The rank-modulation scheme has been recently proposed for efficiently storing data in nonvolatile memories. In this paper, we explore [n, k, d] systematic error-correcting codes for rank modulation. Such codes have length n, k information symbols, and minimum distance d. Systematic codes have the benefits of enabling efficient information retrieval in conjunction with memory-scrubbing schemes. We study systematic codes for rank modulation under Kendall's T-metric as well as under the ℓ _∞ -metric. In Kendall's T-metric, we present [k + 2, k, 3] systematic codes for correcting a single error, which have optimal rates, unless systematic perfect codes exist. We also study the design of multierror-correcting codes, and provide a construction of [k + t + 1, k, 2t + 1] systematic codes, for large-enough k. We use nonconstructive arguments to show that for rank modulation, systematic codes achieve the same capacity as general error-correcting codes. Finally, in the ℓ _∞ -metric, we construct two [n, k, d] systematic multierror-correcting codes, the first for the case of d = 0(1) and the second for d = Θ(n). In the latter case, the codes have the same asymptotic rate as the best codes currently known in this metric.

Deep neural networks based on physical information are expected to be effective alternatives to numerical electromagnetic solvers because they can efficiently solve Maxwell’s equations and obtain the nonlinear physical relationship between the device geometry and its output fields. In this article, we propose a mesh-free and unsupervised time-domain MaxwellNet as a forward model for time-domain electromagnetic simulations, which is qualified to predict the electromagnetic fields of inhomogeneous media of different materials with similar settings in a single inference. We use this network to predict the time-domain electromagnetic fields of several structures with different geometries and achieve good results. Furthermore, experiments show that the model has good generalization ability in both the time and problem-solving range. Finally, we treat the trained model as a Maxwell solver and use it for the inverse design problem. Numerical results show the effectiveness of time-domain MaxwellNet in solving both forward and inverse problems.

View change causes significant differences in the gait appearance. Consequently, recognizing gait in cross-view scenarios is highly challenging. Most recent approaches either convert the gait from the original view to the target view before recognition is carried out or extract the gait feature irrelevant to the camera view through either brute force learning or decouple learning. However, these approaches have many constraints, such as the difficulty of handling unknown camera views. This work treats the view-change issue as a domain-change issue and proposes to tackle this problem through adversarial domain adaptation. This way, gait information from different views is regarded as the data from different sub-domains. The proposed approach focuses on adapting the gait feature differences caused by such sub-domain change and, at the same time, maintaining sufficient discriminability across the different people. For this purpose, a Hierarchical Feature Aggregation (HFA) strategy is proposed for discriminative feature extraction. By incorporating HFA, the feature extractor can well aggregate the spatial-temporal feature across the various stages of the network and thereby comprehensive gait features can be obtained. Then, an Adversarial View-change Elimination (AVE) module equipped with a set of explicit models for recognizing the different gait viewpoints is proposed. Through the adversarial learning process, AVE would not be able to identify the gait viewpoint in the end, given the gait features generated by the feature extractor. That is, the adversarial domain adaptation mitigates the view change factor, and discriminative gait features that are compatible with all sub-domains are effectively extracted. Extensive experiments on three of the most popular public datasets, CASIA-B, OULP, and OUMVLP richly demonstrate the effectiveness of our approach.

Shannon showed that to achieve perfect secrecy in point-to-point communication, the message rate cannot exceed the shared secret key rate giving rise to the simple one-time pad encryption scheme. In this paper, we extend this work from point-to-point to networks. We consider a connected network with pairwise communication between the nodes and assume that each node is provided with a certain amount of secret bits before communication commences. An eavesdropper with unlimited computing power has access to all communication and can hack a subset of the nodes not known to the rest of the nodes. We investigate the limits on information-theoretic secure communication with end-to-end encryption for this network. We establish a tradeoff between the secure channel rate (for a node pair) and the secure network rate (sum over all node pair rates) and show that information-theoretic secrecy can be achieved asymptotically if and only if the sum rate of any subset of unhacked channels does not exceed the shared unhacked-secret-bit rate of these channels. We also propose a practical scheme that achieves a good balance of network and channel rates with information-theoretic secrecy guarantee. This work has a wide range of potential applications for which strong secrecy is desired, such as cyber-physical systems, distributed-control systems, and ad-hoc networks.

Vision-based 3D scene perception and understanding are crucial for autonomous driving, robot navigation, and obstacle avoidance. However, describing objects with arbitrary shapes presents challenges in traditional 3D object detection tasks. In order to better perceive indoor and outdoor scenes, we propose a new method called 3DOPFormer in this paper, which can achieve 3D occupancy perception for autonomous driving through pure vision. Specifically, it first extracts multi-camera image features and uses spatial occupancy cross-attention implicit learning to lift 2D image features to 3D volume features. Then, it uses 3D deconvolution to upsample volume features. For the first time, we integrate the inherent directional and distance properties of LiDAR rays into the 3D occupancy prediction task, leading to outstanding occupancy prediction performance. And we gradually learn about dense visual occupancy perception using sparse point cloud supervision and neural rendering methods, without using expensive dense occupancy annotation supervision for training. Additionally, a cost-effective data collection scheme is introduced, and based on which, we create a dataset named ML3DOP that encompasses both indoor and outdoor scenes using 4-eye cameras and 16-beam LiDAR for 3D occupancy perception. The experimental results demonstrate that our approach can achieve accurate 3D occupancy prediction by only using multiple cameras. Dataset: https://github.com/lvchuandong/ML3DOP .