Deep Landscape Forecasting for Real-time Bidding Advertising

The emergence of real-time auction in online advertising has drawn huge attention of modeling the market competition, i.e., bid landscape forecasting. The problem is formulated as to forecast the probability distribution of market price for each ad auction. With the consideration of the censorship issue which is caused by the second-price auction mechanism, many researchers have devoted their efforts on bid landscape forecasting by incorporating survival analysis from medical research field. However, most existing solutions mainly focus on either counting-based statistics of the segmented sample clusters, or learning a parameterized model based on some heuristic assumptions of distribution forms. Moreover, they neither consider the sequential patterns of the feature over the price space. In order to capture more sophisticated yet flexible patterns at fine-grained level of the data, we propose a Deep Landscape Forecasting (DLF) model which combines deep learning for probability distribution forecasting and survival analysis for censorship handling. Specifically, we utilize a recurrent neural network to flexibly model the conditional winning probability w.r.t. each bid price. Then we conduct the bid landscape forecasting through probability chain rule with strict mathematical derivations. And, in an end-to-end manner, we optimize the model by minimizing two negative likelihood losses with comprehensive motivations. Without any specific assumption for the distribution form of bid landscape, our model shows great advantages over previous works on fitting various sophisticated market price distributions. In the experiments over two large-scale real-world datasets, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: KDD 2019. The reproducible code and dataset link is https://github.com/rk2900/DL

Nematic crossover in BaFe2_2As2_2 under uniaxial stress

Raman scattering can detect spontaneous point-group symmetry breaking without resorting to single-domain samples. Here we use this technique to study $\mathrm{BaFe_2As_2}$, the parent compound of the "122" Fe-based superconductors. We show that an applied compression along the Fe-Fe direction, which is commonly used to produce untwinned orthorhombic samples, changes the structural phase transition at temperature $T_{\mathrm{s}}$ into a crossover that spans a considerable temperature range above $T_{\mathrm{s}}$. Even in crystals that are not subject to any applied force, a distribution of substantial residual stress remains, which may explain phenomena that are seemingly indicative of symmetry breaking above $T_{\mathrm{s}}$. Our results are consistent with an onset of spontaneous nematicity only below $T_{\mathrm{s}}$.Comment: 4 pages, 4 figure

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets: https://github.com/alimamarankgroup/HPM

Design and development of a ceiling-mounted workshop measurement positioning system for large-scale metrology

This paper presents a new ceiling-mounted workshop Measurement Positioning System (C-wMPS) compensating for many deficiencies shown by conventional metrology systems, especially on the possibility of task-oriented designing for coverage ability, measurement accuracy and efficiency. A hybrid calibration system consisting of a high-precision coordinate control field and standard lengths is developed and implemented for the C-wMPS, which can be designed concretely to provide both traceability and the ability of local accuracy enhancement. Layout optimization using a genetic algorithm based on grids is applied to design an appropriate layout of the system, therefore promotes the system’s performance and reduce cost. An experiment carried out at the Guidance, Navigation and Control laboratory (GNC lab, 40×30×12m) validates the prominent characteristic of C-wMPS and the fitness of the new calibration system and layout optimization method.<br/

OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation

Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of large-scale realscanned 3D databases. To facilitate the development of 3D perception, reconstruction, and generation in the real world, we propose OmniObject3D, a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects. OmniObject3D has several appealing properties: 1) Large Vocabulary: It comprises 6,000 scanned objects in 190 daily categories, sharing common classes with popular 2D datasets (e.g., ImageNet and LVIS), benefiting the pursuit of generalizable 3D representations. 2) Rich Annotations: Each 3D object is captured with both 2D and 3D sensors, providing textured meshes, point clouds, multiview rendered images, and multiple real-captured videos. 3) Realistic Scans: The professional scanners support highquality object scans with precise shapes and realistic appearances. With the vast exploration space offered by OmniObject3D, we carefully set up four evaluation tracks: a) robust 3D perception, b) novel-view synthesis, c) neural surface reconstruction, and d) 3D object generation. Extensive studies are performed on these four benchmarks, revealing new observations, challenges, and opportunities for future research in realistic 3D vision.Comment: Project page: https://omniobject3d.github.io

Tetraploidy in Citrus wilsonii Enhances Drought Tolerance via Synergistic Regulation of Photosynthesis, Phosphorylation, and Hormonal Changes

Polyploidy varieties have been reported to exhibit higher stress tolerance relative to their diploid relatives, however, the underlying molecular and physiological mechanisms remain poorly understood. In this study, a batch of autotetraploid Citrus wilsonii were identified from a natural seedling population, and these tetraploid seedlings exhibited greater tolerance to drought stress than their diploids siblings. A global transcriptome analysis revealed that a large number of genes involved in photosynthesis response were enriched in tetraploids under drought stress, which was consistent with the changes in photosynthetic indices including Pn, gs, Tr, Ci, and chlorophyll contents. Compared with diploids, phosphorylation was also modified in the tetraploids after drought stress, as detected through tandem mass tag (TMT)-labeled proteomics. Additionally, tetraploids prioritized the regulation of plant hormone signal transduction at the transcriptional level after drought stress, which was also demonstrated by increased levels of IAA, ABA, and SA and reduced levels of GA3 and JA. Collectively, our results confirmed that the synergistic regulation of photosynthesis response, phosphorylation modification and plant hormone signaling resulted in drought tolerance of autotetraploid C. wilsonii germplasm

Translation and initial psychometric evaluation of the Chinese version of the partners in health scale.

All Published work is licensed under a Creative Commons Attribution 4.0 International LicenseThis study aimed to translate the Partner in Health (PIH) scale (12 items) into the mandarin Chinese language and investigate the psychometric properties of the Chinese version PIH scale in patients with chronic diseases in primary care settings in Changsha, China. The instrument was translated according to the Sousa guideline including the following steps: Forward translation, back translation, expert panel evaluation and pilot study, with achievement of consensus at each step. Psychometric properties of the Chinese version PIH were assessed in a random sample of 300 community-dwelling patients with chronic diseases in Changsha, China. These properties included content validity, internal consistency, test-retest reliability and structural validity. Survey response rate was 93.7%. The results showed that the Chinese PIH scale had satisfactory reliability and validity. The test-retest reliability was 0.832 and the Cronbach’s coefficient was 0.865. The content validity rate (S-CVI/Ave) was 0.965. The correlation between the Patient Activation Measure (PAM) and the PIH was 0.505 (p<0.001). Results of the confirmatory factor analysis suggest that the PIH scale consisted of four factors: knowledge, partnership, management and coping. The Chinese PIH scale yields high reliability and validity. It can be used as a generic self-rated tool to assess self-management of patients with chronic diseases in China

Strain-induced enhancement of TcT_c in infinite-layer Pr0.8_{0.8}Sr0.2_{0.2}NiO2_2 films

The mechanism of unconventional superconductivity in correlated materials remains a great challenge in condensed matter physics. The recent discovery of superconductivity in infinite-layer nickelates, as analog to high-Tc cuprates, has opened a new route to tackle this challenge. By growing 8 nm Pr0.8Sr0.2NiO2 films on the (LaAlO3)0.3(Sr2AlTaO6)0.7 substrate, we successfully raise the transition temperature Tc from 9 K in the widely studied SrTiO3-substrated nickelates into 15 K. By combining x-ray absorption spectroscopy with the first-principles and many-body simulations, we find a positive correlation between Tc and the pre-edge peak intensity, which can be attributed to the hybridization between Ni and O orbitals induced by the strain. Our result suggests that structural engineering can further enhance unconventional superconductivity, and the charge-transfer property plays a crucial role in the pairing strength.Comment: 8 pages, 4 figure