Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects

Leveraging multi-modal fusion, especially between camera and LiDAR, has become essential for building accurate and robust 3D object detection systems for autonomous vehicles. Until recently, point decorating approaches, in which point clouds are augmented with camera features, have been the dominant approach in the field. However, these approaches fail to utilize the higher resolution images from cameras. Recent works projecting camera features to the bird's-eye-view (BEV) space for fusion have also been proposed, however they require projecting millions of pixels, most of which only contain background information. In this work, we propose a novel approach Center Feature Fusion (CFF), in which we leverage center-based detection networks in both the camera and LiDAR streams to identify relevant object locations. We then use the center-based detection to identify the locations of pixel features relevant to object locations, a small fraction of the total number in the image. These are then projected and fused in the BEV frame. On the nuScenes dataset, we outperform the LiDAR-only baseline by 4.9% mAP while fusing up to 100x fewer features than other fusion methods.Comment: Accepted by ICRA 202

Approaching the Intrinsic Bandgap in Suspended High-Mobility Graphene Nanoribbons

We report electrical transport measurements on a suspended ultra-low-disorder graphene nanoribbon(GNR) with nearly atomically smooth edges that reveal a high mobility exceeding 3000 cm2 V-1 s-1 and an intrinsic band gap. The experimentally derived bandgap is in quantitative agreement with the results of our electronic-structure calculations on chiral GNRs with comparable width taking into account the electron-electron interactions, indicating that the origin of the bandgap in non-armchair GNRs is partially due to the magnetic zigzag edges.Comment: 22 pages, 6 figure

Modulation Design and Optimization for RIS-Assisted Symbiotic Radios

In reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR), the RIS acts as a secondary transmitter by modulating its information bits over the incident primary signal and simultaneously assists the primary transmission, then a cooperative receiver is used to jointly decode the primary and secondary signals. Most existing works of SR focus on using RIS to enhance the reflecting link while ignoring the ambiguity problem for the joint detection caused by the multiplication relationship of the primary and secondary signals. Particularly, in case of a blocked direct link, joint detection will suffer from severe performance loss due to the ambiguity, when using the conventional on-off keying and binary phase shift keying modulation schemes for RIS. To address this issue, we propose a novel modulation scheme for RIS-assisted SR that divides the phase-shift matrix into two components: the symbol-invariant and symbol-varying components, which are used to assist the primary transmission and carry the secondary signal, respectively. To design these two components, we focus on the detection of the composite signal formed by the primary and secondary signals, through which a problem of minimizing the bit error rate (BER) of the composite signal is formulated to improve both the BER performance of the primary and secondary ones. By solving the problem, we derive the closed-form solution of the optimal symbol-invariant and symbol-varying components, which is related to the channel strength ratio of the direct link to the reflecting link. Moreover, theoretical BER performance is analyzed. Finally, simulation results show the superiority of the proposed modulation scheme over its conventional counterpart.Comment: 16 pages,15 figure

Study on Spatial Distribution of Soil Available Microelement in Qujing Tobacco Farming Area, China

AbstractDescriptive analysis characteristics and spatial variation characteristics of soil available microelements were studied based on SPSS and GIS Soil available microelements spatial distribution maps were created with ordinary Kriging method. The results indicate that, 7 available microelements in tobacco soil obey lognormal distribution, all the available microelements were intermediate variability; Anisotropic structure of available microelements of tobacco soil varies evidently, spatial variability of available B was mainly caused by random factors, and others’ spatial variability were caused by structural factors and random factors; Spatial distribution maps show that, available B was widely deficient in tobacco soil of Qujing farming area, ‘lower level’ and ‘low level’ taken 7.74% and 68.20%, respectively available Zn distribution was moderate, only 1.32% of the area lack of Zn, available Cu, available Fe and available Mn were extremely high in the whole extension, available Mo was deficient in part of the region with 28.38%, water soluble Cl was higher than critical value(30mgkg−1)in the most of Qujing farming area, which taken 38.86%

In Planta Production of Flock House Virus Transencapsidated RNA and Its Potential Use as a Vaccine

We have developed a transencapsidated vaccine delivery system based on the insect virus, Flock House virus (FHV). FHV is attractive due to its small genome size, simple organization, and non-pathogenic characteristics. With the insertion of a Tobacco mosaic virus (TMV) origin of assembly (Oa), the independently replicating FHV RNA1 can be transencapsidated by TMV coat protein. In this study we demonstrated that the Oa adapted FHV RNA1 transencapsidation process can take place in planta, by using a bipartite plant expression vector system, where TMV coat protein is expressed by another plant virus vector, Foxtail mosaic virus (FoMV). Dual infection in the same cell by both FHV and FoMV was observed. Though an apparent classical coat protein-mediated resistance repressed FHV expression, this was overcome by delaying inoculation of the TMV coat protein vector by three days after FHV vector inoculation. Expression of transgene marker in animals by these in vivo generated transencapsidated nanoparticles was confirmed by mouse vaccination, which also showed an improved vaccine response compared to similar in vitro produced vaccines

How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs

This work focuses on the potential of Vision LLMs (VLLMs) in visual reasoning. Different from prior studies, we shift our focus from evaluating standard performance to introducing a comprehensive safety evaluation suite, covering both out-of-distribution (OOD) generalization and adversarial robustness. For the OOD evaluation, we present two novel VQA datasets, each with one variant, designed to test model performance under challenging conditions. In exploring adversarial robustness, we propose a straightforward attack strategy for misleading VLLMs to produce visual-unrelated responses. Moreover, we assess the efficacy of two jailbreaking strategies, targeting either the vision or language component of VLLMs. Our evaluation of 21 diverse models, ranging from open-source VLLMs to GPT-4V, yields interesting observations: 1) Current VLLMs struggle with OOD texts but not images, unless the visual information is limited; and 2) These VLLMs can be easily misled by deceiving vision encoders only, and their vision-language training often compromise safety protocols. We release this safety evaluation suite at https://github.com/UCSC-VLAA/vllm-safety-benchmark.Comment: H.T., C.C., and Z.W. contribute equally. Work done during H.T. and Z.W.'s internship at UCSC, and C.C. and Y.Z.'s internship at UN

Just ClozE! A Novel Framework for Evaluating the Factual Consistency Faster in Abstractive Summarization

The issue of factual consistency in abstractive summarization has received extensive attention in recent years, and the evaluation of factual consistency between summary and document has become an important and urgent task. Most of the current evaluation metrics are adopted from the question answering (QA) or natural language inference (NLI) task. However, the application of QA-based metrics is extremely time-consuming in practice while NLI-based metrics are lack of interpretability. In this paper, we propose a cloze-based evaluation framework called ClozE and show the great potential of the cloze-based metric. It inherits strong interpretability from QA, while maintaining the speed of NLI- level reasoning. We demonstrate that ClozE can reduce the evaluation time by nearly 96% relative to QA-based metrics while retaining their interpretability and performance through experiments on six human-annotated datasets and a meta-evaluation benchmark GO FIGURE (Gabriel et al., 2021). Finally, we discuss three important facets of ClozE in practice, which further shows better overall performance of ClozE compared to other metrics.Comment: The manuscript for JAI

What Matters for 3D Scene Flow Network

3D scene flow estimation from point clouds is a low-level 3D motion perception task in computer vision. Flow embedding is a commonly used technique in scene flow estimation, and it encodes the point motion between two consecutive frames. Thus, it is critical for the flow embeddings to capture the correct overall direction of the motion. However, previous works only search locally to determine a soft correspondence, ignoring the distant points that turn out to be the actual matching ones. In addition, the estimated correspondence is usually from the forward direction of the adjacent point clouds, and may not be consistent with the estimated correspondence acquired from the backward direction. To tackle these problems, we propose a novel all-to-all flow embedding layer with backward reliability validation during the initial scene flow estimation. Besides, we investigate and compare several design choices in key components of the 3D scene flow network, including the point similarity calculation, input elements of predictor, and predictor & refinement level design. After carefully choosing the most effective designs, we are able to present a model that achieves the state-of-the-art performance on FlyingThings3D and KITTI Scene Flow datasets. Our proposed model surpasses all existing methods by at least 38.2% on FlyingThings3D dataset and 24.7% on KITTI Scene Flow dataset for EPE3D metric. We release our codes at https://github.com/IRMVLab/3DFlow.Comment: Accepted by ECCV 202