Research on Spillover Effect of Paid Search Advertising Channels

With the diversification of paid search advertising channels, e-commerce enterprises are paying more and more attention on how to evaluate the effectiveness of different paid search advertising channels correctly and accurately to choose the optimal advertising channel or channels. We develop a multivariate time series model to investigate the spillover effect of paid search advertising channels based on the ad click-through rate and conversion rate, and calibrate the model using an e-commerce site\u27s web log data. We determine the long-term equilibrium relationship between each channel\u27s advertisement clicks through the co-integration test and evaluate the effect of short-term fluctuations in the interaction between each channel advertisement clicks through the vector error correction model. Based on the empirical results, this paper puts forward suggestions on the advertising strategy of this e-commerce website

Transforming the Interactive Segmentation for Medical Imaging

The goal of this paper is to interactively refine the automatic segmentation on challenging structures that fall behind human performance, either due to the scarcity of available annotations or the difficulty nature of the problem itself, for example, on segmenting cancer or small organs. Specifically, we propose a novel Transformer-based architecture for Interactive Segmentation (TIS), that treats the refinement task as a procedure for grouping pixels with similar features to those clicks given by the end users. Our proposed architecture is composed of Transformer Decoder variants, which naturally fulfills feature comparison with the attention mechanisms. In contrast to existing approaches, our proposed TIS is not limited to binary segmentations, and allows the user to edit masks for arbitrary number of categories. To validate the proposed approach, we conduct extensive experiments on three challenging datasets and demonstrate superior performance over the existing state-of-the-art methods. The project page is: https://wtliu7.github.io/tis/.Comment: Accepted to MICCAI 202

Open-vocabulary Semantic Segmentation with Frozen Vision-Language Models

When trained at a sufficient scale, self-supervised learning has exhibited a notable ability to solve a wide range of visual or language understanding tasks. In this paper, we investigate simple, yet effective approaches for adapting the pre-trained foundation models to the downstream task of interest, namely, open-vocabulary semantic segmentation. To this end, we make the following contributions: (i) we introduce Fusioner, with a lightweight, transformer-based fusion module, that pairs the frozen visual representation with language concept through a handful of image segmentation data. As a consequence, the model gains the capability of zero-shot transfer to segment novel categories; (ii) without loss of generality, we experiment on a broad range of self-supervised models that have been pre-trained with different schemes, e.g. visual-only models (MoCo v3, DINO), language-only models (BERT), visual-language model (CLIP), and show that, the proposed fusion approach is effective to any pair of visual and language models, even those pre-trained on a corpus of uni-modal data; (iii) we conduct thorough ablation studies to analyze the critical components in our proposed Fusioner, while evaluating on standard benchmarks, e.g. PASCAL-5i and COCO-20i , it surpasses existing state-of-the-art models by a large margin, despite only being trained on frozen visual and language features; (iv) to measure the model's robustness on learning visual-language correspondence, we further evaluate on synthetic dataset, named Mosaic-4, where images are constructed by mosaicking the samples from FSS-1000. Fusioner demonstrates superior performance over previous models.Comment: BMVC 2022 Ora

Learning from Winners: A Strategic Perspective of Improving Freelancers’ Bidding Competitiveness in Crowdsourcing

The rapid growth of crowdsourcing grants freelancers unprecedented opportunities to materialize their expertise by bidding in specific tasks. Despite lowering freelancers’ participation costs, the bidding mechanism meanwhile induces intense competition, rendering it difficult for freelancers to submit competitive bids. Although previous research has disentangled several bidding strategies, scant attention was paid to whether and how freelancers should learn to adjust their bidding strategies and improve bidding competitiveness during the journey of participating in multiple tasks. To fill in this gap, we adapt a set of bidding strategies from auction literature into the crowdsourcing context. Leveraging the lens of vicarious learning, we advance that freelancers’ learning from winners on bidding strategies will enhance their bidding competitiveness, which is moderated by task complexity. Our preliminary results suggest a significant relationship between strategic learning and bidding competitiveness, along with the moderating effect of task complexity. Expected contributions and future schemes are discussed finally

Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation

Open-vocabulary semantic segmentation is a challenging task that requires segmenting novel object categories at inference time. Recent works explore vision-language pre-training to handle this task, but suffer from unrealistic assumptions in practical scenarios, i.e., low-quality textual category names. For example, this paradigm assumes that new textual categories will be accurately and completely provided, and exist in lexicons during pre-training. However, exceptions often happen when meet with ambiguity for brief or incomplete names, new words that are not present in the pre-trained lexicons, and difficult-to-describe categories for users. To address these issues, this work proposes a novel decomposition-aggregation framework, inspired by human cognition in understanding new concepts. Specifically, in the decomposition stage, we decouple class names into diverse attribute descriptions to enrich semantic contexts. Two attribute construction strategies are designed: using large language models for common categories, and involving manually labelling for human-invented categories. In the aggregation stage, we group diverse attributes into an integrated global description, to form a discriminative classifier that distinguishes the target object from others. One hierarchical aggregation is further designed to achieve multi-level alignment and deep fusion between vision and text. The final result is obtained by computing the embedding similarity between aggregated attributes and images. To evaluate the effectiveness, we annotate three datasets with attribute descriptions, and conduct extensive experiments and ablation studies. The results show the superior performance of attribute decomposition-aggregation

Cache-Enabled in Cooperative Cognitive Radio Networks for Transmission Performance

The proliferation of mobile devices that support the acceleration of data services (especially smartphones) has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary contents. Thus, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay from the backhaul link to the secondary base station. First, in terms of the content caching and the transmission strategies, we provide a cooperation scheme to maximize the secondary user’s effective data transmission rates under the constraint of the primary users target rate. Then, we investigate the impact of the caching allocation and prove that the formulated problem is a concave problem with regard to the caching capacity allocation for any given power allocation. Furthermore, we obtain the joint caching and power allocation by an effective bisection search algorithm. Finally, our results show that the content caching cooperation scheme can achieve significant performance gain for the primary and secondary systems over the traditional two-hop relay cooperation without caching

Genetic Basis and Expression Pattern Indicate the Biocontrol Potential and Soil Adaption of Lysobacter capsici CK09

Lysobacter species have attracted increasing attention in recent years due to their capacities to produce diverse secondary metabolites against phytopathogens. In this research, we analyzed the genomic and transcriptomic patterns of Lysobacter capsici CK09. Our data showed that L. capsici CK09 harbored various contact-independent biocontrol traits, such as fungal cell wall lytic enzymes and HSAF/WAP-8294A2 biosynthesis, as well as several contact-dependent machineries, including type 2/4/6 secretion systems. Additionally, a variety of hydrolytic enzymes, particularly extracellular enzymes, were found in the L. capsici CK09 genome and predicted to improve its adaption in soil. Furthermore, several systems, including type 4 pili, type 3 secretion system and polysaccharide biosynthesis, can provide a selective advantage to L. capsici CK09, enabling the species to live on the surface in soil. The expression of these genes was then confirmed via transcriptomic analysis, indicating the activities of these genes. Collectively, our research provides a comprehensive understanding of the biocontrol potential and soil adaption of L. capsici CK09 and implies the potential of this strain for application in the future