Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Jinhe Bi, Kristian Kersting, Jeff Z. Pan, Hinrich Schuetze, Volker Tresp, Yunpu Ma. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

We present a new method for synthesizing and implementing high-performancesix-degree-of-freedom($\boldsymbol{6}$-DOF) flight controllers for the Bee$^{++}$, an insect-scale flying robot driven by four independently-actuated flapping wings. Each wing of the Bee$^{++}$is installed with a preset orientation such that the stroke plane generated during flight is inclined, thus enabling reliable roll, pitch, and yaw torque generation. Leveraging this capability, we propose a Lyapunov-based nonlinear control architecture that enables closed-loop position and attitude regulation and tracking. The control algorithms presented in this article simultaneously stabilize position and attitude by independently varying the wingstroke amplitudes of the four flapping wings of the Bee$^{++}$. We use this particular control architecture to exemplify the process of controller synthesis and real-time implementation; however, the aerodynamic design of the Bee$^{++}$is compatible with a great variety of control structures and performance objectives. As a main result, we present the first set of experimental data demonstrating sustained and robust high-performance tracking of a$\boldsymbol{6}$-DOF reference signal during flight at the insect scale, which has been a long-standing control problem in the field of flapping-wing microrobotics. Furthermore, using data obtained through a series of systematic flight tests, we show that the Bee$^{++}$can achieve the highest$\boldsymbol{6}$-DOF performance ever recorded for an insect-scale flapping-wing flying robot during sustained flight.

BACKGROUND: Artificially synthesized RNA molecules provide important ways for creating a variety of novel functional molecules. State-of-the-art RNA inverse folding algorithms can design simple and short RNA sequences of specific GC content, that fold into the target RNA structure. However, their performance is not satisfactory in complicated cases. RESULT: We present a new inverse folding algorithm called MCTS-RNA, which uses Monte Carlo tree search (MCTS), a technique that has shown exceptional performance in Computer Go recently, to represent and discover the essential part of the sequence space. To obtain high accuracy, initial sequences generated by MCTS are further improved by a series of local updates. Our algorithm has an ability to control the GC content precisely and can deal with pseudoknot structures. Using common benchmark datasets for evaluation, MCTS-RNA showed a lot of promise as a standard method of RNA inverse folding. CONCLUSION: MCTS-RNA is available at https://github.com/tsudalab/MCTS-RNA .

Data-intensive applications are increasingly designed to execute on large computing clusters. Our previous observation on Tencent production systems has indicated that join query is one of the most important queries in large-scale data processing. When running a join query on Hive system, the job of the join query is divided into map phase and reduce phase, and requires transferring large amounts of intermediate results over the network, which is inefficient. In this paper, we proposed an algorithm called CHMJ, the general idea of the algorithm is to take advantage of data locality to accelerate calculation. It includes four parts, Data distribution strategy, Parallel HashMapJoin Algorithm, CoLocation Scheduling and Delay scheduling strategy. CHMJ has been adopted in Tencent data warehouse, and plays an important role in Tencent's daily operations. Our relevant experiments demonstrate the feasibility and efficiency of our solution.

Nowadays Hadoop has become a leading architecture for large-scale data processing. One of the efficient ways to accelerate data processing is column-oriented storage technique which has been integrated into Hadoop family recently. However, how to design an appropriate attribute clustering algorithm to achieve optimal data processing performance for column-oriented hadoop system is still a big problem. In this paper, we propose a novel algorithm called CHAC to solve this problem. Both cases of overlapping attribute cluster and non-overlapping attribute cluster are considered in CHAC. In addition, an adjustable parameter is also taken into account to prohibit excessive attribute redundancy via limiting space overhead. The experimental results on TPC-H Benchmark demonstrate the efficiency and effectiveness of the proposed algorithm.