Consistent routing updates through Software-Defined Networking (SDN) can be difficult due to the asynchronous and distributed nature of the data plane. Recent studies have achieved consistent unicast routing updates. However, achieving consistent updates with drop-freeness and duplicate-freeness remains a challenge for multicast with fewer known results. This paper proposes a Novel Ordered Update Scheme called Nous, a novel approach that offers a comprehensive solution for consistently updating multicast routing based on SDN. To avoid duplicate entries, Nous configures the inport match field in the forwarding rules. Nous implements a dependency graph to schedule update operations dynamically. It also solves the Replace Operation Tree Migration Problem (ROTMP) using a greedy solution. To compare the greedy solution with the optimal solution, we employ the state-of-the-art mathematical programming solver Gurobi Optimizer 7.5 (for solving the optimization problem), Mininet 2.0, and Floodlight 1.2 (for simulation and comparison) to obtain a near-optimal solution. Simulation results show that using the greedy solution, Nous can usually achieve near-optimal solutions to the ROTMP with an average of fewer than 1.2 rounds and within 10 ms in different scenarios. This makes Nous the first ordered update scheme to guarantee two consistent states simultaneously.

LiDAR-based 3D object detection plays an essential role in autonomous driving. Although the detector trained on high-resolution data has much better performance than the same detector trained on low-resolution data, the high-resolution LiDAR cannot be widely used due to its high price. In this work, we propose a new distillation method called Focal Distillation to bridge the gap between high-resolution detector (teacher model) and low-resolution detector (student model). It consists of three essential components: focal classification distillation (FCD), focal regression distillation (FRD) and focal feature distillation (FFD). Taking the low-resolution data as input, the student model can learn discriminative features and produce more accurate results with the assistance of the teacher model trained on high-resolution data. We conducted extensive experiments to validate the effectiveness of Focal Distillation. Evaluated on the KITTI validation set, a typical SECOND model trained with Focal Distillation outperformed its non-distilled counterpart by 3.37%, 7.52%, 11.35% mAP on the category Car, Pedestrian, and Cyclist of moderate level, respectively. Moreover, the remarkable improvements observed on different models and different datasets further demonstrate the generalization ability of our proposed method.

Influence Maximization (IM) problem is to select influential users to maximize the influence spread, which plays an important role in many real-world applications such as product recommendation, epidemic control, and network monitoring. Nowadays multiple kinds of information can propagate in online social networks simultaneously, but current literature seldom discuss about this phenomenon. Accordingly, in this article, we propose Multiple Influence Maximization (MIM) problem where multiple information can propagate in a single network with different propagation probabilities. The goal of MIM problems is to maximize the overall accumulative influence spreads of different information with the limit of seed budget . To solve MIM problems, we first propose a greedy framework to solve MIM problems which maintains an -approximate ratio. We further propose parallel algorithms based on semaphores, an inter-thread communication mechanism, which significantly improves our algorithms efficiency. Then we conduct experiments for our framework using complex social network datasets with 12k, 154k, 317k, and 1.1m nodes, and the experimental results show that our greedy framework outperforms other heuristic algorithms greatly for large influence spread and parallelization of algorithms reduces running time observably with acceptable memory overhead.

Due to the distributed and asynchronous nature in data plane, the packets can be forwarded into a loop during routing updates. Software-Defined Networks (SDNs) enable a controller to schedule the update operations of routing rules in a loop-free manner. However, the existing rule replacement mechanism cannot give an efficient solution for large-scale update scenarios. In this paper, we make use of the ingress port field in forwarding rules to design a loop-free update scheme, Inport-Matching Update (IMU), for per-flow and destination-based unicast, which significantly improves efficiency of solving update scenarios and reduces update rounds. In both our proposed and previous mechanisms, a subproblem called Rule Replacement Scheduling (RRS) problem needs to be solved and we prove that the scale of RRS problem can be reduced at least by half. Moreover, we use the inclusion-exclusion principle to rigorously prove that the probability that an RRS problem under IMU has a trivial solution, i.e., one round, is always higher than 83.7%, given a random per-flow unicast update scenario with ingress port rules. Experimental results show that our proposed Inport-Matching Update mechanism remarkably reduces the average number of rounds and runtime for solving the RRS problem compared to the existing rule replacement mechanism.

Consistent routing update based on Software-Defined Networks (SDN) is a complicated problem due to the asynchronous and distributed data plane. Existing ordered update approaches mostly focus on the consistent routing update problem for unicast other than multicast, which should guarantee two consistencies, drop-freeness and duplicate-freeness. In this paper, we propose Shifter, a novel dynamic ordered update scheme for consistent multicast routing update based on SDN to guarantee both consistencies. Shifter advocates configuring inport match field in the forwarding rules to avoid duplicate. In order to guarantee drop-freeness, Shifter employs a dependency graph to dynamically schedule update operations, and uses a greedy solution to solve a subproblem named Replace Operation Tree Migration Problem (ROTMP). We conduct simulations to evaluate Shifter and find that Shifter can give a near optimal solution of ROTMP with very few rounds and little runtime for multicast routing update scenarios. To the best of our knowledge, Shifter is the first ordered update scheme to guarantee the two consistencies simultaneously.

In this paper, techniques of force-feedback control are applied to the hydrodynamic study of a laboratory robotic fish. The experimental apparatus which allows a robotic model to accelerate from rest to a steady speed under self-propelled conditions is clearly described. In the current apparatus, the robotic fish is mounted on a servo guide rail system and the towing speed is not preset but determined by the measured force acting on the body of the fish. Such an apparatus enables the simultaneous measurement of power consumption, thrust efficiency and speed of a robotic model obtained under self-propelled conditions. The thrust efficiency of the robotic fish can be estimated based on a 2-D vortex ring force estimation method. By comparing the thrust performance of carangiform body-shaped robotic swimmer with different typical BCF (body and caudal fin ) swimming modes, i.e. anguilliform, carangiform and thunniform, we show that the robotic swimming fish with the thunniform kinematic movement not only reaches a higher steady swimming speed but is also more efficient than the other two modes However, in the start phase, using the anguilliform kinematic movement, the robotic swimmer accelerates faster among all kinematic movements. Ultimately, we found that the robotic fish always produce a double-row wake structure no matter which swimming mode used.

Hydrodynamic investigation of carangiform robotic fish has been carried out by using a novel experimental method. The laboratory robotic fish model which follows an exact replica of Saithe, is self-propelled on a servo towing system. The forward towing speed is determined by the fluid force acting upon the robotic fish, as the fish undulate its body in the water. The importance of the self-propelled method which allows for simultaneous measurement of internal and extern force and flow analysis of robotic fish has been demonstrated in the hydrodynamic experiment.