Analysis of Crowdsourced Sampling Strategies for HodgeRank with Sparse Random Graphs

Crowdsourcing platforms are now extensively used for conducting subjective pairwise comparison studies. In this setting, a pairwise comparison dataset is typically gathered via random sampling, either \emph{with} or \emph{without} replacement. In this paper, we use tools from random graph theory to analyze these two random sampling methods for the HodgeRank estimator. Using the Fiedler value of the graph as a measurement for estimator stability (informativeness), we provide a new estimate of the Fiedler value for these two random graph models. In the asymptotic limit as the number of vertices tends to infinity, we prove the validity of the estimate. Based on our findings, for a small number of items to be compared, we recommend a two-stage sampling strategy where a greedy sampling method is used initially and random sampling \emph{without} replacement is used in the second stage. When a large number of items is to be compared, we recommend random sampling with replacement as this is computationally inexpensive and trivially parallelizable. Experiments on synthetic and real-world datasets support our analysis

HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation

Recently, crowdsourcing has emerged as an effective paradigm for human-powered large scale problem solving in various domains. However, task requester usually has a limited amount of budget, thus it is desirable to have a policy to wisely allocate the budget to achieve better quality. In this paper, we study the principle of information maximization for active sampling strategies in the framework of HodgeRank, an approach based on Hodge Decomposition of pairwise ranking data with multiple workers. The principle exhibits two scenarios of active sampling: Fisher information maximization that leads to unsupervised sampling based on a sequential maximization of graph algebraic connectivity without considering labels; and Bayesian information maximization that selects samples with the largest information gain from prior to posterior, which gives a supervised sampling involving the labels collected. Experiments show that the proposed methods boost the sampling efficiency as compared to traditional sampling schemes and are thus valuable to practical crowdsourcing experiments.Comment: Accepted by AAAI201

Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are batch methods designed mainly based on the convex optimization, say, the projected gradient descent method. However, they are generally time-consuming due to that the singular value decomposition (SVD) is commonly adopted during the update, especially when the data size is very large. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: (a) SVD-free via dropping convexity, with good scalability by the use of stochastic algorithm, i.e., stochastic variance reduced gradient (SVRG), and (b) adaptive step size choice via introducing a new stabilized Barzilai-Borwein (SBB) method as the original version for convex problems might fail for the considered stochastic \textit{non-convex} optimization problem. Moreover, we show that the proposed algorithm converges to a stationary point at a rate $\mathcal{O}(\frac{1}{T})$ in our setting, where $T$ is the number of total iterations. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm via comparing with the state-of-the-art methods, particularly, much lower computational cost with good prediction performance.Comment: 11 pages, 3 figures, 2 tables, accepted by AAAI201

Quantile autoregressive conditional heteroscedasticity

This paper proposes a novel conditional heteroscedastic time series model by applying the framework of quantile regression processes to the ARCH(\infty) form of the GARCH model. This model can provide varying structures for conditional quantiles of the time series across different quantile levels, while including the commonly used GARCH model as a special case. The strict stationarity of the model is discussed. For robustness against heavy-tailed distributions, a self-weighted quantile regression (QR) estimator is proposed. While QR performs satisfactorily at intermediate quantile levels, its accuracy deteriorates at high quantile levels due to data scarcity. As a remedy, a self-weighted composite quantile regression (CQR) estimator is further introduced and, based on an approximate GARCH model with a flexible Tukey-lambda distribution for the innovations, we can extrapolate the high quantile levels by borrowing information from intermediate ones. Asymptotic properties for the proposed estimators are established. Simulation experiments are carried out to access the finite sample performance of the proposed methods, and an empirical example is presented to illustrate the usefulness of the new model

2'-Fucosyllactose Remits Colitis-Induced Liver Oxygen Stress through the Gut-Liver-Metabolites Axis.

peer reviewedLiver oxygen stress is one of the main extraintestinal manifestations of colitis and 5% of cases develop into a further liver injury and metabolic disease. 2′-fucosyllactose (2′-FL), a main member of human milk oligosaccharides (HMOs), has been found to exert efficient impacts on remitting colitis. However, whether 2′-FL exerts the function to alleviate colitis-induced liver injury and how 2′-FL influences the metabolism via regulating gut microbiota remain unknown. Herein, in our study, liver oxygen stress was measured by measuring liver weight and oxygen-stress-related indicators. Then, 16S full-length sequencing analysis and non-target metabolome in feces were performed to evaluate the overall responses of metabolites and intestinal bacteria after being treated with 2′-FL (400 mg/kg b.w.) in colitis mice. The results showed that, compared with the control group, the liver weight of colitis mice was significantly decreased by 18.30% (p < 0.05). After 2′-FL treatment, the liver weight was significantly increased by 12.65% compared with colitis mice (p < 0.05). Meanwhile, they exhibited higher levels of oxidation in liver tissue with decreasing total antioxidant capacity (T-AOC) (decreased by 17.15%) and glutathione (GSH) levels (dropped by 22.68%) and an increasing malondialdehyde (MDA) level (increased by 36.24%), and 2′-FL treatment could reverse those tendencies. Full-length 16S rRNA sequencing revealed that there were 39 species/genera differentially enriched in the control, dextran sulphate sodium (DSS), and DSS + 2′-FL groups. After treatment with 2′-FL, the intestinal metabolic patterns, especially glycometabolism and the lipid-metabolism-related process, in DSS mice were strikingly altered with 33 metabolites significantly down-regulated and 26 metabolites up-regulated. Further analysis found DSS induced a 40.01%, 41.12%, 43.81%, and 39.86% decline in acetic acid, propionic acid, butyric acid, and total short chain fatty acids (SCFAs) in colitis mice (all p < 0.05), respectively, while these were up-regulated to different degrees in the DSS + 2′-FL group. By co-analyzing the data of gut microbiota and metabolites, glycometabolism and lipid-metabolism-associated metabolites exhibited strong positive/negative relationships with Akkermansia_muciniphila (all p < 0.01) and Paraprevotella spp. (all p < 0.01), suggesting that the two species might play crucial roles in the process of 2′-FL alleviating colitis-induced liver oxygen stress. In conclusion, in the gut−liver−microbiotas axis, 2′-FL mediated in glucose and lipid-related metabolism and alleviated liver oxygen stress via regulating gut microbiota in the DSS-induced colitis model. The above results provide a new perspective to understand the probiotic function of 2′-FL

Dietary Regulation of the Crosstalk between Gut Microbiome and Immune Response in Inflammatory Bowel Disease.

peer reviewedInflammatory bowel disease (IBD), a chronic, recurring inflammatory response, is a growing global public health issue. It results from the aberrant crosstalk among environmental factors, gut microbiota, the immune system, and host genetics, with microbiota serving as the core of communication for differently-sourced signals. In the susceptible host, dysbiosis, characterized by the bloom of facultative anaerobic bacteria and the decline of community diversity and balance, can trigger an aberrant immune response that leads to reduced tolerance against commensal microbiota. In IBD, such dysbiosis has been profoundly proven in animal models, as well as clinic data analysis; however, it has not yet been conclusively ascertained whether dysbiosis actually promotes the disease or is simply a consequence of the inflammatory disorder. Better insight into the complex network of interactions between food, the intestinal microbiome, and host immune response will, therefore, contribute significantly to the diagnosis, treatment, and management of IBD. In this article, we review the ways in which the mutualistic circle of dietary nutrients, gut microbiota, and the immune system becomes anomalous during the IBD process, and discuss the roles of bacterial factors in shaping the intestinal inflammatory barrier and adjusting immune capacity

Protective effects of recombinant lactoferrin with different iron saturations on enteritis injury in young mice.

peer reviewedInfant intestinal development is immature and, thus, is vulnerable to bacterial and viral infections, which damage intestinal development and even induce acute enteritis. Numerous studies have investigated that lactoferrin (LF) has protective effects on the intestine and may play a role in preventing intestinal inflammation in infants. Lactoferrin is divided into 2 types, namely apo-LF and holo-LF, depending on the degree of iron saturation, which may affect its bioactivities. However, the role of LF iron saturation in protecting infant intestinal inflammation has not been clearly clarified. Therefore, in this study, young mice models with intestinal damage induced by lipopolysaccharides (LPS) in vivo and primary intestinal epithelial cells in vitro were constructed to enteritis injury in infants for investigation. The apo-LF and holo-LF were subsequently applied to the mouse models to investigate and compare their levels of protection in the intestinal inflammatory injury, as well as to identify which LF was most active. Moreover, the specific mechanism of the LF with optimal iron saturation was further investigated through Western blot assay. Results demonstrated that disease activity index, shortened length of colon tissue, and histopathological score were significantly decreased in the apo-LF group compared with those of the LPS group and the holo-LF group. In the apo-LF group, the concentration of LPS in the intestinal tract and the number of gram-negative bacteria colonies decreased significantly and the expression levels of proinflammatory factors in the colon tissue were downregulated, in comparison with those in the LPS group. The findings of this study thus verify that apo-LF can significantly alleviate enteritis injury caused by LPS, through regulating the PPAR-γ/PFKFB3/NF-κB inflammatory pathway