Images captured in foggy weather conditions often suffer from bad visibility. In this paper, we propose an efficient regularization method to remove hazes from a single input image. Our method benefits much from an exploration on the inherent boundary constraint on the transmission function. This constraint, combined with a weighted L1−norm based contextual regularization, is modeled into an optimization problem to estimate the unknown scene transmission. A quite efficient algorithm based on variable splitting is also presented to solve the problem. The proposed method requires only a few general assumptions and can restore a high-quality haze-free image with faithful colors and fine image details. Experimental results on a variety of haze images demonstrate the effectiveness and efficiency of the proposed method.

Nanoscale building blocks of many materials exhibit extraordinary mechanical properties due to their defect-free molecular structure. Translation of these high mechanical properties to macroscopic materials represents a difficult materials engineering challenge due to the necessity to organize these building blocks into multiscale patterns and mitigate defects emerging at larger scales. Cellulose nanofibrils (CNFs), the most abundant structural element in living systems, has impressively high strength and stiffness, but natural or artificial cellulose composites are 3-15 times weaker than the CNFs. Here, we report the flow-assisted organization of CNFs into macroscale fibers with nearly perfect unidirectional alignment. Efficient stress transfer from macroscale to individual CNF due to cross-linking and high degree of order enables their Young's modulus to reach up to 86 GPa and a tensile strength of 1.57 GPa, exceeding the mechanical properties of known natural or synthetic biopolymeric materials. The specific strength of our CNF fibers engineered at multiscale also exceeds that of metals, alloys, and glass fibers, enhancing the potential of sustainable lightweight high-performance materials with multiscale self-organization.

Point cloud analysis is very challenging, as the shape implied in irregular points is difficult to capture. In this paper, we propose RS-CNN, namely, Relation-Shape Convolutional Neural Network, which extends regular grid CNN to irregular configuration for point cloud analysis.The key to RS-CNN is learning from relation, i.e., the geometric topology constraint among points. Specifically, the convolutional weight for local point set is forced to learn a high-level relation expression from predefined geometric priors, between a sampled point from this point set and the others. In this way, an inductive local representation with explicit reasoning about the spatial layout of points can be obtained, which leads to much shape awareness and robustness. With this convolution as a basic operator, RS-CNN, a hierarchical architecture can be developed to achieve contextual shape-aware learning for point cloud analysis. Extensive experiments on challenging benchmarks across three tasks verify RS-CNN achieves the state of the arts.

Ferroptosis is a form of nonapoptotic regulated cell death driven by iron-dependent lipid peroxidation. Autophagy involves a lysosomal degradation pathway that can either promote or impede cell death. A high level of autophagy has been associated with ferroptosis, but the mechanisms underpinning this relationship are largely elusive. We characterize the contribution of autophagy to ferroptosis in human cancer cell lines and mouse tumor models. We show that “clockophagy,” the selective degradation of the core circadian clock protein ARNTL by autophagy, is critical for ferroptosis. We identify SQSTM1 as a cargo receptor responsible for autophagic ARNTL degradation. ARNTL inhibits ferroptosis by repressing the transcription of Egln2, thus activating the prosurvival transcription factor HIF1A. Genetic or pharmacological interventions blocking ARNTL degradation or inhibiting EGLN2 activation diminished, whereas destabilizing HIF1A facilitated, ferroptotic tumor cell death. Thus, our findings reveal a new pathway, initiated by the autophagic removal of ARNTL, that facilitates ferroptosis induction.