Effect of ridge-furrow with plastic mulching and organic amendment on fertilizer-N fate in maize-soil system: A 15N isotope tracer study

The implementation of ridge-furrow with plastic film mulching has the potential to enhance crop yields and water productivity, particularly in black soil regions. However, the synergistic impacts of combining ridge-furrow with plastic mulching alongside with various organic amendments on maize yield and nitrogen fertilizer utilization efficiency remain unclear. Using 15N-labeled tracing technology, we investigated fertilizer-N recovery of maize, distribution, fertilizer-N residual in soil, and nitrogen fertilizer loss across six treatments: non-mulched flat with non-organic amendment (FN), non-mulched flat with straw amendment (FS), non-mulched flat with biochar amendment (FBC), ridge-furrow with plastic mulching without organic amendment (RN), ridge-furrow with plastic mulching with straw amendment (RS), and ridge-furrow with plastic mulching with biochar amendment (RBC). The results revealed that ridge-furrow with plastic mulching in comparison to non-mulched flat, led to a significant increase in maize dry biomass accumulation, yield, and the rate of fertilizer-N recovery in maize (NRE) by 8.57%–12.36%, 10.08%–15.13%, and 2.22%–3.18%, respectively. The rate of fertilizer-N residual in soil (NSR) and fertilizer-N loss (NLS) decreased by 0.5%–2.04% and 0.78%–3.21%, respectively. In addition, the straw and biochar amendments under different planting methods promoted NRE in plants and NSR in soil, reducing NLS. Compared with non-organic amendment treatments, the inclusion of straw and biochar amendments resulted in increased NRE and NRS by 1.64%–6.20% and 0.12%–2.18%, while NLS decreased by 1.76%–7.78%. Biochar amendment treatment exhibited significantly higher nitrogen accumulation and NRE compared to the straw amendment treatment. Overall, ridge-furrow with plastic mulching combined with biochar amendment proved to be an effective method to enhance nitrogen fertilizer utilization of maize in the black soil regions, improving both yield and nitrogen fertilizer utilization efficiency

GWAI: Harnessing Artificial Intelligence for Enhancing Gravitational Wave Data Analysis

Gravitational wave (GW) astronomy has opened new frontiers in understanding the cosmos, while the integration of artificial intelligence (AI) in science promises to revolutionize data analysis methodologies. However, a significant gap exists, as there is currently no dedicated platform that enables scientists to develop, test, and evaluate AI algorithms efficiently. To address this gap, we introduce GWAI, a pioneering AI-centered software platform designed for gravitational wave data analysis. GWAI contains a three-layered architecture that emphasizes simplicity, modularity, and flexibility, covering the entire analysis pipeline. GWAI aims to accelerate scientific discoveries, bridging the gap between advanced AI techniques and astrophysical research.Comment: 10 pages, 5 figure

Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer

Space-based gravitational wave detection is one of the most anticipated gravitational wave (GW) detection projects in the next decade, which will detect abundant compact binary systems. However, the precise prediction of space GW waveforms remains unexplored. To solve the data processing difficulty in the increasing waveform complexity caused by detectors' response and second-generation time-delay interferometry (TDI 2.0), an interpretable pre-trained large model named CBS-GPT (Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer) is proposed. For compact binary system waveforms, three models were trained to predict the waveforms of massive black hole binary (MBHB), extreme mass-ratio inspirals (EMRIs), and galactic binary (GB), achieving prediction accuracies of 98%, 91%, and 99%, respectively. The CBS-GPT model exhibits notable interpretability, with its hidden parameters effectively capturing the intricate information of waveforms, even with complex instrument response and a wide parameter range. Our research demonstrates the potential of large pre-trained models in gravitational wave data processing, opening up new opportunities for future tasks such as gap completion, GW signal detection, and signal noise reduction

DECODE: DilatEd COnvolutional neural network for Detecting Extreme-mass-ratio inspirals

The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due to their complex waveforms, extended duration, and low signal-to-noise ratio (SNR), making them more challenging to be identified compared to compact binary coalescences. While matched filtering-based techniques are known for their computational demands, existing deep learning-based methods primarily handle time-domain data and are often constrained by data duration and SNR. In addition, most existing work ignores time-delay interferometry (TDI) and applies the long-wavelength approximation in detector response calculations, thus limiting their ability to handle laser frequency noise. In this study, we introduce DECODE, an end-to-end model focusing on EMRI signal detection by sequence modeling in the frequency domain. Centered around a dilated causal convolutional neural network, trained on synthetic data considering TDI-1.5 detector response, DECODE can efficiently process a year's worth of multichannel TDI data with an SNR of around 50. We evaluate our model on 1-year data with accumulated SNR ranging from 50 to 120 and achieve a true positive rate of 96.3% at a false positive rate of 1%, keeping an inference time of less than 0.01 seconds. With the visualization of three showcased EMRI signals for interpretability and generalization, DECODE exhibits strong potential for future space-based gravitational wave data analyses.Comment: 13 pages, 5 figures, and 2 table

Dawning of a New Era in Gravitational Wave Data Analysis: Unveiling Cosmic Mysteries via Artificial Intelligence -- A Systematic Review

Background: Artificial intelligence (AI), with its vast capabilities, has become an integral part of our daily interactions, particularly with the rise of sophisticated models like Large Language Models. These advancements have not only transformed human-machine interactions but have also paved the way for significant breakthroughs in various scientific domains. Aim of review: This review is centered on elucidating the profound impact of AI, especially deep learning, in the field of gravitational wave data analysis (GWDA). We aim to highlight the challenges faced by traditional GWDA methodologies and how AI emerges as a beacon of hope, promising enhanced accuracy, real-time processing, and adaptability. Key scientific concepts of review: Gravitational wave (GW) waveform modeling stands as a cornerstone in the realm of GW research, serving as a sophisticated method to simulate and interpret the intricate patterns and signatures of these cosmic phenomena. This modeling provides a deep understanding of the astrophysical events that produce gravitational waves. Next in line is GW signal detection, a refined technique that meticulously combs through extensive datasets, distinguishing genuine gravitational wave signals from the cacophony of background noise. This detection process is pivotal in ensuring the authenticity of observed events. Complementing this is the GW parameter estimation, a method intricately designed to decode the detected signals, extracting crucial parameters that offer insights into the properties and origins of the waves. Lastly, the integration of AI for GW science has emerged as a transformative force. AI methodologies harness vast computational power and advanced algorithms to enhance the efficiency, accuracy, and adaptability of data analysis in GW research, heralding a new era of innovation and discovery in the field

WaveFormer: transformer-based denoising method for gravitational-wave data

With the advent of gravitational-wave astronomy and the discovery of more compact binary coalescences, data quality improvement techniques are desired to handle the complex and overwhelming noise in gravitational wave (GW) observational data. Though recent machine learning-based studies have shown promising results for data denoising, they are unable to precisely recover both the GW signal amplitude and phase. To address such an issue, we develop a deep neural network centered workflow, WaveFormer, for significant noise suppression and signal recovery on observational data from the Laser Interferometer Gravitational-Wave Observatory (LIGO). The WaveFormer has a science-driven architecture design with hierarchical feature extraction across a broad frequency spectrum. As a result, the overall noise and glitch are decreased by more than one order of magnitude and the signal recovery error is roughly 1% and 7% for the phase and amplitude, respectively. Moreover, on 75 reported binary black hole (BBH) events of LIGO we obtain a significant improvement of inverse false alarm rate. Our work highlights the potential of large neural networks in gravitational wave data analysis and, while primarily demonstrated on LIGO data, its adaptable design indicates promise for broader application within the International Gravitational-Wave Observatories Network (IGWN) in future observational runs

APICom: Automatic API Completion via Prompt Learning and Adversarial Training-based Data Augmentation

Based on developer needs and usage scenarios, API (Application Programming Interface) recommendation is the process of assisting developers in finding the required API among numerous candidate APIs. Previous studies mainly modeled API recommendation as the recommendation task, which can recommend multiple candidate APIs for the given query, and developers may not yet be able to find what they need. Motivated by the neural machine translation research domain, we can model this problem as the generation task, which aims to directly generate the required API for the developer query. After our preliminary investigation, we find the performance of this intuitive approach is not promising. The reason is that there exists an error when generating the prefixes of the API. However, developers may know certain API prefix information during actual development in most cases. Therefore, we model this problem as the automatic completion task and propose a novel approach APICom based on prompt learning, which can generate API related to the query according to the prompts (i.e., API prefix information). Moreover, the effectiveness of APICom highly depends on the quality of the training dataset. In this study, we further design a novel gradient-based adversarial training method {\atpart} for data augmentation, which can improve the normalized stability when generating adversarial examples. To evaluate the effectiveness of APICom, we consider a corpus of 33k developer queries and corresponding APIs. Compared with the state-of-the-art baselines, our experimental results show that APICom can outperform all baselines by at least 40.02\%, 13.20\%, and 16.31\% in terms of the performance measures EM@1, MRR, and MAP. Finally, our ablation studies confirm the effectiveness of our component setting (such as our designed adversarial training method, our used pre-trained model, and prompt learning) in APICom.Comment: accepted in Internetware 202

Taiji Data Challenge for Exploring Gravitational Wave Universe

The direct observation of gravitational waves (GWs) opens a new window for exploring new physics from quanta to cosmos and provides a new tool for probing the evolution of universe. GWs detection in space covers a broad spectrum ranging over more than four orders of magnitude and enables us to study rich physical and astronomical phenomena. Taiji is a proposed space-based GW detection mission that will be launched in the 2030s. Taiji will be exposed to numerous overlapping and persistent GW signals buried in the foreground and background, posing various data analysis challenges. In order to empower potential scientific discoveries, the Mock LISA Data Challenge and the LISA Data Challenge (LDC) were developed. While LDC provides a baseline framework, the first LDC needs to be updated with more realistic simulations and adjusted detector responses for Taiji's constellation. In this paper, we review the scientific objectives and the roadmap for Taiji, as well as the technical difficulties in data analysis and the data generation strategy, and present the associated data challenges. In contrast to LDC, we utilize second-order Keplerian orbit and second-generation time delay interferometry techniques. Additionally, we employ a new model for the extreme-mass-ratio inspiral waveform and stochastic GW background spectrum, which enables us to test general relativity and measure the non-Gaussianity of curvature perturbations. Furthermore, we present a comprehensive showcase of parameter estimation using a toy dataset. This showcase not only demonstrates the scientific potential of the Taiji Data Challenge but also serves to validate the effectiveness of the pipeline. As the first data challenge for Taiji, we aim to build an open ground for data analysis related to Taiji sources and sciences. More details can be found on the official website at http://taiji-tdc.ictp-ap.org.Comment: 15 pages, 3 figure

Virtual reality-induced motor function of the upper extremity and brain activation in stroke: study protocol for a randomized controlled trial

BackgroundThe benefits of virtual reality (VR)-based rehabilitation were reported in patients after stroke, but there is insufficient evidence about how VR promotes brain activation in the central nervous system. Hence, we designed this study to explore the effects of VR-based intervention on upper extremity motor function and associated brain activation in stroke patients.Methods/designIn this single-center, randomized, parallel-group clinical trial with a blinded assessment of outcomes, a total of 78 stroke patients will be assigned randomly to either the VR group or the control group. All stroke patients who have upper extremity motor deficits will be tested with functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and clinical evaluation. Clinical assessment and fMRI will be performed three times on each subject. The primary outcome is the change in performance on the Fugl-Meyer Assessment Upper Extremity Scale (FMA-UE). Secondary outcomes are functional independence measure (FIM), Barthel Index (BI), grip strength, and changes in the blood oxygenation level-dependent (BOLD) effect in the ipsilesional and contralesional primary motor cortex (M1) on the left and right hemispheres assessed with resting-state fMRI (rs-fMRI), task-state fMRI (ts-fMRI), and changes in EEG at the baseline and weeks 4 and 8.DiscussionThis study aims to provide high-quality evidence for the relationship between upper extremity motor function and brain activation in stroke. In addition, this is the first multimodal neuroimaging study that explores the evidence for neuroplasticity and associated upper motor function recovery after VR in stroke patients.Clinical trial registrationChinese Clinical Trial Registry, identifier: ChiCTR2200063425