The chiplet-based 2.5D/3D multi-die integrated chips have numerous inter-die interconnect lanes, which may suffer from the faults in Through Silicon Vias (TSVs) or micro bumps. The faults are usually clustered in some regions. The redundant lanes are introduced for repairing, to maintain the product yield. The hexagonal array of lanes offers higher interconnect density than the rectangular array, but it requires more redundant lanes if faults are located within the same module. In the Universal Chiplet Interconnect Express (UCIe) standard, each Advanced Package module contains 32 data lanes and 2 redundant lanes, which limit the repair rate for the clustered faults. This work proposes a UCIe-compatible repair scheme which increases the probability of distributing faulty lanes in different modules. It achieves a higher repair rate for the clustered faults with a low proportion of redundant lanes. Simulation results show that for 8 faults in various cluster windows, the proposed scheme achieves a repair rate above 98% while consuming less area than previous approaches.

Federated learning provides privacy protection to the collaborative training of global model based on distributed private data. The local private data is often in the presence of long-tailed distribution in reality, which downgrades the performance and causes biased results. In this paper, we propose a dynamic adaptive federated learning optimization algorithm with the Grey Wolf Optimizer and Markov Chain, named FedWolf, to solve the problems of performance degradation and result bias caused by the local long-tailed data. FedWolf is launched with a set of randomly initialized parameters instead of a shared parameter employed by existing methods. Then multi-level participants are elected based on the F1 scores calculated from the uploaded parameters. A dynamic weighting strategy based on the participant level is used to adaptively update parameters without artificial control. The above parameter updating is modelled as a Markov Process. After all communication rounds are completed, the future performance (including the probability of each participant is elected as different participant level) of participants is predicted through the historical Markov states. Finally, the probability of each participant is elected as the level 1 is used as the contribution weight and the global model is obtained through dynamic contribution weight aggregating. We introduce the Gini index to evaluate the bias of classification results. Extensive experiments are conducted to validate the effectiveness of FedWolf in solving the problems of performance cracks and categorization result bias as well as the robustness of adaptive parameter updating in resisting outliers and malicious users.

Abstract Detailed and accurate feature representation is essential for high‐resolution reconstruction of clothed human. Herein we introduce a unified feature representation for clothed human reconstruction, which can adapt to changeable posture and various clothing details. The whole method can be divided into two parts: the human shape feature representation and the details feature representation. Specifically, we firstly combine the voxel feature learned from semantic voxel with the pixel feature from input image as an implicit representation for human shape. Then, the details feature mixed with the clothed layer feature and the normal feature is used to guide the multi‐layer perceptron to capture geometric surface details. The key difference from existing methods is that we use the clothing semantics to infer clothed layer information, and further restore the layer details with geometric height. We qualitative and quantitative experience results demonstrate that proposed method outperforms existing methods in terms of handling limb swing and clothing details. Our method provides a new solution for clothed human reconstruction with high‐resolution details (style, wrinkles and clothed layers), and has good potential in three‐dimensional virtual try‐on and digital characters.

Existing unsupervised learning methods usually reformulate the depth estimation into the image reconstruction problem by training on stereo image pairs to circumvent the need of dense labeled ground truth depth information. Most of them are designed based on a simple encoder-decoder backbone architecture, which has limited expression for context information and suffers from the loss of depth details. In this paper, we propose a cascaded detail-aware network which contains a contextual network (CN) followed by consecutive spatial networks (SNs) to make an unsupervised coarse-to-fine prediction. CN aims to provide good initialized depth estimation results by introducing a multi-scale attention fusion module to enhance the ability of feature representation. Then, SN is progressively applied on the coarse depth map to produce refined depth outputs by exploiting abundant spatial details from input color image. Moreover, we design a robust loss function that further considers the penalty of photometric errors and the occlusion, and strengthens the recovery of spatial details for better depth estimation. Experimental results show that the proposed method achieves promising performance.