Enhancing Pedestrian-Autonomous Vehicle Safety in Low Visibility Scenarios: A Comprehensive Simulation Method

Self-driving cars raise safety concerns, particularly regarding pedestrian interactions. Current research lacks a systematic understanding of these interactions in diverse scenarios. Autonomous Vehicle (AV) performance can vary due to perception accuracy, algorithm reliability, and environmental dynamics. This study examines AV-pedestrian safety issues, focusing on low visibility conditions, using a co-simulation framework combining virtual reality and an autonomous driving simulator. 40 experiments were conducted, extracting surrogate safety measures (SSMs) from AV and pedestrian trajectories. The results indicate that low visibility can impair AV performance, increasing conflict risks for pedestrians. AV algorithms may require further enhancements and validations for consistent safety performance in low visibility scenarios

Efficient Stitchable Task Adaptation

The paradigm of pre-training and fine-tuning has laid the foundation for deploying deep learning models. However, most fine-tuning methods are designed to meet a specific resource budget. Recently, considering diverse deployment scenarios with various resource budgets, stitchable neural network (SN-Net) is introduced to quickly obtain numerous new networks (stitches) from the pre-trained models (anchors) in a model family via model stitching. Although promising, SN-Net confronts new challenges when adapting it to new target domains, including huge memory and storage requirements and a long and sub-optimal multistage adaptation process. In this work, we present a novel framework, Efficient Stitchable Task Adaptation (ESTA), to efficiently produce a palette of fine-tuned models that adhere to diverse resource constraints. Specifically, we first tailor parameter-efficient fine-tuning to share low-rank updates among the stitches while maintaining independent bias terms. In this way, we largely reduce fine-tuning memory burdens and mitigate the interference among stitches that arises in task adaptation. Furthermore, we streamline a simple yet effective one-stage deployment pipeline, which estimates the important stitches to deploy with training-time gradient statistics. By assigning higher sampling probabilities to important stitches, we also get a boosted Pareto frontier. Extensive experiments on 25 downstream visual recognition tasks demonstrate that our ESTA is capable of generating stitches with smooth accuracy-efficiency trade-offs and surpasses the direct SN-Net adaptation by remarkable margins with significantly lower training time and fewer trainable parameters. Furthermore, we demonstrate the flexibility and scalability of our ESTA framework by stitching LLMs from LLaMA family, obtaining chatbot stitches of assorted sizes.Comment: Source code will be released at https://github.com/ziplab/Stitched_LLaM

Effective targeting of the survivin dimerization interface with small molecule inhibitors

Many oncoproteins are considered undruggable because they lack enzymatic activities. In this study, we present a small-molecule–based anticancer agent that acts by inhibiting dimerization of the oncoprotein survivin, thereby promoting its degradation along with spontaneous apoptosis in cancer cells. Through a combination of computational analysis of the dimerization interface and in silico screening, we identified one compound that induced proteasome-dependent survivin degradation. Analysis of a set of structural analogues led us to identify a lead compound (LQZ-7F), which was effective in blocking the survival of multiple cancer cell lines in a low micromolar concentration range. LQZ-7F induced proteasome-dependent survivin degradation, mitotic arrest, and apoptosis, and it blocked the growth of human tumors in mouse xenograft assays. In addition to providing preclinical proof of concept for a survivin-targeting anticancer agent, our work offers novel in silico screening strategies to therapeutically target homodimeric oncogenic proteins considered undruggable

A Small Molecule Compound Targeting STAT3 DNA-Binding Domain Inhibits Cancer Cell Proliferation, Migration, and Invasion

Signal transducer and activator of transcription 3 (STAT3) plays important roles in multiple aspects of cancer aggressiveness including migration, invasion, survival, self-renewal, angiogenesis, and tumor cell immune evasion by regulating the expression of multiple downstream target genes. STAT3 is constitutively activated in many malignant tumors and its activation is associated with high histological grade and advanced cancer stages. Thus, inhibiting STAT3 promises an attracting strategy for treatment of advanced and metastatic cancers. Herein, we identified a STAT3 inhibitor, inS3-54, by targeting the DNA-binding domain of STAT3 using an improved virtual screening strategy. InS3-54 preferentially suppresses proliferation of cancer over non-cancer cells and inhibits migration and invasion of malignant cells. Biochemical analyses show that inS3-54 selectively inhibits STAT3 binding to DNA without affecting the activation and dimerization of STAT3. Furthermore, inS3-54 inhibits expression of STAT3 downstream target genes and STAT3 binding to chromatin in situ. Thus, inS3-54 represents a novel probe for development of specific inhibitors targeting the DNA-binding domain of STAT3 and a potential therapeutic for cancer treatments

Dynamic Focus-aware Positional Queries for Semantic Segmentation

Most of the latest top semantic segmentation approaches are based on vision Transformers, particularly DETR-like frameworks, which employ a set of queries in the Transformer decoder. Each query is composed of a content query that preserves semantic information and a positional query that provides positional guidance for aggregating the query-specific context. However, the positional queries in the Transformer decoder layers are typically represented as fixed learnable weights, which often encode dataset statistics for segments and can be inaccurate for individual samples. Therefore, in this paper, we propose to generate positional queries dynamically conditioned on the cross-attention scores and the localization information of the preceding layer. By doing so, each query is aware of its previous focus, thus providing more accurate positional guidance and encouraging the cross-attention consistency across the decoder layers. In addition, we also propose an efficient way to deal with high-resolution cross-attention by dynamically determining the contextual tokens based on the low-resolution cross-attention maps to perform local relation aggregation. Our overall framework termed FASeg (Focus-Aware semantic Segmentation) provides a simple yet effective solution for semantic segmentation. Extensive experiments on ADE20K and Cityscapes show that our FASeg achieves state-of-the-art performance, e.g., obtaining 48.3% and 49.6% mIoU respectively for single-scale inference on ADE20K validation set with ResNet-50 and Swin-T backbones, and barely increases the computation consumption from Mask2former. Source code will be made publicly available at https://github.com/zip-group/FASeg.Comment: Tech repor

Noise-Aware Speech Separation with Contrastive Learning

Recently, speech separation (SS) task has achieved remarkable progress driven by deep learning technique. However, it is still challenging to separate target speech from noisy mixture, as the neural model is vulnerable to assign background noise to each speaker. In this paper, we propose a noise-aware SS (NASS) method, which aims to improve the speech quality for separated signals under noisy conditions. Specifically, NASS views background noise as an independent output and predicts it with other speakers in a mask-based manner. Then we conduct patch-wise contrastive learning on feature level to minimize the mutual information between the predicted noise output and other speakers, which suppresses the noise information in separated signals, and vice versa. Experimental results show that NASS could achieve competitive results on different datasets, and significantly improve the noise-robustness for different mask-based SS backbones with less than 0.1M parameter increase