R2 Why compare to COBYLA instead of BOBYQA?: Thank you for the suggestion! We ran BOBYQA (using nlopt) on robot pushing (mean 9.22) and rover (mean 1.40) and it indeed performs better than COBYLA. Thus, we will replace COBYLA by BOBYQA in all experiments. Note that TuRBO still outperforms both by a large margin. R2 The results of EBO for the robot experiments are quite different from the results on the original paper: The results are consistent and we used the code from the authors.

Despite an explosion in the number of experimentally determined, atomically detailed structures of biomolecules, many critical tasks in structural biology remain data-limited. Whether performance in such tasks can be improved by using large repositories of tangentially related structural data remains an open question. To address this question, we focused on a central problem in biology: predicting how proteins interact with one another--that is, which surfaces of one protein bind to those of another protein. We built a training dataset, the Database of Interacting Protein Structures (DIPS), that contains biases but is two orders of magnitude larger than those used previously. We found that these biases significantly degrade the performance of existing methods on gold-standard data. Hypothesizing that assumptions baked into the hand-crafted features on which these methods depend were the source of the problem, we developed the first end-to-end learning model for protein interface prediction, the Siamese Atomic Surfacelet Network (SASNet). Using only spatial coordinates and identities of atoms, SASNet outperforms state-of-the-art methods trained on gold-standard structural data, even when trained on only 3% of our new dataset.

We thank the reviewers for their detailed and thoughtful feedback. We respond to each reviewer individually below. So it is a mean (across training seeds) of the median (across complexes) of AUROC. We will clarify this in the Table 2 and Figure 1 captions. We will clarify this point.

Tyler Saltsman, founder and CEO of EdgeRunner AI, warned that creating artificial general intelligence could "destroy the world as we know it." Scientists say that they have formulated a way to help people in stressful and demanding work environments track their brainwaves and brain usage -- an electronic tattoo device, or "e-tattoo," on the person's face. In a study posted in the science journal Device, the team of researchers wrote that they found e-tattoos to be a more cost-effective and simpler way to track one's mental workload. Dr. Nanshu Lu, the senior author of the research from the University of Texas at Austin, wrote that mental workload is a critical factor in human-in-the-loop systems, directly influencing cognitive performance and decision-making. Lu told Fox News Digital in an email that this device was motivated by high-demand, high-stake jobs such as pilots, air traffic controllers, doctors and emergency dispatchers.

Despite spiking neural networks (SNNs) have demonstrated notable energy efficiency across various fields, the limited firing patterns of spiking neurons within fixed time steps restrict the expression of information, which impedes further improvement of SNN performance. In addition, current implementations of SNNs typically consider the firing rate or average membrane potential of the last layer as the output, lacking exploration of other possibilities. In this paper, we identify that the limited spike patterns of spiking neurons stem from the initial membrane potential (IMP), which is set to 0. By adjusting the IMP, the spiking neurons can generate additional firing patterns and pattern mappings. Furthermore, we find that in static tasks, the accuracy of SNNs at each time step increases as the membrane potential evolves from zero. This observation inspires us to propose a learnable IMP, which can accelerate the evolution of membrane potential and enables higher performance within a limited number of time steps. Additionally, we introduce the last time step (LTS) approach to accelerate convergence in static tasks, and we propose a label smooth temporal efficient training (TET) loss to mitigate the conflicts between optimization objective and regularization term in the vanilla TET. Our methods improve the accuracy by 4.05% on ImageNet compared to baseline and achieve state-of-theart performance of 87.80% on CIFAR10-DVS and 87.86% on N-Caltech101.

We explore the role of softmax attention in an ICL setting where each context encodes a regression task. We show that an attention unit learns a window that it uses to implement a nearest-neighbors predictor adapted to the landscape of the pretraining tasks.

Our modular setting makes it particularly amenable to such generalizations (e.g.

Despite the wide success of deep neural networks (DNN), little progress has been made on end-to-end unsupervised outlier detection (UOD) from high dimensional data like raw images.

Improvements: (1) Please see reply to Q1.

Autonomous driving system aims for safe and social-consistent driving through the behavioral integration among interactive agents. However, challenges remain due to multi-agent scene uncertainty and heterogeneous interaction.