Truthful Auctions for Automated Bidding in Online Advertising

Automated bidding, an emerging intelligent decision making paradigm powered by machine learning, has become popular in online advertising. Advertisers in automated bidding evaluate the cumulative utilities and have private financial constraints over multiple ad auctions in a long-term period. Based on these distinct features, we consider a new ad auction model for automated bidding: the values of advertisers are public while the financial constraints, such as budget and return on investment (ROI) rate, are private types. We derive the truthfulness conditions with respect to private constraints for this multi-dimensional setting, and demonstrate any feasible allocation rule could be equivalently reduced to a series of non-decreasing functions on budget. However, the resulted allocation mapped from these non-decreasing functions generally follows an irregular shape, making it difficult to obtain a closed-form expression for the auction objective. To overcome this design difficulty, we propose a family of truthful automated bidding auction with personalized rank scores, similar to the Generalized Second-Price (GSP) auction. The intuition behind our design is to leverage personalized rank scores as the criteria to allocate items, and compute a critical ROI to transform the constraints on budget to the same dimension as ROI. The experimental results demonstrate that the proposed auction mechanism outperforms the widely used ad auctions, such as first-price auction and second-price auction, in various automated bidding environments

IRGen: Generative Modeling for Image Retrieval

While generative modeling has been ubiquitous in natural language processing and computer vision, its application to image retrieval remains unexplored. In this paper, we recast image retrieval as a form of generative modeling by employing a sequence-to-sequence model, contributing to the current unified theme. Our framework, IRGen, is a unified model that enables end-to-end differentiable search, thus achieving superior performance thanks to direct optimization. While developing IRGen we tackle the key technical challenge of converting an image into quite a short sequence of semantic units in order to enable efficient and effective retrieval. Empirical experiments demonstrate that our model yields significant improvement over three commonly used benchmarks, for example, 22.9\% higher than the best baseline method in precision@10 on In-shop dataset with comparable recall@10 score

The genome of broomcorn millet

Broomcorn millet (Panicum miliaceum L.) is the most water-efficient cereal and one of the earliest domesticated plants. Here we report its high-quality, chromosome-scale genome assembly using a combination of short-read sequencing, single-molecule real-time sequencing, Hi-C, and a high-density genetic map. Phylogenetic analyses reveal two sets of homologous chromosomes that may have merged ~5.6 million years ago, both of which exhibit strong synteny with other grass species. Broomcorn millet contains 55,930 proteincoding genes and 339 microRNA genes. We find Paniceae-specific expansion in several subfamilies of the BTB (broad complex/tramtrack/bric-a-brac) subunit of ubiquitin E3 ligases, suggesting enhanced regulation of protein dynamics may have contributed to the evolution of broomcorn millet. In addition, we identify the coexistence of all three C4 subtypes of carbon fixation candidate genes. The genome sequence is a valuable resource for breeders and will provide the foundation for studying the exceptional stress tolerance as well as C4 biology

Technology Innovation in China’s Home Electric Appliance manufactory industry-Case study of Haier Smart Home Ltd

China is still in the midst of digital transformation revolution. The Internet and advanced mobile communication channels have a profound influence on people’s daily life. The e-commerce boosts the economy as the platform innovation and ecosystem establishment. Even though during the COVID-19, the online consumption has a major contribution to stabilizing the whole economy. The post-pandemic era forces the retail industry and other related service industry to transform digitally. Not only the retail industry and traditional financial industry face the digitalization revolution but also the traditional manufactory industry should follow the step of Industry 4.0 and Smart manufactory era. This article will discuss the digital transformation process in home electric appliance manufactory industry by analysing the case of Haier Smart Home Ltd

DEVELOPMENT OF PHASE RETRIEVAL TECHNIQUES FOR DIGITAL FRINGE PROJECTION PROFILOMETRY

Ph.DDOCTOR OF PHILOSOPH

Dynamic Resource Allocation Scheme and Deep Deterministic Policy Gradient-Based Mobile Edge Computing Slices System

The development of multi-industry compatibility and the coexistence of multiple services and multiple functional communication networks will cause rapid growth in mobile communication system traffic. Users will have increasingly strict requirements for quality of service (QoS), e.g., a high rate, low latency, and low energy consumption. To address these problems, it is helpful to combine network slicing and mobile edge computing (MEC) to provide customized networks while reducing the service processing time. Due to the uncertainty of user requests and the environment, reasonable resource allocation is always particularly challenging. A novel dynamic resource allocation scheme for MEC slice systems, which formulates resource allocation and computation offloading issues as an optimization problem subject to the latency and rate, is proposed. Based on the dynamics of the slice requirements, quantity, and service time, the proposed problem is converted to a Markov decision process (MDP), and a state, action, and reward function are proposed. By exploiting the deep deterministic policy gradient (DDPG) algorithm, the wireless resources and computing resources are configured dynamically according to the requirements of different types of slices to maximize the revenue of the network operator. The simulation results demonstrate the influence of the slice arrival rate and total resources on the allocation policy. Compared with other schemes, the proposed scheme can provide a more effective performance when resources are scarce

Optical breast atlas as a testbed for image reconstruction in optical mammography

10.1038/s41597-021-01037-zScientific Data81257

OST: a heuristic-based orthogonal partitioning algorithm for dynamic hierarchical data visualization

Tools for intuitive visualization of dynamic datasets are highly demanded for capturing information and revealing potential patterns, especially in understanding the trend of data changes. We propose a novel resolution-independent heuristic algorithm, termed Orthogonal Stable Treemap (OST), to implicitly display dynamic hierarchical data value changes. OST adopts a site-based method as the Voronoi treemap (VT), to preserve the layout stability for diversified data values. Meanwhile, OST partitions the whole canvas with horizontal or vertical lines, instead of the lines with arbitrary orientations in VT. Technical innovations are made in three parts: Initialization of site state to speed up the algorithm and preserve the layout; efficient computation of orthogonal rectangular diagram to partition the empty canvas; self-adaption of site state to quickly reach an equilibrium. The performance of OST is quantitatively evaluated in terms of computation complexity, computation time, convergence rate, visibility, and stability. Moreover, qualitative evaluations (use case and user study) are demonstrated on the dynamic work-in-process dataset in the wafer fab. Evaluation results show that OST combines the advantages of layout stability and tidiness, contributing to easier and faster plot understanding.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)This work was partially supported by the A*STAR Cyber-Physical Production System (CPPS)-Towards Contextual and Intelligent Response Research Program, under the RIE2020 IAF-PP Grant A19C1a0018, and Model Factory @SIMTech. This work is also partially supported by a Grant MOE 2017-T1-001-053-04 from Ministry of Education, Singapore

Association of atrial fibrillation and clinical outcomes in adults with chronic kidney disease: A propensity score-matched analysis.

OBJECTIVE:Atrial fibrillation (AF) is associated with adverse outcomes in the general population, but its impact on patients with chronic kidney disease (CKD) remains unclear. In this study, we assessed the association between AF and risks of all-cause mortality and stroke in Chinese adults with CKD. METHODS:We enrolled adults aged 45 years or older with CKD (defined as an estimated glomerular filtration rate <60 mL/min per 1.73 m2 and/or proteinuria identified using the urine dipstick method) from the Kailuan study between 2008 and 2014. AF was identified by 12-lead electrocardiography or hospital discharge diagnostic codes. Mortality data were collected from the provincial vital statistics, and physician-diagnosed ischemic or hemorrhagic stroke was confirmed in the biennial interview. RESULTS:Among the 21587 CKD adults, 216 patients were identified with AF, the median follow-up duration was 5.21 years (5.69 ± 1.96 years); During follow-up, there were 70 cases of death, and 16 cases of ischemic stroke and 6 cases of hemorrhagic stroke in the participants with AF in comparison with 2572 cases of death and 656 cases of ischemic stroke and 184 cases of hemorrhagic stroke among the participants without AF. After adjustment for potential confounders, AF was associated with an 86% increase in the rate of death (hazard ratio [HR], 1.86; 95% confidence interval [CI], 1.33-2.59, P<0.001), a 104% (HR, 2.04; 95% CI, 1.09-3.83, P = 0.026) and 325% (HR, 4.25; 95% CI, 1.74-10.36, P = 0.001) increase in the rate of ischemic stroke and hemorrhagic stroke, respectively. These associations were still consistent and strong after propensity score-matched analysis. CONCLUSION:Our study shows that AF is independently associated with increased risk of all-cause mortality, ischemic and hemorrhagic stroke in Chinese CKD adults. Future studies are required to elucidate the physiological mechanisms underlying this association

Tunable Head-Conducting Microwave-Absorbing Multifunctional Composites with Excellent Microwave Absorption, Thermal Conductivity and Mechanical Properties

Developing composite materials with both thermal conductivity and microwave absorption is an effective strategy to solve the problems of heat dissipation burden and microwave radiation interference caused by the development of miniaturization and high performance of portable electronic equipment. However, these properties are not easy to simultaneously implement due to the limitation of single type fillers with a single particle size, inspiring the possibility of realizing multifunctional composites with the introduction of composite fillers. In this work, using alumina (Al2O3) and zinc oxide (ZnO) as head-conducting fillers, carbonyl iron (Fe(CO)5) as microwave-absorbing fillers, silicone rubber (SR) composites (Al2O3/ZnO/Fe(CO)5/SR) with enhanced microwave absorption, high thermal conductivity and good mechanical properties were successfully mass prepared. It was found that the composites can achieve a thermal conductivity of 3.61 W·m−1·K−1, an effective microwave absorption bandwidth of 10.86–15.47 GHz. Especially, there is an effective microwave absorption efficiency of 99% at 12.46–14.27 GHz, which can realize the integration of electromagnetic shielding and heat dissipation. The compact microstructure, formed by the overlapping of large particle size fillers and the filling of their gaps by small particle size fillers, is helpful to enhance the thermal conduction path and weaken the microwave reflection. The heat-conducting microwave-absorbing Al2O3/ZnO/Fe(CO)5/SR composites also have the advantages of thermal stability, lightness and flexibility, providing a certain experimental basis for the research and development of high-performance and diversified composites