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

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

Efficient and ultra-stable perovskite light-emitting diodes

Perovskite light-emitting diodes (PeLEDs) have emerged as a strong contender for next-generation display and information technologies. However, similar to perovskite solar cells, the poor operational stability remains the main obstacle toward commercial applications. Here we demonstrate ultra-stable and efficient PeLEDs with extraordinary operational lifetimes (T50) of 1.0x10^4 h, 2.8x10^4 h, 5.4x10^5 h, and 1.9x10^6 h at initial radiance (or current densities) of 3.7 W/sr/m2 (~5 mA/cm2), 2.1 W/sr/m2 (~3.2 mA/cm2), 0.42 W/sr/m2 (~1.1 mA/cm2), and 0.21 W/sr/m2 (~0.7 mA/cm2) respectively, and external quantum efficiencies of up to 22.8%. Key to this breakthrough is the introduction of a dipolar molecular stabilizer, which serves two critical roles simultaneously. First, it prevents the detrimental transformation and decomposition of the alpha-phase FAPbI3 perovskite, by inhibiting the formation of lead and iodide intermediates. Secondly, hysteresis-free device operation and microscopic luminescence imaging experiments reveal substantially suppressed ion migration in the emissive perovskite. The record-long PeLED lifespans are encouraging, as they now satisfy the stability requirement for commercial organic LEDs (OLEDs). These results remove the critical concern that halide perovskite devices may be intrinsically unstable, paving the path toward industrial applications.Comment: This is a preprint of the paper prior to peer review. New and updated results may be available in the final version from the publishe

The prognostic value of the tertiary lymphoid structure in gastrointestinal cancers

BackgroundNumerous studies and research papers have provided evidence suggesting that tertiary lymphoid structures (TLS) play a crucial role in combating and suppressing tumor growth and progression. Despite the wealth of information on the significance of TLS in various types of cancer, their prognostic value in gastrointestinal (GI) cancers remains uncertain. Therefore, this meta-analysis investigated the prognostic value of TLS in GI cancers.MethodsWe searched Web of science, Pubmed, Embase and Cochrane Library for studies that met the requirements as of May 1, 2023, and the hazard ratio (HR) and the corresponding 95% confidence interval (CI) were included in the analysis. The bioinformatics analysis results based on the TCGA database are used to supplement our research.ResultsThe meta-analysis included 32 studies involving 5778 patients. The results of comprehensive analysis showed that TLS-High is associated with prolonged OS (HR=0.525,95%CI:0.447-0.616 (P < 0.001), RFS (HR=0.546,95%CI:0.461-0.647, P < 0.001), DFS (HR=0.519,95%CI:0.417-0.646, P < 0.001) and PFS (HR=0.588,95%CI:0.406-0.852, P=0.005) in GI cancer. Among the patients who received immunotherapy, TLS-High is associated with significantly prolonged OS (HR=0.475, 95%CI:0.282-0.799, P=0.005) and PFS(HR=0.576, 95%CI:0.381-0.871, P=0.009). It is worth noting that subgroup analysis showed that there was no significant relationship between TLS and OS(HR=0.775, 95%CI:0.570-1.053,P=0.103) in CRC. And when Present is used as the cut-off criteria of TLS, there is no significant correlation between TLS and OS (HR=0.850, 95%CI:0.721-1.002, P=0.053)in HCC.ConclusionTLS is a significant predictor of the prognosis of GI cancers and has the potential to become a prognostic biomarker of immunotherapy-related patients.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/#recordDetails, identifier CRD42023443562

MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge

We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and runtime for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs $\geq 200$ per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.Comment: 25 pages, 6 figures, 4 tables, additional material available at https://github.com/gwastro/ml-mock-data-challenge-