AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction

Large-scale commercial platforms usually involve numerous business domains for diverse business strategies and expect their recommendation systems to provide click-through rate (CTR) predictions for multiple domains simultaneously. Existing promising and widely-used multi-domain models discover domain relationships by explicitly constructing domain-specific networks, but the computation and memory boost significantly with the increase of domains. To reduce computational complexity, manually grouping domains with particular business strategies is common in industrial applications. However, this pre-defined data partitioning way heavily relies on prior knowledge, and it may neglect the underlying data distribution of each domain, hence limiting the model's representation capability. Regarding the above issues, we propose an elegant and flexible multi-distribution modeling paradigm, named Adaptive Distribution Hierarchical Model (AdaptDHM), which is an end-to-end optimization hierarchical structure consisting of a clustering process and classification process. Specifically, we design a distribution adaptation module with a customized dynamic routing mechanism. Instead of introducing prior knowledge for pre-defined data allocation, this routing algorithm adaptively provides a distribution coefficient for each sample to determine which cluster it belongs to. Each cluster corresponds to a particular distribution so that the model can sufficiently capture the commonalities and distinctions between these distinct clusters. Extensive experiments on both public and large-scale Alibaba industrial datasets verify the effectiveness and efficiency of AdaptDHM: Our model achieves impressive prediction accuracy and its time cost during the training stage is more than 50% less than that of other models

Diachronous end-Permian terrestrial ecosystem collapse with its origin in wildfires

The Permian-Triassic Mass Extinction (PTME) is the greatest biodiversity crisis in Earth history and while the marine crisis is increasingly well constrained, the timing and cause(s) of terrestrial losses remain poorly understood. There have been suggestions that the End-Permian Terrestrial Collapse (EPTC) pre-dated, was synchronous with or post-dated the marine crisis, or even occurred asynchronously in different regions. We address these conflicting interpretations through a detailed geochemical study of a terrestrial sequence in the Liujiang Coalfield on the North China Plate (NCP) in which we apply zircon U-Pb dating of tuffaceous claystone, kerogen identification, and analysis of organic carbon isotopic composition (δ13Corg), total organic carbon (TOC), continental weathering (via the chemical index of alteration; CIA) and Ni concentrations. Our study constrains the Permian-Triassic boundary (PTB) near the base of bed 20 in our sequence at approximately 251.9 ± 1.1 Ma, immediately above a Ni anomaly also known from other terrestrial sequences and the marine PTME. Organic carbon isotope chemostratigraphy together with evidence for algal blooms and the presence of mudstone clasts suggests that the onset of the EPTC in the NCP was synchronous with the crisis in low latitudes (e.g., South China), but was about 310 kyr later than the EPTC in higher southerly latitudes (e.g., Australia). The EPTC predates the marine PTME. Kerogen macerals suggest that a phase of increased wildfire was sustained from the onset of the EPTC in the NCP until the marine PTME interval, implicating wildfire as a major driver of the EPTC (at least in low latitudes) that, in turn, had devastating consequences for the marine realm

OPT-GAN: Black-Box Global Optimization via Generative Adversarial Nets

Black-box optimization (BBO) algorithms are concerned with finding the best solutions for problems with missing analytical details. Most classical methods for such problems are based on strong and fixed a priori assumptions, such as Gaussianity. However, the complex real-world problems, especially when the global optimum is desired, could be very far from the a priori assumptions because of their diversities, causing unexpected obstacles to these methods. In this study, we propose a generative adversarial net-based broad-spectrum global optimizer (OPT-GAN) which estimates the distribution of optimum gradually, with strategies to balance exploration-exploitation trade-off. It has potential to better adapt to the regularity and structure of diversified landscapes than other methods with fixed prior, e.g. Gaussian assumption or separability. Experiments conducted on BBO benchmarking problems and several other benchmarks with diversified landscapes exhibit that OPT-GAN outperforms other traditional and neural net-based BBO algorithms.Comment: M. Lu and S. Ning contribute equally. Submitted to IEEE transactions on Neural Networks and Learning System

Middle Jurassic terrestrial environmental and floral changes linked to volcanism: Evidence from the Qinghai Tibet Plateau, China

The breakup of Pangaea and the rapid opening of the Ligurian and Central Atlantic oceans during the Middle Jurassic resulted in widespread volcanism accompanied by significant shifts in global environments, climates, and floras. Although major volcanism is a plausible driver of such global changes, linking these phenomena in the Middle Jurassic is hindered by a lack of detailed sedimentary records from which to evaluate cause and effect. Here, we link Middle Jurassic environmental, climatic, and floral changes with volcanism using records from the Dameigou section of the Qaidam Basin on the Qinghai-Tibet Plateau. High-resolution chemostratigraphic (δ13Corg patterns) and biostratigraphic (palynological fossils) data reveal three negative organic carbon isotope excursions (NCIE) at the Aalenian-Bajocian boundary, the Bajocian-Bathonian boundary, and in the middle of the Callovian, respectively. The first two NCIEs (NCIE-I and NCIE-II) were accompanied by relatively warm and humid climatic conditions and coal accumulation. In contrast, the third NCIE (NCIE-III) was accompanied by warm but dry climatic conditions, a decrease in coal accumulation, a decline in plant diversity, the significant decline in fern spore diversity and abundance, and a rapid increase in the abundance of Classopollis pollen (based on petrological, palynological, PCA, Hydrophyte/Xerophyte ratio, and nMDS data). Four sedimentary mercury anomalies (Hg/Al spikes) have temporal coincidence with the three NCIEs and climate warming events, suggesting a volcanic origin for these. We suggest that volcanism was a key driver of Middle Jurassic change, with major pulses releasing large amounts of CO2 and Hg into the atmosphere, resulting in Hg loading, NCIEs, climatic warming, and floral changes in terrestrial strata. Our multi-proxy study provides new insights into the links between volcanism and terrestrial environmental, climatic, and floral changes during the Middle Jurassic

Robust Representation Learning for Unified Online Top-K Recommendation

In large-scale industrial e-commerce, the efficiency of an online recommendation system is crucial in delivering highly relevant item/content advertising that caters to diverse business scenarios. However, most existing studies focus solely on item advertising, neglecting the significance of content advertising. This oversight results in inconsistencies within the multi-entity structure and unfair retrieval. Furthermore, the challenge of retrieving top-k advertisements from multi-entity advertisements across different domains adds to the complexity. Recent research proves that user-entity behaviors within different domains exhibit characteristics of differentiation and homogeneity. Therefore, the multi-domain matching models typically rely on the hybrid-experts framework with domain-invariant and domain-specific representations. Unfortunately, most approaches primarily focus on optimizing the combination mode of different experts, failing to address the inherent difficulty in optimizing the expert modules themselves. The existence of redundant information across different domains introduces interference and competition among experts, while the distinct learning objectives of each domain lead to varying optimization challenges among experts. To tackle these issues, we propose robust representation learning for the unified online top-k recommendation. Our approach constructs unified modeling in entity space to ensure data fairness. The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations. Moreover, the proposed method balances conflicting objectives through the homoscedastic uncertainty weights and orthogonality constraints. Various experiments validate the effectiveness and rationality of our proposed method, which has been successfully deployed online to serve real business scenarios.Comment: 14 pages, 6 figures, submitted to ICD

Volcanically-induced floral changes across the Triassic-Jurassic (T-J) transition

The End-Triassic Mass Extinction (ETME) saw the catastrophic loss of ca. 50% of marine genera temporally associated with emplacement of the Central Atlantic Magmatic Province (CAMP). However, the effects of the ETME on land is a controversial topic. Evaluation of the disparate cause(s) and effects of the extinction requires additional, detailed terrestrial records of these events. Here, we present a multidisciplinary record of volcanism and environmental change from an expanded Triassic-Jurassic (T-J) transition preserved in lacustrine sediments from the Jiyuan Basin, North China. High-resolution chemostratigraphy, palynological, kerogen, and sedimentological data reveal that terrestrial conditions responded to and were defined by large-scale volcanism. The record of sedimentary mercury reveals two discrete CAMP eruptive phases during the T-J transition. Each of these can be correlated with large, negative C isotope excursions (CIE-I of -4.7 ‰; CIE-II of -2.9 ‰), significantly reduced plant diversity (with ca. 45% and 44% generic losses respectively), enhanced wildfire (marked by increased fusinite or charcoal content), and major climatic shifts towards drier and hotter conditions (indicated by the occurrence of calcareous nodules, increased Classopollis pollen content, and PCA analysis). Our results show that CAMP eruptions may have followed a bimodal eruptive model and demonstrate the powerful ability of large-scale volcanism to alter the global C cycle and profoundly affect the climate, in turn leading to enhanced wildfires and a collapse in land plant diversity during the T-J transition

Four volcanically driven climatic perturbations led to enhanced continental weathering during the Late Triassic Carnian Pluvial Episode

The arid climate of the Late Triassic was interrupted by a particularly humid episode known as the Carnian Pluvial Episode (CPE; ca. 234–232 million years ago). The CPE is often linked to eruptions in the Wrangellia Large Igneous Province (LIP), and is assumed to have led to global warming, enhanced weathering, water deoxygenation, and biotic changes. However, direct evidence for a temporal link between volcanic activity and chemical weathering has not yet been established due to the lack of comprehensive records across the CPE. In this study, geochemical and mineralogical analyses are applied to a lacustrine stratigraphic succession of the Jiyuan Basin (North China) that captures the CPE in high resolution. We identify four distinct pulses of enhanced continental chemical weathering characterized by elevated Chemical Index of Alteration values and kaolinite contents. These peaks in continental weathering coincide with Hg/TOC enrichments and negative organic carbon isotope excursions that mark four short (~400 kyr) but intense pulses of Wrangellia LIP volcanism. In combination with signs of increased humidity, our findings provide direct and independent evidence that Wrangellia LIP eruptions significantly altered CPE chemical weathering rates in response to global warming and wetting. The lake experienced eutrophication and water deoxygenation after each volcanic pulse but the swift recovery of carbon isotopes suggests that the system rapidly returned to conditions prior to the volcanic perturbation. Organic carbon burial facilitated by widespread dysoxic and anoxic waters, and CO2 consumption via enhanced weathering likely played crucial roles in the rapid climatic recovery after each volcanic pulse