Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes

Tensor decomposition is an important tool for multiway data analysis. In practice, the data is often sparse yet associated with rich temporal information. Existing methods, however, often under-use the time information and ignore the structural knowledge within the sparsely observed tensor entries. To overcome these limitations and to better capture the underlying temporal structure, we propose Dynamic EMbedIngs fOr dynamic Tensor dEcomposition (DEMOTE). We develop a neural diffusion-reaction process to estimate dynamic embeddings for the entities in each tensor mode. Specifically, based on the observed tensor entries, we build a multi-partite graph to encode the correlation between the entities. We construct a graph diffusion process to co-evolve the embedding trajectories of the correlated entities and use a neural network to construct a reaction process for each individual entity. In this way, our model can capture both the commonalities and personalities during the evolution of the embeddings for different entities. We then use a neural network to model the entry value as a nonlinear function of the embedding trajectories. For model estimation, we combine ODE solvers to develop a stochastic mini-batch learning algorithm. We propose a stratified sampling method to balance the cost of processing each mini-batch so as to improve the overall efficiency. We show the advantage of our approach in both simulation study and real-world applications. The code is available at https://github.com/wzhut/Dynamic-Tensor-Decomposition-via-Neural-Diffusion-Reaction-Processes

Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank

Leveraging biased click data for optimizing learning to rank systems has been a popular approach in information retrieval. Because click data is often noisy and biased, a variety of methods have been proposed to construct unbiased learning to rank (ULTR) algorithms for the learning of unbiased ranking models. Among them, automatic unbiased learning to rank (AutoULTR) algorithms that jointly learn user bias models (i.e., propensity models) with unbiased rankers have received a lot of attention due to their superior performance and low deployment cost in practice. Despite their differences in theories and algorithm design, existing studies on ULTR usually use uni-variate ranking functions to score each document or result independently. On the other hand, recent advances in context-aware learning-to-rank models have shown that multivariate scoring functions, which read multiple documents together and predict their ranking scores jointly, are more powerful than uni-variate ranking functions in ranking tasks with human-annotated relevance labels. Whether such superior performance would hold in ULTR with noisy data, however, is mostly unknown. In this paper, we investigate existing multivariate scoring functions and AutoULTR algorithms in theory and prove that permutation invariance is a crucial factor that determines whether a context-aware learning-to-rank model could be applied to existing AutoULTR framework. Our experiments with synthetic clicks on two large-scale benchmark datasets show that AutoULTR models with permutation-invariant multivariate scoring functions significantly outperform those with uni-variate scoring functions and permutation-variant multivariate scoring functions.Comment: 4 pages, 2 figures. It has already been accepted and will show in Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20), October 19--23, 202

Factors influencing small-scale distribution of 10 macro-lichens in King George Island, West Antarctica

Lichens are among the main primary colonists in most terrestrial ecosystems of Antarctica, where the effects of environmental factors on spatial distribution of lichens are essential to understanding the functioning of Antarctic terrestrial ecosystems. We measured abundance of 10 frequently observed macrolichens and 15 environmental factors at a small scale (20 cm×20 cm), in the ice-free areas of Fildes Peninsula and Ardley Island, King George Island, West Antarctica, and assessed the effects of environmental factors on the local distribution of these lichens. Canonical correspondence analyses (CCA) show that 8 out of 15 environmental factors, belonging to 4 sets of variables, are important in spatial distribution of the 10 lichens. Variation partitioning analyses show that most of the variation in distribution of the 10 lichens is described by the spatial heterogeneity of substrate, bird influence and microclimate and topography, whereas human impact has no significant effects

Active Vibration Suppression Based on Piezoelectric Actuator

The piezoelectric constitutive equation states that the inverse piezoelectric effect can convert electrical energy into mechanical energy, resulting in small displacement and force changes with high resolution. The piezoelectric actuator based on inverse piezoelectric effect has an excellent performance in active vibration suppression because of its high frequency response, high positioning accuracy, and large output force. A new active-passive composite vibration suppression system can be formed by cascading it with passive vibration isolation elements in series and parallel. On this basis, by adding different control algorithms and control loops, such as the Sky-Hook damping feedback control algorithm and adaptive feedforward control algorithm, different vibration control effects can be realized

Two Mutations Were Critical for Bat-to-Human Transmission of Middle East Respiratory Syndrome Coronavirus

To understand how Middle East respiratory syndrome coronavirus (MERS-CoV) transmitted from bats to humans, we compared the virus surface spikes of MERS-CoV and a related bat coronavirus, HKU4. Although HKU4 spike cannot mediate viral entry into human cells, two mutations enabled it to do so by allowing it to be activated by human proteases. These mutations are present in MERS-CoV spike, explaining why MERS-CoV infects human cells. These mutations therefore played critical roles in the bat-to-human transmission of MERS-CoV, either directly or through intermediate hosts

Mobile Real-Time Grasshopper Detection and Data Aggregation Framework

nsects of the family Orthoptera: Acrididae including grasshoppers and locust devastate crops and eco-systems around the globe. The effective control of these insects requires large numbers of trained extension agents who try to spot concentrations of the insects on the ground so that they can be destroyed before they take flight. This is a challenging and difficult task. No automatic detection system is yet available to increase scouting productivity, data scale and fidelity. Here we demonstrate MAESTRO, a novel grasshopper detection framework that deploys deep learning within RBG images to detect insects. MAeStRo uses a state-of-the-art two-stage training deep learning approach. the framework can be deployed not only on desktop computers but also on edge devices without internet connection such as smartphones. MAeStRo can gather data using cloud storage for further research and in-depth analysis. In addition, we provide a challenging new open dataset (GHCID) of highly variable grasshopper populations imaged in inner Mongolia. the detection performance of the stationary method and the mobile App are 78 and 49 percent respectively; the stationary method requires around 1000 ms to analyze a single image, whereas the mobile app uses only around 400 ms per image. The algorithms are purely data-driven and can be used for other detection tasks in agriculture (e.g. plant disease detection) and beyond. This system can play a crucial role in the collection and analysis of data to enable more effective control of this critical global pest

Receptor usage and cell entry of bat coronavirus HKU4 provide insight into bat-to-human transmission of MERS coronavirus

A constant and long-term threat to human health is cross-species transmission of Middle East respiratory syndrome coronavirus (MERS-CoV) from bats to humans. However, this process is poorly understood. Examining the cross-species transmissibility of bat coronavirus HKU4, which is genetically related to MERS-CoV, can provide critical information about the likely causes of MERS-CoV infections in humans. Here we investigate the receptor usage and cell entry mechanism of HKU4 compared with MERS-CoV. Our results reveal that MERS-CoV has adapted to use human receptor and cellular proteases for efficient human cell entry, whereas HKU4 can potentially follow-up and also infect human cells. These findings are critical for evaluating emerging disease potentials of bat coronaviruses and for preventing and controlling their spread in humans

Differential Gene Expression in Primary Cultured Sensory and Motor Nerve Fibroblasts

Fibroblasts (Fbs) effectively promote Schwann cells (SCs) migration, proliferation, and neurite regeneration. Whether Fbs express different motor and sensory phenotypes that regulate the cell behavior and peripheral nerve function has not been elucidated. The present study utilized the whole rat genome microarray analysis and identified a total of 121 differentially expressed genes between the primary cultured motor and sensory Fbs. The genes with high expression in sensory Fbs were related to proliferation, migration, chemotaxis, motility activation, protein maturation, defense response, immune system, taxis, and regionalization, while those with high expression in motor Fbs were related to neuron differentiation, segmentation, and pattern specification. Thus, the significant difference in the expression of some key genes was found to be associated with cell migration and proliferation, which was further validated by quantitative real-time PCR (qPCR). The cell proliferation or migration analysis revealed a higher rate of cell migration and proliferation of sensory Fbs than motor Fbs. Moreover, the downregulated expression of chemokine (C-X-C motif) ligand 10 (CXCL10) and chemokine (C-X-C motif) ligand 3 (CXCL3) suppressed the proliferation rate of sensory Fbs, while it enhanced that of the motor Fbs. However, the migration rate of both Fbs was suppressed by the downregulated expression of CXCL10 or CXCL3. Furthermore, a higher proportion of motor or sensory SCs migrated toward their respective (motor or sensory) Fbs; however, few motor or sensory SCs co-cultured with the other type of Fbs (sensory or motor, respectively), migrated toward the Fbs. The current findings indicated that Fbs expressed the distinct motor and sensory phenotypes involved in different patterns of gene expression, biological processes, and effects on SCs. Thus, this study would provide insights into the biological differences between motor and sensory Fbs, including the role in peripheral nerve regeneration