Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure

Ad creatives are one of the prominent mediums for online e-commerce advertisements. Ad creatives with enjoyable visual appearance may increase the click-through rate (CTR) of products. Ad creatives are typically handcrafted by advertisers and then delivered to the advertising platforms for advertisement. In recent years, advertising platforms are capable of instantly compositing ad creatives with arbitrarily designated elements of each ingredient, so advertisers are only required to provide basic materials. While facilitating the advertisers, a great number of potential ad creatives can be composited, making it difficult to accurately estimate CTR for them given limited real-time feedback. To this end, we propose an Adaptive and Efficient ad creative Selection (AES) framework based on a tree structure. The tree structure on compositing ingredients enables dynamic programming for efficient ad creative selection on the basis of CTR. Due to limited feedback, the CTR estimator is usually of high variance. Exploration techniques based on Thompson sampling are widely used for reducing variances of the CTR estimator, alleviating feedback sparsity. Based on the tree structure, Thompson sampling is adapted with dynamic programming, leading to efficient exploration for potential ad creatives with the largest CTR. We finally evaluate the proposed algorithm on the synthetic dataset and the real-world dataset. The results show that our approach can outperform competing baselines in terms of convergence rate and overall CTR

AutoPoster: A Highly Automatic and Content-aware Design System for Advertising Poster Generation

Advertising posters, a form of information presentation, combine visual and linguistic modalities. Creating a poster involves multiple steps and necessitates design experience and creativity. This paper introduces AutoPoster, a highly automatic and content-aware system for generating advertising posters. With only product images and titles as inputs, AutoPoster can automatically produce posters of varying sizes through four key stages: image cleaning and retargeting, layout generation, tagline generation, and style attribute prediction. To ensure visual harmony of posters, two content-aware models are incorporated for layout and tagline generation. Moreover, we propose a novel multi-task Style Attribute Predictor (SAP) to jointly predict visual style attributes. Meanwhile, to our knowledge, we propose the first poster generation dataset that includes visual attribute annotations for over 76k posters. Qualitative and quantitative outcomes from user studies and experiments substantiate the efficacy of our system and the aesthetic superiority of the generated posters compared to other poster generation methods.Comment: Accepted for ACM MM 202

ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models

This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs). Unlike traditional benchmarks that evaluate general mathematical reasoning with an average accuracy, ConceptMath systematically organizes math problems under a hierarchy of math concepts, so that mathematical reasoning can be evaluated at different granularity with concept-wise accuracies. Based on our ConcepthMath, we evaluate a broad range of LLMs, and we observe existing LLMs, though achieving high average accuracies on traditional benchmarks, exhibit significant performance variations across different math concepts and may even fail catastrophically on the most basic ones. Besides, we also introduce an efficient fine-tuning strategy to enhance the weaknesses of existing LLMs. Finally, we hope ConceptMath could guide the developers to understand the fine-grained mathematical abilities of their models and facilitate the growth of foundation models.Comment: The benchmark dataset will be released soo

Controllable ingestion and release of guest components driven by interfacial molecular orientation of host liquid crystal droplets

Controllable construction and manipulation of artificial multi-compartmental structures are crucial in understanding and imitating smart molecular elements such as biological cells and on-demand delivery systems. Here, we report a liquid crystal droplet (LCD) based three-dimensional system for controllable and reversible ingestion and release of guest aqueous droplets (GADs). Induced by interfacial thermodynamic fluctuation and internal topological defect, microscale LCDs with perpendicular anchoring condition at the interface would spontaneously ingest external components from the surroundings and transform them as radially assembled tiny GADs inside LCDs. Landau–de Gennes free-energy model is applied to describe and explain the assembly dynamics and morphologies of these tiny GADs, which presents a good agreement with experimental observations. Furthermore, the release of these ingested GADs can be actively triggered by changing the anchoring conditions at the interface of LCDs. Since those ingestion and release processes are controllable and happen very gently at room temperature and neutral pH environment without extra energy input, these microscale LCDs are very prospective to provide a unique and viable route for constructing hierarchical 3D structures with tunable components and compartments

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbackComment: 16 page

Non-equilibrium molecular-dynamics measurement of the Leslie coefficients of a Gay-Berne nematic liquid crystal

Using non- equilibrium molecular- dynamics ( MD) simulations, we have measured the six Leslie coefficients of a nematic liquid crystal composed of molecules interacting via the Gay Berne potential. In the presence of a simple shear flow, an alignment field is applied to control the molecular orientation and a uniform director is stabilized in the central region of the channel in which the liquid crystal is confined and sheared. With the director tuned by varying the applied field, a number of orientational states are stabilized in the presence of the shear flow and various viscous stress components are measured in these states of differently oriented directors. The six Leslie coefficients alpha(i) are determined by interpreting the MD measurement data for viscous stress according to the constitutive relations in the Ericksen Leslie - Parodi theory. Our measurement of the Leslie coefficients shows the Parodi relation alpha(2)+alpha(3)=alpha(6)-alpha(5) is well satisfied. Given the values of the Leslie coefficients, liquid crystal orientations are evaluated for different alignment fields and shear rates, and then compared with those directly measured in MD simulations, demonstrating a quantitative agreement. Our simulation results show that in the Gay - Berne nematic liquid crystal, the viscous stress and the coupling between orientation and flow are well described by the Ericksen - Leslie Parodi theory

Preparation of Ionic Liquid-Coated Graphene Nanosheets/PTFE Nanocomposite for Stretchable, Flexible Conductor via a Pre-Stretch Processing

The various volume concentrations of ionic liquid-modified graphene nanosheets filled polytetrafluoroethylene nanocomposites (IL-GNs/PTFE) for flexible conductors were fabricated via a pre-stretch processing method after cold-press sintering. The results indicated that pre-stretching has no significant weakening in the electrical conductivity of the nanocomposites, while the Young’s modulus greatly reduced by 62.5%, which is more suitable for flexible conductors. This may be because the reduced conductivity by the destructive conductive pathway cancels out the enhanced conductivity by the increased interlamellar spacing of IL-GNs via a pre-stretch processing, and the nanocomposite exhibits a phase transition from two to three-phase (with the introduction of an air phase) during pre-stretching. It was also found that the tensile strength of the nanocomposites was enhanced by 42.9% and the elongation at break and thermal conductivity decreased slightly with the same filler content after pre-stretching. The electrical conductivity of the pre-stretched nanocomposites tended to stabilize at 5.5 × 10−2 s·m−1, when the volume content of the packings achieved a percolation threshold (1.49 vol%). Meanwhile, the electrical resistivity of the pre-stretched 3.0 vol% IL-GNs/PTFE nanocomposite was slightly reduced by 0.30%, 0.38%, and 0.87% respectively after 180° twisting, 180° bending, and 10% stretching strain for 1000 cycles