SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors

Existing tensor factorization methods assume that the input tensor follows some specific distribution (i.e. Poisson, Bernoulli, and Gaussian), and solve the factorization by minimizing some empirical loss functions defined based on the corresponding distribution. However, it suffers from several drawbacks: 1) In reality, the underlying distributions are complicated and unknown, making it infeasible to be approximated by a simple distribution. 2) The correlation across dimensions of the input tensor is not well utilized, leading to sub-optimal performance. Although heuristics were proposed to incorporate such correlation as side information under Gaussian distribution, they can not easily be generalized to other distributions. Thus, a more principled way of utilizing the correlation in tensor factorization models is still an open challenge. Without assuming any explicit distribution, we formulate the tensor factorization as an optimal transport problem with Wasserstein distance, which can handle non-negative inputs. We introduce SWIFT, which minimizes the Wasserstein distance that measures the distance between the input tensor and that of the reconstruction. In particular, we define the N-th order tensor Wasserstein loss for the widely used tensor CP factorization and derive the optimization algorithm that minimizes it. By leveraging sparsity structure and different equivalent formulations for optimizing computational efficiency, SWIFT is as scalable as other well-known CP algorithms. Using the factor matrices as features, SWIFT achieves up to 9.65% and 11.31% relative improvement over baselines for downstream prediction tasks. Under the noisy conditions, SWIFT achieves up to 15% and 17% relative improvements over the best competitors for the prediction tasks.Comment: Accepted by AAAI-2

Denervation as a Common Mechanism Underlying Different Pulmonary Vein Isolation Strategies for Paroxysmal Atrial Fibrillation: Evidenced by Heart Rate Variability after Ablation

Backgrounds. Segmental and circumferential pulmonary vein isolations (SPVI and CPVI) have been demonstrated to be effective therapies for paroxysmal atrial fibrillation (PAF). PVI is well established as the endpoint of different ablation techniques, whereas it may not completely account for the long-term success. Methods. 181 drug-refractory symptomatic PAF patients were referred for segmental or circumferential PVI (SPVI = 67; CPVI = 114). Heart rate variability (HRV) was assessed before and after the final ablation. Results. After following up for 62.23±12.75 months, patients underwent 1.41±0.68 procedures in average, and the success rates in SPVI and CPVI groups were comparable. 119 patients were free from AF recurrence (SPVI-S, n=43; CPVI-S, n=76). 56 patients had recurrent episodes (SPVI-R, n=21; CPVI-R, n=35). Either ablation technique decreased HRV significantly. Postablation SDNN and rMSSD were significantly lower in SPVI-S and CPVI-S subgroups than in SPVI-R and CPVI-R subgroups (SPVI-S versus SPVI-R: SDNN 91.8±32.6 versus 111.5±36.2 ms, rMSSD 47.4±32.3 versus 55.2±35.2 ms; CPVI-S versus CPVI-R: SDNN 83.0±35.6 versus 101.0±40.7 ms, rMSSD 41.1±22.9 versus 59.2±44.8 ms; all P<0.05). Attenuation of SDNN and rMSSD remained for 12 months in SPVI-S and CPVI-S subgroups, whereas it recovered earlier in SPVI-R and CPVI-R subgroups. Multivariate logistic regression analysis identified SDNN as the only predictor of long-term success. Conclusions. Beyond PVI, denervation may be a common mechanism underlying different ablation strategies for PAF

Effects of mining on the molybdenum absorption and translocation of plants in the Luanchuan molybdenum mine

Background There is a critical need to examine whether mining of molybdenum (Mo) ore will affect Mo absorption and translocation by plants at a community level. Methods Indigenous plants and their rhizospheric soil (0–20 cm) growing in two different areas including the mining and the unexploited areas were collected from the Luanchuan Mo mine—one of the largest Mo mines in Asia. The concentrations of Mo and other heavy metals of plants or soil were measured by ICP-AES. Mo absorption and translocation in plants growing in two areas were investigated and compared. Heavy metal pollution in soil was also evaluated by the potential ecological hazard index method. Results Mo concentration in mining soils was higher with the changes from 108.13 to 268.13 mg kg−1 compared to unexploited area. Mo concentrations in shoots and roots of plants growing in the mining area were also significant higher than those growing in the unexploited area with 2.59 and 2.99 times, respectively. The Mo translocation factor of plants growing in the unexploited area was 1.61, which reached 1.69 times that of plants growing in the mining area. Mo was the main heavy metal pollutant in the soil of both the mining and the unexploited areas. Conclusion Mining of Mo had changed not only the Mo concentration in soil but also Mo absorption and translocation in plants. Plants growing in the mining area absorbed more Mo from the soil but translocated relatively less to shoots than plants of the unexploited area. However, the mechanisms of Mo absorption and translocation of plants in mining area should be further studied in the future

Identifying intentional injuries among children and adolescents based on Machine Learning.

BackgroundCompared to other studies, the injury monitoring of Chinese children and adolescents has captured a low level of intentional injuries on account of self-harm/suicide and violent attacks. Intentional injuries in children and adolescents have not been apparent from the data. It is possible that there has been a misclassification of existing intentional injuries, and there is a lack of research literature on the misclassification of intentional injuries. This study aimed to discuss the feasibility of discriminating the intention of injury based on Machine Learning (ML) modelling and provided ideas for understanding whether there was a misclassification of intentional injuries.MethodsInformation entropy was used to determine the correlation between variables and the intention of injury, and Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Adaboost algorithms and Deep Neural Networks (DNN) were used to create an intention of injury discrimination model. The models were compared by comprehensively testing the discrimination effect to determine stability and consistency.ResultsFor the area under the ROC curve with different intentions of injuries, the NB model was 0.891, 0.880, and 0.897, respectively; the DT model was 0.870, 0.803, and 0.871, respectively; the RF model was 0.850, 0.809, and 0.845, respectively; the Adaboost model was 0.914, 0.846, and 0.914, respectively; the DNN model was 0.927, 0.835, and 0.934, respectively. In a comprehensive comparison of the five models, DNN and Adaboost models had higher values for the determination of the intention of injury. A discrimination of cases with unclear intentions of injury showed that on average, unintentional injuries, violent attacks, and self-harm/suicides accounted for 86.57%, 6.81%, and 6.62%, respectively.ConclusionIt was feasible to use the ML algorithm to determine the injury intention of children and adolescents. The research suggested that the DNN and Adaboost models had higher values for the determination of the intention of injury. This study could build a foundation for transforming the model into a tool for rapid diagnosis and excavating potential intentional injuries of children and adolescents by widely collecting the influencing factors, extracting the influence variables characteristically, reducing the complexity and improving the performance of the models in the future

Catalytic hydrothermal liquefaction of microalgae over metal incorporated mesoporous SBA-15 with high hydrothermal stability

Hydrothermal liquefaction (HTL) is one of the most promising technologies for conversion of microalgae, and catalysts with high hydrothermal stability are required for controllable HTL. In this article, SBA-15 incorporated with transition metals (Ni, Pd, Co and Ru) were synthetized via double-template method for catalytic HTL of microalgae. The results showed that metal incorporated SBA-15 represented high hydrothermal stability at 613 K. The incorporated Ni, Co and Ru was dispersed in SBA-15 enhancing the hydrothermal stability. The catalysts greatly influenced the chemical composition of the obtained bio-oil, which contained a higher percentage of furfural derivatives and a lower content of fatty acids and N-containing compounds, thus bio-oil quality was improved significantly. Higher hydrothermal stability and specific surface areas of Co-SBA-15 contribute to the highest preformation with 78.78% conversion and 24.11 wt% bio-oil yield. Metal incorporated SBA-15 provides a potential application for biomass conversion in high-temperature aqueous phase. Keywords: Hydrothermal stability, Metal incorporation, SBA-15, Catalytic HTL, Microalga

Learning Phenotypes and Dynamic Patient Representations via RNN Regularized Collective Non-Negative Tensor Factorization

Non-negative Tensor Factorization (NTF) has been shown effective to discover clinically relevant and interpretable phenotypes from Electronic Health Records (EHR). Existing NTF based computational phenotyping models aggregate data over the observation window, resulting in the learned phenotypes being mixtures of disease states appearing at different times. We argue that by separating the clinical events happening at different times in the input tensor, the temporal dynamics and the disease progression within the observation window could be modeled and the learned phenotypes will correspond to more specific disease states. Yet how to construct the tensor for data samples with different temporal lengths and properly capture the temporal relationship specific to each individual data sample remains an open challenge. In this paper, we propose a novel Collective Non-negative Tensor Factorization (CNTF) model where each patient is represented by a temporal tensor, and all of the temporal tensors are factorized collectively with the phenotype definitions being shared across all patients. The proposed CNTF model is also flexible to incorporate non-temporal data modality and RNN-based temporal regularization. We validate the proposed model using MIMIC-III dataset, and the empirical results show that the learned phenotypes are clinically interpretable. Moreover, the proposed CNTF model outperforms the state-of-the-art computational phenotyping models for the mortality prediction task

Specific and Sensitive Detection of Tartrazine on the Electrochemical Interface of a Molecularly Imprinted Polydopamine-Coated PtCo Nanoalloy on Graphene Oxide

A novel electrochemical sensor designed to recognize and detect tartrazine (TZ) was constructed based on a molecularly imprinted polydopamine (MIPDA)-coated nanocomposite of platinum cobalt (PtCo) nanoalloy-functionalized graphene oxide (GO). The nanocomposites were characterized and the TZ electrochemical detection performance of the sensor and various reference electrodes was investigated. Interestingly, the synergistic effect of the strong electrocatalytic activity of the PtCo nanoalloy-decorated GO and the high TZ recognition ability of the imprinted cavities of the MIPDA coating resulted in a large and specific response to TZ. Under the optimized conditions, the sensor displayed linear response ranges of 0.003–0.180 and 0.180–3.950 µM, and its detection limit was 1.1 nM (S/N = 3). The electrochemical sensor displayed high anti-interference ability, good stability, and adequate reproducibility, and was successfully used to detect TZ in spiked food samples. Comparison of important indexes of this sensor with those of previous electrochemical sensors for TZ revealed that this sensor showed improved performance. This surface-imprinted sensor provides an ultrasensitive, highly specific, effective, and low-cost method for TZ determination in foodstuffs

Thickness-tunable growth of ultra-large, continuous and high-dielectric h-BN thin films

The outstanding thermal properties, mechanical properties and large optical bandgap of hexagonal boron nitride (h-BN) make it very attractive for various applications in ultrathin 2D microelectronics. However, the synthesis of large lateral size and uniform h-BN thin films with a high breakdown strength still remains a great challenge. Here, we comprehensively investigated the effect of growth conditions on the thickness of h-BN films via low pressure chemical vapor deposition (LPCVD). By optimizing the LPCVD growth parameters with electropolished Cu foils as the deposition substrates and developing customized `` enclosure'' quartz-boat reactors, we achieved thickness-tunable (1.50-10.30 nm) growth of h-BN thin films with a smooth surface (RMS roughness is 0.26 nm) and an ultra-large area (1.0 cm x 1.0 cm), meanwhile, the as-grown h-BN films exhibited an ultra-high breakdown strength of similar to 10.0 MV cm(-1), which is highly promising for the development of electrically reliable 2D microelectronic devices with an ultrathin feature.This work was supported by the China Postdoctoral Science Foundation (Grant No. 2016M602820), the National Natural Science Foundation of China (Grant No. 51607138), the Youth Innovation Foundation of State Key Laboratory of Electrical Insulation and Power Equipment (Grant No. EIPE17312), the Research Foundation of State Key Laboratory of Intense Pulsed Radiation Simulation and Effect (Grant No. SKLIPR.1512) and the Innovative Research Group of National Natural Science Foundation of China (Grant No. 51521065)