LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign

A lot of online marketing campaigns aim to promote user interaction. The average treatment effect (ATE) of campaign strategies need to be monitored throughout the campaign. A/B testing is usually conducted for such needs, whereas the existence of user interaction can introduce interference to normal A/B testing. With the help of link prediction, we design a network A/B testing method LinkLouvain to minimize graph interference and it gives an accurate and sound estimate of the campaign's ATE. In this paper, we analyze the network A/B testing problem under a real-world online marketing campaign, describe our proposed LinkLouvain method, and evaluate it on real-world data. Our method achieves significant performance compared with others and is deployed in the online marketing campaign.Comment: Accepted by the Industrial & Practitioner Track of the 26th International Conference on Database Systems for Advanced Applications (DASFAA 2021

Text Diffusion with Reinforced Conditioning

Diffusion models have demonstrated exceptional capability in generating high-quality images, videos, and audio. Due to their adaptiveness in iterative refinement, they provide a strong potential for achieving better non-autoregressive sequence generation. However, existing text diffusion models still fall short in their performance due to a challenge in handling the discreteness of language. This paper thoroughly analyzes text diffusion models and uncovers two significant limitations: degradation of self-conditioning during training and misalignment between training and sampling. Motivated by our findings, we propose a novel Text Diffusion model called TREC, which mitigates the degradation with Reinforced Conditioning and the misalignment by Time-Aware Variance Scaling. Our extensive experiments demonstrate the competitiveness of TREC against autoregressive, non-autoregressive, and diffusion baselines. Moreover, qualitative analysis shows its advanced ability to fully utilize the diffusion process in refining samples.Comment: 9 pages, 3 figure

Calibrating LLM-Based Evaluator

Recent advancements in large language models (LLMs) on language modeling and emergent capabilities make them a promising reference-free evaluator of natural language generation quality, and a competent alternative to human evaluation. However, hindered by the closed-source or high computational demand to host and tune, there is a lack of practice to further calibrate an off-the-shelf LLM-based evaluator towards better human alignment. In this work, we propose AutoCalibrate, a multi-stage, gradient-free approach to automatically calibrate and align an LLM-based evaluator toward human preference. Instead of explicitly modeling human preferences, we first implicitly encompass them within a set of human labels. Then, an initial set of scoring criteria is drafted by the language model itself, leveraging in-context learning on different few-shot examples. To further calibrate this set of criteria, we select the best performers and re-draft them with self-refinement. Our experiments on multiple text quality evaluation datasets illustrate a significant improvement in correlation with expert evaluation through calibration. Our comprehensive qualitative analysis conveys insightful intuitions and observations on the essence of effective scoring criteria.Comment: 22 pages,11 figure

MicroRNA-708-5p acts as a therapeutic agent against metastatic lung cancer

MicroRNAs (miRNAs) have recently been recognized as targets for anti-metastatic therapy against cancer malignancy. Development of effective miRNA mediated therapies remains a challenge to both basic research and clinical practice. Here we presented the evidence for a miR-708-5p mediated replacement therapy against metastatic lung cancer. Expression of miR-708-5p was substantially reduced in metastatic lung cancer samples and cancer cell lines when compared to non-metastatic counterparts. Expression of the miRNA suppressed cell survival and metastasis in vitro through its direct target p21, and inhibited the PI3K/AKT pathway and stem cell-like characteristics of lung cancer cells. Systemic administration of this miRNA in a mouse model of NSCLC using polyethylenimine (PEI)-mediated delivery of unmodified miRNA mimics induced tumor specific apoptosis. It also effectively protected the tested animals from developing metastatic malignancy without causing any observed toxicity. The findings strongly support miR-708-5p as a novel and effective therapeutic agent against metastatic malignancy of non-small cell lung cancer

Numerical simulation study on suppression effect of water mist on PMMA combustion under external radiant heat flux

Numerical model was built with fire dynamic simulator and theocratical simulation was carried out to investigate the suppression effect of water mist on ignition and combustion process of typical solid material polymethyl methacrylate under external radiant heat flux. Characteristic parameters such as ignition time, surface temperature, heat release rate and temperature distribution of flame central plane during ignition and combustion process under different thermal radiant fluxes were obtained and compared with experimental results. The suppression effect of spray droplets on ignition and combustion process was analyzed and discussed. The results show the theoretical calculations of combustion characteristic parameters are in good agreement with experimental measurements. Water mist droplets can effectively delay the ignition time. Quantitative data proves that the water mist flow rate at 0.9 L/(min·m2) can delay the ignition time of samples by about 1,100 s while the radiant heat flux is 50 kW/m2. The simulation results can provide theoretical support and data reference for typical solid material fire prevention and fire extinguishment in practice

Fatty infiltration in the musculoskeletal system: pathological mechanisms and clinical implications

Fatty infiltration denotes the anomalous accrual of adipocytes in non-adipose tissue, thereby generating toxic substances with the capacity to impede the ordinary physiological functions of various organs. With aging, the musculoskeletal system undergoes pronounced degenerative alterations, prompting heightened scrutiny regarding the contributory role of fatty infiltration in its pathophysiology. Several studies have demonstrated that fatty infiltration affects the normal metabolism of the musculoskeletal system, leading to substantial tissue damage. Nevertheless, a definitive and universally accepted generalization concerning the comprehensive effects of fatty infiltration on the musculoskeletal system remains elusive. As a result, this review summarizes the characteristics of different types of adipose tissue, the pathological mechanisms associated with fatty infiltration in bone, muscle, and the entirety of the musculoskeletal system, examines relevant clinical diseases, and explores potential therapeutic modalities. This review is intended to give researchers a better understanding of fatty infiltration and to contribute new ideas to the prevention and treatment of clinical musculoskeletal diseases

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve