Manual selection of single particles in images acquired using cryo-electron microscopy (cryoEM) will become a significant bottleneck when datasets of a hundred thousand or even a million particles are required for structure determination at near atomic resolution. Algorithm development of fully automated particle selection is thus an important research objective in the cryoEM field. A number of research groups are making promising new advances in this area. Evaluation of algorithms using a standard set of cryoEM images is an essential aspect of this algorithm development. With this goal in mind, a particle selection "bakeoff" was included in the program of the Multidisciplinary Workshop on Automatic Particle Selection for cryoEM. Twelve groups participated by submitting the results of testing their own algorithms on a common dataset. The dataset consisted of 82 defocus pairs of high-magnification micrographs, containing keyhole limpet hemocyanin particles, acquired using cryoEM. The results of the bakeoff are presented in this paper along with a summary of the discussion from the workshop. It was agreed that establishing benchmark particles and using bakeoffs to evaluate algorithms are useful in promoting algorithm development for fully automated particle selection, and that the infrastructure set up to support the bakeoff should be maintained and extended to include larger and more varied datasets, and more criteria for future evaluations.

To meet the application needs of rechargeable Zn−air battery and electrocatalytic overall water splitting (EOWS), developing high-efficiency, cost-effective, and durable trifunctional catalysts for the hydrogen evolution reaction (HER), oxygen evolution, and reduction reaction (OER and ORR) is extremely paramount yet challenging. Herein, the interface engineering concept and nanoscale hollowing design were proposed to fabricate N-doping carbon nanoboxes confined with Co/MoC nanoparticles. Uniform zeolitic imidazolate framework nanocube was employed as the starting material to construct the trifunctional electrocatalyst through the conformal polydopamine−Mo layer coating and the subsequent pyrolysis treatment. The Co@IC/MoC@PC catalyst displayed superior electrochemical ORR performances with a positive half-wave potential of 0.875 V and a high limiting current density of 5.89 mA/cm 2 . When practically employed as an electrocatalyst in regenerative Zn−air battery, a high specific capacity of 728 mAh/g, a large peak power density of 221 mW/cm 2 , a high open-circuit voltage of 1.482 V, and a low charge/discharge voltage gap of 0.41 V were obtained. Moreover, its practicability was further exploited by overall water splitting, affording low overpotentials of 277 and 68 mV at 10 mA/cm 2 for the OER and HER in 1 M KOH solution, respectively, and a decent operating potential of 1.57 V for EOWS. Ultraviolet photoelectron spectroscopy and density functional theory calculation revealed that the Co/MoC interface synergistically facilitated the charge-transfer, thereby contributing to the enhancements of electrocatalytic ORR/OER/HER processes. More importantly, this catalyst design concept can offer some interesting prospects for the construction of outstanding trifunctional catalysts toward various energy conversion and storage devices.

Cross-modality person re-identification is a challenging problem which retrieves a given pedestrian image in RGB modality among all the gallery images in infrared modality. The task can address the limitation of RGB-based person Re-ID in dark environments. Existing researches mainly focus on enlarging inter-class differences of feature to solve the problem. However, few studies investigate improving intra-class cross-modality similarity, which is important for this issue. In this paper, we propose a novel loss function, called Hetero-Center loss (HC loss) to reduce the intra-class cross-modality variations. Specifically, HC loss can supervise the network learning the cross-modality invariant information by constraining the intra-class center distance between two heterogenous modalities. With the joint supervision of Cross-Entropy (CE) loss and HC loss, the network is trained to achieve two vital objectives, inter-class discrepancy and intra-class cross-modality similarity as much as possible. Besides, we propose a simple and high-performance network architecture to learn local feature representations for cross-modality person re-identification, which can be a baseline for future research. Extensive experiments indicate the effectiveness of the proposed methods, which outperform state-of-the-art methods by a wide margin.

Shared autonomy integrates user input with robot autonomy in order to control a robot and help the user to complete a task. Our work aims to improve the performance of such a human-robot team: the robot tries to guide the human towards an effective strategy, sometimes against the human's own preference, while still retaining his trust. We achieve this through a principled human-robot mutual adaptation formalism. We integrate a bounded-memory adaptation model of the human into a partially observable stochastic decision model, which enables the robot to adapt to an adaptable human. When the human is adaptable, the robot guides the human towards a good strategy, maybe unknown to the human in advance. When the human is stubborn and not adaptable, the robot complies with the human's preference in order to retain their trust. In the shared autonomy setting, unlike many other common human-robot collaboration settings, only the robot actions can change the physical state of the world, and the human and robot goals are not fully observable. We address these challenges and show in a human subject experiment that the proposed mutual adaptation formalism improves human-robot team performance, while retaining a high level of user trust in the robot, compared to the common approach of having the robot strictly following participants' preference

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