Adaptive Model Prediction Control-Based Multi-Terrain Trajectory Tracking Framework for Mobile Spherical Robots

Owing to uncertainties in both kinematics and dynamics, the current trajectory tracking framework for mobile robots like spherical robots cannot function effectively on multiple terrains, especially uneven and unknown ones. Since this is a prerequisite for robots to execute tasks in the wild, we enhance our previous hierarchical trajectory tracking framework to handle this issue. First, a modified adaptive RBF neural network (RBFNN) is proposed to represent all uncertainties in kinodynamics. Then the Lyapunov function is utilized to design its adaptive law, and a variable step-size algorithm is employed in the weights update procedure to accelerate convergence and improve stability. Hence, a new adaptive model prediction control-based instruction planner (VAN-MPC) is proposed. Without modifying the bottom controllers, we finally develop the multi-terrain trajectory tracking framework by employing the new instruction planner VAN-MPC. The practical experiments demonstrate its effectiveness and robustness.Comment: 10 pages, 20 figures. This work has been submitted to the IEEE Transactions on Industrial Electronics for possible publicatio

Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack

Previous studies have verified that the functionality of black-box models can be stolen with full probability outputs. However, under the more practical hard-label setting, we observe that existing methods suffer from catastrophic performance degradation. We argue this is due to the lack of rich information in the probability prediction and the overfitting caused by hard labels. To this end, we propose a novel hard-label model stealing method termed \emph{black-box dissector}, which consists of two erasing-based modules. One is a CAM-driven erasing strategy that is designed to increase the information capacity hidden in hard labels from the victim model. The other is a random-erasing-based self-knowledge distillation module that utilizes soft labels from the substitute model to mitigate overfitting. Extensive experiments on four widely-used datasets consistently demonstrate that our method outperforms state-of-the-art methods, with an improvement of at most $8.27\%$. We also validate the effectiveness and practical potential of our method on real-world APIs and defense methods. Furthermore, our method promotes other downstream tasks, \emph{i.e.}, transfer adversarial attacks

An MPC-based Optimal Motion Control Framework for Pendulum-driven Spherical Robots

Motion control is essential for all autonomous mobile robots, and even more so for spherical robots. Due to the uniqueness of the spherical robot, its motion control must not only ensure accurate tracking of the target commands, but also minimize fluctuations in the robot's attitude and motors' current while tracking. In this paper, model predictive control (MPC) is applied to the control of spherical robots and an MPC-based motion control framework is designed. There are two controllers in the framework, an optimal velocity controller ESO-MPC which combines extend states observers (ESO) and MPC, and an optimal orientation controller that uses multilayer perceptron (MLP) to generate accurate trajectories and MPC with changing weights to achieve optimal control. Finally, the performance of individual controllers and the whole control framework are verified by physical experiments. The experimental results show that the MPC-based motion control framework proposed in this work is much better than PID in terms of rapidity and accuracy, and has great advantages over sliding mode controller (SMC) for overshoot, attitude stability, current stability and energy consumption.Comment: This paper has been submitted to Control Engineering Practic

Flames: Benchmarking Value Alignment of Chinese Large Language Models

The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values. Current benchmarks, however, fall short of effectively uncovering safety vulnerabilities in LLMs. Despite numerous models achieving high scores and 'topping the chart' in these evaluations, there is still a significant gap in LLMs' deeper alignment with human values and achieving genuine harmlessness. To this end, this paper proposes the first highly adversarial benchmark named Flames, consisting of 2,251 manually crafted prompts, ~18.7K model responses with fine-grained annotations, and a specified scorer. Our framework encompasses both common harmlessness principles, such as fairness, safety, legality, and data protection, and a unique morality dimension that integrates specific Chinese values such as harmony. Based on the framework, we carefully design adversarial prompts that incorporate complex scenarios and jailbreaking methods, mostly with implicit malice. By prompting mainstream LLMs with such adversarially constructed prompts, we obtain model responses, which are then rigorously annotated for evaluation. Our findings indicate that all the evaluated LLMs demonstrate relatively poor performance on Flames, particularly in the safety and fairness dimensions. Claude emerges as the best-performing model overall, but with its harmless rate being only 63.08% while GPT-4 only scores 39.04%. The complexity of Flames has far exceeded existing benchmarks, setting a new challenge for contemporary LLMs and highlighting the need for further alignment of LLMs. To efficiently evaluate new models on the benchmark, we develop a specified scorer capable of scoring LLMs across multiple dimensions, achieving an accuracy of 77.4%. The Flames Benchmark is publicly available on https://github.com/AIFlames/Flames

Integrated analysis of genome-wide DNA methylation and cancer-associated fibroblasts identified prognostic biomarkers and immune checkpoint blockade in lower grade gliomas

BackgroundCancer-associated fibroblasts (CAFs) are vital components of prominent cellular components in lower-grade gliomas (LGGs) that contribute to LGGs’ progression, treatment resistance, and immunosuppression. Epigenetic modification and immunity have significant implications for tumorigenesis and development.MethodsWe combined aberrant methylation and CAFs abundances to build a prognostic model and the impact on the biological properties of LGGs. Grouping based on the median CAFs abundances score of samples in the TCGA-LGGs dataset, differentially expressed genes and aberrantly methylated genes were combined for subsequent analysis.ResultsWe identified five differentially methylated and expressed genes (LAT32, SWAP70, GSAP, EMP3, and SLC2A10) and established a prognostic gene signature validated in the CGGA-LGGs dataset. Immunohistochemistry (IHC) and in vitro tests were performed to verify these expressions. The high-risk group increased in tumor-promoting immune cells and tumor mutational burden. Notably, risk stratification had different ICB sensitivities in LGGs, and there were also significant sensitivity differences for temozolomide and the other three novel chemotherapeutic agents.ConclusionOur study reveals characteristics of CAFs in LGGs, refines the direct link between epigenetics and tumor stroma, and might provide clinical implications for guiding tailored anti-CAFs therapy in combination with immunotherapy for LGGs patients

Analysis of the Influence of Attitude Error on Underwater Positioning and Its High-Precision Realization Algorithm

GNSS/INS can provide position, attitude, and velocity (PAV) information for shipborne platforms. However, if the ship has a long-term linear motion or a stationary state, and is under the combined actions of sea surface swells, there will be a situation of sideslip and drift; if the ship is traveling slowly or shaking violently, the attitude calculation will not be completed. In the above situation, the traditional single-antenna GNSS/INS measurement mode is not suitable, and the attitude observability is poor; the heading angle attitude information, especially, will gradually diverge. Unreliable information will directly lead to a significant increase in underwater positioning errors. In this paper, a multi-antenna GNSS/INS combination algorithm is developed and used to provide high-precision PAV information, and is thereby able to obtain high-precision underwater positioning results. The experimental results show that the method has improved the acquisition of position and velocity in the horizontal direction and the accuracy of navigation attitude measurement. In particular, the attitude measurement accuracy in the 3 degrees of freedom (DoF) are improved by 10.1% (roll), 8.6% (pitch), and 29.3% (yaw)

Modeling and Analysis of Wireless Cyberphysical Systems Using Stochastic Methods

Wireless mesh network control systems (WMNCSs) are typical Cyberphysical Systems (CPSs) widely used in industries that need to meet stringent performance requirements. These WMNCSs are characteristic of stochasticity at different levels as system behavior, network performance, and wireless signal propagation, which grievously increases the difficulties of system modeling and analysis. In this paper, we propose a three-layered modeling framework to capture the stochastic properties of WMNCSs at different levels with stochastic hybrid system (SHS), stochastic network calculus (SNC), and physical layer models. We also bridge the gaps between these methods with an upper bound approach. All the efforts give us a methodology of modeling and analysis WMNCSs with stochastic methods, so we can know how the factors as channel conditions, network topology, etc. affect the stability and performance of the system. To the best of our knowledge, it is the first work that provides such a unified and flexible framework to model and analyze WMNCSs with stochastic methods

Deep Demand Prediction: An Enhanced Conformer Model With Cold-Start Adaptation for Origin–Destination Ride-Hailing Demand Prediction

In intelligent transportation systems, one key challenge for managing ride-hailing services is the balancing of traffic supply and demand while meeting passenger needs within vehicle availability constraints. Accurate origin–destination (OD) demand predictions can empower platforms to execute timely reallocation of cruising vehicles and improve ride-sharing services. Nonetheless, the complexity of OD-based demand prediction arises from intricate spatiotemporal dependencies and a higher need for precision compared to zone-based predictions, which leads to many unprecedented OD pairs. To tackle this issue, we design a comprehensive set of 102 features, including travel demand, passenger count, travel volume, liveliness, weather, and cross features. We also introduce an enhanced conformer model, which is composed of a single conformer block that integrates feedforward layers, multihead self-attention mechanisms, and depth-wise separable convolution layers. To address the cold-start problem and manage large values, we design a specific algorithm for OD pairs lacking training data and apply a technique to handle larger values. Our approach demonstrates a marked improvement in prediction performance, with an 18% decrease in the total travel demand error and up to a 47% reduction for certain larger values in some cases. Through extensive experiments on a dataset collected from a city, provided by a ride-hailing platform, our proposed methods significantly outperform the most advanced models

Alteration of tumor associated neutrophils by PIK3CA expression in endometrial carcinoma from TCGA data

Abstract Uterine corpus endometrial carcinoma (UCEC) is one of the most common cancer in female worldwide. PIK3CA has been proven to be a strong prognostic biomarker in UCEC. Nevertheless, current studies have not investigated what effects PIK3CA had on tumor associated neutrophils (TANss). Kaplan-Meier methods were used to compute the survival time of TCGA UCEC patients. GO and KEGG enrichment analysis unveiled relevant pathways PIK3CA affected using DEGs between PIK3CA high expression group and PIK3CA low expression group in TCGA UCEC, as well as GSEA. immune infiltration status was calculated using TIMER. We found that PIK3CA influenced a number of pathways including immune related pathways. The fraction of TANs was certainly altered by PIK3CA expression in UCEC. Our findings suggest that PIK3CA expression may play an important role in tumor immune microenvironment and could alter fraction of TANs in UCEC

Flow Characteristics and Heat-Transfer Enhancement of Air Agitation in Ice Storage Air Conditioning Systems

A large number of bubbles generated by the air agitation device in an external melting ice storage system can cause the disturbance of the ice–water mixture, which can enhance the heat transfer and contribute to the reduction in energy consumption. The structural design and optimization of the air agitation device in an external melting ice storage system is the key issue for energy savings. In this study, the influence of different orifice spacings and diameters on the distribution of the gas–liquid flow field, gas holdup, heat-transfer coefficient, and power consumption in the ice storage tank was investigated by numerical simulation. The simulated results showed that the heat-transfer coefficient of the ice–water mixture with air bubbles should be 3–5 times higher than the natural convection when the air superficial velocity is 0.03 m/s. The gas holdup was mainly affected by the orifice spacing, and the maximum varied from 5.0% to 8.2%. When the orifice spacing was less than 150 mm, the gas holdup changed a little in the horizontal direction, and the uniformity became worse when the orifice spacing was larger than 180 mm. An orifice diameter larger than 3 mm can improve the heat transfer and cause less air-compressing energy consumption, which decreased by approximately 1.62%