The Strategy Integration of Network Marketing for the Tourism Enterprises in Shaanxi Province

With the development of modern science and information technology, the Internet has brought about the new opportunity for the development of tourism enterprises, and also made network marketing possible. Starting form concrete situation of tourism industry in Shaanxi Province and making a breakthrough in traditional marketing strategies province, this paper suggests the strategy integration of network marketing for the tourism enterprise in Shaanxi Provinc

Too Large; Data Reduction for Vision-Language Pre-Training

This paper examines the problems of severe image-text misalignment and high redundancy in the widely-used large-scale Vision-Language Pre-Training (VLP) datasets. To address these issues, we propose an efficient and straightforward Vision-Language learning algorithm called TL;DR, which aims to compress the existing large VLP data into a small, high-quality set. Our approach consists of two major steps. First, a codebook-based encoder-decoder captioner is developed to select representative samples. Second, a new caption is generated to complement the original captions for selected samples, mitigating the text-image misalignment problem while maintaining uniqueness. As the result, TL;DR enables us to reduce the large dataset into a small set of high-quality data, which can serve as an alternative pre-training dataset. This algorithm significantly speeds up the time-consuming pretraining process. Specifically, TL;DR can compress the mainstream VLP datasets at a high ratio, e.g., reduce well-cleaned CC3M dataset from 2.82M to 0.67M ($\sim$24\%) and noisy YFCC15M from 15M to 2.5M ($\sim$16.7\%). Extensive experiments with three popular VLP models over seven downstream tasks show that VLP model trained on the compressed dataset provided by TL;DR can perform similar or even better results compared with training on the full-scale dataset. The code will be made available at \url{https://github.com/showlab/data-centric.vlp}.Comment: Work in progress. Code: https://github.com/showlab/data-centric.vl

AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistant

It is still a pipe dream that personal AI assistants on the phone and AR glasses can assist our daily life in addressing our questions like ``how to adjust the date for this watch?'' and ``how to set its heating duration? (while pointing at an oven)''. The queries used in conventional tasks (i.e. Video Question Answering, Video Retrieval, Moment Localization) are often factoid and based on pure text. In contrast, we present a new task called Task-oriented Question-driven Video Segment Retrieval (TQVSR). Each of our questions is an image-box-text query that focuses on affordance of items in our daily life and expects relevant answer segments to be retrieved from a corpus of instructional video-transcript segments. To support the study of this TQVSR task, we construct a new dataset called AssistSR. We design novel guidelines to create high-quality samples. This dataset contains 3.2k multimodal questions on 1.6k video segments from instructional videos on diverse daily-used items. To address TQVSR, we develop a simple yet effective model called Dual Multimodal Encoders (DME) that significantly outperforms several baseline methods while still having large room for improvement in the future. Moreover, we present detailed ablation analyses. Code and data are available at \url{https://github.com/StanLei52/TQVSR}.Comment: 20 pages, 12 figure

Three-Dimensional Microwave Imaging for Concealed Weapon Detection Using Range Stacking Technique

Three-dimensional (3D) microwave imaging has been proven to be well suited for concealed weapon detection application. For the 3D image reconstruction under two-dimensional (2D) planar aperture condition, most of current imaging algorithms focus on decomposing the 3D free space Green function by exploiting the stationary phase and, consequently, the accuracy of the final imagery is obtained at a sacrifice of computational complexity due to the need of interpolation. In this paper, from an alternative viewpoint, we propose a novel interpolation-free imaging algorithm based on wavefront reconstruction theory. The algorithm is an extension of the 2D range stacking algorithm (RSA) with the advantages of low computational cost and high precision. The algorithm uses different reference signal spectrums at different range bins and then forms the target functions at desired range bin by a concise coherent summation. Several practical issues such as the propagation loss compensation, wavefront reconstruction, and aliasing mitigating are also considered. The sampling criterion and the achievable resolutions for the proposed algorithm are also derived. Finally, the proposed method is validated through extensive computer simulations and real-field experiments. The results show that accurate 3D image can be generated at a very high speed by utilizing the proposed algorithm

Review of an Influenza Surveillance System, Beijing, People’s Republic of China

This system enabled detection of the onset and peak of an epidemic

The role of upfront primary tumor resection in asymptomatic patients with unresectable stage IV colorectal cancer: A systematic review and meta-analysis

BackgroundControversy exists over the role of upfront primary tumor resection (PTR) in asymptomatic patients with unresectable stage IV colorectal cancer (CRC). The purpose of this study was to evaluate the effect of upfront PTR on survival outcomes and adverse outcomes.MethodsSearches were conducted on PubMed, EMBASE, Web of Science, and Cochrane Library from inception to August 2021. Studies comparing survival outcomes with or without adverse outcomes between PTR and non-PTR treatments were included. Review Manager 5.3 was applied for meta-analyses with a random-effects model whenever possible.ResultsOverall, 20 studies with 3,088 patients were finally included in this systematic review. Compared with non-PTR, upfront PTR was associated with better 3-year (HR: 0.69, 95% CI, 0.57–0.83, P = 0.0001) and 5-year overall survival (OS) (HR: 0.77, 95% CI, 0.62–0.95, P = 0.01), while subgroup analysis indicated that there was no significant difference between upfront PTR and upfront chemotherapy (CT) group. In addition, grade 3 or higher adverse effects due to CT were more frequent in the PTR group with marginal significance (OR: 1.74, 95% CI, 0.99–3.06, P = 0.05), and other adverse outcomes were comparable.ConclusionsPTR might be related to improved OS for asymptomatic patients with unresectable stage IV CRC, whereas receiving upfront CT is a rational alternative without detrimental influence on survival or adverse outcomes compared with upfront PTR.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=27267

Effects of nitrate on the treatment of lead contaminated groundwater by nanoscale zerovalent iron

Nanoscale zerovalent iron (nZVI) is efficient for removing Pb(2+) and nitrate from water. However, the influence of nitrate, a common groundwater anion, on Pb(2+) removal by nZVI is not well understood. In this study, we showed that under excess Fe(0) conditions (molar ratio of Fe(0)/nitrate>4), Pb(2+) ions were immobilized more quickly (<5 min) than in nitrate-free systems (∼ 15 min) due to increasing pH. With nitrate in excess (molar ratio of Fe(0)/nitrate<4), nitrate stimulated the formation of crystal PbxFe3-xO4 (ferrite), which provided additional Pb(2+) removal. However, ∼ 7% of immobilized Pb(2+) ions were released into aqueous phase within 2h due to ferrite deformation. Oxidation-reduction potential (ORP) values below -600 mV correlated with excess Fe(0) conditions (complete Pb(2+) immobilization), while ORP values ≥-475 mV characterized excess nitrate conditions (ferrite process and Pb(2+) release occurrence). This study indicates that ORP monitoring is important for proper management of nZVI-based remediation in the subsurface to avoid lead remobilization in the presence of nitrate