Positioning and mapping can be conducted accurately by state-of-the-art state estimation methods. However, reliability of these methods is largely based on avoiding degeneracy that can arise from cases such as scarcity of texture features for vision sensors and lack of geometrical structures for range sensors. Since the problems are inevitably solved in uncontrived environments where sensors cannot function with their highest quality, it is important for the estimation methods to be robust to degeneracy. This paper proposes an online method to mitigate for degeneracy in optimizationbased problems, through analysis of geometric structure of the problem constraints. The method determines and separates degenerate directions in the state space, and only partially solves the problem in well-conditioned directions. We demonstrate utility of this method with data from a camera and lidar sensor pack to estimate 6-DOF ego-motion. Experimental results show that the system is able to improve estimation in environmentally degenerate cases, resulting in enhanced robustness for online positioning and mapping.

Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of subject, relation, object triples. In real-world scenarios with large numbers of objects and relations, some are seen very commonly while others are barely seen. We develop a new relationship detection model that embeds objects and relations into two vector spaces where both discriminative capability and semantic affinity are preserved. We learn a visual and a semantic module that map features from the two modalities into a shared space, where matched pairs of features have to discriminate against those unmatched, but also maintain close distances to semantically similar ones. Benefiting from that, our model can achieve superior performance even when the visual entity categories scale up to more than 80, 000, with extremely skewed class distribution. We demonstrate the efficacy of our model on a large and imbalanced benchmark based of Visual Genome that comprises 53, 000+ objects and 29, 000+ relations, a scale at which no previous work has been evaluated at. We show superiority of our model over competitive baselines on the original Visual Genome dataset with 80, 000+ categories. We also show state-of-the-art performance on the VRD dataset and the scene graph dataset which is a subset of Visual Genome with 200 categories.

Hypoxia or hypoxia mimetic has been shown to induce differentiation together with the accumulation of hypoxiainducible factor-1a (HIF-1a) protein of myeloid leukemic cells and normal hematopoietic progenitors. To provide direct evidence for the role of HIF-1a in acute myeloid leukemia (AML) cell differentiation and its mechanisms, we generated myeloid leukemic U937T transformants, in which HIF-1a was tightly induced by tetracycline withdrawal. The results showed that the conditional HIF-1a induction triggered granulocytic differentiation of these transformants, while the suppression of HIF-1a expression by specific short hairpin RNAs (shRNAs) effectively inhibited hypoxia-induced differentiation of U937 cells, as evidenced by morphology, maturation-related antigens as well as expressions of myeloid differentiation signatures and hematopoietic cells-specific cytokine receptors. The specific shRNAs-inhibited expression of HIF-1b, an essential partner for transcription activity of HIF-1, failed, while the inhibition of hematopoietic differentiation-critical CCAAT/enhancer-binding protein-a (C/EBPa) significantly eliminated HIF-1a-mediated myeloid leukemic cell differentiation. Collectively, this work provided several lines of direct evidence for the role of HIF-1a protein through its nontranscriptional activity in myeloid cell differentiation, in which C/EBPa elicits a role as an effector downstream to HIF-1a. These discoveries would shed new insights for understanding mechanisms underlying leukemogenesis and designing the new therapeutic strategy for differentiation induction of AML.