Active Admittance Control with Iterative Learning for General-Purpose Contact-Rich Manipulation

Force interaction is inevitable when robots face multiple operation scenarios. How to make the robot competent in force control for generalized operations such as multi-tasks still remains a challenging problem. Aiming at the reproducibility of interaction tasks and the lack of a generalized force control framework for multi-task scenarios, this paper proposes a novel hybrid control framework based on active admittance control with iterative learning parameters-tunning mechanism. The method adopts admittance control as the underlying algorithm to ensure flexibility, and iterative learning as the high-level algorithm to regulate the parameters of the admittance model. The whole algorithm has flexibility and learning ability, which is capable of achieving the goal of excellent versatility. Four representative interactive robot manipulation tasks are chosen to investigate the consistency and generalisability of the proposed method. Experiments are designed to verify the effectiveness of the whole framework, and an average of 98.21% and 91.52% improvement of RMSE is obtained relative to the traditional admittance control as well as the model-free adaptive control, respectively.

Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning

Hierarchical federated learning (HFL) shows great advantages over conventional two-layer federated learning (FL) in reducing network overhead and interaction latency while still retaining the data privacy of distributed FL clients. However, the communication and energy overhead still pose a bottleneck for HFL performance, especially as the number of clients raises dramatically. To tackle this issue, we propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation in this paper, aiming to minimize the total cost of time and energy at each HFL global round. Specifically, we first propose a novel fuzzy logic based client orchestration policy considering client heterogenerity in multiple aspects, including channel quality, data quantity and model staleness. Subsequently, given the fuzzy based client-edge association, a joint edge server scheduling and resource allocation problem is formulated. Utilizing problem decomposition, we firstly derive the closed-form solution for the edge server scheduling subproblem via the penalty dual decomposition (PDD) method. Next, a deep deterministic policy gradient (DDPG) based algorithm is proposed to tackle the resource allocation subproblem considering time-varying environments. Finally, extensive simulations demonstrate that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.

MIM-GAN-based Anomaly Detection for Multivariate Time Series Data

The loss function of Generative adversarial network(GAN) is an important factor that affects the quality and diversity of the generated samples for anomaly detection. In this paper, we propose an unsupervised multiple time series anomaly detection algorithm based on the GAN with message importance measure(MIM-GAN). In particular, the time series data is divided into subsequences using a sliding window. Then a generator and a discriminator designed based on the Long Short-Term Memory (LSTM) are employed to capture the temporal correlations of the time series data. To avoid the local optimal solution of loss function and the model collapse, we introduce an exponential information measure into the loss function of GAN. Additionally, a discriminant reconstruction score consisting on discrimination and reconstruction loss is taken into account. The global optimal solution for the loss function is derived and the model collapse is proved to be avoided in our proposed MIM-GAN-based anomaly detection algorithm. Experimental results show that the proposed MIM-GAN-based anomaly detection algorithm has superior performance in terms of precision, recall, and F1 score.

Towards Better Entity Linking with Multi-View Enhanced Distillation

Dense retrieval is widely used for entity linking to retrieve entities from large-scale knowledge bases. Mainstream techniques are based on a dual-encoder framework, which encodes mentions and entities independently and calculates their relevances via rough interaction metrics, resulting in difficulty in explicitly modeling multiple mention-relevant parts within entities to match divergent mentions. Aiming at learning entity representations that can match divergent mentions, this paper proposes a Multi-View Enhanced Distillation (MVD) framework, which can effectively transfer knowledge of multiple fine-grained and mention-relevant parts within entities from cross-encoders to dual-encoders. Each entity is split into multiple views to avoid irrelevant information being over-squashed into the mention-relevant view. We further design cross-alignment and self-alignment mechanisms for this framework to facilitate fine-grained knowledge distillation from the teacher model to the student model. Meanwhile, we reserve a global-view that embeds the entity as a whole to prevent dispersal of uniform information. Experiments show our method achieves state-of-the-art performance on several entity linking benchmarks.

Energy-Efficient Design of STAR-RIS Aided MIMO-NOMA Networks

Simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) can provide expanded coverage compared with the conventional reflection-only RIS. This paper exploits the energy efficient potential of STAR-RIS in a multiple-input and multiple-output (MIMO) enabled non-orthogonal multiple access (NOMA) system. Specifically, we mainly focus on energy-efficient resource allocation with MIMO technology in the STAR-RIS assisted NOMA network. To maximize the system energy efficiency, we propose an algorithm to optimize the transmit beamforming and the phases of the low-cost passive elements on the STAR-RIS alternatively until the convergence. Specifically, we first decompose the formulated energy efficiency problem into beamforming and phase shift optimization problems. To efficiently address the non-convex beamforming optimization problem, we exploit signal alignment and zero-forcing precoding methods in each user pair to decompose MIMO-NOMA channels into single-antenna NOMA channels. Then, the Dinkelbach approach and dual decomposition are utilized to optimize the beamforming vectors. In order to solve non-convex phase shift optimization problem, we propose a successive convex approximation (SCA) based method to efficiently obtain the optimized phase shift of STAR-RIS. Simulation results demonstrate that the proposed algorithm with NOMA technology can yield superior energy efficiency performance over the orthogonal multiple access (OMA) scheme and the random phase shift scheme.

Time-aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answering

Knowledge graphs (KGs) have received increasing attention due to its wide applications on natural language processing. However, its use case on temporal question answering (QA) has not been well-explored. Most of existing methods are developed based on pre-trained language models, which might not be capable to learn \emph{temporal-specific} presentations of entities in terms of temporal KGQA task. To alleviate this problem, we propose a novel \textbf{T}ime-aware \textbf{M}ultiway \textbf{A}daptive (\textbf{TMA}) fusion network. Inspired by the step-by-step reasoning behavior of humans. For each given question, TMA first extracts the relevant concepts from the KG, and then feeds them into a multiway adaptive module to produce a \emph{temporal-specific} representation of the question. This representation can be incorporated with the pre-trained KG embedding to generate the final prediction. Empirical results verify that the proposed model achieves better performance than the state-of-the-art models in the benchmark dataset. Notably, the Hits@1 and Hits@10 results of TMA on the CronQuestions dataset's complex questions are absolutely improved by 24\% and 10\% compared to the best-performing baseline. Furthermore, we also show that TMA employing an adaptive fusion mechanism can provide interpretability by analyzing the proportion of information in question representations.

Energy-Efficiency Maximization for a WPT-D2D Pair in a MISO-NOMA Downlink Network

The combination of non-orthogonal multiple access (NOMA) and wireless power transfer (WPT) is a promising solution to enhance the energy efficiency of Device-to-Device (D2D) enabled wireless communication networks. In this paper, we focus on maximizing the energy efficiency of a WPT-D2D pair in a multiple-input single-output (MISO)-NOMA downlink network, by alternatively optimizing the beamforming vectors of the base station (BS) and the time switching coefficient of the WPT assisted D2D transmitter. The formulated energy efficiency maximization problem is non-convex due to the highly coupled variables. To efficiently address the non-convex problem, we first divide it into two subproblems. Afterwards, an alternating algorithm based on the Dinkelbach method and quadratic transform is proposed to solve the two subproblems iteratively. To verify the proposed alternating algorithm's accuracy, partial exhaustive search algorithm is proposed as a benchmark. We also utilize a deep reinforcement learning (DRL) method to solve the non-convex problem and compare it with the proposed algorithm. To demonstrate the respective superiority of the proposed algorithm and DRL-based method, simulations are performed for two scenarios of perfect and imperfect channel state information (CSI). Simulation results are provided to compare NOMA and orthogonal multiple access (OMA), which demonstrate the superior performance of energy efficiency of the NOMA scheme.

FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Prediction

Modern scientific research and applications very often encounter "fragmentary data" which brings big challenges to imputation and prediction. By leveraging the structure of response patterns, we propose a unified and flexible framework based on Generative Adversarial Nets (GAN) to deal with fragmentary data imputation and label prediction at the same time. Unlike most of the other generative model based imputation methods that either have no theoretical guarantee or only consider Missing Completed At Random (MCAR), the proposed FragmGAN has theoretical guarantees for imputation with data Missing At Random (MAR) while no hint mechanism is needed. FragmGAN trains a predictor with the generator and discriminator simultaneously. This linkage mechanism shows significant advantages for predictive performances in extensive experiments.