The Cox Proportional Hazards (CPH) model has long been the preferred survival model for its explainability. However, to increase its predictive power beyond its linear log-risk, it was extended to utilize deep neural networks, sacrificing its explainability. In this work, we explore the potential of self-explaining neural networks (SENN) for survival analysis. We propose a new locally explainable Cox proportional hazards model, named CoxSE, by estimating a locally-linear log-hazard function using the SENN. We also propose a modification to the Neural additive (NAM) model, hybrid with SENN, named CoxSENAM, which enables the control of the stability and consistency of the generated explanations. Several experiments using synthetic and real datasets are presented, benchmarking CoxSE and CoxSENAM against a NAM-based model, a DeepSurv model explained with SHAP, and a linear CPH model. The results show that, unlike the NAM-based model, the SENN-based model can provide more stable and consistent explanations while maintaining the predictive power of the black-box model. The results also show that, due to their structural design, NAM-based models demonstrate better robustness to non-informative features. Among the models, the hybrid model exhibits the best robustness. Full implementation is available on GitHub

Vision Language Models (VLMs) have shown remarkable capabilities in multimodal under-standing, yet their susceptibility to adversarial perturbations poses a significant threat to their reliability in real-world applications. Despite often being imperceptible to humans, these perturbations can drastically alter model outputs, leading to erroneous interpretations and decisions. This paper introduces DiffCAP, a novel diffusion-based purification strategy that can effectively neutralize adversarial corruptions in VLMs. We theoretically establish a provable recovery region in the forward diffusion process and meanwhile quantify the convergence rate of semantic variation with respect to VLMs. These findings manifest that adversarial effects monotonically fade as diffusion unfolds. Guided by this principle, DiffCAP leverages noise injection with a similarity threshold of VLM embeddings as an adaptive criterion, before reverse diffusion restores a clean and reliable representation for VLM inference. Through extensive experiments across six datasets with three VLMs under varying attack strengths in three task scenarios, we show that DiffCAP outperforms existing defense techniques by a substantial margin. Notably, DiffCAP significantly reduces both hyperparameter tuning complexity and the required diffusion time, thereby accelerating the denoising process. Equipped with theorems and empirical support, DiffCAP provides a robust and practical solution for securely deploying VLMs in adversarial environments. The source code is available at https://github.com/JasonFu1998/DiffCAP

Accurate energy consumption prediction is crucial for optimizing the operation of electric commercial heavy-duty vehicles, e.g., route planning for charging. Moreover, understanding why certain predictions are cast is paramount for such a predictive model to gain user trust and be deployed in practice. Since commercial vehicles operate differently as transportation tasks, ambient, and drivers vary, a heterogeneous population is expected when building an AI system for forecasting energy consumption. The dependencies between the input features and the target values are expected to also differ across sub-populations. One well-known example of such a statistical phenomenon is Simpson’s paradox. In this paper, we illustrate that such a setting poses a challenge for existing XAI methods that produce global feature statistics, e.g., LIME or SHAP, causing them to yield misleading results. We demonstrate a potential solution by training multiple regression models on subsets of data via a divide-and-conquer approach. It not only leads to superior regression performance but also more relevant and consistent LIME explanations. Given that the employed groupings correspond to relevant sub-populations, the associations between the input features and the target values are consistent within each cluster but different across clusters. Experiments on both synthetic and real-world datasets show that such splitting of a complex problem into simpler ones yields better regression performance and interpretability

Anomaly detection is an essential component for ensuring the safety and reliability of critical systems. Currently, most machine learning-based anomaly detection approaches rely purely on correlations among sensor signals, rather than causal relations, making them susceptible to spurious associations. This limitation can lead to poor generalization and unreliable anomaly detection in practical scenarios. To address this, we propose an anomaly detection approach which leverages score-based causal discovery, NTS-DAGMA. This causal discovery method advances over prior work by combining the network architecture from NTS-NOTEARS with the acyclicity constraint from DAGMA. Like other prediction-based anomaly detection methods, it can predict future states; however, it does this by learning and utilizing causal relations in the time series data. Through comprehensive experiments, we demonstrate that: (i) on causal discovery tasks NTS-NOTEARS and NTS-DAGMA achieve similar performances; (ii) on anomaly detection tasks NTS-NOTEARS and NTS-DAGMA also perform similar to each other, and have comparable performance to state-of-the-art ML approaches; and (iii) most importantly, our results show that NTS-DAGMA provides causally meaningful models: detects anomalies which propagate through child nodes in agreement with the inferred causal graph. Incorporating causal structure into the model enables improved interpretability and aligns anomaly detection with the physical dynamics of the system

Statistical modeling of multivariate time-series data poses significant challenges due to their high dimensionality and complex inter-variable relationships. Reliable forecasts or anomaly detection on these datasets require capturing such relationships within and between the features. While traditional deep learning architectures are good at capturing temporal non-linear patterns within features, they are less efficient at modeling inter-variable relationships explicitly structured as graphs-a capability where Graph Neural Networks (GNNs) excel. Inspired by the success of GNNs, Graph Deviation Network (GDN) was originally proposed for anomaly detection on industrial multivariate time-series data. After proving its merits through experiments with real-world data, GDN gained significant popularity in the research community, claiming to learn the hidden graph structure in any multivariate time-series data. Various modifications to GDN were proposed over the years, but essentially all of them kept its Graph Structure Learning (GSL) module intact. However, until now, this module has never been rigorously evaluated. This work scrutinizes the contribution of the GSL module. Our experiments reveal that the graph learned by GSL is relatively ineffective, and the key to the overall performance achieved by GDN lies almost entirely in the downstream Graph Attention Network (GAT) module. We hope our findings will garner attention for further development of the GSL module of GDN, whose fidelity can improve the performance of GDN variants. 

In industrial settings, anomalies often indicate critical events such as equipment failures or system faults. These events are rare but highly impactful and require urgent attention and often have financial or safety consequences. Deep learning models, especially Graph Neural Networks (GNNs) have gained prominence due to their ability to capture intricate dependencies between sensor signals as graphs. Understanding the reasons behind the predicted anomalies is essential for effective response, however, the black-box nature of GNNs poses a significant challenge. To address this limitation, we propose a counterfactual explanation framework that offers human-understandable insights by identifying minimal input changes capable of altering the model’s decision. Our method employs a two-stage process: (i) selecting the most relevant nodes contributing to the anomaly using graphs, and (ii) generating counterfactual instances by perturbing only these selected nodes. We evaluate our approach on two real-world CPS datasets: SWaT and WADI. Experimental results show that our method produces significantly sparser explanations compared to existing techniques. Additionally, our ablation study shows using graph information for node selection helps in generating sparse explanations. These counterfactual insights enhance model transparency, support better operational decision-making, and ultimately foster greater trust in anomaly detection systems.

In the ever-evolving adversarial machine learning landscape, developing effective defenses against patch attacks has become a critical challenge, necessitating reliable solutions to safeguard real-world AI systems. Although diffusion models have shown remarkable capacity in image synthesis and have been recently utilized to counter lp-norm bounded attacks, their potential in mitigating localized patch attacks remains largely underexplored. In this work, we propose DiffPAD, a novel framework that harnesses the power of diffusion models for adversarial patch decontamination. DiffPAD first performs super-resolution restoration on downsampled input images, then adopts binarization, dynamic thresholding scheme and sliding window for effective localization of adversarial patches. Such a design is inspired by the theoretically derived correlation between patch size and diffusion restoration error that is generalized across diverse patch attack scenarios. Finally, DiffPAD applies inpainting techniques to the original input images with the estimated patch region being masked. By integrating closed-form solutions for super-resolution restoration and image inpainting into the conditional reverse sampling process of a pre-trained diffusion model, DiffPAD obviates the need for text guidance or fine-tuning. Through comprehensive experiments, we demonstrate that DiffPAD not only achieves state-of-the-art adversarial robustness against patch attacks but also excels in recovering naturalistic images without patch remnants. The source code is available at https://github.com/JasonFu1998/DiffPAD. 

Venue-specific training has, over the years, proven to be a key asset for sailing teams performing at major championships and at the Olympics. A comprehensive understanding of the environmental features and possible weather scenarios in an outdoor sport like sailing can provide athletes with a significant strategic advantage. At the same time, GPS tracking is becoming a readily available technology that athletes use both in training and during races to analyse their own and their competitor’s performance. This work couples environmental and meteorological data with GPS tracks for Olympic sailing classes, linking weather features with strategic decisions on the race track. We propose a greedy algorithm to search for an optimal route based on weather forecasts to present the best strategy prior to the race. Our results show the potential of this approach to provide valuable decision support for athletes in Olympic sailing competitions, demonstrated for the 470 Olympic class.

Accurate energy consumption prediction is crucial for optimising the operation of electric commercial heavy-duty vehicles, particularly for efficient route planning, refining charging strategies, and ensuring optimal truck configuration for specific tasks. This study investigates the application of multi-task curriculum learning to enhance machine learning models for forecasting the energy consumption of various onboard systems in electric vehicles. Multi-task learning, unlike traditional training approaches, leverages auxiliary tasks to provide additional training signals, which has been shown to enhance predictive performance in many domains. By further incorporating curriculum learning, where simpler tasks are learned before progressing to more complex ones, neural network training becomes more efficient and effective. We evaluate the suitability of these methodologies in the context of electric vehicle energy forecasting, examining whether the combination of multi-task learning and curriculum learning enhances algorithm generalisation, even with limited training data. We primarily focus on understanding the efficacy of different curriculum learning strategies, including sequential learning and progressive continual learning, using complex, real-world industrial data. Our research further explores a set of auxiliary tasks designed to facilitate the learning process by targeting key consumption characteristics projected into future time frames. The findings illustrate the potential of multi-task curriculum learning to advance energy consumption forecasting, significantly contributing to the optimisation of electric heavy-duty vehicle operations. This work offers a novel perspective on integrating advanced machine learning techniques to enhance energy efficiency in the exciting field of electromobility. 

Batteries are a safety-critical and the most expensive component for electric buses (EBs). Monitoring their condition, or the state of health (SoH), is crucial for ensuring the reliability of EB operation. However, EBs come in many models and variants, including different mechanical configurations, and deploy to operate under various conditions. Developing new degradation models for each combination of settings and faults quickly becomes challenging due to the unavailability of data for novel conditions and the low evidence for less popular vehicle populations. Therefore, building machine learning models that can generalize to new and unseen settings becomes a vital challenge for practical deployment. This study aims to develop and evaluate feature selection methods for robust machine learning models that allow estimating the SoH of batteries across various settings of EB configuration and usage. Building on our previous work, we propose two approaches, a genetic algorithm for domain invariant features (GADIF) and causal discovery for selecting invariant features (CDIF). Both aim to select features that are invariant across multiple domains. While GADIF utilizes a specific fitness function encompassing both task performance and domain shift, the CDIF identifies pairwise causal relations between features and selects the common causes of the target variable across domains. Experimental results confirm that selecting only invariant features leads to a better generalization of machine learning models to unseen domains. The contribution of this work comprises the two novel invariant feature selection methods, their evaluation on real-world EBs data, and a comparison against state-of-the-art invariant feature selection methods. Moreover, we analyze how the selected features vary under different settings.