A.1 Descriptions of backdoor attack algorithms In addition to the basic information in Table 1 of the main manuscript, here we describe the general idea of eight implemented backdoor attack algorithms in BackdoorBench, as follows. A.2 Descriptions of backdoor defense algorithms In addition to the basic information in Table 2 of the main manuscript, here we describe the general idea of nine implemented backdoor defense algorithms in BackdoorBench, as follows. It is used to determine the number of pruned neurons. Running environments Our evaluations are conducted on GPU servers with 2 Intel(R) Xeon(R) Platinum 8170 CPU @ 2.10GHz, RTX3090 GPU (32GB) and 320 GB RAM (2666MHz). With these hyper-3 Table 2: Hyper-parameter settings of all implemented defense methods.

However, we find that the evaluations of new methods are often unthorough to verify their claims and accurate performance, mainly due to the rapid development, diverse settings, and thedifficulties ofimplementationand reproducibility.

Such diversity hampers knowledge sharing, learning, and generalization across tasks and environments.

Motion forecasting is an essential task for autonomous driving, and utilizing information from infrastructure and other vehicles can enhance forecasting capabilities.

It also tends towards high capacity storage of all pieces of information which may be relevant for future reasoning [42, 3, 4]. By contrast, longterm memory changes slowly [45, 41], is highly selective and involves repeated consolidation. It contains a set of memories that summarize the entire past, only storing details about observations whicharemostrelevant[28,6]. Deep Learning has seen a variety of architectures for processing sequential data [36, 57, 18].