PillarNeSt: Embracing Backbone Scaling and Pretraining for Pillar-based 3D Object Detection

This paper shows the effectiveness of 2D backbone scaling and pretraining for pillar-based 3D object detectors. Pillar-based methods mainly employ randomly initialized 2D convolution neural network (ConvNet) for feature extraction and fail to enjoy the benefits from the backbone scaling and pretraining in the image domain. To show the scaling-up capacity in point clouds, we introduce the dense ConvNet pretrained on large-scale image datasets (e.g., ImageNet) as the 2D backbone of pillar-based detectors. The ConvNets are adaptively designed based on the model size according to the specific features of point clouds, such as sparsity and irregularity. Equipped with the pretrained ConvNets, our proposed pillar-based detector, termed PillarNeSt, outperforms the existing 3D object detectors by a large margin on the nuScenes and Argoversev2 datasets. Our code shall be released upon acceptance

GPT detectors are biased against non-native English writers

The rapid adoption of generative language models has brought about substantial advancements in digital communication, while simultaneously raising concerns regarding the potential misuse of AI-generated content. Although numerous detection methods have been proposed to differentiate between AI and human-generated content, the fairness and robustness of these detectors remain underexplored. In this study, we evaluate the performance of several widely-used GPT detectors using writing samples from native and non-native English writers. Our findings reveal that these detectors consistently misclassify non-native English writing samples as AI-generated, whereas native writing samples are accurately identified. Furthermore, we demonstrate that simple prompting strategies can not only mitigate this bias but also effectively bypass GPT detectors, suggesting that GPT detectors may unintentionally penalize writers with constrained linguistic expressions. Our results call for a broader conversation about the ethical implications of deploying ChatGPT content detectors and caution against their use in evaluative or educational settings, particularly when they may inadvertently penalize or exclude non-native English speakers from the global discourse

Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection

To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-boxes, which need a sequence of post-processing with fine-tuned hyper-parameters. In this work, we propose replacing the sparse pseudo-boxes with the dense prediction as a united and straightforward form of pseudo-label. Compared to the pseudo-boxes, our Dense Pseudo-Label (DPL) does not involve any post-processing method, thus retaining richer information. We also introduce a region selection technique to highlight the key information while suppressing the noise carried by dense labels. We name our proposed SS-OD algorithm that leverages the DPL as Dense Teacher. On COCO and VOC, Dense Teacher shows superior performance under various settings compared with the pseudo-box-based methods.Comment: ECCV202

ADriver-I: A General World Model for Autonomous Driving

Typically, autonomous driving adopts a modular design, which divides the full stack into perception, prediction, planning and control parts. Though interpretable, such modular design tends to introduce a substantial amount of redundancy. Recently, multimodal large language models (MLLM) and diffusion techniques have demonstrated their superior performance on comprehension and generation ability. In this paper, we first introduce the concept of interleaved vision-action pair, which unifies the format of visual features and control signals. Based on the vision-action pairs, we construct a general world model based on MLLM and diffusion model for autonomous driving, termed ADriver-I. It takes the vision-action pairs as inputs and autoregressively predicts the control signal of the current frame. The generated control signals together with the historical vision-action pairs are further conditioned to predict the future frames. With the predicted next frame, ADriver-I performs further control signal prediction. Such a process can be repeated infinite times, ADriver-I achieves autonomous driving in the world created by itself. Extensive experiments are conducted on nuScenes and our large-scale private datasets. ADriver-I shows impressive performance compared to several constructed baselines. We hope our ADriver-I can provide some new insights for future autonomous driving and embodied intelligence.Comment: Tech Repor

Molecular cloning, characterization and expression analysis of CpCBF2 gene in harvested papaya fruit under temperature stresses

Background: C-repeat binding factors (CBFs) are transcription factors that regulate the expression of a number of genes related to abiotic stresses. Few CBF genes have been cloned from other plants but no report in papaya. In present study, a full-length cDNA, designated as CpCBF2, was cloned from papaya using in silico cloning and 5\u2019- rapid amplification cDNA ends (RACE). Sequence analysis was performed to understand the gene function. The expression pattern of CpCBF2 in papaya under low (7\ubaC) and high temperature (35\ubaC) stresses was examined using real-time quantitative polymerase chain reaction (RT-qPCR). Results: The full-length cDNA of CpCBF2 was 986-bp, with a 762-bp open reading frame (ORF) encoding a 254 amino acid polypeptide. CpCBF2 contained several major highly conserved regions including the CBF-family signature PKRRAGRKKFQETRHP and FADSAW in its amino acid sequence. Phylogenetic tree and three-dimensional structure analysis showed that CpCBF2 had a relatively close relationship with other plant CBFs. Gene expression analysis showed that high temperature stress had little effect on the expression of CpCBF2 but low temperature repressed CpCBF2 expression. Conclusion: The results showed that CpCBF2 may involve in different roles in temperature stress tolerance. This study provided a candidate gene potentially useful for fruit temperature stress tolerance, although its function still needs further confirmation

Circulating tumor DNA clearance predicts prognosis across treatment regimen in a large real-world longitudinally monitored advanced non-small cell lung cancer cohort

Background: Although growth advantage of certain clones would ultimately translate into a clinically visible disease progression, radiological imaging does not reflect clonal evolution at molecular level. Circulating tumor DNA (ctDNA), validated as a tool for mutation detection in lung cancer, could reflect dynamic molecular changes. We evaluated the utility of ctDNA as a predictive and a prognostic marker in disease monitoring of advanced non-small cell lung cancer (NSCLC) patients.Methods: This is a multicenter prospective cohort study. We performed capture-based ultra-deep sequencing on longitudinal plasma samples utilizing a panel consisting of 168 NSCLC-related genes on 949 advanced NSCLC patients with driver mutations to monitor treatment responses and disease progression. The correlations between ctDNA and progression-free survival (PFS)/overall survival (OS) were performed on 248 patients undergoing various treatments with the minimum of 2 ctDNA tests.Results: The results of this study revealed that higher ctDNA abundance (P=0.012) and mutation count (P=8.5x10(-4)) at baseline are associated with shorter OS. We also found that patients with ctDNA clearance, not just driver mutation clearance, at any point during the course of treatment were associated with longer PFS (P=2.2x10(-1)6, HR 0.28) and OS (P=4.5x10(-6), HR 0.19) regardless of type of treatment and evaluation schedule.Conclusions: This prospective real-world study shows that ctDNA clearance during treatment may serve as predictive and prognostic marker across a wide spectrum of treatment regimens

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data