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

EIF3i Promotes Colon Oncogenesis by Regulating COX-2 Protein Synthesis and β-Catenin Activation

Translational control of gene expression has recently been recognized as an important mechanism controlling cell proliferation and oncogenesis and it mainly occurs in the initiation step of protein synthesis that involves multiple eukaryotic initiation factors (eIFs). Many eIFs have been found to have aberrant expression in human tumors and the aberrant expression may contribute to oncogenesis. However, how these previously considered house-keeping proteins are potentially oncogenic remains elusive. In this study, we investigated the expression of eIF3i in human colon cancers, tested its contribution to colon oncogenesis, and determined the mechanism of eIF3i action in colon oncogenesis. We found that eIF3i expression was up-regulated in both human colon adenocarcinoma and adenoma polyps as well as in model inducible colon tumorigenic cell lines. Over-expression of ectopic eIF3i in intestinal epithelial cells causes oncogenesis by directly up-regulating synthesis of COX-2 protein and activates the β-catenin/TCF4 signaling pathway that mediates the oncogenic function of eIF3i. Together, we conclude that eIF3i is a proto-oncogene that drives colon oncogenesis by translationally up-regulating COX-2 and activating β-catenin signaling pathway. These findings imply that protooncogenic eIFs likely exert their tumorigenic function by regulating/altering the synthesis level of down-stream tumor suppressor or oncogenes

Stitched ViTs are Flexible Vision Backbones

Large pretrained plain vision Transformers (ViTs) have been the workhorse for many downstream tasks. However, existing works utilizing off-the-shelf ViTs are inefficient in terms of training and deployment, because adopting ViTs with individual sizes requires separate trainings and is restricted by fixed performance-efficiency trade-offs. In this paper, we are inspired by stitchable neural networks (SN-Net), which is a new framework that cheaply produces a single model that covers rich subnetworks by stitching pretrained model families, supporting diverse performance-efficiency trade-offs at runtime. Building upon this foundation, we introduce SN-Netv2, a systematically improved model stitching framework to facilitate downstream task adaptation. Specifically, we first propose a two-way stitching scheme to enlarge the stitching space. We then design a resource-constrained sampling strategy that takes into account the underlying FLOPs distributions in the space for better sampling. Finally, we observe that learning stitching layers as a low-rank update plays an essential role on downstream tasks to stabilize training and ensure a good Pareto frontier. With extensive experiments on ImageNet-1K, ADE20K, COCO-Stuff-10K and NYUv2, SN-Netv2 demonstrates superior performance over SN-Netv1 on downstream dense predictions and shows strong ability as a flexible vision backbone, achieving great advantages in both training efficiency and deployment flexibility. Code is available at https://github.com/ziplab/SN-Netv2.Comment: Tech repor

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