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Code Large Language Models (Code LLMs), such as Code llama and DeepSeek-Coder, have demonstrated exceptional performance in the code generation tasks. However, most existing models focus on the abilities of generating correct code, but often struggle with bug repair. We introduce a suit of methods to enhance LLM's SQL bug-fixing abilities. The methods are mainly consisted of two parts: A Progressive Dataset Construction (PDC) from scratch and Dynamic Mask Supervised Fine-tuning (DM-SFT). PDC proposes two data expansion methods from the perspectives of breadth first and depth first respectively. DM-SFT introduces an efficient bug-fixing supervised learning approach, which effectively reduce the total training steps and mitigate the "disorientation" in SQL code bug-fixing training. In our evaluation, the code LLM models trained with two methods have exceeds all current best performing model which size is much larger., Comment: COLING-Industry 2025 accepted

The metaverse, emerging as a revolutionary platform for social and economic activities, provides various virtual services while posing security and privacy challenges. Wearable devices serve as bridges between the real world and the metaverse. To provide intelligent services without revealing users' privacy in the metaverse, leveraging federated learning (FL) to train models on local wearable devices is a promising solution. However, centralized model aggregation in traditional FL may suffer from external attacks, resulting in a single point of failure. Furthermore, the absence of incentive mechanisms may weaken users' participation during FL training, leading to degraded performance of the trained model and reduced quality of intelligent services. In this paper, we propose BF-Meta, a secure blockchain-empowered FL framework with decentralized model aggregation, to mitigate the negative influence of malicious users and provide secure virtual services in the metaverse. In addition, we design an incentive mechanism to give feedback to users based on their behaviors. Experiments conducted on five datasets demonstrate the effectiveness and applicability of BF-Meta.

The propagation of millimeter wave (MMW) and sub-terahertz (THz) signals through plasma sheaths is a critical concern for maintaining communication with hypersonic vehicles, yet the impact of complex plasma structures on these high-frequency channels remains insufficiently understood. In this work, we aim to characterize the transmission properties of MMW and sub-THz waves through plasma sheaths with various density profiles and ripple structures, addressing the gap in knowledge regarding the effects of plasma inhomogeneities on signal propagation. We employ an approach combining Inductively Coupled Plasma (ICP) data with transfer matrix methods (TMM) to model propagation through both flat and rippled plasma layers. Our findings reveal that ripple structures in plasma sheaths significantly affect channel performance, with periodic ripples reducing cutoff frequency and introducing frequency-selective behavior, while random ripples cause more unpredictable transmission characteristics. Our results explore the impact of the arrangement of plasma density layers and the parameters of ripple structures (period and amplitude) on channel transmission, group velocity dispersion, and angular dependence of wave propagation. These results provide crucial insights for the design and optimization of communication systems for hypersonic vehicles, potentially enabling the development of adaptive technologies capable of maintaining reliable communication in complex plasma environments., Comment: Submitted to IEEE Transactions on Plasma Science

Unmanned Aerial Vehicle (UAV) assisted terahertz (THz) wireless communications have been expected to play a vital role in the next generation of wireless networks. UAVs can serve as either repeaters or data collectors within the communication link, thereby potentially augmenting the efficacy of communication systems. Despite their promise, the channel analysis and modeling specific to THz wireless channels leveraging UAVs remain under explored. This work delves into a ground-to-UAV channel at 140 GHz, with a specific focus on the influence of UAV hovering behavior on channel performance. Employing experimental measurements through an unmodulated channel setup and a geometry-based stochastic model (GBSM) that integrates three-dimensional positional coordinates and beamwidth, this work evaluates the impact of UAV dynamic movements and antenna orientation on channel performance. Our findings highlight the minimal impact of UAV orientation adjustments on channel performance and underscore the diminishing necessity for precise alignment between UAVs and ground stations as beamwidth increases., Comment: To be published in Optics Express

Force interaction is inevitable when robots face multiple operation scenarios. How to make the robot competent in force control for generalized operations such as multi-tasks still remains a challenging problem. Aiming at the reproducibility of interaction tasks and the lack of a generalized force control framework for multi-task scenarios, this paper proposes a novel hybrid control framework based on active admittance control with iterative learning parameters-tunning mechanism. The method adopts admittance control as the underlying algorithm to ensure flexibility, and iterative learning as the high-level algorithm to regulate the parameters of the admittance model. The whole algorithm has flexibility and learning ability, which is capable of achieving the goal of excellent versatility. Four representative interactive robot manipulation tasks are chosen to investigate the consistency and generalisability of the proposed method. Experiments are designed to verify the effectiveness of the whole framework, and an average of 98.21% and 91.52% improvement of RMSE is obtained relative to the traditional admittance control as well as the model-free adaptive control, respectively.

Non-line-of-sight (NLOS) data transmission through surface reflection is pivotal for enhancing the reach and efficiency of terahertz (THz) communication systems. However, this innovation also introduces significant eavesdropping risks, exacerbated by the complex bistatic scattering effects during adverse weather conditions like rain. This work delves into the assessment of the vulnerabilities of NLOS THz communication channels to eavesdropping under simulated rain conditions using metallic wavy surfaces (MWS). The observation reveals the feasibility of successful signal interception under these conditions, highlighting a prevalent security concern for outdoor terahertz communication networks utilizing NLOS channels to broaden coverage. This insight underscores the critical need for addressing and mitigating potential eavesdropping threats to ensure secure and reliable terahertz communications in varied environmental conditions., Comment: To be published in Journal of the Optical Society of America B

The Terahertz (THz) frequency band offers a wide range of bandwidths, from tens to hundreds of gigahertz (GHz) and also supports data speeds of several terabits per second (Tbps). Because of this, maintaining THz channel reliability and efficiency in adverse weather conditions is crucial. Rain, in particular, disrupts THz channel propagation significantly and there is still lack of comprehensive investigations due to the involved experimental difficulties. This work explores how rain affects THz channel performance by conducting experiments in a rain emulation chamber and under actual rainy conditions outdoors. We focus on variables like rain intensity, raindrop size distribution (RDSD), and the channel's gradient height. We observe that the gradient height (for air-to-ground channel) can induce changes of the RDSD along the channel's path, impacting the precision of modeling efforts. To address this, we propose a theoretical model, integrating Mie scattering theory with considerations of channel's gradient height. Both our experimental and theoretical findings confirm this model's effectiveness in predicting THz channel behavior in rainy conditions. This work underscores the necessary in incorporating the variation of RDSD when THz channel travels in scenarios involving ground-to-air or air-to-ground communications., Comment: submitted to Journal of Infrared, Millimeter and Terahertz Waves

In the evolving landscape of terahertz communication, the behavior of channels within indoor environments, particularly through glass doors, has garnered significant attention. This paper comprehensively investigates terahertz channel performance under such conditions, employing a measurement setup operational between 113 and 170 GHz. Analyzing scenarios frequently induced by human activity and environmental factors, like door movements, we established a comprehensive theoretical model. This model seamlessly integrates transmission, reflection, absorption, and diffraction mechanisms, leveraging the Fresnel formula, multi-layer transmission paradigm, and knife-edge diffraction theory. Our experimental results and theoretical predictions harmoniously align, revealing intricate dependencies, such as increased power loss at higher frequencies and larger incident angles. Furthermore, door interactions, whether opening or oscillations, significantly impact the terahertz channel. Notably, door edges lead to a power blockage surpassing the transmission loss of the glass itself but remaining inferior to metallic handle interferences. This paper's insights are pivotal for the design and fabrication of terahertz communication systems within indoor settings, pushing the boundaries of efficient and reliable communication., Comment: Scheduled to publish in Nano Communication Networks

The advancement of millimeter wave and terahertz technologies have revolutionized high speed wireless networks and precise tracking systems. These technologies offer unique penetration capabilities in specific scenarios, significantly enhancing the capability to investigation plasma. Recent breakthroughs include the precise diagnosis of plasma electron density using terahertz time domain spectroscopy and the modeling of plasma sheaths in re-entry spacecraft through scattering matrices. Concurrently, extensive research efforts have been dedicated to comprehending plasma's influence on electromagnetic wave behaviors, encompassing reflection, transmission, absorption and also phase shift. In this paper, we employ COMSOL Multiphysics software to create an inductively coupled plasma (ICP) device, enabling the simulation of various plasma density arrangements. Our investigation focuses on unraveling the intricate interplay between plasma configurations and millimeter-wave transmission characteristics. The findings underscore the substantial impact of diverse plasma concentration arrangements on the behavior of electromagnetic waves traversing through them. Additionally, these arrangements endow the plasma with a discernible degree of frequency selectivity, thus expanding our understanding of plasma behavior in novel ways., Comment: To be published in 2023 Cross Strait Radio Science and Wireless Technology Conference (2023 CSRSWTC)

The neutron rich neck zone created in heavy ion reaction is experimentally probed by the production of the $A=3$ isobars. The energy spectra and angular distributions of triton and $^3$He are measured with the CSHINE detector in $^{86}$Kr +$^{208}$Pb reactions at 25 MeV/u. While the energy spectrum of $^{3}$He is harder than that of triton, known as "$^{3}$He-puzzle", the yield ratio $R({\rm t/^3He})$ presents a robust rising trend with the polar angle in laboratory. Using the fission fragments to reconstruct the fission plane, the enhancement of out-plane $R({\rm t/^3He})$ is confirmed in comparison to the in-plane ratios. Transport model simulations reproduce qualitatively the experimental trends, but the quantitative agreement is not achieved. The results demonstrate that a neutron rich neck zone is formed in the reactions. Further studies are called for to understand the clustering and the isospin dynamics related to neck formation.

Music as an emotional intervention medium has important applications in scenarios such as music therapy, games, and movies. However, music needs real-time arrangement according to changing emotions, bringing challenges to balance emotion real-time fit and soft emotion transition due to the fine-grained and mutable nature of the target emotion. Existing studies mainly focus on achieving emotion real-time fit, while the issue of smooth transition remains understudied, affecting the overall emotional coherence of the music. In this paper, we propose REMAST to address this trade-off. Specifically, we recognize the last timestep's music emotion and fuse it with the current timestep's input emotion. The fused emotion then guides REMAST to generate the music based on the input melody. To adjust music similarity and emotion real-time fit flexibly, we downsample the original melody and feed it into the generation model. Furthermore, we design four music theory features by domain knowledge to enhance emotion information and employ semi-supervised learning to mitigate the subjective bias introduced by manual dataset annotation. According to the evaluation results, REMAST surpasses the state-of-the-art methods in objective and subjective metrics. These results demonstrate that REMAST achieves real-time fit and smooth transition simultaneously, enhancing the coherence of the generated music.

Lobster eye telescopes are ideal monitors to detect X-ray transients, because they could observe celestial objects over a wide field of view in X-ray band. However, images obtained by lobster eye telescopes are modified by their unique point spread functions, making it hard to design a high efficiency target detection algorithm. In this paper, we integrate several machine learning algorithms to build a target detection framework for data obtained by lobster eye telescopes. Our framework would firstly generate two 2D images with different pixel scales according to positions of photons on the detector. Then an algorithm based on morphological operations and two neural networks would be used to detect candidates of celestial objects with different flux from these 2D images. At last, a random forest algorithm will be used to pick up final detection results from candidates obtained by previous steps. Tested with simulated data of the Wide-field X-ray Telescope onboard the Einstein Probe, our detection framework could achieve over 94% purity and over 90% completeness for targets with flux more than 3 mCrab (9.6 * 10-11 erg/cm2/s) and more than 94% purity and moderate completeness for targets with lower flux at acceptable time cost. The framework proposed in this paper could be used as references for data processing methods developed for other lobster eye X-ray telescopes., Comment: Accepted by the APJS Journal. Full source code could be downloaded from the China VO with DOI of https://doi.org/10.12149/101175. Docker version of the code could be obtained under request to the corresponding author

Real-time music accompaniment generation has a wide range of applications in the music industry, such as music education and live performances. However, automatic real-time music accompaniment generation is still understudied and often faces a trade-off between logical latency and exposure bias. In this paper, we propose SongDriver, a real-time music accompaniment generation system without logical latency nor exposure bias. Specifically, SongDriver divides one accompaniment generation task into two phases: 1) The arrangement phase, where a Transformer model first arranges chords for input melodies in real-time, and caches the chords for the next phase instead of playing them out. 2) The prediction phase, where a CRF model generates playable multi-track accompaniments for the coming melodies based on previously cached chords. With this two-phase strategy, SongDriver directly generates the accompaniment for the upcoming melody, achieving zero logical latency. Furthermore, when predicting chords for a timestep, SongDriver refers to the cached chords from the first phase rather than its previous predictions, which avoids the exposure bias problem. Since the input length is often constrained under real-time conditions, another potential problem is the loss of long-term sequential information. To make up for this disadvantage, we extract four musical features from a long-term music piece before the current time step as global information. In the experiment, we train SongDriver on some open-source datasets and an original \`aiSong Dataset built from Chinese-style modern pop music scores. The results show that SongDriver outperforms existing SOTA (state-of-the-art) models on both objective and subjective metrics, meanwhile significantly reducing the physical latency., Comment: *Both Zihao Wang and Qihao Liang contribute equally to the paper and share the co-first authorship. This paper has been accepted by ACM Multimedia 2022, oral session, full paper (main track)

Two-body correlations of the isotope-resolved light and heavy clusters are measured in $^{86}$Kr+$^{\rm 208}$Pb reactions at 25 MeV/u. The yield and kinetic variables of the $A=3$ isobars, triton and $^3$He, are analyzed in coincidence with the heavy clusters of $7\le A \le 14$ emitted at the earlier chance. While the velocity spectra of both triton and $^3$He exhibit scaling behavior over the type of the heavy clusters, the yield ratios of ${\rm t/^3He}$ correlate reversely to the neutron-to-proton ratio $N/Z$ of the latter, showing the ping-pong modality of the $N/Z$ of emitted clusters. The commonality that the $N/Z$ of the residues keeps the initial system value is extended to the cluster emission in heavy ion reactions. The comparison of transport model calculations to the data is discussed.

In this research, an integrated detection model, Swin-transformer-YOLOv5 or Swin-T-YOLOv5, was proposed for real-time wine grape bunch detection to inherit the advantages from both YOLOv5 and Swin-transformer. The research was conducted on two different grape varieties of Chardonnay (always white berry skin) and Merlot (white or white-red mix berry skin when immature; red when matured) from July to September in 2019. To verify the superiority of Swin-T-YOLOv5, its performance was compared against several commonly used/competitive object detectors, including Faster R-CNN, YOLOv3, YOLOv4, and YOLOv5. All models were assessed under different test conditions, including two different weather conditions (sunny and cloudy), two different berry maturity stages (immature and mature), and three different sunlight directions/intensities (morning, noon, and afternoon) for a comprehensive comparison. Additionally, the predicted number of grape bunches by Swin-T-YOLOv5 was further compared with ground truth values, including both in-field manual counting and manual labeling during the annotation process. Results showed that the proposed Swin-T-YOLOv5 outperformed all other studied models for grape bunch detection, with up to 97% of mean Average Precision (mAP) and 0.89 of F1-score when the weather was cloudy. This mAP was approximately 44%, 18%, 14%, and 4% greater than Faster R-CNN, YOLOv3, YOLOv4, and YOLOv5, respectively. Swin-T-YOLOv5 achieved its lowest mAP (90%) and F1-score (0.82) when detecting immature berries, where the mAP was approximately 40%, 5%, 3%, and 1% greater than the same. Furthermore, Swin-T-YOLOv5 performed better on Chardonnay variety with achieved up to 0.91 of R2 and 2.36 root mean square error (RMSE) when comparing the predictions with ground truth. However, it underperformed on Merlot variety with achieved only up to 0.70 of R2 and 3.30 of RMSE., Comment: 30 pages; 15 figures;Corresponding author: Xin Zhang Department of Agricultural and Biological Engineering Mississippi State University Mississippi State, MS 39762, USA (xzhang@abe.msstate.edu)