LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention

LiDAR-based semantic segmentation is critical in the fields of robotics and autonomous driving as it provides a comprehensive understanding of the scene. This paper proposes a lightweight and efficient projection-based semantic segmentation network called LENet with an encoder-decoder structure for LiDAR-based semantic segmentation. The encoder is composed of a novel multi-scale convolutional attention (MSCA) module with varying receptive field sizes to capture features. The decoder employs an Interpolation And Convolution (IAC) mechanism utilizing bilinear interpolation for upsampling multi-resolution feature maps and integrating previous and current dimensional features through a single convolution layer. This approach significantly reduces the network's complexity while also improving its accuracy. Additionally, we introduce multiple auxiliary segmentation heads to further refine the network's accuracy. Extensive evaluations on publicly available datasets, including SemanticKITTI, SemanticPOSS, and nuScenes, show that our proposed method is lighter, more efficient, and robust compared to state-of-the-art semantic segmentation methods. Full implementation is available at https://github.com/fengluodb/LENet

Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models

Data poisoning attacks manipulate training data to introduce unexpected behaviors into machine learning models at training time. For text-to-image generative models with massive training datasets, current understanding of poisoning attacks suggests that a successful attack would require injecting millions of poison samples into their training pipeline. In this paper, we show that poisoning attacks can be successful on generative models. We observe that training data per concept can be quite limited in these models, making them vulnerable to prompt-specific poisoning attacks, which target a model's ability to respond to individual prompts. We introduce Nightshade, an optimized prompt-specific poisoning attack where poison samples look visually identical to benign images with matching text prompts. Nightshade poison samples are also optimized for potency and can corrupt an Stable Diffusion SDXL prompt in <100 poison samples. Nightshade poison effects "bleed through" to related concepts, and multiple attacks can composed together in a single prompt. Surprisingly, we show that a moderate number of Nightshade attacks can destabilize general features in a text-to-image generative model, effectively disabling its ability to generate meaningful images. Finally, we propose the use of Nightshade` and similar tools as a last defense for content creators against web scrapers that ignore opt-out/do-not-crawl directives, and discuss possible implications for model trainers and content creators

Parametric knocking performance investigation of spark ignition natural gas engines and dual fuel engines

Both spark ignition (SI) natural gas engines and compression ignition (CI) dual fuel (DF) engines suffer from knocking when the unburnt mixture ignites spontaneously prior to the flame front arrival. In this study, a parametric investigation is performed on the knocking performance of these two engine types by using the GT-Power software. An SI natural gas engine and a DF engine are modelled by employing a two-zone zero-dimensional combustion model, which uses Wiebe function to determine the combustion rate and provides adequate prediction of the unburnt zone temperature, which is crucial for the knocking prediction. The developed models are validated against experimentally measured parameters and are subsequently used for performing parametric investigations. The derived results are analysed to quantify the effect of the compression ratio, air-fuel equivalence ratio and ignition timing on both engines as well as the effect of pilot fuel energy proportion on the DF engine. The results demonstrate that the compression ratio of the investigated SI and DF engines must be limited to 11 and 16.5, respectively, for avoiding knocking occurrence. The ignition timing for the SI and the DF engines must be controlled after −38 ◦CA and 3 ◦CA, respectively. A higher pilot fuel energy proportion between 5% and 15% results in increasing the knocking tendency and intensity for the DF Engine at high loads. This study results in better insights on the impacts of the investigated engine design and operating settings for natural gas (NG)-fuelled engines, thus it can provide useful support for obtaining the optimal settings targeting a desired combustion behaviour and engine performance while attenuating the knocking tendency