Low Frequency Quasi-periodic Oscillation in MAXI J1820+070: Revealing distinct Compton and Reflection Contributions

X-ray low frequency quasi-periodic oscillations (LFQPOs) of black hole X-ray binaries, especially those type-C LFQPOs, are representative timing signals of black hole low/hard state and intermediate state, which has been suspected as to originate due to Lense-Thirring precession of the accretion flow. Here we report an analysis of one of the \emph{Insight}-HXMT observations of the black hole transient MAXI J1820$+$070 taken near the flux peak of its hard spectral state during which strong type-C LFQPOs were detected in all three instruments up to photon energies above 150 keV. We obtained and analyzed the short-timescale X-ray spectra corresponding to high- and low-intensity phases of the observed LFQPO waveform with a spectral model composed of Comptonization and disk reflection components. We found that the normalization of the spectral model is the primary parameter that varied between the low and high-intensity phases. The variation in the LFQPO flux at the hard X-ray band (> 100 keV) is from the Compton component alone, while the energy-dependent variation in the LFQPO flux at lower energies (< 30 keV) is mainly caused by the reflection component with a large reflection fraction in response to the incident Compton component. The observed X-ray LFQPOs thus should be understood as manifesting the original timing signals or beats in the hard Compton component, which gives rise to additional variability in softer energies due to disk reflection.Comment: 8 pages, 4 figures, accepted for publication in MNRA

How to Retrain Recommender System? A Sequential Meta-Learning Method

Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since it can account for both long-term and short-term user preference. However, a full model retraining could be very time-consuming and memory-costly, especially when the scale of historical data is large. In this work, we study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research community. Our first belief is that retraining the model on historical data is unnecessary, since the model has been trained on it before. Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference. To address this dilemma, we propose a new training method, aiming to abandon the historical data during retraining through learning to transfer the past training experience. Specifically, we design a neural network-based transfer component, which transforms the old model to a new model that is tailored for future recommendations. To learn the transfer component well, we optimize the "future performance" -- i.e., the recommendation accuracy evaluated in the next time period. Our Sequential Meta-Learning(SML) method offers a general training paradigm that is applicable to any differentiable model. We demonstrate SML on matrix factorization and conduct experiments on two real-world datasets. Empirical results show that SML not only achieves significant speed-up, but also outperforms the full model retraining in recommendation accuracy, validating the effectiveness of our proposals. We release our codes at: https://github.com/zyang1580/SML.Comment: Appear in SIGIR 202

MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model

This paper studies the human image animation task, which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion. Despite achieving reasonable results, these approaches face challenges in maintaining temporal consistency throughout the animation due to the lack of temporal modeling and poor preservation of reference identity. In this work, we introduce MagicAnimate, a diffusion-based framework that aims at enhancing temporal consistency, preserving reference image faithfully, and improving animation fidelity. To achieve this, we first develop a video diffusion model to encode temporal information. Second, to maintain the appearance coherence across frames, we introduce a novel appearance encoder to retain the intricate details of the reference image. Leveraging these two innovations, we further employ a simple video fusion technique to encourage smooth transitions for long video animation. Empirical results demonstrate the superiority of our method over baseline approaches on two benchmarks. Notably, our approach outperforms the strongest baseline by over 38% in terms of video fidelity on the challenging TikTok dancing dataset. Code and model will be made available.Comment: Project Page at https://showlab.github.io/magicanimat

Simulation study of BESIII with stitched CMOS pixel detector using ACTS

Reconstruction of tracks of charged particles with high precision is very crucial for HEP experiments to achieve their physics goals. As the tracking detector of BESIII experiment, the BESIII drift chamber has suffered from aging effects resulting in degraded tracking performance after operation for about 15 years. To preserve and enhance the tracking performance of BESIII, one of the proposals is to add one layer of thin CMOS pixel sensor in cylindrical shape based on the state-of-the-art stitching technology, between the beam pipe and the drift chamber. The improvement of tracking performance of BESIII with such an additional pixel detector compared to that with only the existing drift chamber is studied using the modern common tracking software ACTS, which provides a set of detector-agnostic and highly performant tracking algorithms that have demonstrated promising performance for a few high energy physics and nuclear physics experiments

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

Aggregation-Induced Emission (AIE), Life and Health

Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Influence of the Steel Fiber Content on the Flexural Fatigue Behavior of Recycled Aggregate Concrete

Steel fiber recycled aggregate concrete (SFRAC) is mainly used in roads, bridges, and railways that are subjected to bear wheel load. This paper presents a comparative experimental study on the flexural fatigue behavior of the SFRAC, the natural aggregate concrete (NAC), and the recycled aggregate concrete (RAC). The results show that, with the use of 1.0% volume fraction steel fiber, the flexural strength of SFRAC exceeds the flexural strength of NAC (around 0.3%), and the fatigue lives of RAC have been found to be lower by 19.9% and 53.4% compared to SFRAC at stress levels S = 0.9 and S = 0.7. The fatigue strain of SFRAC follows the three-stage law, and the fatigue strain of SFRAC develops more slowly than that of RAC at the same stress level. Two-parameter Weibull distribution is fitted to the test data to generate fatigue models at different survival probabilities, and fatigue life can be accurately predicted using the developed model. Therefore, it is feasible to replace the natural concrete with the recycled aggregate concrete with appropriate steel fiber content in some aspects, which is of great significance to green development

Probabilistic load margin assessment considering forecast error of wind power generation

The increasing integration of wind power in power systems necessitates the probabilistic assessment of various uncertain factors. In operational planning, modeling short-term scale uncertainties, i.e., wind power forecast errors, plays an important role. In this paper, according to the different forecast values, the corresponding probability distributions of wind power forecast errors are developed using a data-driven manner. Then, the polynomial chaos expansion surrogate is developed to facilitate the probabilistic load margin assessment considering wind power forecast errors. The effectiveness of the forecast error model is verified using the historical data of realistic wind power plants. The results show that the probability distributions of forecast errors vary with the level of forecast values. Moreover, the performance of the polynomial chaos expansion surrogate in estimating probabilistic load margin is validated in the IEEE 30-bus system. The results demonstrate that the versatile forecast error distributions significantly impact the characteristics of load margin. Moreover, the polynomial chaos expansion surrogate can accelerate the load margin assessment compared to the Monter Carlo simulation while retaining the same accuracy