Does the dataset contain all possible instancesorisitasample(notnecessarilyrandom) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified.

PROSPECT provides value to proteomics and machine learning researchers by including several high-quality annotations and by being accessible in terms of format and structure for applying machinelearning.

For each benchmark, we generate three different training datasets. If the dataset is a sample, then what is the larger set? Is the samplerepresentativeofthe larger set(e.g., geographic coverage)? The provided datasets are self-contained and will remain constant. However, more datasets could be generated using the proposed benchmarking platform.

Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)?

Is there anything afuture user could do to mitigate theseundesirableharms? Although ConfLab's long-term vision is towards developing technology to assist individuals in navigating social interactions, the data could also affect a community in unintended ways: for instance, cause worsened social satisfaction, alackofagency,stereotype newcomers andveterans, or benefit only those members of the community who make use of resulting applications at the expense of the rest. More nefarious uses involve exploiting the data for developing methods that harmfully surveilorprofile people.

Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly(i.e.,incombinationwithotherdata)fromthedataset?

Asabaseline for DCLM, we conduct extensive experiments and find that model-based filtering is key to assembling a high-quality training set.

Additionally, for FVDcalculation, we generate 256 videosforevaluation.

Wu et al. use a feathered saturated mask to replace the light source.

In this section, we conduct an ablation study analyzing the effects of iterations in our proposed approach. Moreover, we see that as the iterations progress, the 2D landmark prediction error continues to reduce as seen in Figure 1a. The red points represent the frames that were given initial 2D input labels. The colorbar of these scatter plots represents the reprojection error (Eq. Weassume that only asingle object of interest (Chimpanzee in Figure 1is visible in each frame.