The utility of an explanation method critically depends on its fidelity to the underlying machine learning model. Especially in high-stakes medical settings, clinicians and regulators require explanations that faithfully reflect the model's decision process. Existing fidelity metrics such as Infidelity rely on Monte Carlo approximation, which demands numerous model evaluations and introduces uncertainty due to random sampling. This work proposes a novel metric for evaluating the fidelity of local feature attribution methods by modifying the existing Prediction Change (PC) metric within the Guided Perturbation Experiment. By incorporating the direction of both perturbation and attribution, the proposed Directed Prediction Change (DPC) metric achieves an almost tenfold speedup and eliminates randomness, resulting in a deterministic and trustworthy evaluation procedure that measures the same property as local Infidelity. DPC is evaluated on two datasets (skin lesion images and financial tabular data), two black-box models, seven explanation algorithms, and a wide range of hyperparameters. Across $4\,744$ distinct explanations, the results demonstrate that DPC, together with PC, enables a holistic and computationally efficient evaluation of both baseline-oriented and local feature attribution methods, while providing deterministic and reproducible outcomes.

Ireland's data privacy regulator is investigating Elon Musk's X. The country's Data Protection Commission (DPC) said on Friday (via Reuters) that it's opening an inquiry into the social platform's use of European users' public posts to train its Grok AI chatbot. In this case, Ireland handles EU regulation enforcement because X's European headquarters are in Dublin. The DPC said it will probe "the processing of personal data comprised in publicly-accessible posts posted on the'X' social media platform by EU/EEA users." Under Europe's General Data Protection Regulation (GDPR) rules, Ireland has the legal muscle to fine X up to four percent of its global revenue.

Center-based clustering algorithms (e.g., K-means) are popular for clustering tasks, but they usually struggle to achieve high accuracy on complex datasets. We believe the main reason is that traditional center-based clustering algorithms identify only one clustering center in each cluster. Once the distribution of the dataset is complex, a single clustering center cannot strongly represent distant objects within the cluster. How to optimize the existing center-based clustering algorithms will be valuable research. In this paper, we propose a general optimization method called ECAC, and it can optimize different center-based clustering algorithms. ECAC is independent of the clustering principle and is embedded as a component between the center process and the category assignment process of center-based clustering algorithms. Specifically, ECAC identifies several extended-centers for each clustering center. The extended-centers will act as relays to expand the representative capability of the clustering center in the complex cluster, thus improving the accuracy of center-based clustering algorithms. We conducted numerous experiments to verify the robustness and effectiveness of ECAC. ECAC is robust to diverse datasets and diverse clustering centers. After ECAC optimization, the accuracy (NMI as well as RI) of center-based clustering algorithms improves by an average of 33.4% and 64.1%, respectively, and even K-means accurately identifies complex-shaped clusters.

Model Predictive Control (MPC) is effective at generating safe control strategies in constrained scenarios, at the cost of computational complexity. This is especially the case in robots that require high sampling rates and have limited computing resources. Differentiable Predictive Control (DPC) trains offline a neural network approximation of the parametric MPC problem leading to computationally efficient online control laws at the cost of losing safety guarantees. DPC requires a differentiable model, and performs poorly when poorly conditioned. In this paper we propose a system decomposition technique based on relative degree to overcome this. We also develop a novel safe set generation technique based on the DPC training dataset and a novel event-triggered predictive safety filter which promotes convergence towards the safe set. Our empirical results on a quadcopter demonstrate that the DPC control laws have comparable performance to the state-of-the-art MPC whilst having up to three orders of magnitude reduction in computation time and satisfy safety requirements in a scenario that DPC was not trained on.

X will permanently avoid training its AI chatbot Grok on the public posts of users in the European Union and European Economic Area following pressure from a regulator in the region. Last month, the company temporarily suspended the practice after Ireland's Data Protection Commission (DPC) opened High Court proceedings against it. X has now made that commitment a permanent one, which prompted the DPC to end its legal action. The DPC, which is the chief EU regulator for X, raised concerns that X may have been violating data protection rules and users' rights. Since May, X had offered users the option to opt-out of having their public posts being used to train Grok, implying that the company had enabled that setting for public accounts by default.

In this paper, we propose a novel method for plane clustering specialized in cluttered scenes using an RGB-D camera and validate its effectiveness through robot grasping experiments. Unlike existing methods, which focus on large-scale indoor structures, our approach -- Multi-Object RANSAC emphasizes cluttered environments that contain a wide range of objects with different scales. It enhances plane segmentation by generating subplanes in Deep Plane Clustering (DPC) module, which are then merged with the final planes by post-processing. DPC rearranges the point cloud by voting layers to make subplane clusters, trained in a self-supervised manner using pseudo-labels generated from RANSAC. Multi-Object RANSAC demonstrates superior plane instance segmentation performances over other recent RANSAC applications. We conducted an experiment on robot suction-based grasping, comparing our method with vision-based grasping network and RANSAC applications. The results from this real-world scenario showed its remarkable performance surpassing the baseline methods, highlighting its potential for advanced scene understanding and manipulation.

Precise glucose level monitoring is critical for people with diabetes to avoid serious complications. While there are several methods for continuous glucose level monitoring, research on maintenance devices is limited. To mitigate the gap, we provide a novel neural control system for continuous glucose monitoring and management that uses differential predictive control. Our approach, led by a sophisticated neural policy and differentiable modeling, constantly adjusts insulin supply in real-time, thereby improving glucose level optimization in the body. This end-to-end method maximizes efficiency, providing personalized care and improved health outcomes, as confirmed by empirical evidence.

Meta has been hit with a €265 million ($277 million) fine for failing to prevent millions of Facebook users' mobile phone numbers and other data from being scraped and dumped online, Independent.ie It's the second fine levied by the Irish Data Protection Commission (DPC) in just the past few months, following a €405 million ($402 million at the time) penalty issued in September. In just the last 18 months, Meta has tallied nearly €1 billion in fines. The penalty was issued in response to the leak of 533 million Facebook users' data reported in April last year. That included phone numbers, birth dates, email addresses and locations, information that could be exploited in phishing and other attacks.

We propose a novel gray-box modeling algorithm for physical systems governed by stochastic differential equations (SDE). The proposed approach, referred to as the Deep Physics Corrector (DPC), blends approximate physics represented in terms of SDE with deep neural network (DNN). The primary idea here is to exploit DNN to model the missing physics. We hypothesize that combining incomplete physics with data will make the model interpretable and allow better generalization. The primary bottleneck associated with training surrogate models for stochastic simulators is often associated with selecting the suitable loss function. Among the different loss functions available in the literature, we use the conditional maximum mean discrepancy (CMMD) loss function in DPC because of its proven performance. Overall, physics-data fusion and CMMD allow DPC to learn from sparse data. We illustrate the performance of the proposed DPC on four benchmark examples from the literature. The results obtained are highly accurate, indicating its possible application as a surrogate model for stochastic simulators.

Dynamic polarization control (DPC) is beneficial for many optical applications. It uses adjustable waveplates to perform automatic polarization tracking and manipulation. Efficient algorithms are essential to realizing an endless polarization control process at high speed. However, the standard gradientbased algorithm is not well analyzed. Here we model the DPC with a Jacobian-based control theory framework that finds a lot in common with robot kinematics. We then give a detailed analysis of the condition of the Stokes vector gradient as a Jacobian matrix. We identify the multi-stage DPC as a redundant system enabling control algorithms with null-space operations. An efficient, reset-free algorithm can be found. We anticipate more customized DPC algorithms to follow the same framework in various optical systems.