AToM: Amortized Text-to-Mesh using 2D Diffusion

We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously. In contrast to existing text-to-3D methods that often entail time-consuming per-prompt optimization and commonly output representations other than polygonal meshes, AToM directly generates high-quality textured meshes in less than 1 second with around 10 times reduction in the training cost, and generalizes to unseen prompts. Our key idea is a novel triplane-based text-to-mesh architecture with a two-stage amortized optimization strategy that ensures stable training and enables scalability. Through extensive experiments on various prompt benchmarks, AToM significantly outperforms state-of-the-art amortized approaches with over 4 times higher accuracy (in DF415 dataset) and produces more distinguishable and higher-quality 3D outputs. AToM demonstrates strong generalizability, offering finegrained 3D assets for unseen interpolated prompts without further optimization during inference, unlike per-prompt solutions.

CF4J: Collaborative Filtering for Java

Recommender Systems (RS) provide a relevant tool to mitigate the information overload problem. A large number of researchers have published hundreds of papers to improve different RS features. It is advisable to use RS frameworks that simplify RS researchers: a) to design and implement recommendations methods and, b) to speed up the execution time of the experiments. In this paper, we present CF4J, a Java library designed to carry out Collaborative Filtering based RS research experiments. CF4J has been designed from researchers to researchers. It allows: a) RS datasets reading, b) full and easy access to data and intermediate or final results, c) to extend their main functionalities, d) to concurrently execute the implemented methods, and e) to provide a thorough evaluation for the implementations by quality measures. In summary, CF4J serves as a library specifically designed for the research trial and error process.

Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning

Addressing biases in AI models is crucial for ensuring fair and accurate predictions. However, obtaining large, unbiased datasets for training can be challenging. This paper proposes a comprehensive approach using multiple methods to remove bias in AI models, with only a small dataset and a potentially biased pretrained model. We train multiple models with the counter-bias of the pre-trained model through data splitting, local training, and regularized fine-tuning, gaining potentially counter-biased models. Then, we employ ensemble learning for all models to reach unbiased predictions. To further accelerate the inference time of our ensemble model, we conclude our solution with knowledge distillation that results in a single unbiased neural network. We demonstrate the effectiveness of our approach through experiments on the CIFAR10 and HAM10000 datasets, showcasing promising results. This work contributes to the ongoing effort to create more unbiased and reliable AI models, even with limited data availability.

Scalable Federated Unlearning via Isolated and Coded Sharding

Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models. However, the federated unlearning process often introduces extensive storage overhead and consumes substantial computational resources, thus hindering its implementation in practice. To address this issue, this paper proposes a scalable federated unlearning framework based on isolated sharding and coded computing. We first divide distributed clients into multiple isolated shards across stages to reduce the number of clients being affected. Then, to reduce the storage overhead of the central server, we develop a coded computing mechanism by compressing the model parameters across different shards. In addition, we provide the theoretical analysis of time efficiency and storage effectiveness for the isolated and coded sharding. Finally, extensive experiments on two typical learning tasks, i.e., classification and generation, demonstrate that our proposed framework can achieve better performance than three state-of-the-art frameworks in terms of accuracy, retraining time, storage overhead, and F1 scores for resisting membership inference attacks.

Clinically meaningful timeline summarisation in social media for mental health monitoring

We introduce the new task of clinically meaningful summarisation of social media user timelines, appropriate for mental health monitoring. We develop a novel approach for unsupervised abstractive summarisation that produces a two-layer summary consisting of both high-level information, covering aspects useful to clinical experts, as well as accompanying time sensitive evidence from a user's social media timeline. A key methodological novelty comes from the timeline summarisation component based on a version of hierarchical variational autoencoder (VAE) adapted to represent long texts and guided by LLM-annotated key phrases. The resulting timeline summary is input into a LLM (LLaMA-2) to produce the final summary containing both the high level information, obtained through instruction prompting, as well as corresponding evidence from the user's timeline. We assess the summaries generated by our novel architecture via automatic evaluation against expert written summaries and via human evaluation with clinical experts, showing that timeline summarisation by TH-VAE results in logically coherent summaries rich in clinical utility and superior to LLM-only approaches in capturing changes over time.

GOODAT: Towards Test-time Graph Out-of-Distribution Detection

Graph neural networks (GNNs) have found widespread application in modeling graph data across diverse domains. While GNNs excel in scenarios where the testing data shares the distribution of their training counterparts (in distribution, ID), they often exhibit incorrect predictions when confronted with samples from an unfamiliar distribution (out-of-distribution, OOD). To identify and reject OOD samples with GNNs, recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN. Despite their effectiveness, these methods come with heavy training resources and costs, as they need to optimize the GNN-based models on training data. Moreover, their reliance on modifying the original GNNs and accessing training data further restricts their universality. To this end, this paper introduces a method to detect Graph Out-of-Distribution At Test-time (namely GOODAT), a data-centric, unsupervised, and plug-and-play solution that operates independently of training data and modifications of GNN architecture. With a lightweight graph masker, GOODAT can learn informative subgraphs from test samples, enabling the capture of distinct graph patterns between OOD and ID samples. To optimize the graph masker, we meticulously design three unsupervised objective functions based on the graph information bottleneck principle, motivating the masker to capture compact yet informative subgraphs for OOD detection. Comprehensive evaluations confirm that our GOODAT method outperforms state-of-the-art benchmarks across a variety of real-world datasets. The code is available at Github: https://github.com/Ee1s/GOODAT

GS++: Error Analyzing and Optimal Gaussian Splatting

3D Gaussian Splatting has garnered extensive attention and application in real-time neural rendering. Concurrently, concerns have been raised about the limitations of this technology in aspects such as point cloud storage, performance , and robustness in sparse viewpoints , leading to various improvements. However, there has been a notable lack of attention to the projection errors introduced by the local affine approximation inherent in the splatting itself, and the consequential impact of these errors on the quality of photo-realistic rendering. This paper addresses the projection error function of 3D Gaussian Splatting, commencing with the residual error from the first-order Taylor expansion of the projection function $\phi$. The analysis establishes a correlation between the error and the Gaussian mean position. Subsequently, leveraging function optimization theory, this paper analyzes the function's minima to provide an optimal projection strategy for Gaussian Splatting referred to Optimal Gaussian Splatting. Experimental validation further confirms that this projection methodology reduces artifacts, resulting in a more convincingly realistic rendering.

Short: Benchmarking transferable adversarial attacks

The robustness of deep learning models against adversarial attacks remains a pivotal concern. This study presents, for the first time, an exhaustive review of the transferability aspect of adversarial attacks. It systematically categorizes and critically evaluates various methodologies developed to augment the transferability of adversarial attacks. This study encompasses a spectrum of techniques, including Generative Structure, Semantic Similarity, Gradient Editing, Target Modification, and Ensemble Approach. Concurrently, this paper introduces a benchmark framework \textit{TAA-Bench}, integrating ten leading methodologies for adversarial attack transferability, thereby providing a standardized and systematic platform for comparative analysis across diverse model architectures. Through comprehensive scrutiny, we delineate the efficacy and constraints of each method, shedding light on their underlying operational principles and practical utility. This review endeavors to be a quintessential resource for both scholars and practitioners in the field, charting the complex terrain of adversarial transferability and setting a foundation for future explorations in this vital sector. The associated codebase is accessible at: https://github.com/KxPlaug/TAA-Bench

Multi-agent Path Finding for Cooperative Autonomous Driving

Anticipating possible future deployment of connected and automated vehicles (CAVs), cooperative autonomous driving at intersections has been studied by many works in control theory and intelligent transportation across decades. Simultaneously, recent parallel works in robotics have devised efficient algorithms for multi-agent path finding (MAPF), though often in environments with simplified kinematics. In this work, we hybridize insights and algorithms from MAPF with the structure and heuristics of optimizing the crossing order of CAVs at signal-free intersections. We devise an optimal and complete algorithm, Order-based Search with Kinematics Arrival Time Scheduling (OBS-KATS), which significantly outperforms existing algorithms, fixed heuristics, and prioritized planning with KATS. The performance is maintained under different vehicle arrival rates, lane lengths, crossing speeds, and control horizon. Through ablations and dissections, we offer insight on the contributing factors to OBS-KATS's performance. Our work is directly applicable to many similarly scaled traffic and multi-robot scenarios with directed lanes.

Rapid Integrator for a Class of Multi-Contact Systems

Many problems in robotics involve creating or breaking multiple contacts nearly simultaneously or in an indeterminate order. We present a novel general purpose numerical integrator based on the theory of Event Selected Systems (ESS). Many multicontact models are ESS, which has recently been shown to imply that despite a discontinuous vector field, the flow of these systems is continuous, piecewise smooth, and has a well defined orbital derivative for all trajectories, which can be rapidly computed. We provide an elementary proof that our integrator is first-order accurate and verify numerically that it is in fact second-order accurate as its construction anticipated. We also compare our integrator, implemented in NumPy, to a MuJoCo simulation on models with 2 to 100 contacts, and confirm that the increase in simulation time per contact is nearly identical. The results suggest that this novel integrator can be invaluable for modelling and control in many robotics applications.