AutoMLP: Automated MLP for Sequential Recommendations

Sequential recommender systems aim to predict users' next interested item given their historical interactions. However, a long-standing issue is how to distinguish between users' long/short-term interests, which may be heterogeneous and contribute differently to the next recommendation. Existing approaches usually set pre-defined short-term interest length by exhaustive search or empirical experience, which is either highly inefficient or yields subpar results. The recent advanced transformer-based models can achieve state-of-the-art performances despite the aforementioned issue, but they have a quadratic computational complexity to the length of the input sequence. To this end, this paper proposes a novel sequential recommender system, AutoMLP, aiming for better modeling users' long/short-term interests from their historical interactions. In addition, we design an automated and adaptive search algorithm for preferable short-term interest length via end-to-end optimization. Through extensive experiments, we show that AutoMLP has competitive performance against state-of-the-art methods, while maintaining linear computational complexity.Comment: Accepted by WWW'2

Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting

With the acceleration of urbanization, traffic forecasting has become an essential role in smart city construction. In the context of spatio-temporal prediction, the key lies in how to model the dependencies of sensors. However, existing works basically only consider the micro relationships between sensors, where the sensors are treated equally, and their macroscopic dependencies are neglected. In this paper, we argue to rethink the sensor's dependency modeling from two hierarchies: regional and global perspectives. Particularly, we merge original sensors with high intra-region correlation as a region node to preserve the inter-region dependency. Then, we generate representative and common spatio-temporal patterns as global nodes to reflect a global dependency between sensors and provide auxiliary information for spatio-temporal dependency learning. In pursuit of the generality and reality of node representations, we incorporate a Meta GCN to calibrate the regional and global nodes in the physical data space. Furthermore, we devise the cross-hierarchy graph convolution to propagate information from different hierarchies. In a nutshell, we propose a Hierarchical Information Enhanced Spatio-Temporal prediction method, HIEST, to create and utilize the regional dependency and common spatio-temporal patterns. Extensive experiments have verified the leading performance of our HIEST against state-of-the-art baselines. We publicize the code to ease reproducibility.Comment: 9 pages, accepted by CIKM'2

Quercetin and Bornyl Acetate Regulate T-Lymphocyte Subsets and INF-γ/IL-4 Ratio In Utero in Pregnant Mice

The objective of this study is to investigate the antiabortive effects of Quercetin and Bornvl Acetate and their immunological modulation at maternal-fetal interface. Lipopolysaccharide (LPS) was injected via tail vein to induce abortion in mice which received Quercetin and Bornvl Acetate at days 4–7 of gestation. Uterine CD4+/CD8+ T lymphocytes and IFN-γ/IL-4 of each group (n = 10) were detected by immunohistochemistry and enzyme-linked immunosorbent assay, respectively. The ratio of CD4+/CD8+ increased significantly (P < .01) in the uterus of LPS-induced abortion mice. In the Quercetin and Bornvl Acetate pretreated mice followed by LPS administration, the ratio of CD4+/CD8+ dropped to 0.562 ± 0.021, lower than that of LPS-abortion group (P < .01). The mean value of IFN-γ/IL-4 in LPS-treated mice was 0.310 ± 0.066, higher than that of Quercetin and Bornyl Acetate group. The results indicate that Quercetin and Bornyl Acetate have an antiabortive effect through modulation of immunological balance at maternal-fetal interface

ADJUVANT EFFECTS OF SIJUNZI DECOCTION IN CHICKENS ORALLY VACCINATED WITH ATTENUATED NEWCASTLE-DISEASE VACCINE

Many Chinese Herbal medicines (CHMs) and their components have been reported to enhance immunity. In this study, the capacity for the Chinese herbal medicine, oral administration Sijunzi Decoction (SJZD) in stimulating Newcastle disease virus(NDV) immunity in chickens was examined. Serum was sampled on days 20,30,40,50 and 60 and tissues were collected on days 20, 40 and 60, respectively. The immune responses were determined by means of hemagglutination inhibition test, immunohistochemistry examination and semi-quantitative RT–PCR. The results showed that SJZD could increase the antibody titers and the area coefficient of IgA secreting cells, promote the expression of IL-2 mRNA in the whole immune period and IFN-γ mRNA was increased in the initial stage. The SJZD used was safe with no adverse effects on chicken weight or survival, providing evidence for the use of SJZD as an oral adjuvant

FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows

Despite recent progress in open-domain dialogue evaluation, how to develop automatic metrics remains an open problem. We explore the potential of dialogue evaluation featuring dialog act information, which was hardly explicitly modeled in previous methods. However, defined at the utterance level in general, dialog act is of coarse granularity, as an utterance can contain multiple segments possessing different functions. Hence, we propose segment act, an extension of dialog act from utterance level to segment level, and crowdsource a large-scale dataset for it. To utilize segment act flows, sequences of segment acts, for evaluation, we develop the first consensus-based dialogue evaluation framework, FlowEval. This framework provides a reference-free approach for dialog evaluation by finding pseudo-references. Extensive experiments against strong baselines on three benchmark datasets demonstrate the effectiveness and other desirable characteristics of our FlowEval, pointing out a potential path for better dialogue evaluation.Comment: EMNLP 2022 camera-ready versio

MLPST: MLP is All You Need for Spatio-Temporal Prediction

Traffic prediction is a typical spatio-temporal data mining task and has great significance to the public transportation system. Considering the demand for its grand application, we recognize key factors for an ideal spatio-temporal prediction method: efficient, lightweight, and effective. However, the current deep model-based spatio-temporal prediction solutions generally own intricate architectures with cumbersome optimization, which can hardly meet these expectations. To accomplish the above goals, we propose an intuitive and novel framework, MLPST, a pure multi-layer perceptron architecture for traffic prediction. Specifically, we first capture spatial relationships from both local and global receptive fields. Then, temporal dependencies in different intervals are comprehensively considered. Through compact and swift MLP processing, MLPST can well capture the spatial and temporal dependencies while requiring only linear computational complexity, as well as model parameters that are more than an order of magnitude lower than baselines. Extensive experiments validated the superior effectiveness and efficiency of MLPST against advanced baselines, and among models with optimal accuracy, MLPST achieves the best time and space efficiency

The Influence of an EPS Concrete Buffer Layer Thickness on Debris Dams Impacted by Massive Stones in the Debris Flow

The failure of debris dams impacted by the massive stones in a debris flow represents a difficult design problem. Reasonable materials selection and structural design can effectively improve the resistance impact performance of debris dams. Based on the cushioning properties of expanded polystyrene (EPS) concrete, EPS concrete as a buffer layer poured on the surface of a rigid debris dam was proposed. A three-dimensional numerical calculation model of an EPS concrete buffer layer/rigid debris dam was established. The single-factor theory revealed change rules for the thickness of the buffer layer concerning the maximal impact force of the rigid debris dam surface through numerical simulation. Moreover, the impact force-time/history curves under different calculation conditions for the rigid debris dam surface were compared. Simulation results showed that the EPS concrete buffer layer can not only effectively extend the impact time of massive stones affecting the debris dam but also reduce the impact force of the rigid debris dam caused by massive stones in the debris flow. The research results provide theoretical guidance for transferring the energy of the massive stone impact, creating a structural design and optimizing debris dams

Analysis of the characteristics and development trends of the “7•5” catastrophic debris flow in Xiangjiao gully, Muli County, Sichuan

On July 5, 2021, a catastrophic debris flow disaster occured in Xiangjiao gully, Muli County, Sichuan Province. This study analyzed the formation conditions, eruption process and eruption characteristics of the debris flow through field investigation and characteristic parameter calculation. Based on the results of on-site inspection, this debris flow was mainly caused by the combined effects of forest fires, short-term heavy rainfall, and channel topography. It was a post-fire debris flow caused by rainfall runoff erosion. The heavy rain caused powerful flood erosion on the slope surface, eroded the channel, led to collapse and landslides on both sides of the channel, and resulted in significant damming effects, which enlarged the scale of the debris flow. The forest fire area in Xiangjiao gully reached 74.61%, and the high-intensity burned area was 57.98%. The critical rainfall intensity for this debris flow was 77.84 mm/h, and the cumulative rainfall was 141.60 mm. According to the calculation results of characteristic parameters, the density of this debris flow was in the range of 1.83 ～1.93 g/cm3, indicating it was a viscous debris flow. The flow velocity at the downstream outlet of the main channel was 7.22 m/s, and the peak flow rate was 759.08 m3/s. Combined with the results of the rainfall-runoff method and the morphology investigation method, the recurrence interval of this debris flow was estimated to be once in a hundred years. Considering the development trend of debris flow, it is believed that there is still a possibility of large-scale debris flow in the basin. Therefore, prevention and control suggestions including slope reinforcement in the upstream, regulation in the middle reach, and drainage in the downstream are proposed