updating the pseudo annotations and parameters. Experimental results show that our proposed unsupervised multimodal domain adaptation method achieves significantly higher detection performance than the approach without domain adaptation, and is competitive with the supervised multispectral pedestrian detectors

The Emerging Roles of the RNA Binding Protein QKI in Cardiovascular Development and Function

RNA binding proteins (RBPs) have a broad biological and physiological function and are critical in regulating pre-mRNA posttranscriptional processing, intracellular migration, and mRNA stability. QKI, also known as Quaking, is a member of the signal transduction and activation of RNA (STAR) family, which also belongs to the heterogeneous nuclear ribonucleoprotein K- (hnRNP K-) homology domain protein family. There are three major alternatively spliced isoforms, QKI-5, QKI-6, and QKI-7, differing in carboxy-terminal domains. They share a common RNA binding property, but each isoform can regulate pre-mRNA splicing, transportation or stability differently in a unique cell type-specific manner. Previously, QKI has been known for its important role in contributing to neurological disorders. A series of recent work has further demonstrated that QKI has important roles in much broader biological systems, such as cardiovascular development, monocyte to macrophage differentiation, bone metabolism, and cancer progression. In this mini-review, we will focus on discussing the emerging roles of QKI in regulating cardiac and vascular development and function and its potential link to cardiovascular pathophysiology

Real-space renormalization group for the random-field Ising model

We present real--space renormalization group (RG) calculations of the critical properties of the random--field Ising model on a cubic lattice in three dimensions. We calculate the RG flows in a two--parameter truncation of the Hamiltonian space. As predicted, the transition at finite randomness is controlled by a zero temperature, disordered critical fixed point, and we exhibit the universal crossover trajectory from the pure Ising critical point. We extract scaling fields and critical exponents, and study the distribution of barrier heights between states as a function of length scale.Comment: 12 pages, CU-MSC-757

Critical Exponents of the pure and random-field Ising models

We show that current estimates of the critical exponents of the three-dimensional random-field Ising model are in agreement with the exponents of the pure Ising system in dimension 3 - theta where theta is the exponent that governs the hyperscaling violation in the random case.Comment: 9 pages, 4 encapsulated Postscript figures, REVTeX 3.

Critical Behavior of the 3d Random Field Ising Model: Two-Exponent Scaling or First Order Phase Transition?

In extensive Monte Carlo simulations the phase transition of the random field Ising model in three dimensions is investigated. The values of the critical exponents are determined via finite size scaling. For a Gaussian distribution of the random fields it is found that the correlation length $\xi$ diverges with an exponent $\nu=1.1\pm0.2$ at the critical temperature and that $\chi\sim\xi^{2-\eta}$ with $\eta=0.50\pm0.05$ for the connected susceptibility and $\chi_{\rm dis}\sim\xi^{4-\bar{\eta}}$ with $\bar{\eta}=1.03\pm0.05$ for the disconnected susceptibility. Together with the amplitude ratio $A=\lim_{T\to T_c}\chi_{\rm dis}/\chi^2(h_r/T)^2$ being close to one this gives further support for a two exponent scaling scenario implying $\bar{\eta}=2\eta$. The magnetization behaves discontinuously at the transition, i.e. $\beta=0$, indicating a first order transition. However, no divergence for the specific heat and in particular no latent heat is found. Also the probability distribution of the magnetization does not show a multi-peak structure that is characteristic for the phase-coexistence at first order phase transition points.Comment: 14 pages, RevTeX, 11 postscript figures (fig9.ps and fig11.ps should be printed separately

BMP10 preserves cardiac function through its dual activation of SMAD-mediated and STAT3-mediated pathways

Bone morphogenetic protein 10 (BMP10) is a cardiac peptide growth factor belonging to the transforming growth factor β superfamily that critically controls cardiovascular development, growth, and maturation. It has been shown that BMP10 elicits its intracellular signaling through a receptor complex of activin receptor-like kinase 1 with morphogenetic protein receptor type II or activin receptor type 2A. Previously, we generated and characterized a transgenic mouse line expressing BMP10 from the α-myosin heavy chain gene promoter and found that these mice have normal cardiac hypertrophic responses to both physiological and pathological stimuli. In this study, we report that these transgenic mice exhibit significantly reduced levels of cardiomyocyte apoptosis and cardiac fibrosis in response to a prolonged administration of the β-adrenoreceptor agonist isoproterenol. We further confirmed this cardioprotective function with a newly generated conditional Bmp10 transgenic mouse line, in which Bmp10 was activated in adult hearts by tamoxifen. Moreover, the intraperitoneal administration of recombinant human BMP10 was found to effectively protect hearts from injury, suggesting potential therapeutic utility of using BMP10 to prevent heart failure. Gene profiling and biochemical analyses indicated that BMP10 activates the SMAD-mediated canonical pathway and, unexpectedly, also the signal transducer and activator of transcription 3 (STAT3)-mediated signaling pathway both in vivo and in vitro Additional findings further supported the notion that BMP10's cardioprotective function likely is due to its dual activation of SMAD- and STAT3-regulated signaling pathways, promoting cardiomyocyte survival and suppressing cardiac fibrosis

Phase Diagram of the 3D Bimodal Random-Field Ising Model

The one-parametric Wang-Landau (WL) method is implemented together with an extrapolation scheme to yield approximations of the two-dimensional (exchange-energy, field-energy) density of states (DOS) of the 3D bimodal random-field Ising model (RFIM). The present approach generalizes our earlier WL implementations, by handling the final stage of the WL process as an entropic sampling scheme, appropriate for the recording of the required two-parametric histograms. We test the accuracy of the proposed extrapolation scheme and then apply it to study the size-shift behavior of the phase diagram of the 3D bimodal RFIM. We present a finite-size converging approach and a well-behaved sequence of estimates for the critical disorder strength. Their asymptotic shift-behavior yields the critical disorder strength and the associated correlation length's exponent, in agreement with previous estimates from ground-state studies of the model.Comment: 18 pages, 7 figure