Localized primary gastrointestinal diffuse large B cell lymphoma received a surgical approach: an analysis of prognostic factors and comparison of staging systems in 101 patients from a single institution

Clinical characteristics and survival rate of patients with localized PG-DLBCL. It shows the clinical characteristics and Rituximab treatment between localized PG-DLBCL patients with surgery and those with chemotherapy alone. (PDF 276 kb

Specialized Courses Teaching Mode Innovation of the Independent College Based on MOOCS

Independent college is a new kind of school-running pattern, on the basis of independent college computer professional course teaching, based on the background of MOOCS, specialized course teaching mode principle, on the basis of design is given priority to, the class online course of classroom teaching mode. To a certain extent can motivate we will accelerate reform of the teaching mode of independent colleges, improve the teaching quality of education. Keywords: Moocs, Independent college, Specialized courses, Teaching mod

Optical event horizon-based complete transformation and control of dark solitons

We propose a manipulation approach to vary the wave speed, as well as the grayness, of dark solitons under the optical event horizon arising from the interaction between a dark soliton and a probe wave. To the best of our knowledge, the optical event horizon effect is demonstrated for the first time to be capable of inducing a reversible conversion between a black soliton and a gray one. This reversible soliton transformation and control process originates from the intrinsic competition between the probe-induced nonlinear phase shift and the internal phase of the dark soliton. In a cascaded system consisting of two optical event horizons, we also observe the new optical soliton tunneling phenomena where a dark soliton can be reset longitudinally purposely. The results may find applications in information cloaking such as effectively hiding the presence of intermediate fiber section to the receiver

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

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