Stochastic Two Points Method for Deep Model Zeroth-order Optimization

Large foundation models, such as large language models, have performed exceptionally well in various application scenarios. Building or fully fine-tuning such large models is usually prohibitive due to either hardware budget or lack of access to backpropagation. The zeroth-order methods offer a promising direction for tackling this challenge, where only forward passes are needed to update the model. This paper introduces an efficient Stochastic Two-Point (S2P) approach within the gradient-free regime. We present the theoretical convergence properties of S2P under the general and relaxed smoothness assumptions, and the derived results help understand and inherently connect the two popular types of zeroth-order methods, basic random search and stochastic three-point method. The theoretical properties also shed light on a Variant of S2P (VS2P), through exploiting our new convergence properties that better represent the dynamics of deep models in training. Our comprehensive empirical results show that VS2P is highly effective in optimizing objectives for deep models. It outperforms or achieves competitive performance compared to standard methods across various model types and scales

RUSH: Robust Contrastive Learning via Randomized Smoothing

Recently, adversarial training has been incorporated in self-supervised contrastive pre-training to augment label efficiency with exciting adversarial robustness. However, the robustness came at a cost of expensive adversarial training. In this paper, we show a surprising fact that contrastive pre-training has an interesting yet implicit connection with robustness, and such natural robustness in the pre trained representation enables us to design a powerful robust algorithm against adversarial attacks, RUSH, that combines the standard contrastive pre-training and randomized smoothing. It boosts both standard accuracy and robust accuracy, and significantly reduces training costs as compared with adversarial training. We use extensive empirical studies to show that the proposed RUSH outperforms robust classifiers from adversarial training, by a significant margin on common benchmarks (CIFAR-10, CIFAR-100, and STL-10) under first-order attacks. In particular, under $\ell_{\infty}$-norm perturbations of size 8/255 PGD attack on CIFAR-10, our model using ResNet-18 as backbone reached 77.8% robust accuracy and 87.9% standard accuracy. Our work has an improvement of over 15% in robust accuracy and a slight improvement in standard accuracy, compared to the state-of-the-arts.Comment: 12 pages, 2 figure

Cross-modality debiasing: using language to mitigate sub-population shifts in imaging

Sub-population shift is a specific type of domain shift that highlights changes in data distribution within specific sub-groups or populations between training and testing. Sub-population shift accounts for a significant source of algorithmic bias and calls for distributional robustness. Recent studies found inherent distributional robustness in multi-modality foundation models, such as the vision-language model CLIP, yet this robustness is vulnerable through parameter fine-tuning. In this paper, we propose leveraging the connection of robustness among different modalities and reshaping the distributional robustness of one modality with another. Specifically, in the context of the distributional robustness of CLIP, we propose to leverage natural language inputs to debias the image feature representations, to improve worst-case performance on sub-populations. Our extensive empirical studies show that image representations debiased by natural language can achieve significant performance improvement and reduction of performance instability under sub-population shifts

Trust-Aware Reflective Control for Fault-Resilient Dynamic Task Response in Human–Swarm Cooperation

Due to the complexity of real-world deployments, a robot swarm is required to dynamically respond to tasks such as tracking multiple vehicles and continuously searching for victims. Frequent task assignments eliminate the need for system calibration time, but they also introduce uncertainty from previous tasks, which can undermine swarm performance. Therefore, responding to dynamic tasks presents a significant challenge for a robot swarm compared to handling tasks one at a time. In human–human cooperation, trust plays a crucial role in understanding each other’s performance expectations and adjusting one’s behavior for better cooperation. Taking inspiration from human trust, this paper introduces a trust-aware reflective control method called “Trust-R”. Trust-R, based on a weighted mean subsequence reduced algorithm (WMSR) and human trust modeling, enables a swarm to self-reflect on its performance from a human perspective. It proactively corrects faulty behaviors at an early stage before human intervention, mitigating the negative influence of uncertainty accumulated from dynamic tasks. Three typical task scenarios {Scenario 1: flocking to the assigned destination; Scenario 2: a transition between destinations; and Scenario 3: emergent response} were designed in the real-gravity simulation environment, and a human user study with 145 volunteers was conducted. Trust-R significantly improves both swarm performance and trust in dynamic task scenarios, marking a pivotal step forward in integrating trust dynamics into swarm robotics

ABCC1 deficiency potentiated noise-induced hearing loss in mice by impairing cochlear antioxidant capacity

The ABCC1 gene belongs to the ATP-binding cassette membrane transporter superfamily, which plays a crucial role in the efflux of various endogenous and exogenous substances. Mutations in ABCC1 can result in autosomal dominant hearing loss. However, the specific roles of ABCC1 in auditory function are not fully understood. Through immunofluorescence, we found that ABCC1 was expressed in microvascular endothelial cells (ECs) of the stria vascularis (StV) in the murine cochlea. Then, an Abcc1 knockout mouse model was established by using CRISPR/Cas9 technology to elucidate the role of ABCC1 in the inner ear. The ABR threshold did not significantly differ between WT and Abcc1−/− mice at any age studied. After noise exposure, the ABR thresholds of the WT and Abcc1−/− mice were significantly elevated. Interestingly, after 14 days of noise exposure, ABR thresholds largely returned to pre-exposure levels in WT mice but not in Abcc1−/− mice. Our subsequent experiments showed that microvascular integrity in the StV was compromised and that the number of outer hair cells and the number of ribbons were significantly decreased in the cochleae of Abcc1−/− mice post-exposure. Besides, the production of ROS and the accumulation of 4-HNE significantly increased. Furthermore, StV microvascular ECs were cultured to elucidate the role of ABCC1 in these cells under glucose oxidase challenge. Notably, 30 U/L glucose oxidase (GO) induced severe oxidative stress damage in Abcc1−/− cells. Compared with WT cells, the ROS and 4-HNE levels and the apoptotic rate were significantly elevated in Abcc1−/− cells. In addition, the reduced GSH/GSSG ratio was significantly decreased in Abcc1−/− cells after GO treatment. Taken together, Abcc1−/− mice are more susceptible to noise-induced hearing loss, possibly because ABCC1 knockdown compromises the GSH antioxidant system of StV ECs. The exogenous antioxidant N-acetylcysteine (NAC) may protect against oxidative damage in Abcc1−/− murine cochleae and ECs