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

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

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

A data-manifold based scoring system and its application to cardiac arrest prediction

Scoring systems have been widely used in medical applications, for example to assess the severity of illness in intensive care units (ICU). In this Final Year Project (FYP), the author researched into different ways of developing novel scoring systems for predicting cardiac arrest within 72 hours. This report is the documentation of the works done and the presentation of the novel scoring system developed. Several approaches were looked into and eventually one scoring system with best performance is proposed in this report. The proposed scoring system computes the scores based on the data manifolds possessed by training and testing data, and therefore global consistency of the data is utilized. This proposed scoring system is essentially a supervised learning process. Other approaches include semi-supervised learning and supervised learning with pre-known scores. The validation experiment is conducted on real patients’ data, including both vital signs and heart rate variability (HRV) parameters. Performance is evaluated under leave-one-out cross-validation (LOOCV) framework. Moreover, comparison of the proposed scoring system with previous work can be found in terms of sensitivity, specificity and positive predictive value (PPV), negative predictive value (NPV) and receiver operating characteristic (ROC). The proposed Data-manifold based Scoring System is able to achieve better performance in generating meaningful risk scores than the Distance-based Scoring System previously developed.Bachelor of Engineerin

Clustering and semi-supervised classification with application to driver distraction detection

Clustering and Semi-Supervised Classification (SSC) algorithms can make use of unlabeled training data and thus have the potential to alleviate labeling costs. For example, Extreme Learning Machine (ELM) was recently extended to semi-supervised learning and clustering with promising performance. Meanwhile, it is either costly or infeasible to obtain labeled training samples in some real-world applications. The thesis investigates clustering and SSC algorithms with application to driver distraction detection. Firstly, the thesis investigates embedding-based clustering. The desirable properties of embedding are reviewed in the literature, e.g., preserving the intrinsic data structure and maximizing the class separability. To obtain better embedding for clustering, the thesis considers both properties together and develops a novel clustering algorithm referred to as ELM for Joint Embedding and Clustering (ELM-JEC). Experimental studies on a wide range of benchmark datasets have show that ELM-JEC is competitive with the related methods. Secondly, the thesis investigates graph-based clustering. One limitation of existing graph learning methods is that they adjust the graph based on either the original data or the linearly projected data, which may not effectively reveal the underlying low- dimensional structures. To address this limitation, this thesis develops dual data representations, i.e., the original data and their nonlinear embedding obtained via an ELM- based neural network, and uses them as the basis for graph learning. The resulting algorithm is named as clustering based on ELM and Constrained Laplacian Rank (ELM- CLR). The experimental results show that ELM-CLR outperforms other adaptive graph learning methods on most benchmark datasets. Finally, the thesis applies the proposed clustering algorithms, i.e., ELM-JEC and ELM- CLR, and several SSC algorithms to driver distraction detection. The clustering algorithms are used on unlabeled data to generate preliminary labels as reference to assist human experts in the labeling process. In terms of the clustering accuracy, both proposed clustering algorithms perform better or on par with the related algorithms. The best clustering accuracy is achieved by ELM-JEC. Moreover, the research question of “which type of SSC method is more suitable for driver distraction detection?” is answered by evaluating two popular types of semi-supervised methods on a real-world dataset of drivers’ eye and head movements. The experimental results show that the graph-based methods achieve twice the improvement by the low-density-separation based method. It has also been shown that 1) the graph-based methods reduce the required amount of labeled training data, and 2) the benefits in detection accuracy increase with the size of unlabeled datasets. Overall, the thesis contributes two novel clustering algorithms by making use of ELM- based embedding and discovers that 1) better clustering performance on some datasets is expected, if the embedding preserves the intrinsic local structure and maximizes the class separability simultaneously, and 2) Both original and nonlinear embedded spaces are crucial to learning graphs with clear clusters. Moreover, the thesis contributes to the research on driver distraction detection by putting forward a semi-supervised driver distraction detection system with efficient labeling assistance and verifies it on an on- road driver distraction dataset.Doctor of Philosoph