Light axigluon and single top production at the LHC

The light axigluon model can explain the Tevatron $t\overline{t}$ forward-backward asymmetry and at the same time satisfy the constraints from the electroweak precision measurement and the $ATLAS$ and $CMS$ data, which induces the flavor changing ($FC$) couplings of axigluon with the $SM$ and new quarks. We investigate the effects of these $FC$ couplings on the s- and t-channel single top productions at the $LHC$ and the $FC$ decays $Z\rightarrow \overline{b}s+b\overline{s}$, $t\rightarrow c\gamma$ and $cg$. Our numerical results show that the light axigluon can give significantly contributions to single top production and the rare top decays $t\rightarrow c\gamma$ and $cg$.Comment: 22 pages, 8 figures; references added, contributions of new quarks to rare decay processes adde

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

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

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

Secure semi-automated GDPR compliance service with restrictive fine-grained access control

Sharing personal data with service providers is a contentious issue that led to the birth of data regulations such as the EU General Data Protection Regulation (GDPR) and similar laws in the US. Complying with these regulations is a must for service providers. For users, this compliance assures them that their data is handled the way the service provider says it will be via their privacy policy. Auditing service providers' compliance is usually carried out by specific authorities when there is a need to do so (e.g., data breach). Nonetheless, these irregular compliance checks could lead to non-compliant actions being undetected for long periods. Users need an improved way to make sure their data is managed properly, giving them the ability to control and enforce detailed, restricted access to their data, in line with the policies set by the service provider. This work addresses these issues by providing a secure semi-automated GDPR compliance service for both users and service providers using smart contracts and attribute-based encryption with accountability. Privacy policies will be automatically checked for compliance before a service commences. Users can then upload their personal data with restrictive access controls extracted from the approved privacy policy. Operations' logs on the personal data during its full lifecycle will be immutably recorded and regularly checked for compliance to ensure the privacy policy is adhered to at all times. Evaluation results, using a real-world organization policy and example logs, show that the proposed service achieves these goals with low time overhead and high throughput

Synthesis and characterization of 2-(2-benzhydrylnaphthyliminomethyl)pyridylnickel halides: formation of branched polyethylene

A series of 2-(2-benzhydrylnaphthyliminomethyl)pyridine derivatives (L1–L3) was prepared and used to synthesize the corresponding bis-ligated nickel(II) halide complexes (Ni1–Ni6) in good yield. The molecular structures of representative complexes, namely the bromide Ni3 and the chloride complex Ni6, were confirmed by single crystal X-ray diffraction, and revealed a distorted octahedral geometry at nickel. Upon activation with either methylaluminoxane (MAO) or modified methylaluminoxane (MMAO), all nickel complex pre-catalysts exhibited high activities (up to 2.02 × 10⁷ g(PE) mol⁻¹(Ni) h⁻¹) towards ethylene polymerization, producing branched polyethylene of low molecular weight and narrow polydispersity. The influence of the reaction parameters and the nature of the ligands on the catalytic behavior of the title nickel complexes were investigated

Delay-dependent stabilization of stochastic interval delay systems with nonlinear disturbances

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.In this paper, a delay-dependent approach is developed to deal with the robust stabilization problem for a class of stochastic time-delay interval systems with nonlinear disturbances. The system matrices are assumed to be uncertain within given intervals, the time delays appear in both the system states and the nonlinear disturbances, and the stochastic perturbation is in the form of a Brownian motion. The purpose of the addressed stochastic stabilization problem is to design a memoryless state feedback controller such that, for all admissible interval uncertainties and nonlinear disturbances, the closed-loop system is asymptotically stable in the mean square, where the stability criteria are dependent on the length of the time delay and therefore less conservative. By using Itô's differential formula and the Lyapunov stability theory, sufficient conditions are first derived for ensuring the stability of the stochastic interval delay systems. Then, the controller gain is characterized in terms of the solution to a delay-dependent linear matrix inequality (LMI), which can be easily solved by using available software packages. A numerical example is exploited to demonstrate the effectiveness of the proposed design procedure.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany

One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale

This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Our key insight is -- learning diffusion models for marginal, conditional, and joint distributions can be unified as predicting the noise in the perturbed data, where the perturbation levels (i.e. timesteps) can be different for different modalities. Inspired by the unified view, UniDiffuser learns all distributions simultaneously with a minimal modification to the original diffusion model -- perturbs data in all modalities instead of a single modality, inputs individual timesteps in different modalities, and predicts the noise of all modalities instead of a single modality. UniDiffuser is parameterized by a transformer for diffusion models to handle input types of different modalities. Implemented on large-scale paired image-text data, UniDiffuser is able to perform image, text, text-to-image, image-to-text, and image-text pair generation by setting proper timesteps without additional overhead. In particular, UniDiffuser is able to produce perceptually realistic samples in all tasks and its quantitative results (e.g., the FID and CLIP score) are not only superior to existing general-purpose models but also comparable to the bespoken models (e.g., Stable Diffusion and DALL-E 2) in representative tasks (e.g., text-to-image generation).Comment: Accepted to ICML202

Prediction of necrotizing enterocolitis in very low birth weight infants by superior mesenteric artery ultrasound of postnatal day 1: A nested prospective study

BackgroundNecrotizing enterocolitis (NEC) is a devastating intestinal complication that occurs mainly in very-low-birth-weight infants (VLBWI). The study's aim was to investigate the possibility of early prediction of NEC on postnatal day 1 based on superior mesenteric artery (SMA) doppler ultrasonograpy.MethodsA prospective, observational, nested case control study (ChiCTR1900026197) was conducted to enroll VLBWIs (birth weight <1,500 grams) between October 2019 and September 2021. Doppler ultrasound measurement was done during the first 12 h of life and before first feeding. Infants developing NEC (stage II or III) subsequently were included in NEC group and infants spare of NEC were included in control group.Results370 VLBWIs were enrolled (30 NEC cases). Among the ultrasound parameters, S/D was significantly higher in the NEC group (OR: 2.081, 95% CI: 1.411–3.069, P = 0.000). The area under the receiver operating curve (AUROC) following the Logistic regression was 0.704 (95% CI: 0.566–0.842, P = 0.001). The sensitivity of S/D for predicting NEC was 52.2% and the specificity was 92.7%. The critical value of S/D was 6.944 and Youden index was 0.449. Preplanned subgroup analysis confirmed that NEC infants of different stages were characterized by different SMA bloodstream. Small for gestational age (SGA) might be a confounding factor affecting intestinal bloodflow. And infants with delayed initiation or slow advancement of feeding exhibited characteristic intestinal perfusion.ConclusionsIn VLBWI, early SMA ultrasound shows the potential to predict NEC. It is reasonable to speculate that SMA bloodstream is related to intestinal structural and functional integrity