OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive Artificial Time Delay Control for Robotic Systems
Venue: arXiv cs.RO 2024
Year: 2024

Artificial time delay controller was conceptualised for nonlinear systems to reduce dependency on precise system modelling unlike the conventional adaptive and robust control strategies. In this approach unknown dynamics is compensated by using input and state measurements collected at immediate past time instant (i.e., artificially delayed). The advantage of this kind of approach lies in its simplicity and ease of implementation. However, the applications of artificial time delay controllers in robotics, which are also robust against unknown state-dependent uncertainty, are still missing at large. This thesis presents the study of this control approach toward two important classes of robotic systems, namely a fully actuated bipedal walking robot and an underactuated quadrotor system. In the first work, we explore the idea of a unified control design instead of multiple controllers for different walking phases in adaptive bipedal walking control while bypassing computing constraint forces, since they often lead to complex designs. The second work focuses on quadrotors employed for applications such as payload delivery, inspection and search-and-rescue. The effectiveness of this controller is validated using experimental results.
Towards a Generative AI Design Dialogue
Venue: arXiv cs.HC 2024
Year: 2024

Traditional visualisation designers often start with sketches before implementation. With generative AI, these sketches can be turned into AI-generated visualisations using specific prompts. However, guiding AI to create compelling visuals can be challenging. We propose a new design process where designers verbalise their thoughts during work, later converting these narratives into AI prompts. This approach helps AI generate accurate visuals and assists designers in refining their concepts, enhancing the overall design process. Blending human creativity with AI capabilities enables rapid iteration, leading to higher quality and more innovative visualisations, making design more accessible and efficient.
Topological degree as a discrete diagnostic for disentanglement, with applications to the $Δ$VAE
Venue: arXiv cs.LG 2024
Year: 2024

We investigate the ability of Diffusion Variational Autoencoder ($Δ$VAE) with unit sphere $\mathcal{S}^2$ as latent space to capture topological and geometrical structure and disentangle latent factors in datasets. For this, we introduce a new diagnostic of disentanglement: namely the topological degree of the encoder, which is a map from the data manifold to the latent space. By using tools from homology theory, we derive and implement an algorithm that computes this degree. We use the algorithm to compute the degree of the encoder of models that result from the training procedure. Our experimental results show that the $Δ$VAE achieves relatively small LSBD scores, and that regardless of the degree after initialization, the degree of the encoder after training becomes $-1$ or $+1$, which implies that the resulting encoder is at least homotopic to a homeomorphism.
Representing Neural Network Layers as Linear Operations via Koopman Operator Theory
Venue: arXiv cs.LG 2024
Year: 2024

The strong performance of simple neural networks is often attributed to their nonlinear activations. However, a linear view of neural networks makes understanding and controlling networks much more approachable. We draw from a dynamical systems view of neural networks, offering a fresh perspective by using Koopman operator theory and its connections with dynamic mode decomposition (DMD). Together, they offer a framework for linearizing dynamical systems by embedding the system into an appropriate observable space. By reframing a neural network as a dynamical system, we demonstrate that we can replace the nonlinear layer in a pretrained multi-layer perceptron (MLP) with a finite-dimensional linear operator. In addition, we analyze the eigenvalues of DMD and the right singular vectors of SVD, to present evidence that time-delayed coordinates provide a straightforward and highly effective observable space for Koopman theory to linearize a network layer. Consequently, we replace layers of an MLP trained on the Yin-Yang dataset with predictions from a DMD model, achieving a mdoel accuracy of up to 97.3%, compared to the original 98.4%. In addition, we replace layers in an MLP trained on the MNIST dataset, achieving up to 95.8%, compared to the original 97.2% on the test set.
LoGex: Improved tail detection of extremely rare histopathology classes via guided diffusion
Venue: arXiv cs.CV 2024
Year: 2024

In realistic medical settings, the data are often inherently long-tailed, with most samples concentrated in a few classes and a long tail of rare classes, usually containing just a few samples. This distribution presents a significant challenge because rare conditions are critical to detect and difficult to classify due to limited data. In this paper, rather than attempting to classify rare classes, we aim to detect these as out-of-distribution data reliably. We leverage low-rank adaption (LoRA) and diffusion guidance to generate targeted synthetic data for the detection problem. We significantly improve the OOD detection performance on a challenging histopathological task with only ten samples per tail class without losing classification accuracy on the head classes.
External Steering of Vine Robots via Magnetic Actuation
Venue: arXiv cs.RO 2024
Year: 2024

This paper explores the concept of external magnetic control for vine robots to enable their high curvature steering and navigation for use in endoluminal applications. Vine robots, inspired by natural growth and locomotion strategies, present unique shape adaptation capabilities that allow passive deformation around obstacles. However, without additional steering mechanisms, they lack the ability to actively select the desired direction of growth. The principles of magnetically steered growing robots are discussed, and experimental results showcase the effectiveness of the proposed magnetic actuation approach. We present a 25 mm diameter vine robot with integrated magnetic tip capsule, including 6 Degrees of Freedom (DOF) localization and camera and demonstrate a minimum bending radius of 3.85 cm with an internal pressure of 30 kPa. Furthermore, we evaluate the robot's ability to form tight curvature through complex navigation tasks, with magnetic actuation allowing for extended free-space navigation without buckling. The suspension of the magnetic tip was also validated using the 6 DOF localization system to ensure that the shear-free nature of vine robots was preserved. Additionally, by exploiting the magnetic wrench at the tip, we showcase preliminary results of vine retraction. The findings contribute to the development of controllable vine robots for endoluminal applications, providing high tip force and shear-free navigation.
Grounding Language Models in Autonomous Loco-manipulation Tasks
Venue: arXiv cs.RO 2024
Year: 2024

Humanoid robots with behavioral autonomy have consistently been regarded as ideal collaborators in our daily lives and promising representations of embodied intelligence. Compared to fixed-based robotic arms, humanoid robots offer a larger operational space while significantly increasing the difficulty of control and planning. Despite the rapid progress towards general-purpose humanoid robots, most studies remain focused on locomotion ability with few investigations into whole-body coordination and tasks planning, thus limiting the potential to demonstrate long-horizon tasks involving both mobility and manipulation under open-ended verbal instructions. In this work, we propose a novel framework that learns, selects, and plans behaviors based on tasks in different scenarios. We combine reinforcement learning (RL) with whole-body optimization to generate robot motions and store them into a motion library. We further leverage the planning and reasoning features of the large language model (LLM), constructing a hierarchical task graph that comprises a series of motion primitives to bridge lower-level execution with higher-level planning. Experiments in simulation and real-world using the CENTAURO robot show that the language model based planner can efficiently adapt to new loco-manipulation tasks, demonstrating high autonomy from free-text commands in unstructured scenes.
Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domain
Venue: arXiv cs.CL 2024
Year: 2024

Hybrid search has emerged as an effective strategy to offset the limitations of different matching paradigms, especially in out-of-domain contexts where notable improvements in retrieval quality have been observed. However, existing research predominantly focuses on a limited set of retrieval methods, evaluated in pairs on domain-general datasets exclusively in English. In this work, we study the efficacy of hybrid search across a variety of prominent retrieval models within the unexplored field of law in the French language, assessing both zero-shot and in-domain scenarios. Our findings reveal that in a zero-shot context, fusing different domain-general models consistently enhances performance compared to using a standalone model, regardless of the fusion method. Surprisingly, when models are trained in-domain, we find that fusion generally diminishes performance relative to using the best single system, unless fusing scores with carefully tuned weights. These novel insights, among others, expand the applicability of prior findings across a new field and language, and contribute to a deeper understanding of hybrid search in non-English specialized domains.
A Survey and Comparison of Post-quantum and Quantum Blockchains
Venue: arXiv cs.CR 2024
Year: 2024

Blockchains have gained substantial attention from academia and industry for their ability to facilitate decentralized trust and communications. However, the rapid progress of quantum computing poses a significant threat to the security of existing blockchain technologies. Notably, the emergence of Shor's and Grover's algorithms raises concerns regarding the compromise of the cryptographic systems underlying blockchains. Consequently, it is essential to develop methods that reinforce blockchain technology against quantum attacks. In response to this challenge, two distinct approaches have been proposed. The first approach involves post-quantum blockchains, which aim to utilize classical cryptographic algorithms resilient to quantum attacks. The second approach explores quantum blockchains, which leverage the power of quantum computers and networks to rebuild the foundations of blockchains. This paper aims to provide a comprehensive overview and comparison of post-quantum and quantum blockchains while exploring open questions and remaining challenges in these domains. It offers an in-depth introduction, examines differences in blockchain structure, security, privacy, and other key factors, and concludes by discussing current research trends.
Polaris: Sampling from the Multigraph Configuration Model with Prescribed Color Assortativity
Venue: arXiv cs.SI 2024
Year: 2024

We introduce Polaris, a network null model for colored multi-graphs that preserves the Joint Color Matrix. Polaris is specifically designed for studying network polarization, where vertices belong to a side in a debate or a partisan group, represented by a vertex color, and relations have different strengths, represented by an integer-valued edge multiplicity. The key feature of Polaris is preserving the Joint Color Matrix (JCM) of the multigraph, which specifies the number of edges connecting vertices of any two given colors. The JCM is the basic property that determines color assortativity, a fundamental aspect in studying homophily and segregation in polarized networks. By using Polaris, network scientists can test whether a phenomenon is entirely explained by the JCM of the observed network or whether other phenomena might be at play. Technically, our null model is an extension of the configuration model: an ensemble of colored multigraphs characterized by the same degree sequence and the same JCM. To sample from this ensemble, we develop a suite of Markov Chain Monte Carlo algorithms, collectively named Polaris-*. It includes Polaris-B, an adaptation of a generic Metropolis-Hastings algorithm, and Polaris-C, a faster, specialized algorithm with higher acceptance probabilities. This new null model and the associated algorithms provide a more nuanced toolset for examining polarization in social networks, thus enabling statistically sound conclusions.
Debiasing Graph Representation Learning based on Information Bottleneck
Venue: arXiv cs.LG 2024
Year: 2024

Graph representation learning has shown superior performance in numerous real-world applications, such as finance and social networks. Nevertheless, most existing works might make discriminatory predictions due to insufficient attention to fairness in their decision-making processes. This oversight has prompted a growing focus on fair representation learning. Among recent explorations on fair representation learning, prior works based on adversarial learning usually induce unstable or counterproductive performance. To achieve fairness in a stable manner, we present the design and implementation of GRAFair, a new framework based on a variational graph auto-encoder. The crux of GRAFair is the Conditional Fairness Bottleneck, where the objective is to capture the trade-off between the utility of representations and sensitive information of interest. By applying variational approximation, we can make the optimization objective tractable. Particularly, GRAFair can be trained to produce informative representations of tasks while containing little sensitive information without adversarial training. Experiments on various real-world datasets demonstrate the effectiveness of our proposed method in terms of fairness, utility, robustness, and stability.
Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation
Venue: arXiv cs.LG 2024
Year: 2024

Federated Learning (FL) enables multiple clients to collaboratively train a model without sharing their local data. Yet the FL system is vulnerable to well-designed Byzantine attacks, which aim to disrupt the model training process by uploading malicious model updates. Existing robust aggregation rule-based defense methods overlook the diversity of magnitude and direction across different layers of the model updates, resulting in limited robustness performance, particularly in non-IID settings. To address these challenges, we propose the Layer-Adaptive Sparsified Model Aggregation (LASA) approach, which combines pre-aggregation sparsification with layer-wise adaptive aggregation to improve robustness. Specifically, LASA includes a pre-aggregation sparsification module that sparsifies updates from each client before aggregation, reducing the impact of malicious parameters and minimizing the interference from less important parameters for the subsequent filtering process. Based on sparsified updates, a layer-wise adaptive filter then adaptively selects benign layers using both magnitude and direction metrics across all clients for aggregation. We provide the detailed theoretical robustness analysis of LASA and the resilience analysis for the FL integrated with LASA. Extensive experiments are conducted on various IID and non-IID datasets. The numerical results demonstrate the effectiveness of LASA. Code is available at \url{https://github.com/JiiahaoXU/LASA}.
EarthGen: Generating the World from Top-Down Views
Venue: arXiv cs.CV 2024
Year: 2024

In this work, we present a novel method for extensive multi-scale generative terrain modeling. At the core of our model is a cascade of superresolution diffusion models that can be combined to produce consistent images across multiple resolutions. Pairing this concept with a tiled generation method yields a scalable system that can generate thousands of square kilometers of realistic Earth surfaces at high resolution. We evaluate our method on a dataset collected from Bing Maps and show that it outperforms super-resolution baselines on the extreme super-resolution task of 1024x zoom. We also demonstrate its ability to create diverse and coherent scenes via an interactive gigapixel-scale generated map. Finally, we demonstrate how our system can be extended to enable novel content creation applications including controllable world generation and 3D scene generation.
A practical generalization metric for deep networks benchmarking
Venue: arXiv cs.LG 2024
Year: 2024

There is an ongoing and dedicated effort to estimate bounds on the generalization error of deep learning models, coupled with an increasing interest with practical metrics that can be used to experimentally evaluate a model's ability to generalize. This interest is not only driven by practical considerations but is also vital for theoretical research, as theoretical estimations require practical validation. However, there is currently a lack of research on benchmarking the generalization capacity of various deep networks and verifying these theoretical estimations. This paper aims to introduce a practical generalization metric for benchmarking different deep networks and proposes a novel testbed for the verification of theoretical estimations. Our findings indicate that a deep network's generalization capacity in classification tasks is contingent upon both classification accuracy and the diversity of unseen data. The proposed metric system is capable of quantifying the accuracy of deep learning models and the diversity of data, providing an intuitive and quantitative evaluation method, a trade-off point. Furthermore, we compare our practical metric with existing generalization theoretical estimations using our benchmarking testbed. It is discouraging to note that most of the available generalization estimations do not correlate with the practical measurements obtained using our proposed practical metric. On the other hand, this finding is significant as it exposes the shortcomings of theoretical estimations and inspires new exploration.
Lecture Notes from the NaijaCoder Summer Camp
Venue: arXiv cs.CY 2024
Year: 2024

The NaijaCoder in-person summer camps are intensive programs for high school and pre-college students in Nigeria. The programs are meant to provide free instruction on the basics of algorithms and computer programming. In 2024, the camps were held in two locations within the country: (i) the Federal Capital Territory (F.C.T.), Abuja; and (ii) Lagos state. Both locations relied on the same set of notes for instructional purposes. We are providing these notes in a publicly-available medium for both students and teachers to review after the main in-person programs are over.
From Data to Insights: A Covariate Analysis of the IARPA BRIAR Dataset for Multimodal Biometric Recognition Algorithms at Altitude and Range
Venue: arXiv cs.CV 2024
Year: 2024

This paper examines covariate effects on fused whole body biometrics performance in the IARPA BRIAR dataset, specifically focusing on UAV platforms, elevated positions, and distances up to 1000 meters. The dataset includes outdoor videos compared with indoor images and controlled gait recordings. Normalized raw fusion scores relate directly to predicted false accept rates (FAR), offering an intuitive means for interpreting model results. A linear model is developed to predict biometric algorithm scores, analyzing their performance to identify the most influential covariates on accuracy at altitude and range. Weather factors like temperature, wind speed, solar loading, and turbulence are also investigated in this analysis. The study found that resolution and camera distance best predicted accuracy and findings can guide future research and development efforts in long-range/elevated/UAV biometrics and support the creation of more reliable and robust systems for national security and other critical domains.
Auditable and reusable crosswalks for fast, scaled integration of scattered tabular data
Venue: arXiv cs.DB 2024
Year: 2024

This paper presents an open-source curatorial toolkit intended to produce well-structured and interoperable data. Curation is divided into discrete components, with a schema-centric focus for auditable restructuring of complex and scattered tabular data to conform to a destination schema. Task separation allows development of software and analysis without source data being present. Transformations are captured as high-level sequential scripts describing schema-to-schema mappings, reducing complexity and resource requirements. Ultimately, data are transformed, but the objective is that any data meeting a schema definition can be restructured using a crosswalk. The toolkit is available both as a Python package, and as a 'no-code' visual web application. A visual example is presented, derived from a longitudinal study where scattered source data from hundreds of local councils are integrated into a single database.
It is Time to Develop an Auditing Framework to Promote Value Aware Chatbots
Venue: arXiv cs.CL 2024
Year: 2024

The launch of ChatGPT in November 2022 marked the beginning of a new era in AI, the availability of generative AI tools for everyone to use. ChatGPT and other similar chatbots boast a wide range of capabilities from answering student homework questions to creating music and art. Given the large amounts of human data chatbots are built on, it is inevitable that they will inherit human errors and biases. These biases have the potential to inflict significant harm or increase inequity on different subpopulations. Because chatbots do not have an inherent understanding of societal values, they may create new content that is contrary to established norms. Examples of concerning generated content includes child pornography, inaccurate facts, and discriminatory posts. In this position paper, we argue that the speed of advancement of this technology requires us, as computer and data scientists, to mobilize and develop a values-based auditing framework containing a community established standard set of measurements to monitor the health of different chatbots and LLMs. To support our argument, we use a simple audit template to share the results of basic audits we conduct that are focused on measuring potential bias in search engine style tasks, code generation, and story generation. We identify responses from GPT 3.5 and GPT 4 that are both consistent and not consistent with values derived from existing law. While the findings come as no surprise, they do underscore the urgency of developing a robust auditing framework for openly sharing results in a consistent way so that mitigation strategies can be developed by the academic community, government agencies, and companies when our values are not being adhered to. We conclude this paper with recommendations for value-based strategies for improving the technologies.