Camouflaged object detection (COD) aims to identify the objects that seamlessly blend into the surrounding backgrounds. Due to the intrinsic similarity between the camouflaged objects and the background region, it is extremely challenging to precisely distinguish the camouflaged objects by existing approaches. In this paper, we propose a hierarchical graph interaction network termed HGINet for camouflaged object detection, which is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Specifically, we first design a region-aware token focusing attention (RTFA) with dynamic token clustering to excavate the potentially distinguishable tokens in the local region. Afterwards, a hierarchical graph interaction transformer (HGIT) is proposed to construct bi-directional aligned communication between hierarchical features in the latent interaction space for visual semantics enhancement. Furthermore, we propose a decoder network with confidence aggregated feature fusion (CAFF) modules, which progressively fuses the hierarchical interacted features to refine the local detail in ambiguous regions. Extensive experiments conducted on the prevalent datasets, i.e. COD10K, CAMO, NC4K and CHAMELEON demonstrate the superior performance of HGINet compared to existing state-of-the-art methods. Our code is available at https://github.com/Garyson1204/HGINet.

Adaptive optimization has achieved notable success for distributed learning while extending adaptive optimizer to federated Learning (FL) suffers from severe inefficiency, including (i) rugged convergence due to inaccurate gradient estimation in global adaptive optimizer; (ii) client drifts exacerbated by local over-fitting with the local adaptive optimizer. In this work, we propose a novel momentum-based algorithm via utilizing the global gradient descent and locally adaptive amended optimizer to tackle these difficulties. Specifically, we incorporate a locally amended technique to the adaptive optimizer, named Federated Local ADaptive Amended optimizer (FedLADA), which estimates the global average offset in the previous communication round and corrects the local offset through a momentum-like term to further improve the empirical training speed and mitigate the heterogeneous over-fitting. Theoretically, we establish the convergence rate of FedLADA with a linear speedup property on the non-convex case under the partial participation settings. Moreover, we conduct extensive experiments on the real-world dataset to demonstrate the efficacy of our proposed FedLADA, which could greatly reduce the communication rounds and achieves higher accuracy than several baselines.

Imitation learning from observation (LfO) is more preferable than imitation learning from demonstration (LfD) because of the nonnecessity of expert actions when reconstructing the expert policy from the expert data. However, previous studies imply that the performance of LfO is inferior to LfD by a tremendous gap, which makes it challenging to employ LfO in practice. By contrast, this article proves that LfO is almost equivalent to LfD in the deterministic robot environment, and more generally even in the robot environment with bounded randomness. In the deterministic robot environment, from the perspective of the control theory, we show that the inverse dynamics disagreement between LfO and LfD approaches zero, meaning that LfO is almost equivalent to LfD. To further relax the deterministic constraint and better adapt to the practical environment, we consider bounded randomness in the robot environment and prove that the optimizing targets for both LfD and LfO remain almost the same in the more generalized setting. Extensive experiments for multiple robot tasks are conducted to demonstrate that LfO achieves comparable performance to LfD empirically. In fact, the most common robot systems in reality are the robot environment with bounded randomness (i.e., the environment this article considered). Hence, our findings greatly extend the potential of LfO and suggest that we can safely apply LfO in practice without sacrificing the performance compared to LfD.

In high performance computing (HPC) systems, collecting and replaying communication traces are fundamental approaches to analyze performance. With increasingly large-scale HPC systems and applications, tracing tools can produce huge trace data that is costly and challenging to store and analyze. Due to the inherent repetition of behaviors of HPC applications, domain-aware data compression methods can effectively reduce the storage cost of trace data. This study proposes LCR (Lossy Compression and Replay), a framework that aggressively compresses and replays MPI communication traces. Differing from existing trace compression methods, which explicitly identify loop and synchronization structures of communication events, LCR models traces as time series and compactly represents them by lightweight recurrent neural networks. Experimental results demonstrate that LCR can further reduce the size of irregular traces by three orders of magnitude at most, compared with existing structural methods. Meanwhile, LCR accurately reproduces performance and communication patterns of original MPI programs.

Graph neural networks (GNNs) are effective models to address learning problems on graphs and have been successfully applied to numerous domains. To improve the productivity of implementing GNNs, various GNN programming frameworks have been developed. Both the effectiveness (accuracy, loss, etc) and the performance (latency, bandwidth, etc) are essential metrics to evaluate the implementation of GNNs. There are many comparative studies related to the effectiveness of different GNN models on domain tasks. However, the performance characteristics of different GNN frameworks are still lacking. In this study, we evaluate the effectiveness and performance of six popular GNN models, GCN, GIN, GAT, GraphSAGE, MoNet, and GatedGCN, across several common benchmarks under two popular GNN frameworks, PyTorch Geometric and Deep Graph Library. We analyze the training time, GPU utilization, and memory usage of different evaluation settings and the performance of models across different hardware configurations under the two frameworks. Our evaluation provides in-depth observations of performance bottlenecks of GNNs and the performance differences between the two popular GNN frameworks. Our work helps GNN researchers understand the performance differences of the popular GNN frameworks, and gives guidelines for developers to find potential performance bugs of frameworks and optimization possibilities of GNNs.