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Despite being widely applied due to their exceptional capabilities, Large Language Models (LLMs) have been proven to be vulnerable to backdoor attacks. These attacks introduce targeted vulnerabilities into LLMs by poisoning training samples and full-parameter fine-tuning. However, this kind of backdoor attack is limited since they require significant computational resources, especially as the size of LLMs increases. Besides, parameter-efficient fine-tuning (PEFT) offers an alternative but the restricted parameter updating may impede the alignment of triggers with target labels. In this study, we first verify that backdoor attacks with PEFT may encounter challenges in achieving feasible performance. To address these issues and improve the effectiveness of backdoor attacks with PEFT, we propose a novel backdoor attack algorithm from weak to strong based on contrastive knowledge distillation (W2SAttack). Specifically, we poison small-scale language models through full-parameter fine-tuning to serve as the teacher model. The teacher model then covertly transfers the backdoor to the large-scale student model through contrastive knowledge distillation, which employs PEFT. Theoretical analysis reveals that W2SAttack has the potential to augment the effectiveness of backdoor attacks. We demonstrate the superior performance of W2SAttack on classification tasks across four language models, four backdoor attack algorithms, and two different architectures of teacher models. Experimental results indicate success rates close to 100% for backdoor attacks targeting PEFT.

Earth Observation (EO) systems play a crucial role in achieving Sustainable Development Goals by collecting and analyzing vital global data through satellite networks. These systems are essential for tasks like mapping, disaster monitoring, and resource management, but they face challenges in processing and transmitting large volumes of EO data, especially in specialized fields such as agriculture and real-time disaster response. Domain-adapted Large Language Models (LLMs) provide a promising solution by facilitating data fusion between extensive EO data and semantic EO data. By improving integration and interpretation of diverse datasets, LLMs address the challenges of processing specialized information in agriculture and disaster response applications. This fusion enhances the accuracy and relevance of transmitted data. This paper presents a framework for semantic communication in EO satellite networks, aimed at improving data transmission efficiency and overall system performance through cognitive processing techniques. The proposed system employs Discrete-Task-Oriented Source-Channel Coding (DT-JSCC) and Semantic Data Augmentation (SA) to focus on relevant information while minimizing communication overhead. By integrating cognitive semantic processing and inter-satellite links, the framework enhances the analysis and transmission of multispectral satellite imagery, improving object detection, pattern recognition, and real-time decision-making. The introduction of Cognitive Semantic Augmentation (CSA) allows satellites to process and transmit semantic information, boosting adaptability to changing environments and application needs. This end-to-end architecture is tailored for next-generation satellite networks, such as those supporting 6G, and demonstrates significant improvements in efficiency and accuracy.

Tactile sensing plays a vital role in enabling robots to perform fine-grained, contact-rich tasks. However, the high dimensionality of tactile data, due to the large coverage on dexterous hands, poses significant challenges for effective tactile feature learning, especially for 3D tactile data, as there are no large standardized datasets and no strong pretrained backbones. To address these challenges, we propose a novel canonical representation that reduces the difficulty of 3D tactile feature learning and further introduces a force-based self-supervised pretraining task to capture both local and net force features, which are crucial for dexterous manipulation. Our method achieves an average success rate of 78% across four fine-grained, contact-rich dexterous manipulation tasks in real-world experiments, demonstrating effectiveness and robustness compared to other methods. Further analysis shows that our method fully utilizes both spatial and force information from 3D tactile data to accomplish the tasks. The videos can be viewed at //3dtacdex.github.io.

We propose a standalone monocular visual Simultaneous Localization and Mapping (vSLAM) initialization pipeline for autonomous robots in space. Our method, a state-of-the-art factor graph optimization pipeline, enhances classical Structure from Small Motion (SfSM) to robustly initialize a monocular agent in weak-perspective projection scenes. Furthermore, it overcomes visual estimation challenges introduced by spacecraft inspection trajectories, such as: center-pointing motion, which exacerbates the bas-relief ambiguity, and the presence of a dominant plane in the scene, which causes motion estimation degeneracies in classical Structure from Motion (SfM). We validate our method on realistic, simulated satellite inspection images exhibiting weak-perspective projection, and we demonstrate its effectiveness and improved performance compared to other monocular initialization procedures.

Deep State-Space Models (SSM) demonstrate state-of-the art performance on long-range sequence modeling tasks. While the recurrent structure of SSMs can be efficiently implemented as a convolution or as a parallel scan during training, recurrent token-by-token processing cannot currently be implemented efficiently on GPUs. Here, we demonstrate efficient token-by-token inference of the SSM S4D on Intel's Loihi 2 state-of-the-art neuromorphic processor. We compare this first ever neuromorphic-hardware implementation of an SSM on sMNIST, psMNIST, and sCIFAR to a recurrent and a convolutional implementation of S4D on Jetson Orin Nano (Jetson). While we find Jetson to perform better in an offline sample-by-sample based batched processing mode, Loihi 2 outperforms during token-by-token based processing, where it consumes 1000 times less energy with a 75 times lower latency and a 75 times higher throughput compared to the recurrent implementation of S4D on Jetson. This opens up new avenues towards efficient real-time streaming applications of SSMs.

Object detection in remote sensing imagery plays a vital role in various Earth observation applications. However, unlike object detection in natural scene images, this task is particularly challenging due to the abundance of small, often barely visible objects across diverse terrains. To address these challenges, multimodal learning can be used to integrate features from different data modalities, thereby improving detection accuracy. Nonetheless, the performance of multimodal learning is often constrained by the limited size of labeled datasets. In this paper, we propose to use Masked Image Modeling (MIM) as a pre-training technique, leveraging self-supervised learning on unlabeled data to enhance detection performance. However, conventional MIM such as MAE which uses masked tokens without any contextual information, struggles to capture the fine-grained details due to a lack of interactions with other parts of image. To address this, we propose a new interactive MIM method that can establish interactions between different tokens, which is particularly beneficial for object detection in remote sensing. The extensive ablation studies and evluation demonstrate the effectiveness of our approach.

The rising use of Large Language Models (LLMs) to create and disseminate malware poses a significant cybersecurity challenge due to their ability to generate and distribute attacks with ease. A single prompt can initiate a wide array of malicious activities. This paper addresses this critical issue through a multifaceted approach. First, we provide a comprehensive overview of LLMs and their role in malware detection from diverse sources. We examine five specific applications of LLMs: Malware honeypots, identification of text-based threats, code analysis for detecting malicious intent, trend analysis of malware, and detection of non-standard disguised malware. Our review includes a detailed analysis of the existing literature and establishes guiding principles for the secure use of LLMs. We also introduce a classification scheme to categorize the relevant literature. Second, we propose performance metrics to assess the effectiveness of LLMs in these contexts. Third, we present a risk mitigation framework designed to prevent malware by leveraging LLMs. Finally, we evaluate the performance of our proposed risk mitigation strategies against various factors and demonstrate their effectiveness in countering LLM-enabled malware. The paper concludes by suggesting future advancements and areas requiring deeper exploration in this fascinating field of artificial intelligence.

Solving Singularly Perturbed Differential Equations (SPDEs) poses computational challenges arising from the rapid transitions in their solutions within thin regions. The effectiveness of deep learning in addressing differential equations motivates us to employ these methods for solving SPDEs. In this manuscript, we introduce Component Fourier Neural Operator (ComFNO), an innovative operator learning method that builds upon Fourier Neural Operator (FNO), while simultaneously incorporating valuable prior knowledge obtained from asymptotic analysis. Our approach is not limited to FNO and can be applied to other neural network frameworks, such as Deep Operator Network (DeepONet), leading to potential similar SPDEs solvers. Experimental results across diverse classes of SPDEs demonstrate that ComFNO significantly improves accuracy compared to vanilla FNO. Furthermore, ComFNO exhibits natural adaptability to diverse data distributions and performs well in few-shot scenarios, showcasing its excellent generalization ability in practical situations.

We investigate the problem of automatically determining what type of shoe left an impression found at a crime scene. This recognition problem is made difficult by the variability in types of crime scene evidence (ranging from traces of dust or oil on hard surfaces to impressions made in soil) and the lack of comprehensive databases of shoe outsole tread patterns. We find that mid-level features extracted by pre-trained convolutional neural nets are surprisingly effective descriptors for this specialized domains. However, the choice of similarity measure for matching exemplars to a query image is essential to good performance. For matching multi-channel deep features, we propose the use of multi-channel normalized cross-correlation and analyze its effectiveness. Our proposed metric significantly improves performance in matching crime scene shoeprints to laboratory test impressions. We also show its effectiveness in other cross-domain image retrieval problems: matching facade images to segmentation labels and aerial photos to map images. Finally, we introduce a discriminatively trained variant and fine-tune our system through our proposed metric, obtaining state-of-the-art performance.

Visual Question Answering (VQA) models have struggled with counting objects in natural images so far. We identify a fundamental problem due to soft attention in these models as a cause. To circumvent this problem, we propose a neural network component that allows robust counting from object proposals. Experiments on a toy task show the effectiveness of this component and we obtain state-of-the-art accuracy on the number category of the VQA v2 dataset without negatively affecting other categories, even outperforming ensemble models with our single model. On a difficult balanced pair metric, the component gives a substantial improvement in counting over a strong baseline by 6.6%.