Topological Data Analysis (TDA) has emerged as a powerful tool for extracting meaningful features from complex data structures, driving advancements in neuroscience, biology, machine learning, and financial modeling. However, its integration with time-series forecasting remains underexplored due to three key challenges: limited use of temporal dependencies in topological features, computational bottlenecks in persistent homology, and the deterministic nature of TDA pipelines that restrict generalized learning. To address these issues, we propose the Topological Information Supervised (TIS) Prediction framework, which employs neural networks and Conditional Generative Adversarial Networks (CGANs) to generate synthetic topological features that preserve distributional properties while reducing computation time. A novel training strategy incorporating a topological consistency loss further enhances predictive accuracy. We evaluate TIS across recurrent and Transformer-based models, showing that integrating topological information yields consistent performance improvements across diverse architectures and forecasting horizons. While the magnitude of gains varies, tending to be more modest for recent architectures, the results highlight TIS as a lightweight, complementary inductive bias that augments existing models beyond architectural refinements. This work advances TDA-based time-series prediction and opens new directions for embedding topological insights into deep learning frameworks.

Transparent object grasping remains a persistent challenge in robotics, largely due to the difficulty of acquiring precise 3D information. Conventional optical 3D sensors struggle to capture transparent objects, and machine learning methods are often hindered by their reliance on high-quality datasets. Leveraging NeRF's capability for continuous spatial opacity modeling, our proposed architecture integrates a NeRF-based approach for reconstructing the 3D information of transparent objects. Despite this, certain portions of the reconstructed 3D information may remain incomplete. To address these deficiencies, we introduce a shape-prior-driven completion mechanism, further refined by a geometric pose estimation method we have developed. This allows us to obtain a complete and reliable 3D information of transparent objects. Utilizing this refined data, we perform scene-level grasp prediction and deploy the results in real-world robotic systems. Experimental validation demonstrates the efficacy of our architecture, showcasing its capability to reliably capture 3D information of various transparent objects in cluttered scenes, and correspondingly, achieve high-quality, stable, and executable grasp predictions.

Human-machine interactions (HMIs), e.g., touchscreens, are essential for users to interact with mobile devices. They are also beneficial in resisting emerging active attacks, which aim at maliciously controlling mobile devices, e.g., smartphones and tablets. With touchscreen-like HMIs, users can notice and interrupt malicious actions conducted by the attackers timely and perform necessary countermeasures, e.g., tapping the ‘Quit’ button on the touchscreen. However, the effect of HMI-oriented active attacks has not been investigated yet. In this paper, we present a practical attack towards touch-based devices, namely Expelliarmus. It reveals a new attack surface of active attacks for hijacking users’ operations and thus taking full control over victim devices. Expelliarmus neutralizes users’ touch commands by producing a reverse current via electromagnetic interference (EMI). Since the reverse current offsets the current change caused by a touch, the touchscreen detects no current change and thus ignores users’ commands. Besides this basic denial-of-service attack, we also realize a target cancellation attack, which can neutralize target commands, e.g., ‘Quit’ without interference in irrelevant operations. Thus, the active attack can be completely performed without interruption from users, even if they are alerted by the abnormal events. Extensive evaluations demonstrate the effectiveness of Expelliarmus on 29 off-the-shelf devices.