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

A novel role for IGF-1R in p53-mediated apoptosis through translational modulation of the p53-Mdm2 feedback loop

Insulin-like growth factor 1 receptor (IGF-1R) is important in cancer cell growth and survival and has been implicated in cancer pathophysiology and treatment. Here we report a novel function for IGF-1R in p53-dependent apoptotic response. We show that inhibition or loss of IGF-1R activity reduces translational synthesis of p53 and Mdm2 protein. Notably, IGF-1R inhibition increases p53 protein stability by reducing p53 ubiquitination and maintains p53 at low levels by decreasing p53 synthesis, thus rendering p53 insensitive to stabilization after DNA damage. The accumulation and apoptosis of DNA-damage–induced p53 is therefore reduced in Igf-1r−/− mouse embryonic fibroblasts or tumor cells treated with the IGF-1R inhibitor. Furthermore, we find that inhibition of IGF-1R reduces p53 and Mdm2 translation through a gene-specific mechanism mediated by the respective 5′ untranslated region of p53 and mdm2 messenger RNA. The eukaryotic translation initiation factor 4F complex is also involved in this translational inhibition. These results demonstrate an unexpected role for translational control by IGF-1R in p53-mediated apoptosis

Using Mendelian randomization analysis to determine the causal connection between unpleasant emotions and coronary atherosclerosis

ObjectiveObservational studies have shown a correlation between unpleasant emotions and coronary atherosclerosis, but the underlying causal linkages are still uncertain. We conducted a Mendelian randomization (MR) investigation on two samples for this purpose.MethodsIn genome-wide association studies in the UK Biobank (total = 459,561), we selected 40 distinct single-nucleotide polymorphisms (SNPs) related to unpleasant emotions as genome-wide statistically significant instrumental variables. FinnGen consortium provided summary-level data on coronary atherosclerosis for 211,203 individuals of Finnish descent. MR-Egger regression, the inverse variance weighted technique (IVW), and the weighted median method were used in the process of conducting data analysis.ResultsThere was sufficient evidence to establish a causal connection between unpleasant emotions and coronary atherosclerosis risk. For each unit increase in the log-odds ratio of unpleasant feelings, the odds ratios were 3.61 (95% CI: 1.64–7.95; P = 0.001). The outcomes of sensitivity analyses were comparable. There was no indication of heterogeneity or directional pleiotropy.ConclusionOur findings provide causal evidence for the effects of unpleasant emotions on coronary atherosclerosis

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

Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport

Human activity recognition (HAR) with wearables is one of the serviceable technologies in ubiquitous and mobile computing applications. The sliding-window scheme is widely adopted while suffering from the multi-class windows problem. As a result, there is a growing focus on joint segmentation and recognition with deep-learning methods, aiming at simultaneously dealing with HAR and time-series segmentation issues. However, obtaining the full activity annotations of wearable data sequences is resource-intensive or time-consuming, while unsupervised methods yield poor performance. To address these challenges, we propose a novel method for joint activity segmentation and recognition with timestamp supervision, in which only a single annotated sample is needed in each activity segment. However, the limited information of sparse annotations exacerbates the gap between recognition and segmentation tasks, leading to sub-optimal model performance. Therefore, the prototypes are estimated by class-activation maps to form a sample-to-prototype contrast module for well-structured embeddings. Moreover, with the optimal transport theory, our approach generates the sample-level pseudo-labels that take advantage of unlabeled data between timestamp annotations for further performance improvement. Comprehensive experiments on four public HAR datasets demonstrate that our model trained with timestamp supervision is superior to the state-of-the-art weakly-supervised methods and achieves comparable performance to the fully-supervised approaches.Comment: Under Review (submitted to IEEE TMC

Spatial heterogeneity in sediment and carbon accretion rates within a seagrass meadow correlated with the hydrodynamic intensity

The majority of the carbon stored in seagrass sediments originates outside the meadow, such that the carbon storage capacity within a meadow is strongly dependent on hydrodynamic conditions that favor deposition and retention of fine organic matter within the meadow. By extension, if hydrodynamic conditions vary across a meadow, they may give rise to spatial gradients in carbon. This study considered whether the spatial gradients in sediment and carbon accretion rates correlated with the spatial variation in hydrodynamic intensity within a single meadow. Field measurements were conducted in three depth zones across a Zostera marina L. (eelgrass) meadow in Nahant Harbor, Massachusetts. Four sediment cores were collected in each zone, including one outside the meadow (control) and three within the meadow at increasing distances from the nearest meadow edge. Sedimentation and carbon accretion rates were estimated by combining the measurements of dry bulk density, organic carbon fraction (% OC), 210Pb, and 226Ra. Tilt current meters measured wave velocities within each zone, which were used to estimate turbulent kinetic energy (TKE). Both sediment and carbon accretion rates exhibited spatial heterogeneity across the meadow, which were correlated with the spatial variation in near-bed TKE. Specifically, both accretion rates increased with decreasing TKE, which was consistent with diminished resuspension associated with lower TKE. A method is proposed for using spatial gradients in hydrodynamic intensity to improve the estimation of total meadow accretion rates

Dynamic alterations in the amplitude of low-frequency fluctuation in patients with cerebral small vessel disease

Background and purposePrevious studies have focused on the changes of dynamic and static functional connections in cerebral small vessel disease (CSVD). However, the dynamic characteristics of local brain activity are poorly understood. The purpose of this study was to investigate the dynamic cerebral activity changes in patients with CSVD using the dynamic amplitude of low-frequency fluctuation (d-ALFF).MethodsA total of 104 CSVD patients with cognitive impairment (CSVD-CI, n = 52) or normal cognition (CSVD-NC, n = 52) and 63 matched healthy controls (HCs) were included in this study. Every participant underwent magnetic resonance imaging scans and a battery of neuropsychological examinations. The dynamics of spontaneous brain activity were assessed using dynamic changes in the amplitude of low-frequency fluctuation (ALFF) with the sliding-window method. We used voxel-wise one-way analysis of variance (ANOVA) to compare dynamic ALFF variability among the three groups. Post-hoc t-tests were used to evaluate differences between each group pair. Finally, the brain regions with d-ALFF values with differences between CSVD subgroups were taken as regions of interest (ROI), and the d-ALFF values corresponding to the ROI were extracted for partial correlation analysis with memory.Results(1) There was no significant difference in age (p = 0.120), sex (p = 0.673) and education (p = 0.067) among CSVD-CI, CSVD-NC and HC groups, but there were significant differences Prevalence of hypertension and diabetes mellitus among the three groups (p < 10−3). There were significant differences in scores of several neuropsychological scales among the three groups (p < 10−3). (2) ANOVA and post-hoc t-test showed that there were dynamic abnormalities of spontaneous activity in several brain regions in three groups, mainly located in bilateral parahippocampal gyrus and bilateral hippocampus, bilateral insular and frontal lobes, and the static activity abnormalities in bilateral parahippocampal gyrus and bilateral hippocampal regions were observed at the same time, suggesting that bilateral parahippocampal gyrus and bilateral hippocampus may be the key brain regions for cognitive impairment caused by CSVD. (3) The correlation showed that d-ALFF in the bilateral insular was slightly correlated with the Mini-Mental State Examination (MMSE) score and disease progression rate. The d-ALFF value of the left postcentral gyrus was negatively correlated with the Clock Drawing Test (CDT) score (r = −0.416, p = 0.004), and the d-ALFF value of the right postcentral gyrus was negatively correlated with the Rey’s Auditory Verbal Learning Test (RAVLT) word recognition (r = −0.320, p = 0.028).ConclusionThere is a wide range of dynamic abnormalities of spontaneous brain activity in patients with CSVD, in which the abnormalities of this activity in specific brain regions are related to memory and execution or emotion

Spatial heterogeneity in sediment and carbon accretion rates within a seagrass meadow correlated with the hydrodynamic intensity

Unidad de excelencia María de Maeztu CEX2019-000940-MThe majority of the carbon stored in seagrass sediments originates outside the meadow, such that the carbon storage capacity within a meadow is strongly dependent on hydrodynamic conditions that favor deposition and retention of fine organic matter within the meadow. By extension, if hydrodynamic conditions vary across a meadow, they may give rise to spatial gradients in carbon. This study considered whether the spatial gradients in sediment and carbon accretion rates correlated with the spatial variation in hydrodynamic intensity within a single meadow. Field measurements were conducted in three depth zones across a Zostera marina L. (eelgrass) meadow in Nahant Harbor, Massachusetts. Four sediment cores were collected in each zone, including one outside the meadow (control) and three within the meadow at increasing distances from the nearest meadow edge. Sedimentation and carbon accretion rates were estimated by combining the measurements of dry bulk density, organic carbon fraction (%OC), 210Pb, and 226Ra. Tilt current meters measured wave velocities within each zone, which were used to estimate turbulent kinetic energy (TKE). Both sediment and carbon accretion rates exhibited spatial heterogeneity across the meadow, which were correlated with the spatial variation in near-bed TKE. Specifically, both accretion rates increased with decreasing TKE, which was consistent with diminished resuspension associated with lower TKE. A method is proposed for using spatial gradients in hydrodynamic intensity to improve the estimation of total meadow accretion rates

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