Extending state-of-the-art object detectors from image to video is challenging. The accuracy of detection suffers from degenerated object appearances in videos, e.g., motion blur, video defocus, rare poses, etc. Existing work attempts to exploit temporal information on box level, but such methods are not trained end-to-end. We present flowguided feature aggregation, an accurate and end-to-end learning framework for video object detection. It leverages temporal coherence on feature level instead. It improves the per-frame features by aggregation of nearby features along the motion paths, and thus improves the video recognition accuracy. Our method significantly improves upon strong single-frame baselines in ImageNet VID [33], especially for more challenging fast moving objects. Our framework is principled, and on par with the best engineered systems winning the ImageNet VID challenges 2016, without additional bells-and-whistles. The proposed method, together with Deep Feature Flow [49], powered the winning entry of ImageNet VID challenges 2017 1 . The code is available at https://github.com/msracver/ Flow-Guided-Feature-Aggregation. * This work is done when Xizhou Zhu and Yujie Wang are interns at Microsoft Research Asia 1 http://image-net.org/challenges/LSVRC/2017/ results 1

Stomatal regulation presumably evolved to optimize CO for H O exchange in response to changing conditions. If the optimization criterion can be readily measured or calculated, then stomatal responses can be efficiently modelled without recourse to empirical models or underlying mechanism. Previous efforts have been challenged by the lack of a transparent index for the cost of losing water. Yet it is accepted that stomata control water loss to avoid excessive loss of hydraulic conductance from cavitation and soil drying. Proximity to hydraulic failure and desiccation can represent the cost of water loss. If at any given instant, the stomatal aperture adjusts to maximize the instantaneous difference between photosynthetic gain and hydraulic cost, then a model can predict the trajectory of stomatal responses to changes in environment across time. Results of this optimization model are consistent with the widely used Ball-Berry-Leuning empirical model (r > 0.99) across a wide range of vapour pressure deficits and ambient CO concentrations for wet soil. The advantage of the optimization approach is the absence of empirical coefficients, applicability to dry as well as wet soil and prediction of plant hydraulic status along with gas exchange.

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Animals living in the extremely cold environment, such as polar bears, have shown amazing capability to keep warm, benefiting from their hollow hairs. Mimicking such a strategy in synthetic fibers would stimulate smart textiles for efficient personal thermal management, which plays an important role in preventing heat loss and improving efficiency in house warming energy consumption. Here, a "freeze-spinning" technique is used to realize continuous and large-scale fabrication of fibers with aligned porous structure, mimicking polar bear hairs, which is difficult to achieve by other methods. A textile woven with such biomimetic fibers shows an excellent thermal insulation property as well as good breathability and wearability. In addition to passively insulating heat loss, the textile can also function as a wearable heater, when doped with electroheating materials such as carbon nanotubes, to induce fast thermal response and uniform electroheating while maintaining its soft and porous nature for comfortable wearing.