STAR-RIS Assisted Wireless-Powered and Backscattering Mobile Edge Computing Networks

Wireless powered and backscattering mobile edge computing (WPB-MEC) network is a novel network paradigm to supply energy supplies and computing resource to wireless sensors (WSs). However, its performance is seriously affected by severe attenuations and inappropriate assumptions of infinite computing capability at the hybrid access point (HAP). To address the above issues, in this paper, we propose a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided scheme for boosting the performance of WPB-MEC network under the constraint of finite computing capability. Specifically, energy-constrained WSs are able to offload tasks actively or passively from them to the HAP. In this process, the STAR-RIS is utilized to improve the quantity of harvested energy and strengthen the offloading efficiency by adapting its operating protocols. We then maximize the sum computational bits (SCBs) under the finite computing capability constraint. To handle the solving challenges, we first present interesting results in closed-form and then design a block coordinate descent (BCD) based algorithm, ensuring a near-optimal solution. Finally, simulation results are provided to confirm that our proposed scheme can improve the SCBs by 9.9 times compared to the local computing only scheme.

Fine-Grained and High-Faithfulness Explanations for Convolutional Neural Networks

Recently, explaining CNNs has become a research hotspot. CAM (Class Activation Map)-based methods and LRP (Layer-wise Relevance Propagation) method are two common explanation methods. However, due to the small spatial resolution of the last convolutional layer, the CAM-based methods can often only generate coarse-grained visual explanations that provide a coarse location of the target object. LRP and its variants, on the other hand, can generate fine-grained explanations. But the faithfulness of the explanations is too low. In this paper, we propose FG-CAM (fine-grained CAM), which extends the CAM-based methods to generate fine-grained visual explanations with high faithfulness. FG-CAM uses the relationship between two adjacent layers of feature maps with resolution difference to gradually increase the explanation resolution, while finding the contributing pixels and filtering out the pixels that do not contribute at each step. Our method not only solves the shortcoming of CAM-based methods without changing their characteristics, but also generates fine-grained explanations that have higher faithfulness than LRP and its variants. We also present FG-CAM with denoising, which is a variant of FG-CAM and is able to generate less noisy explanations with almost no change in explanation faithfulness. Experimental results show that the performance of FG-CAM is almost unaffected by the explanation resolution. FG-CAM outperforms existing CAM-based methods significantly in the both shallow and intermediate convolutional layers, and outperforms LRP and its variations significantly in the input layer.

BabyAI++: Towards Grounded-Language Learning beyond Memorization

Despite success in many real-world tasks (e.g., robotics), reinforcement learning (RL) agents still learn from tabula rasa when facing new and dynamic scenarios. By contrast, humans can offload this burden through textual descriptions. Although recent works have shown the benefits of instructive texts in goal-conditioned RL, few have studied whether descriptive texts help agents to generalize across dynamic environments. To promote research in this direction, we introduce a new platform, BabyAI++, to generate various dynamic environments along with corresponding descriptive texts. Moreover, we benchmark several baselines inherited from the instruction following setting and develop a novel approach towards visually-grounded language learning on our platform. Extensive experiments show strong evidence that using descriptive texts improves the generalization of RL agents across environments with varied dynamics.

Removing the Feature Correlation Effect of Multiplicative Noise

Multiplicative noise, including dropout, is widely used to regularize deep neural networks (DNNs), and is shown to be effective in a wide range of architectures and tasks. From an information perspective, we consider injecting multiplicative noise into a DNN as training the network to solve the task with noisy information pathways, which leads to the observation that multiplicative noise tends to increase the correlation between features, so as to increase the signal-to-noise ratio of information pathways. However, high feature correlation is undesirable, as it increases redundancy in representations. In this work, we propose non-correlating multiplicative noise (NCMN), which exploits batch normalization to remove the correlation effect in a simple yet effective way. We show that NCMN significantly improves the performance of standard multiplicative noise on image classification tasks, providing a better alternative to dropout for batch-normalized networks. Additionally, we present a unified view of NCMN and shake-shake regularization, which explains the performance gain of the latter.