A triphenylamine derivative STPA was designed and synthesized by introducing thermally cross-linked vinyl group at the end of 4methoxytriphenylamine. The corresponding polymer pSTPA was prepared by a thermal polymerization reaction without any initiator. CV tests reveal that pSTPA has a pair of distinct redox peaks. Spectroelectrochemical results demonstrates that pSTPA has a maximum absorption peak at 354 nm and a new absorption peak at 730 nm with increasing voltage, accompanied with the polymer from transparent to blue. In order to realize multicolor showing and improve the cycling stability, the corresponding copolymer pSTPA-co-TPA-OCH 3 was further prepared via thermally cross-linked vinyl groups by combining STPA with methoxy-modified triphenylamine derivative TPA-OCH 3 monomers in a molar ratio of 1:1. As expected, the copolymer film presents a multicolored appearance with four color changes (transparent−dark yellow−sky blue−purple), high optical contrast (45.0% at 480 nm, 56.1% at 700 nm, and 82.4% at 910 nm, respectively), large coloration efficiency of 287 cm 2 /C (at 910 nm), and good cycling stability (essentially no degradation of optical contrast over 2000 cycles). Therefore, this work puts forward an effective method to achieve a highly transparent to multicolor-showing electrochromic polymer via thermally cross-linked copolymerization, which shows potential applications in smart windows, sunglasses, and electronic tags.
We formulate an asymmetric (or non-commutative) distance between tasks based on Fisher Information Matrices, called Fisher task distance. This distance represents the complexity of transferring the knowledge of one task to another. We provide a proof of consistency for our distance through theorems and experiments on various classification tasks from MNIST, CIFAR-10, CIFAR-100, ImageNet, and Taskonomy datasets. Next, we construct an online neural architecture search framework using the Fisher task distance, in which we have access to the past learned tasks. By using the Fisher task distance, we can identify the closest learned tasks to the target task, and utilize the knowledge learned from these related tasks on the target task. Here, we show how the proposed distance between a target task and a set of learned tasks can be used to reduce the neural architecture search space for the target task. The complexity reduction in search space for task-specific architectures is achieved by building on the optimized architectures for similar tasks instead of doing a full search and without using this side information. Experimental results for tasks in MNIST, CIFAR-10, CIFAR-100, ImageNet datasets demonstrate the efficacy of the proposed approach and its improvements, in terms of the performance and the number of parameters, over other gradient-based search methods, such as ENAS, DARTS, PC-DARTS.
Recently Reinforcement Learning (RL) has been applied as an antiadversarial remedy in wireless communication networks. However studying the RL-based approaches from the adversary's perspective has received little attention. Additionally, RL-based approaches in an anti-adversary or adversarial paradigm mostly consider singlechannel communication (either channel selection or single channel power control), while multi-channel communication is more common in practice. In this paper, we propose a multi-agent adversary system (MAAS) for modeling and analyzing adversaries in a wireless communication scenario by careful design of the reward function under realistic communication scenarios. In particular, by modeling the adversaries as learning agents, we show that the proposed MAAS is able to successfully choose the transmitted channel(s) and their respective allocated power(s) without any prior knowledge of the sender strategy. Compared to the single-agent adversary (SAA), multi-agents in MAAS can achieve significant reduction in signal-to-noise ratio (SINR) under the same power constraints and partial observability, while providing improved stability and a more efficient learning process. Moreover, through empirical studies we show that the results in simulation are close to the ones in communication in reality, a conclusion that is pivotal to the validity of performance of agents evaluated in simulations.
We consider a class of assortment optimization problems in an offline data-driven setting. A firm does not know the underlying customer choice model but has access to an offline dataset consisting of the historically offered assortment set, customer choice, and revenue. The objective is to use the offline dataset to find an optimal assortment. Due to the combinatorial nature of assortment optimization, the problem of insufficient data coverage is likely to occur in the offline dataset. Therefore, designing a provably efficient offline learning algorithm becomes a significant challenge. To this end, we propose an algorithm referred to as Pessimistic ASsortment opTimizAtion (PASTA for short) designed based on the principle of pessimism, that can correctly identify the optimal assortment by only requiring the offline data to cover the optimal assortment under general settings. In particular, we establish a regret bound for the offline assortment optimization problem under the celebrated multinomial logit model. We also propose an efficient computational procedure to solve our pessimistic assortment optimization problem. Numerical studies demonstrate the superiority of the proposed method over the existing baseline method.
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