Flexible spines are critical to the remarkable agility and speed of animals. Translating this biological advantage to quadruped robots presents a significant control challenge, particularly in coordinating the spine and limbs for maximal velocity. In this work, we utilize reinforcement learning (RL) to develop high-speed locomotion for a bioinspired mouse robot with a lateral flexible spine. The resulting controller achieves motor performance that demonstrably surpasses non-spined and model-based methods. More importantly, our analysis reveals the principles behind this performance: the emergence of two distinct motion templates. For high-speed walking, the robot learns a “whip-like” spinal oscillation to increase leg swing frequency, while for agile turning, it adopts a dynamic “bend-and-straighten” pattern. These findings demonstrate the capability of RL to not only generate high-performance controllers but also to produce emergent strategies that, upon analysis, reveal underlying principles of high-speed, spine-driven locomotion.

3D Gaussian Splatting (3DGS) allows flexible adjustments to scene representation, enabling continuous optimization of scene quality during dense visual simultaneous localization and mapping (SLAM) in static environments. However, 3DGS faces challenges in handling environmental disturbances from dynamic objects with irregular movement, leading to degradation in both camera tracking accuracy and map reconstruction quality. To address this challenge, we develop an RGB-D dense SLAM which is called Gaussian Splatting SLAM in Dynamic Environments (Gassidy). This approach calculates Gaussians to generate rendering loss flows for each environmental component based on a designed photometricgeometric loss function. To distinguish and filter environmental disturbances, we iteratively analyze rendering loss flows to detect features characterized by changes in loss values between dynamic objects and static components. This process ensures a clean environment for accurate scene reconstruction. Compared to state-of-the-art SLAM methods, experimental results on open datasets show that Gassidy improves camera tracking precision by up to 97.9 % and enhances map quality by up to 6 %. Video of experiments is available here: https://www.wixsite.com.com/wen-Gassidy.

The whisker-inspired tactile sensor is advantageous for enhancing robotic perception in proximate range and darkness via non-intrusive contacts. However, localizing contact along the whisker shaft is challenging due to the non-injective mapping between tangential contacts and the resulting bending moments at the whisker base. Previous studies suggest that incorporating axial force measurements can resolve this ambiguity. In this work, we develop a magnetically transduced whisker sensor that integrates axial force sensing as an additional mechanical signal. The sensor features a tapered whisker with a custom slope and a 3-DoF suspension mechanism, enabling axial displacement at the base, which is proportional to the applied axial force. We construct a Penalized Gaussian Process model trained on synthetic data to estimate the whisker’s motion and refine it with real-data constraints. The design is compact, low-cost, and validated through simulations and real-world experiments to differentiate tangential contacts. Furthermore, we propose an optimization-based approach for estimating instantaneous contact locations. Experimental results demonstrate that the proposed method can effectively track contacts in millimeter-level accuracy with a mean error of 7.17 mm, achieving a higher accuracy with only 4.02 mm in large-deflection and close-to-base regions.

This paper introduces a new multiple-input multiple-output (MIMO) communication system, referred to as "pseudo MIMO". Unlike conventional MIMO systems, the pseudo MIMO technology enables the transmission of a greater number of parallel data streams than the available radio frequency (RF) chains at the receiver. This is achieved by connecting multiple antenna elements to each receiving RF chain and applying a sequence of analog combining patterns for signal reception within the orthogonal frequency division multiplexing (OFDM) sampling interval. The entire signal processing procedures are formulated using matrix representations, leading to a compact expression of the equivalent channel matrix for each OFDM subcarrier. This matrix is further decomposed into the product of the frequency domain wireless channel matrix and the analog combining matrix. Numerical results and prototype testing demonstrate the benefits of pseudo MIMO in terms of both spectral and energy efficiency.

The precise control of cell fate is driven by a hierarchical regulatory network (HRNet) where transcription factors (TFs) and cis-regulatory elements (CREs) orchestrate the expression of target genes (TGs) through complex causal actions. While single-cell multi-omics technologies provide multi-dimensional data to resolve regulatory networks, existing methods often fail to capture their hierarchical and causal properties. We propose SMOGT (Single-cell Multi-Omics Graph Transformer), a graph representation learning method to decipher HRNet. SMOGT embeds epigenetic mechanism into Heterogeneous Graph Transformer (HGT) by structuring information flow along a hierarchical-guided meta-path (TF-TF → TF-CRE → CRE-CRE → CRE-TG), and employs a semi-supervised strategy to ensure network accuracy. Validated against ChIP-seq and HiC-seq benchmarked datasets, SMOGT showed significantly higher accuracy in predicting transcriptional regulation (TF-CRE) and long-range chromatin conformation (CRE-CRE). The HRNet scaffolds downstream modules that mechanistically link network architecture to cell fate. The multi-layer random walk (MRWR) module identifies driver regulators and their TGs. The BioStreamNet module predicts shifts in cell fate trajectories following in silico perturbations within gene-specific HRNet formed by extracting regulatory weights during TG expression prediction. In hematopoietic stem cell differentiation, SMOGT elucidated the hierarchical causal cascade from driver TFs that governs lineage commitment. In melanoma epithelial-to-mesenchymal transition (EMT), it revealed a critical therapeutic window for reversing the process, and in Acute Myeloid Leukemia (AML), it uncovered hub-CREs with significant prognostic value. By accurately modeling hierarchical causality, SMOGT provides a robust tool to dissect and predict cell fate dynamics in both development and disease.

The massive scale of deployment, hundreds of parameters, differentiated scenarios and interworking with existing mobile networks leads to high complexity and high cost of optimization, operation and maintenance of the 5th generation mobile network (5G), which inspires that 6th generation mobile network (6G) should support high level autonomy at the beginning of deployment. Digital twin network (DTN) technology, with its advantages of intelligent decision making, low-cost experimentation, and preverification, has emerged as a key enabling technology for autonomous network. To address the need for flexibility to fulfill more diverse scenarios and high-level autonomy toward 2030, this article discusses the typical usage cases of DTN, and proposes an innovative and native design for 6G DTN, encompassing logical framework, architecture, functions, and deployment modes. Furthermore, the efficient DTN Model Construction and Intelligent Orchestration and Management are introduced to enable fully automated and high-performance DTN tasks. Finally, the future direction for DTN research is presented.

Autonomous robots operating in diverse scenarios are expected to safely and efficiently adapt to new, unknown, and cluttered environments. In this paper, we introduce a real-time goal-seeking and exploration framework incorporating novel meta-signed distance functions (MetaSDFs) and metabuffer robust control barrier functions (Meta-BRCBFs). To adapt to environmental changes in real time, we employ Bayesian meta-learning to construct MetaSDFs. Deep neural network weights are initially trained offline, followed by efficient online adaptation at the last Bayesian layer, allowing for online updates at linear time complexity. Each MetaSDF is individually trained for its corresponding obstacle class, enhancing online distance estimation accuracy. Subsequently, buffer zones are constructed around the MetaSDFs to establish corresponding Meta-BRCBFs. These Meta-BRCBFs are activated only when the robot enters these zones, substantially reducing the number of CBFs required. Outside these specified buffer zones, the robot remains ingoal-seekingmode, focusing on task completion. After entering a buffer zone, it transitions toexplorationmode, prioritizing safety and exploring safe pathways, effectively balancing task execution with environmental adaptability. We demonstrate that, under this framework, the system achieves both safety and asymptotic stabilization. Extensive simulations and experiments are conducted to demonstrate our framework’s effectiveness in both simulated scenarios and real-world environments. These tests confirm our framework’s real-time capabilities and safety assurances in dynamic settings where state-of-the-art methods fail. The video is available at: https://www.youtube.com/watch?v=C6eshldAMxA.

As an extension of machine unlearning in distributed scenarios, federated unlearning gains significant attention. However, federated unlearning remains challenging, as many studies require additional resources, such as auxiliary dataset or storage, to achieve high-quality models. These requirements incur extra costs and are often difficult to satisfy in practical applications. To address these issues, we propose a flexible client-level federated unlearning algorithm with prototypes, called FedUP. Specifically, our algorithm consists of two components: prototype-based unlearning and model recovering. First, we design a prototype-based unlearning strategy that uses prototypes of the erased client to guide the unlearning process, and maximizes the prototype loss between the remaining and erased clients to unlearn the information. It does not rely on historical storage updates or additional standard datasets, making the unlearning process more streamlined. To mitigate performance degradation from the unlearning process, we develop a brief model recovering approach guided by global prototypes to swiftly and efficiently restore models' accuracy on the remaining datasets. Unlike other unlearning algorithms, our approach exchanges prototypes instead of model parameters, significantly reducing communication overhead. Finally, we empirically evaluate the proposed algorithm from multiple perspectives on two datasets, demonstrating that our algorithm can achieve high-quality unlearned models with minimal communication cost.

Balancing oneself using the spine is a physiological alignment of the body posture in the most efficient manner by the muscular forces for mammals. For this reason, we can see many disabled quadruped animals can still stand or walk even with three limbs. This paper investigates the optimization of dynamic balance during trot gait based on the spatial relationship between the center of mass (CoM) and support area influenced by spinal flexion. During trotting, the robot balance is significantly influenced by the distance of the CoM to the support area formed by diagonal footholds. In this context, lateral spinal flexion, which is able to modify the position of footholds, holds promise for optimizing balance during trotting. This paper explores this phenomenon using a rat robot equipped with a soft actuated spine. Based on the lateral flexion of the spine, we establish a kinematic model to quantify the impact of spinal flexion on robot balance during trot gait. Subsequently, we develop an optimized controller for spinal flexion, designed to enhance balance without altering the leg locomotion. The effectiveness of our proposed controller is evaluated through extensive simulations and physical experiments conducted on a rat robot. Compared to both a non-spine based trot gait controller and a trot gait controller with lateral spinal flexion, our proposed optimized controller effectively improves the dynamic balance of the robot and retains the desired locomotion during trotting.

Entering the 5G era, the mobile network operators (MNO) are facing greater challenges in providing services cost effectively than any other previous generations. The potential solutions to this are lying on the emerging trend of deep convergence of information technology (IT), communication technology (CT) and data technology (DT). In particular, the O-RAN technology, the representation of such ICDT convergence and proposed by the O-RAN ALLIANCE in 2018, is transforming Radio Access Networks towards a new paradigm featuring openness, cloudification and intelligence. O-RAN has gained huge attention from both industry and academia since its inception. In this paper, we presented the recent endeavors from China Mobile, including our deployment scenarios, various test results from open fronthaul, cloud platform to the intelligent controller. Our rich and comprehensive tests have demonstrated the viability and superiority of current O-RAN technologies. Furthermore, we also provide our deep thinking on the O-RAN future evolution in order to better serve the emerging applications such as Metaverse, cloud extended-reality (XR), extensive enterprise private 5G verticals and so on.

Massive multiple-input multiple-output (MIMO) has gained widespread popularity in recent years due to its ability to increase data rates, improve signal quality, and provide better coverage in challenging environments. In this paper, we investigate the MIMO beam selection (MBS) problem, which is proven to be NP-hard and computationally intractable. To deal with this problem, quantum computing that can provide faster and more efficient solutions to large-scale combinatorial optimization is considered. MBS is formulated in a quadratic unbounded binary optimization form and solved with Coherent Ising Machine (CIM) physical machine. We compare the performance of our solution with two classic heuristics, simulated annealing and Tabu search. The results demonstrate an average performance improvement by a factor of 261.23 and 20.6, respectively, which shows that CIM-based solution performs significantly better in terms of selecting the optimal subset of beams. This work shows great promise for practical 5G operation and promotes the application of quantum computing in solving computationally hard problems in communication.

The aim of this research is to investigate the relationship between spinal flexion and quadruped locomotion in a rat robot equipped with a compliant spine, controlled by a central pattern generator (CPG). The study reveals that spinal flexion can enhance limb stride length, but it may also cause significant and unexpected motion disturbances during stride length variations. To address this issue, this paper proposes a CPG model driven by spinal flexion and a novel oscillator that incorporates a circular limit cycle and accounts for the anticipated stride length transition process. This approach effectively matches the torque change with the dynamics of stride length changes, leading to lower energy consumption. Extensive simulations are conducted to evaluate the efficacy of the proposed oscillator and compare it with the original kinetic model and other CPG models. The results demonstrate that the designed CPG model with the proposed oscillator yields smoother gait transitions during stride length variations and reduces energy consumption.

Lane keeping, as a fundamental functionality of autonomous navigation, remains a challenging task for autonomous robots and vehicles. Recently, spiking neural networks (SNNs) have gained attention and research interest due to their biological plausibility and application potential on neuromorphic processors. SNNs have also been successfully deployed on robots to solve autonomous navigation problems. However, lane keeping with a LiDAR sensor is still an open problem for SNNs. In this work, we propose an end-to-end approach based on an SNN to solve the lane-keeping problem using a 3D LiDAR sensor. For the first time, we explore the capability of the proposed SNN controller to perceive the LiDAR input and exploit the features to perform reward-based feedback learning. To ensure the effectiveness of the controller, the proposed method is deployed and evaluated on two high-fidelity simulators. The experimental results demonstrate the high applicability and performance in different scenarios. Furthermore, experiments show that the SNN is capable of performing lane keeping in a simulated urban environment with only 18 control neurons and 32 synapse connections, producing on average only a 17cm deviation from lane center, which is 4.3 % of the lane width.

LiDAR (Light Detection And Ranging) sensors have been widely used in autonomous vehicles as the main sensors. According to the specification details of the widely used 3D LiDAR products in the market, the distribution of vertical beam channels is set according to a uniform angular resolution, which is not ideally efficient for specific autonomous tasks. In this paper, we propose a novel approach to find the optimized angular distribution of the vertical beam channels for different application scenarios and installation configurations. The experimental results in a study case suggest that concerning the vehicle detection task, the optimized LiDARs perform almost two times better than the ones with the same number of channels in terms of the detection range, and have perception performances close to the LiDARs with double channels in the long distance.

In nature, the movement of quadrupeds is completed under the combined action of the spine and the legs. Inspired by this, this paper explores the effect of a lateral flexing spine on the locomotion of a rat robot. Benefiting from the regular lateral flexion of a soft actuated spine, the rat robot exhibits enhance step length of its hind legs and increased translational velocity by coordinating the opposite movements of the left and right sides. Furthermore, this paper introduces a mathematical model of the effect of the flexible spine on the robot velocity. Finally, extensive experiments are conducted in simulations and on the physical rat robot. Compared with the locomotion without a flexing spine, the simulation results show that the velocity of the robot can be increased up to 218.29%, which is in line with the theoretical results from the proposed mathematical model. Limited by the gap between simulation and the real world, the experiment results of the physical rat robot show a slight performance than the theoretical results. But the physical rat robot can still enhance its translational velocity with the help of a lateral flexing spine.

Simultaneous localization and mapping (SLAM) is one of the essential techniques and functionalities used by robots to perform autonomous navigation tasks. Inspired by the rodent hippocampus, this paper presents a biologically inspired SLAM system based on a LiDAR sensor using a hippocampal model to build a cognitive map and estimate the robot pose in indoor environments. Based on the biologically inspired models mimicking boundary cells, place cells, and head direction cells, the SLAM system using LiDAR point cloud data is capable of leveraging the self-motion cues from the LiDAR odometry and the boundary cues from the LiDAR boundary cells to build a cognitive map and estimate the robot pose. Experiment results show that with the LiDAR boundary cells the proposed SLAM system greatly outperforms the camera-based brain-inspired method in both simulation and indoor environments, and is competitive with the conventional LiDAR-based SLAM methods.

In this paper, we propose the robust transmission design for an intelligent reflecting surface (IRS) aided distributed multi-antenna system under imperfect cascaded channel state information at transmitter (CSIT). The active transmit beamforming at access points and reflection coefficients at IRS are jointly optimized to maximize the worst-case sum rate subject to the transmit power constraint and unit-modulus reflection coefficients for all possible channel realizations in the uncertainty region. We propose the conservative approximation (CA) based robust optimization. Under the framework of alternative optimization algorithm, the transmit beamforming is first solved via weighted minimum mean square error method with the fixed reflection coefficients, and then the reflection coefficients are optimized via nearest point projection method. Since the CA method can introduce the rate saturation for non-scaling CSIT as signal-to-noise ratio scales, we investigate another robust optimization algorithm, cutting-set (CS) method, which solves the problem by alternating between an optimization step for a finite subset of the uncertainty region, and a pessimization step for updating the subset. The numerical simulation results illustrate that the CS method can reap better sum-rate performance than CA, and the deleterious effect of imperfect cascaded CSIT on the performance becomes more significant with the increase of the reflecting elements.

This paper proposes an offline and runtime combined framework for the autonomous motion of snake robots. With the dynamic feedback of its state during runtime, the robot utilizes the linear regression to update its control parameters for better performance and thus adaptively reacts to the environment. To reduce interference from infeasible samples and improve efficiency, the data set for runtime training is chosen from one in several clusters categorized from samples collected in offline practice. Moreover, only the most sensitive control parameter is updated at one iteration for better robustness and efficiency. The effectiveness and efficiency of our approach are evaluated by a set of case studies of pole climbing. Experimental results demonstrate that with the proposed framework, the snake robot can adapt its locomotion gait to poles with different unknown diameters.

Term weight algorithms based on inverse document analysis are widely used in the expression of characteristic information for text. According to the finding that frequently occurring terms always cover less feature information for the text, the terms with lower frequency will be endowed higher weight. However, the terms with little occurrences always display unimportant information or even error information, such as rare terms and misspelled terms. To tackle such a problem, this paper proposed a novel term weight algorithm that focuses on the terms with low frequency rather than little occurrences. With the statistics based on non-homogeneous compression of term frequency, the action of terms with concerned frequency will be highlighted. And logarithmic function combined with the number of terms with the same frequency is utilized to weight the terms with different frequency based on different compression intervals. Comparing with TF-IDF and SIF, the proposed approach has a similar performance with SIF and a little better than TF-IDF. According to the difference among such methods, a finding shows that the term with a low frequency rather than little occurrences may dominate the feature information of the text.

Three-dimensional (3D) multiple input and multiple output (3D-MIMO) with massive antennas is a key technology to achieve high spectral efficiency and user experienced data rate for the fifth generation (5G) mobile communication system. To implement 3D-MIMO in 5G system, practical constraints on the product design should be considered. This paper proposes a systematic design for the 3D-MIMO product by considering the restrictions of both base band and the hardware, including cost, size, weight, and heat dissipation. The design has been implemented for 2.6-GHz time-division duplex band, and field trials have been conducted for performance validation with practical intercell interference in commercial network. The trial results show that this 3D-MIMO design can meet the spectral efficiency requirement of the 5G enhanced mobile broadband services. The performance gain of 3D-MIMO varies with the traffic load. When the traffic load is heavy, 3D-MIMO can enhance the cell throughput by 4~6.7 times. When the traffic load is low, the performance gain of this 3D-MIMO design decreases. The results from field trial also show that the performance of 3D-MIMO degrades in mobility scenarios, where further enhancement on acquiring instant channel status information are necessary to improve the robustness of 3D-MIMO to mobility.

In this paper, a new algorithm for non-parametric model identification and curve fitting is presented. The discrete impulse response function of a linear system is first estimated by using M-sequence, which enable the real-time robust control. Then, the discrete curve is fitted into some continuous functions using a hierarchy fitting strategy. The experiments show high precision of the algorithm. Besides, the formula we give in this approach can be used as the scale model formula for the H/sub /spl infin// control system design.