Selective Kalman Filter: When and How to Fuse Multi-Sensor Information to Overcome Degeneracy in SLAM

Research trends in SLAM systems are now focusing more on multi-sensor fusion to handle challenging and degenerative environments. However, most existing multi-sensor fusion SLAM methods mainly use all of the data from a range of sensors, a strategy we refer to as the all-in method. This method, while merging the benefits of different sensors, also brings in their weaknesses, lowering the robustness and accuracy and leading to high computational demands. To address this, we propose a new fusion approach -- Selective Kalman Filter -- to carefully choose and fuse information from multiple sensors (using LiDAR and visual observations as examples in this paper). For deciding when to fuse data, we implement degeneracy detection in LiDAR SLAM, incorporating visual measurements only when LiDAR SLAM exhibits degeneracy. Regarding degeneracy detection, we propose an elegant yet straightforward approach to determine the degeneracy of LiDAR SLAM and to identify the specific degenerative direction. This method fully considers the coupled relationship between rotational and translational constraints. In terms of how to fuse data, we use visual measurements only to update the specific degenerative states. As a result, our proposed method improves upon the all-in method by greatly enhancing real-time performance due to less processing visual data, and it introduces fewer errors from visual measurements. Experiments demonstrate that our method for degeneracy detection and fusion, in addressing degeneracy issues, exhibits higher precision and robustness compared to other state-of-the-art methods, and offers enhanced real-time performance relative to the all-in method. The code is openly available.

MDHP-Net: Detecting Injection Attacks on In-vehicle Network using Multi-Dimensional Hawkes Process and Temporal Model

The integration of intelligent and connected technologies in modern vehicles, while offering enhanced functionalities through Electronic Control Unit and interfaces like OBD-II and telematics, also exposes the vehicle's in-vehicle network (IVN) to potential cyberattacks. In this paper, we consider a specific type of cyberattack known as the injection attack. As demonstrated by empirical data from real-world cybersecurity adversarial competitions(available at https://mimic2024.xctf.org.cn/race/qwmimic2024 ), these injection attacks have excitation effect over time, gradually manipulating network traffic and disrupting the vehicle's normal functioning, ultimately compromising both its stability and safety. To profile the abnormal behavior of attackers, we propose a novel injection attack detector to extract long-term features of attack behavior. Specifically, we first provide a theoretical analysis of modeling the time-excitation effects of the attack using Multi-Dimensional Hawkes Process (MDHP). A gradient descent solver specifically tailored for MDHP, MDHP-GDS, is developed to accurately estimate optimal MDHP parameters. We then propose an injection attack detector, MDHP-Net, which integrates optimal MDHP parameters with MDHP-LSTM blocks to enhance temporal feature extraction. By introducing MDHP parameters, MDHP-Net captures complex temporal features that standard Long Short-Term Memory (LSTM) cannot, enriching temporal dependencies within our customized structure. Extensive evaluations demonstrate the effectiveness of our proposed detection approach.

Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery

In the drug discovery process, the low success rate of drug candidate screening often leads to insufficient labeled data, causing the few-shot learning problem in molecular property prediction. Existing methods for few-shot molecular property prediction overlook the sample selection bias, which arises from non-random sample selection in chemical experiments. This bias in data representativeness leads to suboptimal performance. To overcome this challenge, we present a novel method named contextual representation anchor Network (CRA), where an anchor refers to a cluster center of the representations of molecules and serves as a bridge to transfer enriched contextual knowledge into molecular representations and enhance their expressiveness. CRA introduces a dual-augmentation mechanism that includes context augmentation, which dynamically retrieves analogous unlabeled molecules and captures their task-specific contextual knowledge to enhance the anchors, and anchor augmentation, which leverages the anchors to augment the molecular representations. We evaluate our approach on the MoleculeNet and FS-Mol benchmarks, as well as in domain transfer experiments. The results demonstrate that CRA outperforms the state-of-the-art by 2.60% and 3.28% in AUC and $\Delta$AUC-PR metrics, respectively, and exhibits superior generalization capabilities.

Dual-Label Learning With Irregularly Present Labels

In multi-task learning, we often encounter the case when the presence of labels across samples exhibits irregular patterns: samples can be fully labeled, partially labeled or unlabeled. Taking drug analysis as an example, multiple toxicity properties of a drug molecule may not be concurrently available due to experimental limitations. It triggers a demand for a new training and inference mechanism that could accommodate irregularly present labels and maximize the utility of any available label information. In this work, we focus on the two-label learning task, and propose a novel training and inference framework, Dual-Label Learning (DLL). The DLL framework formulates the problem into a dual-function system, in which the two functions should simultaneously satisfy standard supervision, structural duality and probabilistic duality. DLL features a dual-tower model architecture that explicitly captures the information exchange between labels, aimed at maximizing the utility of partially available labels in understanding label correlation. During training, label imputation for missing labels is conducted as part of the forward propagation process, while during inference, labels are regarded as unknowns of a bivariate system of equations and are solved jointly. Theoretical analysis guarantees the feasibility of DLL, and extensive experiments are conducted to verify that by explicitly modeling label correlation and maximizing the utility of available labels, our method makes consistently better predictions than baseline approaches by up to a 10% gain in F1-score or MAPE. Remarkably, our method provided with data at a label missing rate as high as 60% can achieve similar or even better results than baseline approaches at a label missing rate of only 10%.

GNN-MolKAN: Harnessing the Power of KAN to Advance Molecular Representation Learning with GNNs

Effective molecular representation learning is crucial for molecular property prediction and drug design. However, existing approaches struggle with limitations in insufficient annotations and suboptimal architecture design. For instance, Graph Neural Networks (GNNs) suffer from over-squashing, causing the loss of important structural details in molecules, thus impairing molecular representations. In this work, we propose a new class of GNNs, GNN-MolKAN and its augmented variant, GNN-MolKAN+, that integrate the Kolmogorov-Arnold Networks (KAN) architecture from AI + Science into GNNs to address these challenges. Additionally, we introduce Adaptive FastKAN (AdFastKAN), an advanced KAN that offers increased stability and speed, further enhancing the performance of standard GNNs. Notably, our approach holds three key benefits: 1) Superior Performance: GNN-MolKAN and GNN-MolKAN+ demonstrate superior prediction ability, robust generalization to unseen scaffolds, and versatile transferability across different GNN architectures. 2) Efficiency: These models require less computational time and fewer parameters while matching or surpassing the state-of-the-art (SOTA) self-supervised methods. 3) Few-shot Learning Ability: GNN-MolKAN demonstrates great potential in few-shot learning scenarios, achieving an average improvement of 6.97% across few-shot benchmarks. Overall, we validate our architecture on 6 classification datasets, 6 regression datasets, and 4 few-shot learning datasets, consistently achieving highly competitive results across all of them.

Physical formula enhanced multi-task learning for pharmacokinetics prediction

Artificial intelligence (AI) technology has demonstrated remarkable potential in drug dis-covery, where pharmacokinetics plays a crucial role in determining the dosage, safety, and efficacy of new drugs. A major challenge for AI-driven drug discovery (AIDD) is the scarcity of high-quality data, which often requires extensive wet-lab work. A typical example of this is pharmacokinetic experiments. In this work, we develop a physical formula enhanced mul-ti-task learning (PEMAL) method that predicts four key parameters of pharmacokinetics simultaneously. By incorporating physical formulas into the multi-task framework, PEMAL facilitates effective knowledge sharing and target alignment among the pharmacokinetic parameters, thereby enhancing the accuracy of prediction. Our experiments reveal that PEMAL significantly lowers the data demand, compared to typical Graph Neural Networks. Moreover, we demonstrate that PEMAL enhances the robustness to noise, an advantage that conventional Neural Networks do not possess. Another advantage of PEMAL is its high flexibility, which can be potentially applied to other multi-task machine learning scenarios. Overall, our work illustrates the benefits and potential of using PEMAL in AIDD and other scenarios with data scarcity and noise.

OFVL-MS: Once for Visual Localization across Multiple Indoor Scenes

In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task. We propose OFVL-MS, a unified framework that dispenses with the traditional practice of training a model for each individual scene and relieves gradient conflict induced by optimizing multiple scenes collectively, enabling efficient storage yet precise visual localization for all scenes. Technically, in the forward pass of OFVL-MS, we design a layer-adaptive sharing policy with a learnable score for each layer to automatically determine whether the layer is shared or not. Such sharing policy empowers us to acquire task-shared parameters for a reduction of storage cost and task-specific parameters for learning scene-related features to alleviate gradient conflict. In the backward pass of OFVL-MS, we introduce a gradient normalization algorithm that homogenizes the gradient magnitude of the task-shared parameters so that all tasks converge at the same pace. Furthermore, a sparse penalty loss is applied on the learnable scores to facilitate parameter sharing for all tasks without performance degradation. We conduct comprehensive experiments on multiple benchmarks and our new released indoor dataset LIVL, showing that OFVL-MS families significantly outperform the state-of-the-arts with fewer parameters. We also verify that OFVL-MS can generalize to a new scene with much few parameters while gaining superior localization performance.

Multiscale Positive-Unlabeled Detection of AI-Generated Texts

Recent releases of Large Language Models (LLMs), e.g. ChatGPT, are astonishing at generating human-like texts, but they may get misused for fake scholarly texts, fake news, fake tweets, et cetera. Previous works have proposed methods to detect these multiscale AI-generated texts, including simple ML classifiers, pretrained-model-based training-agnostic methods, and finetuned language classification models. However, mainstream detectors are formulated without considering the factor of corpus length: shorter corpuses are harder to detect compared with longer ones for shortage of informative features. In this paper, a Multiscale Positive-Unlabeled (MPU) training framework is proposed to address the challenge of multiscale text detection. Firstly, we acknowledge the human-resemblance property of short machine texts, and rephrase text classification as a Positive-Unlabeled (PU) problem by marking these short machine texts as "unlabeled" during training. In this PU context, we propose the length-sensitive Multiscale PU Loss, where we use a recurrent model in abstraction to estimate positive priors of scale-variant corpuses. Additionally, we introduce a Text Multiscaling module to enrich training corpuses. Experiments show that our MPU method augments detection performance on long AI-generated text, and significantly improves short-corpus detection of language model detectors. Language Models trained with MPU could outcompete existing detectors by large margins on multiscale AI-generated texts. The codes are available at https://github.com/mindspore-lab/mindone/tree/master/examples/detect_chatgpt and https://github.com/YuchuanTian/AIGC_text_detector.

Pillar R-CNN for Point Cloud 3D Object Detection

The performance of point cloud 3D object detection hinges on effectively representing raw points, grid-based voxels or pillars. Recent two-stage 3D detectors typically take the point-voxel-based R-CNN paradigm, i.e., the first stage resorts to the 3D voxel-based backbone for 3D proposal generation on bird-eye-view (BEV) representation and the second stage refines them via the intermediate point representation. Their primary mechanisms involve the utilization of intermediary keypoints to restore the substantial 3D structure context from the converted BEV representation. The skilled point-voxel feature interaction, however, makes the entire detection pipeline more complex and compute-intensive. In this paper, we take a different viewpoint -- the pillar-based BEV representation owns sufficient capacity to preserve the 3D structure. In light of the latest advances in BEV-based perception, we devise a conceptually simple yet effective two-stage 3D detection architecture, named Pillar R-CNN. On top of densified BEV feature maps, Pillar R-CNN can easily introduce the feature pyramid architecture to generate 3D proposals at various scales and take the simple 2D R-CNN style detect head for box refinement. Our Pillar R-CNN performs favorably against state-of-the-art 3D detectors on the large-scale Waymo Open Dataset but at a small extra cost. It should be highlighted that further exploration into BEV perception for applications involving autonomous driving is now possible thanks to the effective and elegant Pillar R-CNN architecture.

OAMatcher: An Overlapping Areas-based Network for Accurate Local Feature Matching

Local feature matching is an essential component in many visual applications. In this work, we propose OAMatcher, a Tranformer-based detector-free method that imitates humans behavior to generate dense and accurate matches. Firstly, OAMatcher predicts overlapping areas to promote effective and clean global context aggregation, with the key insight that humans focus on the overlapping areas instead of the entire images after multiple observations when matching keypoints in image pairs. Technically, we first perform global information integration across all keypoints to imitate the humans behavior of observing the entire images at the beginning of feature matching. Then, we propose Overlapping Areas Prediction Module (OAPM) to capture the keypoints in co-visible regions and conduct feature enhancement among them to simulate that humans transit the focus regions from the entire images to overlapping regions, hence realizeing effective information exchange without the interference coming from the keypoints in non overlapping areas. Besides, since humans tend to leverage probability to determine whether the match labels are correct or not, we propose a Match Labels Weight Strategy (MLWS) to generate the coefficients used to appraise the reliability of the ground-truth match labels, while alleviating the influence of measurement noise coming from the data. Moreover, we integrate depth-wise convolution into Tranformer encoder layers to ensure OAMatcher extracts local and global feature representation concurrently. Comprehensive experiments demonstrate that OAMatcher outperforms the state-of-the-art methods on several benchmarks, while exhibiting excellent robustness to extreme appearance variants. The source code is available at https://github.com/DK-HU/OAMatcher.