Monocular depth prediction plays a crucial role in understanding 3D scene geometry. Although recent methods have achieved impressive progress in evaluation metrics such as the pixel-wise relative error, most methods neglect the geometric constraints in the 3D space. In this work, we show the importance of the high-order 3D geometric constraints for depth prediction. By designing a loss term that enforces one simple type of geometric constraints, namely, virtual normal directions determined by randomly sampled three points in the reconstructed 3D space, we can considerably improve the depth prediction accuracy. Significantly, the byproduct of this predicted depth being sufficiently accurate is that we are now able to recover good 3D structures of the scene such as the point cloud and surface normal directly from the depth, eliminating the necessity of training new sub-models as was previously done. Experiments on two benchmarks: NYU Depth-V2 and KITTI demonstrate the effectiveness of our method and state-of-the-art performance.

We have found three errors in our paper [1], and thus would like to make the following corrections to this paper:On page 302, in the second paragraph, line 12, "tenabiliby" should be changed to "tunability". On page 302, in the fourth paragraph, "In a typical cell, LC passing the cell, where d is the cell gap. When a sufficiently high voltage is applied to the indium tin oxide (ITO) electrodes, the LC directors will be reoriented material is sandwiched between two substrates coated with electrodes (e.g., indium tin oxide, ITO) and surface alignment layers (e.g., polyimide, PI) [53]" should be changed to "In a typical cell, LC material is sandwiched between two substrates coated with electrodes (e.g., indium tin oxide, ITO) and surface alignment layers (e.g., polyimide, PI) [53]".On page 303, in the first paragraph, "It will experience an optical path of L = dne after in vertical direction and the optical path becomes L = dno (Figure 1b)" should be changed to "It will experience an optical path of L = dne after passing the cell, where d is the cell gap. When a sufficiently high voltage is applied to the ITO electrodes, the LC directors will be reoriented in vertical direction and the optical path becomes L = dno (Figure 1b)." OPEN ACCESS

In this paper, we consider transferring the structure information from large networks to small ones for dense prediction tasks. Previous knowledge distillation strategies used for dense prediction tasks often directly borrow the distillation scheme for image classification and perform knowledge distillation for each pixel separately, leading to sub-optimal performance. Here we propose to distill structured knowledge from large networks to small networks, taking into account the fact that dense prediction is a structured prediction problem. Specifically, we study two structured distillation schemes: i) pair-wise distillation that distills the pairwise similarities by building a static graph; and ii) holistic distillation that uses adversarial training to distill holistic knowledge. The effectiveness of our knowledge distillation approaches is demonstrated by extensive experiments on three dense prediction tasks: semantic segmentation, depth estimation and object detection.

We developed a mathematical model of a cows-to-consumers supply chain associated with a single milk-processing facility that is the victim of a deliberate release of botulinum toxin. Because centralized storage and processing lead to substantial dilution of the toxin, a minimum amount of toxin is required for the release to do damage. Irreducible uncertainties regarding the dose-response curve prevent us from quantifying the minimum effective release. However, if terrorists can obtain enough toxin, and this may well be possible, then rapid distribution and consumption result in several hundred thousand poisoned individuals if detection from early symptomatics is not timely. Timely and specific in-process testing has the potential to eliminate the threat of this scenario at a cost of <1 cent per gallon and should be pursued aggressively. Investigation of improving the toxin inactivation rate of heat pasteurization without sacrificing taste or nutrition is warranted.bioterrorism ͉ mathematical modeling A mong bioterror attacks not involving genetic engineering, the three scenarios that arguably pose the greatest threats to humans are a smallpox attack, an airborne anthrax attack, and a release of botulinum toxin in cold drinks (1). The methods of dissemination in these three scenarios are, respectively, the person-to-person spread of a contagious disease, the outdoor dispersal of a highly durable and lethal agent, and the large-scale storage and production and rapid widespread distribution and consumption of beverages containing the most poisonous substance known. The first two scenarios have been the subject of recent systems modeling studies (2-5), and here we present a detailed systems analysis of the third scenario. For concreteness, we consider a release in the milk supply, which, in addition to its symbolic value as a target, is characterized by the rapid distribution of 20 billion gallons per year in the U.S.; indeed, two natural Salmonella outbreaks in the dairy industry each infected Ϸ200,000 people (6). Nonetheless, our methods are applicable to similar food products, such as fruit and vegetable juices, canned foods (e.g., processed tomato products), and perhaps grainbased and other foods possessing the bow-tie-shaped supply chain pictured in Fig. 1. The ModelThe mathematical model considers the flow of milk through a nine-stage cows-to-consumers supply chain associated with a single milk-processing facility (Fig. 1). Supporting Appendix, which is published as supporting information on the PNAS web site, contains a detailed mathematical formulation of the model, a discussion of the modeling assumptions, and the specification of parameter values, some of which are listed in Table 1. The supply-chain parameter values are representative of the California dairy industry, which produces Ͼ20% of the nation's milk (California dairy facts, www.dairyforum.org͞cdf.html, accessed on May 18, 2004). In our model, cows are milked twice daily, and the milk from each farm is picked up once per day by a 5,500-gallon truc...

Transition metal complexes of Ru(II), Pt(II) and Ir(III) with strong absorption of visible light and long-lived T 1 excited states were summarized. A design rationale of these complexes, i.e. direct metalation of organic chromophore, was proposed. Alternatively an organic chromophore can be dangled on the peripheral moiety of the coordination center. In both cases the long-lived intraligand triplet excited state ( 3 IL) can be accessed. However, the 3 IL excited state is usually emissive for the former case and it is very often non-emissive for the latter case. Two methods used for study of the long-lived triplet excited state, i.e. the time-resolved transient difference absorption spectroscopy and the spin density analysis, are briefly introduced. Preliminary applications of the complexes in luminescent O 2 sensing and triplet-triplet annihilation (TTA) upconversions were discussed.

Room temperature near-IR phosphorescence of naphthalenediimide (NDI) was observed with N^N Pt(II) bisacetylide complex (Pt-NDI) in which the NDI was connected to Pt(II) center via acetylide. Pt-NDI shows intense absorption of visible light and long-lived NDI-localized excited state ((3)IL) (τ(T) = 22.3 μs). Pt-NDI was used as a triplet sensitizer for upconversion.

No abstract

Electrochemical carbon monoxide reduction is a promising strategy for the production of value-added multicarbon compounds, albeit yielding diverse products with low selectivities and Faradaic efficiencies. Here, copper single atoms anchored to Ti3C2Tx MXene nanosheets are firstly demonstrated as effective and robust catalysts for electrochemical carbon monoxide reduction, achieving an ultrahigh selectivity of 98% for the formation of multicarbon products. Particularly, it exhibits a high Faradaic efficiency of 71% towards ethylene at −0.7 V versus the reversible hydrogen electrode, superior to the previously reported copper-based catalysts. Besides, it shows a stable activity during the 68-h electrolysis. Theoretical simulations reveal that atomically dispersed Cu–O3 sites favor the C–C coupling of carbon monoxide molecules to generate the key *CO-CHO species, and then induce the decreased free energy barrier of the potential-determining step, thus accounting for the high activity and selectivity of copper single atoms for carbon monoxide reduction.