Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search

On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance

Porous single crystalline-like titanium dioxide monolith with enhanced photoelectrochemical performance

Macro-sized porous single crystalline-like (PSC-like) TiO2 is endowed with unique structural advantages due to its structural consistency and porosity in a large area, which would significantly enhance its photoelectrochemical function. However, there are significant technical challenges in the growth of porous single crystalline-like monoliths. The consistency of structure dominates the structure so that the grain boundary is reduced to the minimum, which is in contradiction with the three-dimensional percolation structure. Here we report a lattice reconstruction strategy based on solid-solid transformation to grow porous single crystal-like anatase TiO2 dominated by (200) and (101) facets at 2 cm scale. In comparison with the traditional definition of porous single crystal, it has two different lattice orientations, but still has good photoelectrochemical properties. The band gap engineering introduces Ti3+ gap into the lattice to generate TinO2n−1 with Magneli phase, limiting the created active structure to the lattice with two-dimensional surface, which would open a new avenue to create highly active surfaces to capture photons and transport electrons stably. The PSC-like TinO2n−1 provides enhanced exciton lifetime (3–5 ns) as a photocatalytic catalyst and shows significant visible light absorption. The independent PSC-like TinO2n−1 delivers high photocurrent of 1.8–5.5 mA · cm−2 at room temperature and does not decay for 10 h

Cyclooxygenase-2-Prostaglandin E2 pathway: A key player in tumor-associated immune cells

Cyclooxygenases-2 (COX-2) and Prostaglandin E2 (PGE2), which are important in chronic inflammatory diseases, can increase tumor incidence and promote tumor growth and metastasis. PGE2 binds to various prostaglandin E receptors to activate specific downstream signaling pathways such as PKA pathway, β-catenin pathway, NF-κB pathway and PI3K/AKT pathway, all of which play important roles in biological and pathological behavior. Nonsteroidal anti-inflammatory drugs (NSAIDs), which play as COX-2 inhibitors, and EP antagonists are important in anti-tumor immune evasion. The COX-2-PGE2 pathway promotes tumor immune evasion by regulating myeloid-derived suppressor cells, lymphocytes (CD8+ T cells, CD4+ T cells and natural killer cells), and antigen presenting cells (macrophages and dendritic cells). Based on conventional treatment, the addition of COX-2 inhibitors or EP antagonists may enhance immunotherapy response in anti-tumor immune escape. However, there are still a lot of challenges in cancer immunotherapy. In this review, we focus on how the COX-2-PGE2 pathway affects tumor-associated immune cells

Optimal control and ultimate bounds of 1:2 nonlinear quantum systems

Using optimal control, we establish and link the ultimate bounds in time (referred to as quantum speed limit) and energy of two- and three-level quantum nonlinear systems which feature 1:2 resonance. Despite the unreachable complete inversion, by using the Pontryagin maximum principle, we determine the optimal time, pulse area, or energy, for a given arbitrary accuracy. We show that the third-order Kerr terms can be absorbed in the detuning in order to lock the dynamics to the resonance. In the two-level problem, we determine the non-linear counterpart of the optimal $\pi$-pulse inversion for a given accuracy. In the three-level problem, we obtain an intuitive pulse sequence similar to the linear counterpart but with different shapes. We prove the (slow) logarithmic increasing of the optimal time as a function of the accuracy

Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search

On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance

DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching

Dataset distillation plays a crucial role in creating compact datasets with similar training performance compared with original large-scale ones. This is essential for addressing the challenges of data storage and training costs. Prevalent methods facilitate knowledge transfer by matching the gradients, embedding distributions, or training trajectories of synthetic images with those of the sampled original images. Although there are various matching objectives, currently the strategy for selecting original images is limited to naive random sampling. We argue that random sampling overlooks the evenness of the selected sample distribution, which may result in noisy or biased matching targets. Besides, the sample diversity is also not constrained by random sampling. Additionally, current methods predominantly focus on single-dimensional matching, where information is not fully utilized. To address these challenges, we propose a novel matching strategy called Dataset Distillation by Bidirectional REpresentAtive Matching (DREAM+), which selects representative original images for bidirectional matching. DREAM+ is applicable to a variety of mainstream dataset distillation frameworks and significantly reduces the number of distillation iterations by more than 15 times without affecting performance. Given sufficient training time, DREAM+ can further improve the performance and achieve state-of-the-art results. We have released the code at github.com/NUS-HPC-AI-Lab/DREAM+.Comment: This is an extension of the ICCV conference versio

A Brief Online Mindfulness-Based Group Intervention for Psychological Distress Among Chinese Residents During COVID-19: a Pilot Randomized Controlled Trial

Objectives The coronavirus (COVID-19) global pandemic has increased psychological distress among the general population. The objective of this study is to evaluate a mindfulness-based intervention for psychological distress among Chinese residents during COVID-19. Methods This study used a switching replications design to test the feasibility and efficacy of a brief online mindfulness-based intervention for Chinese residents’ psychological distress. Fifty-one residents in the Hubei province were randomly allocated to two groups (experimental group and waitlist control group) with three waves of measurement at time 1, time 2, and time 3 for changes in mindfulness and psychological distress. Results In addition to significant within-group improvements over time for both groups, OLS linear regression with full information likelihood estimation revealed statistically significant between-group treatment effects across outcome domains, including mindfulness awareness, b = 2.84, p < 0.001, g = 6.92, psychological distress, b = −21.33, p < 0.001, g = 6.62, somatic symptoms, b = −6.22, p < 0.001, g = 4.42, depressive symptoms, b = −7.16, p < 0.001, g = 5.07, and anxiety symptoms, b = −8.09, p < 0.001, g = 6.84. Conclusions Results suggest that a brief online mindfulness-based intervention can be a feasible and promising intervention for improving mindfulness and decreasing psychological distress among Chinese residents staying at home during the COVID-19 outbreak. The study used a small convenience sample which led to a concern of external generalizability and with limited evaluation of long-term change.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167607/1/Zhang_2021_Article_ABriefOnlineMindfulness-BasedG.pdfDescription of Zhang_2021_Article_ABriefOnlineMindfulness-BasedG.pdf : Main articleSEL

TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training

Contrastive Language-Image Pre-training (CLIP) has demonstrated impressive capabilities in open-vocabulary classification. The class token in the image encoder is trained to capture the global features to distinguish different text descriptions supervised by contrastive loss, making it highly effective for single-label classification. However, it shows poor performance on multi-label datasets because the global feature tends to be dominated by the most prominent class and the contrastive nature of softmax operation aggravates it. In this study, we observe that the multi-label classification results heavily rely on discriminative local features but are overlooked by CLIP. As a result, we dissect the preservation of patch-wise spatial information in CLIP and proposed a local-to-global framework to obtain image tags. It comprises three steps: (1) patch-level classification to obtain coarse scores; (2) dual-masking attention refinement (DMAR) module to refine the coarse scores; (3) class-wise reidentification (CWR) module to remedy predictions from a global perspective. This framework is solely based on frozen CLIP and significantly enhances its multi-label classification performance on various benchmarks without dataset-specific training. Besides, to comprehensively assess the quality and practicality of generated tags, we extend their application to the downstream task, i.e., weakly supervised semantic segmentation (WSSS) with generated tags as image-level pseudo labels. Experiments demonstrate that this classify-then-segment paradigm dramatically outperforms other annotation-free segmentation methods and validates the effectiveness of generated tags. Our code is available at https://github.com/linyq2117/TagCLIP.Comment: Accepted by AAAI202