Trailing-arm suspension vehicles (TAV) often en-counter difficulties when navigating uneven terrains due to their unique mechanisms and coupling dynamics, rendering them susceptible to compromised ride comfort and significant posture changes. To address these challenges, we propose a decentralized position-based impedance control (PIC) approach, which integrates posture stability metrics with impedance characteristics through a centralized skyhook model. Furthermore, we introduce the generalized momentum (GM) method to estimate the external torque at the arm joint induced by road disturbances, acting as the mapped Cartesian external force in the PIC loop. Through simulation experiments, we verify the efficacy of the proposed controller and juxtapose its performance against traditional PD suspension controllers. The results attest to the substantial enhancement in ride comfort and posture stability afforded by the proposed controller, thereby presenting a compelling control solution for TAV applications.

A/B testing is one of the most successful applications of statistical theory in the Internet age. A crucial problem of Null Hypothesis Statistical Testing (NHST), the backbone of A/B testing methodology, is that experimenters are not allowed to continuously monitor the results and make decisions in real time. Many people see this restriction as a setback against the trend in the technology toward real time data analytics. Recently, Bayesian Hypothesis Testing, which intuitively is more suitable for real time decision making, attracted growing interest as a viable alternative to NHST. While corrections of NHST for the continuous monitoring setting are well established in the existing literature and known in A/B testing community, the debate over the issue of whether continuous monitoring is a proper practice in Bayesian testing exists among both academic researchers and general practitioners. In this paper, we formally prove the validity of Bayesian testing under proper stopping rules, and illustrate the theoretical results with concrete simulation illustrations. We point out common bad practices where stopping rules are not proper, and discuss how priors can be learned objectively. General guidelines for researchers and practitioners are also provided.

Recommender systems are faced with new challenges that are beyond traditional techniques. For example, most traditional techniques are based on similarity or overlap among existing data, however, there may not exist sufficient historical records for some new users to predict their preference, or users can hold diverse interest, but the similarity based methods may probably over-narrow it. To address the above challenges, we develop a principled approach called contextual combinatorial bandit in which a learning algorithm can dynamically identify diverse items that interest a new user. Specifically, each item is represented as a feature vector, and each user is represented as an unknown preference vector. On each of n rounds, the bandit algorithm sequentially selects a set of items according to the item-selection strategy that balances exploration and exploitation, and collects the user feedback on these selected items. A reward function is further designed to measure the quality (e.g. relevance or diversity) of the selected set based on observed feedback, and the goal of the algorithm is to maximize the total rewards of n rounds. The reward function only needs to satisfy two mild assumptions that is general enough to accommodate a large class of nonlinear functions. To solve this bandit problem, we provide algorithm that achieves Õ(√n) regret after playing n rounds. Experiments conducted on real-wold movie recommendation dataset demonstrate that our approach can effectively address the above challenges and hence improve the performance of recommendation task.

Tensor completion from incomplete observations is a problem of significant practical interest. However, it is unlikely that there exists an efficient algorithm with provable guarantee to recover a general tensor from a limited number of observations. In this paper, we study the recovery algorithm for pairwise interaction tensors, which has recently gained considerable attention for modeling multiple attribute data due to its simplicity and effectiveness. Specifically, in the absence of noise, we show that one can exactly recover a pairwise interaction tensor by solving a constrained convex program which minimizes the weighted sum of nuclear norms of matrices from $O(nr\log^2(n))$ observations. For the noisy cases, we also prove error bounds for a constrained convex program for recovering the tensors. Our experiments on the synthetic dataset demonstrate that the recovery performance of our algorithm agrees well with the theory. In addition, we apply our algorithm on a temporal collaborative filtering task and obtain state-of-the-art results.

With the increasing of physical-layer (PHY) data rate in modern wireless local area networks (WLANs) (e.g., 802.11n), the overhead of media access control (MAC) progressively degrades data throughput efficiency. This trend reflects a fundamental aspect of the current MAC protocol, which allocates the channel as a single resource at a time. This paper argues that, in a high data rate WLAN, the channel should be divided into separate subchannels whose width is commensurate with the PHY data rate and typical frame size. Multiple stations can then contend for and use subchannels simultaneously according to their traffic demands, thereby increasing overall efficiency. We introduce FICA, a fine-grained channel access method that embodies this approach to media access using two novel techniques. First, it proposes a new PHY architecture based on orthogonal frequency division multiplexing (OFDM) that retains orthogonality among subchannels while relying solely on the coordination mechanisms in existing WLAN, carrier sensing and broadcasting. Second, FICA employs a frequency-domain contention method that uses physical-layer Request to Send/Clear to Send (RTS/CTS) signaling and frequency domain backoff to efficiently coordinate subchannel access. We have implemented FICA, both MAC and PHY layers, using a software radio platform, and our experiments demonstrate the feasibility of the FICA design. Furthermore, our simulation results show FICA can improve the efficiency of WLANs from a few percent to 600% compared to existing 802.11.

Modern communication technologies are steadily advancing the physical layer (PHY) data rate in wireless LANs, from hundreds of Mbps in current 802.11n to over Gbps in the near future. As PHY data rates increase, however, the overhead of media access control (MAC) progressively degrades data throughput efficiency. This trend reflects a fundamental aspect of the current MAC protocol, which allocates the channel as a single resource at a time.

This paper presents a novel method of generating extractive summaries for multiple documents. Given a cluster of documents, we firstly construct a graph where each vertex represents a sentence and edges are created according to the asymmetric relationship between sentences. Then we develop a method to measure the importance of a subset of vertices by adding a super-vertex into the original graph. The importance of such a super-vertex is quantified as super-centrality, a quantitative measure for the importance of a subset of vertices within the whole graph. Finally, we propose a heuristic algorithm to find the best summary. Our method is evaluated with extensive experiments. The comparative results show that the proposed method outperforms other methods on several datasets.