OpenRaiser/Intern-Atlas huggingface dataset:
Compromising Embodied Agents with Contextual Backdoor Attacks
Venue: arXiv cs.AI 2024
Year: 2024

Large language models (LLMs) have transformed the development of embodied intelligence. By providing a few contextual demonstrations, developers can utilize the extensive internal knowledge of LLMs to effortlessly translate complex tasks described in abstract language into sequences of code snippets, which will serve as the execution logic for embodied agents. However, this paper uncovers a significant backdoor security threat within this process and introduces a novel method called \method{}. By poisoning just a few contextual demonstrations, attackers can covertly compromise the contextual environment of a black-box LLM, prompting it to generate programs with context-dependent defects. These programs appear logically sound but contain defects that can activate and induce unintended behaviors when the operational agent encounters specific triggers in its interactive environment. To compromise the LLM's contextual environment, we employ adversarial in-context generation to optimize poisoned demonstrations, where an LLM judge evaluates these poisoned prompts, reporting to an additional LLM that iteratively optimizes the demonstration in a two-player adversarial game using chain-of-thought reasoning. To enable context-dependent behaviors in downstream agents, we implement a dual-modality activation strategy that controls both the generation and execution of program defects through textual and visual triggers. We expand the scope of our attack by developing five program defect modes that compromise key aspects of confidentiality, integrity, and availability in embodied agents. To validate the effectiveness of our approach, we conducted extensive experiments across various tasks, including robot planning, robot manipulation, and compositional visual reasoning. Additionally, we demonstrate the potential impact of our approach by successfully attacking real-world autonomous driving systems.
Intermediate direct preference optimization
Venue: arXiv cs.CL 2024
Year: 2024

We propose the intermediate direct preference optimization (DPO) method to calculate the DPO loss at selected intermediate layers as an auxiliary loss for finetuning large language models (LLMs). The conventional DPO method fine-tunes a supervised fine-tuning (SFT) model by calculating the DPO loss using logits from the final layer. In our intermediate DPO approach, DPO losses are calculated using the logits from K-selected intermediate layers and averaged to obtain the intermediate DPO loss. For training the intermediate DPO model, the final loss is obtained by calculating the weighted sum of the DPO and intermediate DPO losses. During inference, the intermediate DPO model decodes using the final layer logits similarly to the conventional DPO model. In experiments using the ultrafeedback dataset, the performance of the intermediate DPO model was evaluated using GPT-4. As a result, the intermediate DPO model trained using the intermediate DPO loss calculated at the 22nd layer of a 32-layer SFT model achieved win rates of 52.5% and 67.5% against the conventional DPO and SFT models, respectively, demonstrating the effectiveness of the proposed method. Furthermore, we report the relationships among the position of the selected intermediate layers, the number of layers, and performance.
Reinforcement Learning based Workflow Scheduling in Cloud and Edge Computing Environments: A Taxonomy, Review and Future Directions
Venue: arXiv cs.DC 2024
Year: 2024

Deep Reinforcement Learning (DRL) techniques have been successfully applied for solving complex decision-making and control tasks in multiple fields including robotics, autonomous driving, healthcare and natural language processing. The ability of DRL agents to learn from experience and utilize real-time data for making decisions makes it an ideal candidate for dealing with the complexities associated with the problem of workflow scheduling in highly dynamic cloud and edge computing environments. Despite the benefits of DRL, there are multiple challenges associated with the application of DRL techniques including multi-objectivity, curse of dimensionality, partial observability and multi-agent coordination. In this paper, we comprehensively analyze the challenges and opportunities associated with the design and implementation of DRL oriented solutions for workflow scheduling in cloud and edge computing environments. Based on the identified characteristics, we propose a taxonomy of workflow scheduling with DRL. We map reviewed works with respect to the taxonomy to identify their strengths and weaknesses. Based on taxonomy driven analysis, we propose novel future research directions for the field.
Are Female Carpenters like Blue Bananas? A Corpus Investigation of Occupation Gender Typicality
Venue: arXiv cs.CL 2024
Year: 2024

People tend to use language to mention surprising properties of events: for example, when a banana is blue, we are more likely to mention color than when it is yellow. This fact is taken to suggest that yellowness is somehow a typical feature of bananas, and blueness is exceptional. Similar to how a yellow color is typical of bananas, there may also be genders that are typical of occupations. In this work, we explore this question using information theoretic techniques coupled with corpus statistic analysis. In two distinct large corpora, we do not find strong evidence that occupations and gender display the same patterns of mentioning as do bananas and color. Instead, we find that gender mentioning is correlated with femaleness of occupation in particular, suggesting perhaps that woman-dominated occupations are seen as somehow ``more gendered'' than male-dominated ones, and thereby they encourage more gender mentioning overall.
Cross-cultural analysis of pedestrian group behaviour influence on crossing decisions in interactions with autonomous vehicles
Venue: arXiv cs.HC 2024
Year: 2024

Understanding cultural backgrounds is crucial for the seamless integration of autonomous driving into daily life as it ensures that systems are attuned to diverse societal norms and behaviours, enhancing acceptance and safety in varied cultural contexts. In this work, we investigate the impact of co-located pedestrians on crossing behaviour, considering cultural and situational factors. To accomplish this, a full-scale virtual reality (VR) environment was created in the CARLA simulator, enabling the identical experiment to be replicated in both Spain and Australia. Participants (N=30) attempted to cross the road at an urban crosswalk alongside other pedestrians exhibiting conservative to more daring behaviours, while an autonomous vehicle (AV) approached with different driving styles. For the analysis of interactions, we utilized questionnaires and direct measures of the moment when participants entered the lane. Our findings indicate that pedestrians tend to cross the same traffic gap together, even though reckless behaviour by the group reduces confidence and makes the situation perceived as more complex. Australian participants were willing to take fewer risks than Spanish participants, adopting more cautious behaviour when it was uncertain whether the AV would yield.
Causal Vector-valued Witsenhausen Counterexamples with Feedback
Venue: arXiv cs.IT 2024
Year: 2024

We study the continuous vector-valued Witsenhausen counterexample through the lens of empirical coordination coding. We characterize the region of achievable pairs of costs in three scenarios: (i) causal encoding and causal decoding, (ii) causal encoding and causal decoding with channel feedback, and (iii) causal encoding and noncausal decoding with channel feedback. In these vector-valued versions of the problem, the optimal coding schemes must rely on a time-sharing strategy, since the region of achievable pairs of costs might not be convex in the scalar version of the problem. We examine the role of the channel feedback when the encoder is causal and the decoder is either causal or non-causal, and we show that feedback improves the performance, only when the decoder is non-causal.
Comb, Prune, Distill: Towards Unified Pruning for Vision Model Compression
Venue: arXiv cs.CV 2024
Year: 2024

Lightweight and effective models are essential for devices with limited resources, such as intelligent vehicles. Structured pruning offers a promising approach to model compression and efficiency enhancement. However, existing methods often tie pruning techniques to specific model architectures or vision tasks. To address this limitation, we propose a novel unified pruning framework Comb, Prune, Distill (CPD), which addresses both model-agnostic and task-agnostic concerns simultaneously. Our framework employs a combing step to resolve hierarchical layer-wise dependency issues, enabling architecture independence. Additionally, the pruning pipeline adaptively remove parameters based on the importance scoring metrics regardless of vision tasks. To support the model in retaining its learned information, we introduce knowledge distillation during the pruning step. Extensive experiments demonstrate the generalizability of our framework, encompassing both convolutional neural network (CNN) and transformer models, as well as image classification and segmentation tasks. In image classification we achieve a speedup of up to x4.3 with a accuracy loss of 1.8% and in semantic segmentation up to x1.89 with a 5.1% loss in mIoU.
Learning to Turn: Diffusion Imitation for Robust Row Turning in Under-Canopy Robots
Venue: arXiv cs.RO 2024
Year: 2024

Under-canopy agricultural robots require robust navigation capabilities to enable full autonomy but struggle with tight row turning between crop rows due to degraded GPS reception, visual aliasing, occlusion, and complex vehicle dynamics. We propose an imitation learning approach using diffusion policies to learn row turning behaviors from demonstrations provided by human operators or privileged controllers. Simulation experiments in a corn field environment show potential in learning this task with only visual observations and velocity states. However, challenges remain in maintaining control within rows and handling varied initial conditions, highlighting areas for future improvement.
Extend Model Merging from Fine-Tuned to Pre-Trained Large Language Models via Weight Disentanglement
Venue: arXiv cs.CL 2024
Year: 2024

Merging Large Language Models (LLMs) aims to amalgamate multiple homologous LLMs into one with all the capabilities. Ideally, any LLMs sharing the same backbone should be mergeable, irrespective of whether they are Fine-Tuned (FT) with minor parameter changes or Pre-Trained (PT) with substantial parameter shifts. However, existing methods often manually assign the model importance, rendering them feasible only for LLMs with similar parameter alterations, such as multiple FT LLMs. The diverse parameter changed ranges between FT and PT LLMs pose challenges for current solutions in empirically determining the optimal combination. In this paper, we make a pioneering effort to broaden the applicability of merging techniques from FT to PT LLMs. We initially examine the efficacy of current methods in merging FT and PT LLMs, discovering that they struggle to deal with PT LLMs. Subsequently, we introduce an approach based on WeIght DisENtanglement (WIDEN) to effectively extend the merging scope, which first disentangles model weights into magnitude and direction components, and then performs adaptive fusion by considering their respective contributions. In the experiments, we merge Qwen1.5-Chat (an FT LLM with instruction-following skills) with Sailor (a PT LLM with multilingual abilities) across 7B and 14B model scales. Results reveal that: (1) existing solutions usually fail when merging Sailor, either losing both abilities or only retaining instruction-following skills; (2) WIDEN successfully injects the multilingual abilities of Sailor into Qwen1.5-Chat and make it proficient in Southeast Asian languages, achieving enhancements in the fundamental capabilities. In light of previous research, we also merge multiple 13B FT LLMs and observe that WIDEN achieves a balanced amalgamation of instruction following, mathematical reasoning, and code generation skills.
Inference Optimizations for Large Language Models: Effects, Challenges, and Practical Considerations
Venue: arXiv cs.CL 2024
Year: 2024

Large language models are ubiquitous in natural language processing because they can adapt to new tasks without retraining. However, their sheer scale and complexity present unique challenges and opportunities, prompting researchers and practitioners to explore novel model training, optimization, and deployment methods. This literature review focuses on various techniques for reducing resource requirements and compressing large language models, including quantization, pruning, knowledge distillation, and architectural optimizations. The primary objective is to explore each method in-depth and highlight its unique challenges and practical applications. The discussed methods are categorized into a taxonomy that presents an overview of the optimization landscape and helps navigate it to understand the research trajectory better.
Integration vs segregation: network analysis of interdisciplinarity in funded and unfunded research on infectious diseases
Venue: arXiv cs.SI 2024
Year: 2024

Interdisciplinary research fuels innovation. In this paper, we examine the interdisciplinarity of research output driven by funding. Considering 36 major infectious diseases, we model interdisciplinarity through temporal correlation networks based on funded and unfunded research from 1995-2022. Using hierarchical clustering, we identify coherent periods of time or regimes characterised by important research topics like vaccinations or the Zika outbreak. We establish that funded research is less interdisciplinary than unfunded research, but the effect has decreased markedly over time. In terms of network growth, we find a tendency of funded research to focus on readily established connections leading to compartmentalisation and conservatism. In contrast, unfunded research tends to be exploratory and bridge distant knowledge leading to knowledge integration. Our results show that interdisciplinary research on prominent infectious diseases like HIV and tuberculosis tends to have strong bridging effects facilitating global knowledge integration in the network. At the periphery of the network, we observe the emergence of vaccination-related and Zika-related knowledge clusters, both with limited systemic impact. We further show that despite the surge in publications related to COVID-19, its systematic impact on the disease network remains relatively low. Overall, this research provides a generalisable framework to examine the impact of funding in interdisciplinary knowledge creation. It can assist in priority setting, for example with horizon scanning for new and emerging threats to health, such as pandemic planning. Policymakers, funding agencies, and research institutions should consider revamping evaluation systems to reward interdisciplinary work and implement mechanisms that promote and support intelligent risk-taking.
Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization
Venue: arXiv cs.CV 2024
Year: 2024

The rapid increase in multimedia data has spurred advancements in Multimodal Summarization with Multimodal Output (MSMO), which aims to produce a multimodal summary that integrates both text and relevant images. The inherent heterogeneity of content within multimodal inputs and outputs presents a significant challenge to the execution of MSMO. Traditional approaches typically adopt a holistic perspective on coarse image-text data or individual visual objects, overlooking the essential connections between objects and the entities they represent. To integrate the fine-grained entity knowledge, we propose an Entity-Guided Multimodal Summarization model (EGMS). Our model, building on BART, utilizes dual multimodal encoders with shared weights to process text-image and entity-image information concurrently. A gating mechanism then combines visual data for enhanced textual summary generation, while image selection is refined through knowledge distillation from a pre-trained vision-language model. Extensive experiments on public MSMO dataset validate the superiority of the EGMS method, which also prove the necessity to incorporate entity information into MSMO problem.
TSC: A Simple Two-Sided Constraint against Over-Smoothing
Venue: arXiv cs.LG 2024
Year: 2024

Graph Convolutional Neural Network (GCN), a widely adopted method for analyzing relational data, enhances node discriminability through the aggregation of neighboring information. Usually, stacking multiple layers can improve the performance of GCN by leveraging information from high-order neighbors. However, the increase of the network depth will induce the over-smoothing problem, which can be attributed to the quality and quantity of neighbors changing: (a) neighbor quality, node's neighbors become overlapping in high order, leading to aggregated information becoming indistinguishable, (b) neighbor quantity, the exponentially growing aggregated neighbors submerges the node's initial feature by recursively aggregating operations. Current solutions mainly focus on one of the above causes and seldom consider both at once. Aiming at tackling both causes of over-smoothing in one shot, we introduce a simple Two-Sided Constraint (TSC) for GCNs, comprising two straightforward yet potent techniques: random masking and contrastive constraint. The random masking acts on the representation matrix's columns to regulate the degree of information aggregation from neighbors, thus preventing the convergence of node representations. Meanwhile, the contrastive constraint, applied to the representation matrix's rows, enhances the discriminability of the nodes. Designed as a plug-in module, TSC can be easily coupled with GCN or SGC architectures. Experimental analyses on diverse real-world graph datasets verify that our approach markedly reduces the convergence of node's representation and the performance degradation in deeper GCN.
Training on the Fly: On-device Self-supervised Learning aboard Nano-drones within 20 mW
Venue: arXiv cs.RO 2024
Year: 2024

Miniaturized cyber-physical systems (CPSes) powered by tiny machine learning (TinyML), such as nano-drones, are becoming an increasingly attractive technology. Their small form factor (i.e., ~10cm diameter) ensures vast applicability, ranging from the exploration of narrow disaster scenarios to safe human-robot interaction. Simple electronics make these CPSes inexpensive, but strongly limit the computational, memory, and sensing resources available on board. In real-world applications, these limitations are further exacerbated by domain shift. This fundamental machine learning problem implies that model perception performance drops when moving from the training domain to a different deployment one. To cope with and mitigate this general problem, we present a novel on-device fine-tuning approach that relies only on the limited ultra-low power resources available aboard nano-drones. Then, to overcome the lack of ground-truth training labels aboard our CPS, we also employ a self-supervised method based on ego-motion consistency. Albeit our work builds on top of a specific real-world vision-based human pose estimation task, it is widely applicable for many embedded TinyML use cases. Our 512-image on-device training procedure is fully deployed aboard an ultra-low power GWT GAP9 System-on-Chip and requires only 1MB of memory while consuming as low as 19mW or running in just 510ms (at 38mW). Finally, we demonstrate the benefits of our on-device learning approach by field-testing our closed-loop CPS, showing a reduction in horizontal position error of up to 26% vs. a non-fine-tuned state-of-the-art baseline. In the most challenging never-seen-before environment, our on-device learning procedure makes the difference between succeeding or failing the mission.
MaskAnyone Toolkit: Offering Strategies for Minimizing Privacy Risks and Maximizing Utility in Audio-Visual Data Archiving
Venue: arXiv cs.CR 2024
Year: 2024

This paper introduces MaskAnyone, a novel toolkit designed to navigate some privacy and ethical concerns of sharing audio-visual data in research. MaskAnyone offers a scalable, user-friendly solution for de-identifying individuals in video and audio content through face-swapping and voice alteration, supporting multi-person masking and real-time bulk processing. By integrating this tool within research practices, we aim to enhance data reproducibility and utility in social science research. Our approach draws on Design Science Research, proposing that MaskAnyone can facilitate safer data sharing and potentially reduce the storage of fully identifiable data. We discuss the development and capabilities of MaskAnyone, explore its integration into ethical research practices, and consider the broader implications of audio-visual data masking, including issues of consent and the risk of misuse. The paper concludes with a preliminary evaluation framework for assessing the effectiveness and ethical integration of masking tools in such research settings.
DRL-Assisted Dynamic QoT-Aware Service Provisioning in Multi-Band Elastic Optical Networks
Venue: arXiv cs.NI 2024
Year: 2024

We propose a DRL-assisted approach for service provisioning in multi-band elastic optical networks. Our simulation environment uses an accurate QoT estimator based on the GN/EGN model. Results show that the proposed approach reduces request blocking by 50% compared with heuristics from the literature.
Don't Think It Twice: Exploit Shift Invariance for Efficient Online Streaming Inference of CNNs
Venue: arXiv cs.LG 2024
Year: 2024

Deep learning time-series processing often relies on convolutional neural networks with overlapping windows. This overlap allows the network to produce an output faster than the window length. However, it introduces additional computations. This work explores the potential to optimize computational efficiency during inference by exploiting convolution's shift-invariance properties to skip the calculation of layer activations between successive overlapping windows. Although convolutions are shift-invariant, zero-padding and pooling operations, widely used in such networks, are not efficient and complicate efficient streaming inference. We introduce StreamiNNC, a strategy to deploy Convolutional Neural Networks for online streaming inference. We explore the adverse effects of zero padding and pooling on the accuracy of streaming inference, deriving theoretical error upper bounds for pooling during streaming. We address these limitations by proposing signal padding and pooling alignment and provide guidelines for designing and deploying models for StreamiNNC. We validate our method in simulated data and on three real-world biomedical signal processing applications. StreamiNNC achieves a low deviation between streaming output and normal inference for all three networks (2.03 - 3.55% NRMSE). This work demonstrates that it is possible to linearly speed up the inference of streaming CNNs processing overlapping windows, negating the additional computation typically incurred by overlapping windows.