Both the arrest and thecreationofmovement are fundamental aspects of dynamics on macroscopic as well as microscopic levels. Brakes and motors dominate the operation of machines, be they those of daily life, such as vehicles and appliances, or those of living systems. like muscles and Ilagellae. On the molecular level motion is thenorm;spontanwus freerotationaroundsinglebonds is thus the rule, not the exception. In machines of ordinary experience, such as automobiles, the brake is often as important as the accelerator. We now report the first molecular analog: a reversible molecular brake. Figure I presents theconcept inbothgeneral andspecific terms. 1 (biako ofl) 3 (brako on) -"W 2 (brake ott) 4 (brake on) Figure 1. Conceptualandaetualdepictionoftheoperationofa molecular brake. With the brakedisengaged. the wheel-(a) representedasa threetoothed gear ( I ) and (b) constructed as a triptycene (2)' -spins rapidly. Engagement ofthe brake (3and4) slowsorstopsrotation. With the actual system, the brake is activated remotely (2 -4) by addition of Hg2+ ion. In the absence of Hgz+ (or other metal ions), the triptycene wheel spins rapidly a t 30 OC, as evidenced by the simplicity of the 'H NMR spectrum of 2 (Figure 2b). wherein by virtue of C, symmetry arising from relatively rapid rotation, the 12 triptycene aromatic protons give rise to only four sets (asterisked) of resonances. Addition of Hg(OzCCF3)z to 2 results in profound changes in the 30 OC (and other) 'H NMR spectrum (Figure 3a). Most noteworthy are the change in the extraordinarychemicalshiftoftheB-ringmethoxyin 2 (6 2.1 to a normal 6 4. I 3 value (not shown) and the obvious broadening of the four resonances attributable to the hydrogens in the three benzo rings of the triptycene. Variable-temperature 'H NMR experiments3 (see Figure 3) document the engagement of the brake. In particular, a t -30 OC (contrast Figure 2),thethreearomaticringsofthetriptyceneare nolongerequivalent becauseofthearrest ofrotation on theNMR . . ( I ) For the use of triptycenaas gean see, inter alia (a) Guenzi. A,: Johnson. C.(2) The 9-ring mcthary group is evidently (see models) in the shielding zone of the lriplycene knzo rings; the B-ring melhory group in 9 is similarly shielded (6 2.W).'(3) See supplementary material for specifis.

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Zhou et al. Ours 1× Input Cameras 30× Extrapolated Right Extrapolated Left Zhou Figure 1: We propose a novel view synthesis method that can generate extreme views, i.e., images synthesized from a small number of cameras (two in this example) and from significantly different viewpoints. In this comparison with the method by Zhou et al.[34], we show the left view from the camera setup depicted above. Even at a 30× baseline magnification our method produces sharper results. AbstractWe present Extreme View Synthesis, a solution for novel view extrapolation that works even when the number of input images is small-as few as two. In this context, occlusions and depth uncertainty are two of the most pressing issues, and worsen as the degree of extrapolation increases. We follow the traditional paradigm of performing depthbased warping and refinement, with a few key improvements. First, we estimate a depth probability volume, rather than just a single depth value for each pixel of the novel view. This allows us to leverage depth uncertainty in challenging regions, such as depth discontinuities. After using it to get an initial estimate of the novel view, we explicitly combine learned image priors and the depth uncertainty to synthesize a refined image with less artifacts. Our method is the first to show visually pleasing results for baseline magnifications of up to 30×. The code is available at https: /

Capturing spatially-varying bidirectional reflectance distribution functions (SVBRDFs) of 3D objects with just a single, hand-held camera (such as an off-the-shelf smartphone or a DSLR camera) is a difficult, open problem. Previous works are either limited to planar geometry, or rely on previously scanned 3D geometry, thus limiting their practicality. There are several technical challenges that need to be overcome: First, the built-in flash of a camera is almost colocated with the lens, and at a fixed position; this severely hampers sampling procedures in the light-view space. Moreover, the near-field flash lights the object partially and unevenly. In terms of geometry, existing multiview stereo techniques assume diffuse reflectance only, which leads to overly smoothed 3D reconstructions, as we show in this paper. We present a simple yet powerful framework that removes the need for expensive, dedicated hardware, enabling practical acquisition of SVBRDF information from real-world, 3D objects with a single, off-the-shelf camera with a built-in flash. In addition, by removing the diffuse reflection assumption and leveraging instead such SVBRDF information, our method outputs high-quality 3D geometry reconstructions, including more accurate high-frequency details than state-of-the-art multiview stereo techniques. We formulate the joint reconstruction of SVBRDFs, shading normals, and 3D geometry as a multi-stage, iterative inverse-rendering reconstruction pipeline. Our method is also directly applicable to any existing multiview 3D reconstruction technique. We present results of captured objects with complex geometry and reflectance; we also validate our method numerically against other existing approaches that rely on dedicated hardware, additional sources of information, or both.

We present a novel hyperspectral image reconstruction algorithm, which overcomes the long-standing tradeoff between spectral accuracy and spatial resolution in existing compressive imaging approaches. Our method consists of two steps: First, we learn nonlinear spectral representations from real-world hyperspectral datasets; for this, we build a convolutional autoencoder which allows reconstructing its own input through its encoder and decoder networks. Second, we introduce a novel optimization method, which jointly regularizes the fidelity of the learned nonlinear spectral representations and the sparsity of gradients in the spatial domain, by means of our new fidelity prior. Our technique can be applied to any existing compressive imaging architecture, and has been thoroughly tested both in simulation, and by building a prototype hyperspectral imaging system. It outperforms the state-of-the-art methods from each architecture, both in terms of spectral accuracy and spatial resolution, while its computational complexity is reduced by two orders of magnitude with respect to sparse coding techniques. Moreover, we present two additional applications of our method: hyperspectral interpolation and demosaicing. Last, we have created a new high-resolution hyperspectral dataset containing sharper images of more spectral variety than existing ones, available through our project website.