Nonhuman primate and human studies have suggested that populations of neurons in the posterior parietal cortex (PPC) may represent high-level aspects of action planning that can be used to control external devices as part of a brain-machine interface. However, there is no direct neuron-recording evidence that human PPC is involved in action planning, and the suitability of these signals for neuroprosthetic control has not been tested. We recorded neural population activity with arrays of microelectrodes implanted in the PPC of a tetraplegic subject. Motor imagery could be decoded from these neural populations, including imagined goals, trajectories, and types of movement. These findings indicate that the PPC of humans represents high-level, cognitive aspects of action and that the PPC can be a rich source for cognitive control signals for neural prosthetics that assist paralyzed patients.

Lithium-sulfur (Li-S) batteries are highly appealing for next-generation electrochemical energy storage owing to their high theoretical energy density, environmental friendliness, and low cost. However, the insulating nature of sulfur and migration of dissolved polysulfide intermediates lead to low active material utilization and fast capacity decay, which pose a significant challenge to their practical applications. Here, this paper reports a multifunctional carbon hybrid with metal-organic frameworks (MOFs)-derived nitrogen-doped porous carbon anchored on graphene sheets (NPC/G) serving as a sulfur host. On the one hand, the high surface area and nitrogen-doping of the carbon nanoparticles enable effective polysulfide immobilization through both physical confinement and chemical adsorption; on the other hand, the highly conductive graphene provides an interconnected conductive framework to facilitate fast electron transport, improving the sulfur utilization. As a result, the NPC/G-based sulfur cathode exhibits a high specific capacity of 1372 mAh g −1 with good cycling stability over 300 cycles. This approach provides a promising approach for the design of MOFs-derived carbon materials for high performance Li-S batteries.

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Recently deep neural networks (DNNs) have been used to learn speaker features. However, the quality of the learned features is not sufficiently good, so a complex back-end model, either neural or probabilistic, has to be used to address the residual uncertainty when applied to speaker verification, just as with raw features. This paper presents a convolutional timedelay deep neural network structure (CT-DNN) for speaker feature learning. Our experimental results on the Fisher database demonstrated that this CT-DNN can produce highquality speaker features: even with a single feature (0.3 seconds including the context), the EER can be as low as 7.68%. This effectively confirmed that the speaker trait is largely a deterministic short-time property rather than a long-time distributional pattern, and therefore can be extracted from just dozens of frames.