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We design a full cell configuration having Na$_{3}$V$_{2}$(PO$_{4}$)$_{3}$ as cathode and pre-sodiated hard carbon as an anode with Cellulosic Paper Separators and compare the electrochemical performance of these ceramic-impregnated polymer-coated cellulose paper separators with commercial glass fiber separator. Notably, the paper-based multilayer separators provide desirable characteristics such as excellent electrolyte wettability, thermal stability up to 200\degree C, and ionic conductivity, which are essential for the efficient operation of SIBs. The cellulose separator is coated by a layer of polyvinylidene fluoride polymer, followed by a second layer of styrene butadiene rubber (SBR) polymer in which ferroelectric fillers BaTiO$_{3}$ are integrated, which interacts with the polymer hosts through Lewis acid-base interactions ion and improves the conduction mechanism for the Na$^{+}$ ions. The final lamination is performed by varying the SBR concentrations (0.5, 0.75, and 1.0 w/v\%). The incorporated polymer matrices improve the flexibility, adhesion and dispersion of the nanoparticles and affinity of the electrolyte to the electrode. The morphology of the paper separators shows the uniform interconnected fibers with the porous structure. Interestingly, we find that the paper separator with 0.75 w/v\% content of SBR exhibit decreased interfacial resistance and improved electrochemical performance, having retention of 62\% and nearly 100\% Coulombic efficiency up to 240 cycles, as compared to other concentrations. Moreover, we observe the energy density around 376 Wh kg$^{-1}$ (considering cathode weight), which found to be comparable to the commercially available glass fiber separator. Our results demonstrate the potential of these multilayer paper separators towards achieving sustainability and safety in energy storage systems., Comment: submitted

The current autonomous stack is well modularized and consists of perception, decision making and control in a handcrafted framework. With the advances in artificial intelligence (AI) and computing resources, researchers have been pushing the development of end-to-end AI for autonomous driving, at least in problems of small searching space such as in highway scenarios, and more and more photorealistic simulation will be critical for efficient learning. In this research, we propose a novel game-based end-to-end learning and testing framework for autonomous vehicle highway driving, by learning from human driving skills. Firstly, we utilize the popular game Grand Theft Auto V (GTA V) to collect highway driving data with our proposed programmable labels. Then, an end-to-end architecture predicts the steering and throttle values that control the vehicle by the image of the game screen. The predicted control values are sent to the game via a virtual controller to keep the vehicle in lane and avoid collisions with other vehicles on the road. The proposed solution is validated in GTA V games, and the results demonstrate the effectiveness of this end-to-end gamification framework for learning human driving skills., Comment: Accepted by ITSC

Cognitive biases are mental shortcuts humans use in dealing with information and the environment, and which result in biased actions and behaviors (or, actions), unbeknownst to themselves. Biases take many forms, with cognitive biases occupying a central role that inflicts fairness, accountability, transparency, ethics, law, medicine, and discrimination. Detection of biases is considered a necessary step toward their mitigation. Herein, we focus on two cognitive biases - anchoring and recency. The recognition of cognitive bias in computer science is largely in the domain of information retrieval, and bias is identified at an aggregate level with the help of annotated data. Proposing a different direction for bias detection, we offer a principled approach along with Machine Learning to detect these two cognitive biases from Web logs of users' actions. Our individual user level detection makes it truly personalized, and does not rely on annotated data. Instead, we start with two basic principles established in cognitive psychology, use modified training of an attention network, and interpret attention weights in a novel way according to those principles, to infer and distinguish between these two biases. The personalized approach allows detection for specific users who are susceptible to these biases when performing their tasks, and can help build awareness among them so as to undertake bias mitigation.