A New Species of Gracixalus (Anura: Rhacophoridae) from West Guangxi, China

We discovered a new species of the genus Gracixalus, Gracixalus tianlinensis sp. nov. which is morphologically almost similar to G. jinggangensis, G. jinxiuensis and G. sapaensis, but is distinguished from these species and all other rhacophorids in China and adjoining countries by a combination of the following characters: (1) SVL 30.3-35.9 mm in male, 35.6-38.7 mm in female, (2) head length less than head width, (3) vomerine teeth absent, (4) supratympanic fold distinct, (5) axilla and posterior surface of flanks pale yellow, (6) nuptial pads distinct on Finger I and slightly visible on Finger II, (7) dorsum brown to beige, with an inverse Y-shaped dark brown marking, (8) single subgular vocal sac. Our preliminary phylogenetic analyses implied G. tianlinensis sp. nov. is sister to G. sapaensis with well-supported values. Currently, this new species is known to be distributed in montane evergreen forests in association with montane bamboo in Cenwanglaoshan National Nature Reserve, Tianlin County, Guangxi, China

Speed Control of Magnetic Drive-Trains with Pole-Slipping Amelioration

The paper introduces new techniques to reduce the potential for pole-slipping induced by control systems and presents a low-cost pole-slipping detection and recovery scheme for magnetic drive-trains (MDTs). For the first time, the paper shows that a combination of electromagnetic and load-torque excitations which individually are not greater than the maximum coupling torque can initiate pole-slipping. For applications where acceleration feedback is unavailable, the motor-side inertia is virtually increased with a tracking differentiator to provide feedback of acceleration. Subse- quently, controller design and parameter optimization are discussed. Experimental measurements on a custom test facility verify the presented principles that low-bandwidth controller designs with low inertia ratios can accommodate a wider range of on-load startup torque and load-torque disturbances without pole-slipping. To address overload issues, a pole-slipping detection method based on the kurtosis of electromagnetic torque and a recovery strategy based on converting the state of pole- slipping into that of on-load startup are presented. Experimental results demonstrate that detecting slip anomalies without load-side information, and recovery from pole-slipping without auxiliary mechanical devices are both feasible

Robust Speed Control of Magnetic Drive-Trains with Low-Cost Drives

The paper presents a methodology to improve the operating robustness of low-cost magnetic drive-train (MDT) systems in which load-side sensing is not a preferred option for addressing pole-slipping and variable torsional stiffness issues. Firstly, through dynamically analysing the relative displacement angle between both sides of the MDT (resulting from the developed electromagnetic- and load-torque), the paper offers an operating criteria using the inertia ratio and electromagnetic- and load-torque excitations to prevent the MDT from pole-slipping. Subsequently, the relationship between controller parameters and dominant/resonant poles of closed-loop MDT control system, is discussed. It is shown that controller parameters for MDTs to accommodate a wide range of torsional stiffness variations can be determined from natural frequencies that are bounded by operating constraints. Using the presented principles, desired performance with respect to speed reference tracking and load-torque disturbance accommodation can be achieved by simply determining the natural frequency of the dominant poles. Simulation studies and experimental measurements on a custom MDT test facility are used to underpin the efficacy of the proposed analysis and design techniques

Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks

With the recent prevalence of reinforcement learning (RL), there have been tremendous interests in utilizing RL for ads allocation in recommendation platforms (e.g., e-commerce and news feed sites). For better performance, recent RL-based ads allocation agent makes decisions based on representations of list-wise item arrangement. This results in a high-dimensional state-action space, which makes it difficult to learn an efficient and generalizable list-wise representation. To address this problem, we propose a novel algorithm to learn a better representation by leveraging task-specific signals on Meituan food delivery platform. Specifically, we propose three different types of auxiliary tasks that are based on reconstruction, prediction, and contrastive learning respectively. We conduct extensive offline experiments on the effectiveness of these auxiliary tasks and test our method on real-world food delivery platform. The experimental results show that our method can learn better list-wise representations and achieve higher revenue for the platform.Comment: arXiv admin note: text overlap with arXiv:2109.04353, arXiv:2204.0037

MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed

Nowadays, the mainstream approach in position allocation system is to utilize a reinforcement learning model to allocate appropriate locations for items in various channels and then mix them into the feed. There are two types of data employed to train reinforcement learning (RL) model for position allocation, named strategy data and random data. Strategy data is collected from the current online model, it suffers from an imbalanced distribution of state-action pairs, resulting in severe overestimation problems during training. On the other hand, random data offers a more uniform distribution of state-action pairs, but is challenging to obtain in industrial scenarios as it could negatively impact platform revenue and user experience due to random exploration. As the two types of data have different distributions, designing an effective strategy to leverage both types of data to enhance the efficacy of the RL model training has become a highly challenging problem. In this study, we propose a framework named Multi-Distribution Data Learning (MDDL) to address the challenge of effectively utilizing both strategy and random data for training RL models on mixed multi-distribution data. Specifically, MDDL incorporates a novel imitation learning signal to mitigate overestimation problems in strategy data and maximizes the RL signal for random data to facilitate effective learning. In our experiments, we evaluated the proposed MDDL framework in a real-world position allocation system and demonstrated its superior performance compared to the previous baseline. MDDL has been fully deployed on the Meituan food delivery platform and currently serves over 300 million users.Comment: 4 pages, 2 figures, accepted by SIGIR 202

Servo Control of Drive-Trains Incorporating Magnetic Couplings

The paper presents a high performance and low-cost design methodology for the servo control of magnetic drive-trains (MDTs) operating in direct drive mode. For the first time, this paper considers using sensitivity peaks to analyse the robustness and stability of MDT control systems. Initially, through analysis of a dynamic model, the key spring characteristic parameters with respect to operating points, are developed. It is also shown that a wider dynamic performance envelope can be achieved by linearizing the MDT model at around 60%-80% of the maximum coupling torque, as opposed to traditional linearization under zero torque conditions. Subsequently, the paper exploits the spring characteristics for a design methodology based on the H∞ mixed sensitivity approach to determine suitable control parameters. Following this, the maximum exogenous load-torque disturbance and speed reference that will not induce pole-slipping can be determined. Finally, preferential position reference profiles and optimal gains for position controllers are given to prevent demand-induced speed oscillations. The proposed methodologies are validated through simulation and experimental studies

An On-demand Photonic Ising Machine with Simplified Hamiltonian Calculation by Phase-encoding and Intensity Detection

Photonic Ising machine is a new paradigm of optical computing, which is based on the characteristics of light wave propagation, parallel processing and low loss transmission. Thus, the process of solving the combinatorial optimization problems can be accelerated through photonic/optoelectronic devices. In this work, we have proposed and demonstrated the so-called Phase-Encoding and Intensity Detection Ising Annealer (PEIDIA) to solve arbitrary Ising problems on demand. The PEIDIA is based on the simulated annealing algorithm and requires only one step of optical linear transformation with simplified Hamiltonian calculation. With PEIDIA, the Ising spins are encoded on the phase term of the optical field and only intensity detection is required during the solving process. As a proof of principle, several 20 and 30-dimensional Ising problems have been solved with high ground state probability

Predicting human microRNA precursors based on an optimized feature subset generated by GA–SVM

AbstractMicroRNAs (miRNAs) are non-coding RNAs that play important roles in post-transcriptional regulation. Identification of miRNAs is crucial to understanding their biological mechanism. Recently, machine-learning approaches have been employed to predict miRNA precursors (pre-miRNAs). However, features used are divergent and consequently induce different performance. Thus, feature selection is critical for pre-miRNA prediction. We generated an optimized feature subset including 13 features using a hybrid of genetic algorithm and support vector machine (GA–SVM). Based on SVM, the classification performance of the optimized feature subset is much higher than that of the two feature sets used in microPred and miPred by five-fold cross-validation. Finally, we constructed the classifier miR-SF to predict the most recently identified human pre-miRNAs in miRBase (version 16). Compared with microPred and miPred, miR-SF achieved much higher classification performance. Accuracies were 93.97%, 86.21% and 64.66% for miR-SF, microPred and miPred, respectively. Thus, miR-SF is effective for identifying pre-miRNAs