KYOTO - Nidec President Mitsuya Kishida has said the major Japanese motor maker will suspend business acquisitions for the time being to focus its efforts on reconstructing the firm rocked by accounting and product quality fraud. Business acquisitions have been a growth driver for Nidec, based in Kyoto. "I will work on rebuilding our company's governance system," Kishida said in an interview Friday, showing a plan to spend ¥130 billion over five years on measures to prevent irregularities. A panel of outside experts that investigated the accounting fraud has concluded that excessive pressure from Nidec's founder, Shigenobu Nagamori, on company staff to meet performance targets was among the factors behind the irregularities. Pointing out that Nidec had "a corporate culture to pursue short-term profits," Kishida said, "We will build a system that makes it impossible to commit irregularities regarding accounting and product quality control." On future business management, he said, "We will review our operations, including the possibility of ceding what we have in our group to partner entities," suggesting that consolidating some of its existing operations could be an option.

We introduce TRiCo, a novel triadic game-theoretic co-training framework that rethinks the structure of semi-supervised learning by incorporating a teacher, two students, and an adversarial generator into a unified training paradigm. Unlike existing co-training or teacher-student approaches, TRiCo formulates SSL as a structured interaction among three roles: (i) two student classifiers trained on frozen, complementary representations, (ii) a meta-learned teacher that adaptively regulates pseudo-label selection and loss balancing via validation-based feedback, and (iii) a non-parametric generator that perturbs embeddings to uncover decision boundary weaknesses. Pseudo-labels are selected based on mutual information rather than confidence, providing a more robust measure of epistemic uncertainty. This triadic interaction is formalized as a Stackelberg game, where the teacher leads strategy optimization and students follow under adversarial perturbations. By addressing key limitations in existing SSL frameworks, such as static view interactions, unreliable pseudo-labels, and lack of hard sample modeling, TRiCo provides a principled and generalizable solution. Extensive experiments on CIFAR-10, SVHN, STL-10, and ImageNet demonstrate that TRiCo consistently achieves state-of-the-art performance in low-label regimes, while remaining architecture-agnostic and compatible with frozen vision backbones.Code:https://github.com/HoHongYeung/NeurIPS25-TRiCo.

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" To this end, we investigate extensive experiments on

Speed bumps and potholes are the most common road anomalies, significantly affecting ride comfort and vehicle stability. Preview-based suspension control mitigates their impact by detecting such irregularities in advance and adjusting suspension parameters proactively. Accurate and real-time detection is essential, but embedded deployment is constrained by limited computational resources and the small size of targets in input images.To address these challenges, this paper proposes SBP-YOLO, an efficient detection framework for speed bumps and potholes in embedded systems. Built upon YOLOv11n, it integrates GhostConv and VoVGSCSPC modules in the backbone and neck to reduce computation while enhancing multi-scale semantic features. A P2-level branch improves small-object detection, and a lightweight and efficient detection head (LEDH) maintains accuracy with minimal overhead. A hybrid training strategy further enhances robustness under varying road and environmental conditions, combining NWD loss, BCKD knowledge distillation, and Albumentations-based augmentation. Experiments show that SBP-YOLO achieves 87.0% mAP, outperforming the YOLOv11n baseline by 5.8%. After TensorRT FP16 quantization, it runs at 139.5 FPS on Jetson AGX Xavier, yielding a 12.4% speedup over the P2-enhanced YOLOv11. These results demonstrate the framework's suitability for fast, low-latency road condition perception in embedded suspension control systems.

Engineering design is traditionally performed by hand: an expert makes design proposals based on past experience, and these proposals are then tested for compliance with certain target specifications. Testing for compliance is performed first by computer simulation using what is called a discipline model. Such a model can be implemented by a finite element analysis, multibody systems approach, etc. Designs passing this simulation are then considered for physical prototyping. The overall process may take months, and is a significant cost in practice. We have developed a Bayesian optimization system for partially automating this process by directly optimizing compliance with the target specification with respect to the design parameters. The proposed method is a general framework for computing a generalized inverse of a high-dimensional non-linear function that does not require e.g. gradient information, which is often unavailable from discipline models. We furthermore develop a two-tier convergence criterion based on (i) convergence to a solution optimally satisfying all specified design criteria, or (ii) convergence to a minimum-norm solution. We demonstrate the proposed approach on a vehicle chassis design problem motivated by an industry setting using a state-of-the-art commercial discipline model. We show that the proposed approach is general, scalable, and efficient, and that the novel convergence criteria can be implemented straightforwardly based on existing concepts and subroutines in popular Bayesian optimization software packages.

--While current onboard state estimation methods are adequate for most driving and safety-related applications, they do not provide insights into the interaction between tires and road surfaces. This paper explores a novel communication concept for efficiently transmitting integrated wheel sensor data from an ESP32 microcontroller . Our proposed approach utilizes a publish-subscribe system, surpassing comparable solutions in the literature regarding data transmission volume. We tested this approach on a drum tire test rig with our prototype sensors system utilizing a diverse selection of sample frequencies between 1 Hz and 32 000 Hzto demonstrate the efficacy of our communication concept. The implemented prototype sensor showcases minimal data loss, approximately 0. 1 % of the sampled data, validating the reliability of our developed communication system. This work contributes to advancing real-time data acquisition, providing insights into optimizing integrated wheel sensor communication. A. Motivation Intelligent transportation systems rely on sensors to estimate their own state and that of surrounding objects. Traditional onboard methods, using inertial measurement units (IMUs), wheel encoders, and Global Navigation Satellite Systems (GNSS), operate at frequencies from 1 Hz to several hundred Hz, providing sufficient accuracy for trajectory reconstruction and stability control. However, these methods do not directly capture tire-road interactions. Even safety systems like an-tilock brake (ABS) and electronic stability programs (ESP) rely on conservative assumptions about forces and friction rather than real-time estimation.

Despite exceptional capabilities in knowledge-intensive tasks, Large Language Models (LLMs) face a critical gap in understanding how they internalize new knowledge, particularly how to structurally embed acquired knowledge in their neural computations. We address this issue through the lens of knowledge circuit evolution, identifying computational subgraphs that facilitate knowledge storage and processing. Our systematic analysis of circuit evolution throughout continual pre-training reveals several key findings: (1) the acquisition of new knowledge is influenced by its relevance to pre-existing knowledge; (2) the evolution of knowledge circuits exhibits a distinct phase shift from formation to optimization; (3) the evolution of knowledge circuits follows a deep-to-shallow pattern. These insights not only advance our theoretical understanding of the mechanisms of new knowledge acquisition in LLMs, but also provide potential implications for improving continual pre-training strategies to enhance model performance. Code and data will be available at https://github.com/zjunlp/DynamicKnowledgeCircuits.

This study examines the capabilities of the Vision Transformer (ViT) model in generating visual embeddings for images of auto parts sourced from online marketplaces, such as Craigslist and OfferUp. By focusing exclusively on single-modality data, the analysis evaluates ViT's potential for detecting patterns indicative of illicit activities. The workflow involves extracting high-dimensional embeddings from images, applying dimensionality reduction techniques like Uniform Manifold Approximation and Projection (UMAP) to visualize the embedding space, and using K-Means clustering to categorize similar items. Representative posts nearest to each cluster centroid provide insights into the composition and characteristics of the clusters. While the results highlight the strengths of ViT in isolating visual patterns, challenges such as overlapping clusters and outliers underscore the limitations of single-modal approaches in this domain. This work contributes to understanding the role of Vision Transformers in analyzing online marketplaces and offers a foundation for future advancements in detecting fraudulent or illegal activities.

Mechanisms are designed to perform functions in various fields. Often, there is no unique mechanism that performs a well-defined function. For example, vehicle suspensions are designed to improve driving performance and ride comfort, but different types are available depending on the environment. This variability in design makes performance comparison difficult. Additionally, the traditional design process is multi-step, gradually reducing the number of design candidates while performing costly analyses to meet target performance. Recently, AI models have been used to reduce the computational cost of FEA. However, there are limitations in data availability and different analysis environments, especially when transitioning from low-fidelity to high-fidelity analysis. In this paper, we propose a multi-fidelity design framework aimed at recommending optimal types and designs of mechanical mechanisms. As an application, vehicle suspension systems were selected, and several types were defined. For each type, mechanism parameters were generated and converted into 3D CAD models, followed by low-fidelity rigid body dynamic analysis under driving conditions. To effectively build a deep learning-based multi-fidelity surrogate model, the results of the low-fidelity analysis were analyzed using DBSCAN and sampled at 5% for high-cost flexible body dynamic analysis. After training the multi-fidelity model, a multi-objective optimization problem was formulated for the performance metrics of each suspension type. Finally, we recommend the optimal type and design based on the input to optimize ride comfort-related performance metrics. To validate the proposed methodology, we extracted basic design rules of Pareto solutions using data mining techniques. We also verified the effectiveness and applicability by comparing the results with those obtained from a conventional deep learning-based design process.