Code-mixing, the practice of switching between languages within a conversation, poses unique challenges for traditional NLP.Existing benchmarks are limited by their narrow language pairs and tasks, failing to adequately assess large language models' (LLMs) codemixing abilities.Despite the recognized importance of code-mixing for multilingual users, research on LLMs in this context remains sparse.Additionally, current techniques for synthesizing code-mixed data are underdeveloped to generate code-mixing.In response, we introduce CodeMixBench, a comprehensive benchmark covering eight tasks, including three specific to LLMs and five traditional NLP tasks, and 18 languages across seven language families.We also propose a new method for generating largescale synthetic code-mixed texts by combining word substitution with GPT-4 prompting.Our evaluation reveals consistent underperformance of LLMs on code-mixed datasets involving different language families.Enhancements in training data size, model scale, and fewshot learning could improve their performance.The code and dataset are available at https:// github.com/Jeromeyluck/CodeMixBench.

With the advancement of urbanization, communication-based train control (CBTC) systems for urban rail transit and train-to-train (T2T) communication have garnered significant attention. T2T communication establishes mobile ad hoc networks (MANETs), similar to those in vehicular ad hoc networks (VANETs). Building upon this foundation, we propose an adaptive cooperative (ADCO) MAC protocol for T2T communication. The scheme introduces clustering and cooperative transmission mechanisms, which enhance the efficiency and reliability of safety packet transmission. Additionally, the protocol assigns different priorities to packets engaging in contention on the control channel (CCH) and enables trains to access service channels (SCHs) without contention through pre-reserved time slots. To analyze the transmission probabilities and success rates of packets with varying priorities, a Markov-based model is utilized, ultimately determining the optimal ratio of the CCH interval (CCHI) to the SCH interval (SCHI) for maximizing channel utilization. Theoretical analysis and simulation results demonstrate that the proposed MAC protocol ensures reliable transmission of safety packets while simultaneously optimizing the throughput on SCHs.

Insects and hummingbirds exhibit extraordinary flight performance and can simultaneously master seemingly conflicting goals: stable hovering and aggressive maneuvering, which are unmatched by conventional small scale man-made vehicles. Flapping Wing Micro Air Vehicles (FWMAVs) hold great promise for closing this performance gap. However, design and control of such systems remain challenging. Here, we present an open source high fidelity dynamic simulation for FWMAVs. The simulator serves as a testbed for the design, optimization and flight control of FWMAVs. To validate the simulation, we recreated the at-scale hummingbird robot developed in our lab in the simulation. System identification was performed to obtain the model parameters. Force generation and dynamic response of open-loop and closed loop systems between simulated and experimental flights were compared. The unsteady aerodynamics and the highly nonlinear flight dynamics present challenging control problems for conventional and learning control algorithms such as Reinforcement Learning. The interface of the simulation is fully compatible with OpenAI Gym environment. As a benchmark study, we present a linear controller for hovering stabilization and a Deep Reinforcement Learning control policy for goal-directed maneuvering. Finally, we demonstrate direct simulation-to-real transfer of both control policies onto the physical robot, further demonstrating the fidelity of the simulation.

Unlike conventional aerial vehicles of fixed or rotary wings, realtime on-board attitude estimation of insect or hummingbird scale Flapping Wing Micro Aerial Vehicles (FWMAVs) is very challenging due to the severe instantaneous oscillations (approximately ten times of gravity on our platform) induced by high-frequency wing flapping. In this work, we present a novel sensor fusion algorithm for realtime on-board attitude estimation of FWMAVs. The algorithm is proposed with adaptive model-based compensation for both sensing drift and aerodynamic forces induced by flapping wings. We validated our approach on a 12.5 grams hummingbird robot. The experimental results demonstrated the accuracy, convergence, and robustness of the proposed algorithm.