A Model of Customer Lifetime Value Consider with Word-of-mouth Marketing Value

With the rapid development of IT technology and fierce competition of market, the customer relationship management(CRM) has gained its importance in the market. Companies have attached importance to acquiring and retaining the most profitable customers. So calculating customer’s value is a significant segment for every effective CRM. Many researches have been performed to calculate customer’s value based on customer lifetime value (LTV). But, these calculations can’t effectively include the whole customer value, especially for the word-of-mouth marketing value. This paper proposes a new LTV model which considers the customer’s past profit contribution, potential value and word-of-mouth marketing value, and gives a more reasonable LTV value in CRM for the company to make a decision

OTS: A One-shot Learning Approach for Text Spotting in Historical Manuscripts

Historical manuscript processing poses challenges like limited annotated training data and novel class emergence. To address this, we propose a novel One-shot learning-based Text Spotting (OTS) approach that accurately and reliably spots novel characters with just one annotated support sample. Drawing inspiration from cognitive research, we introduce a spatial alignment module that finds, focuses on, and learns the most discriminative spatial regions in the query image based on one support image. Especially, since the low-resource spotting task often faces the problem of example imbalance, we propose a novel loss function called torus loss which can make the embedding space of distance metric more discriminative. Our approach is highly efficient and requires only a few training samples while exhibiting the remarkable ability to handle novel characters, and symbols. To enhance dataset diversity, a new manuscript dataset that contains the ancient Dongba hieroglyphics (DBH) is created. We conduct experiments on publicly available VML-HD, TKH, NC datasets, and the new proposed DBH dataset. The experimental results demonstrate that OTS outperforms the state-of-the-art methods in one-shot text spotting. Overall, our proposed method offers promising applications in the field of text spotting in historical manuscripts

Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution

Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video superresolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the SpatialTemporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https: //vlis2022.github.io/cvpr23/egvsr.Comment: Accepted by CVPR202

Adaptive White-Box Watermarking with Self-Mutual Check Parameters in Deep Neural Networks

Artificial Intelligence (AI) has found wide application, but also poses risks due to unintentional or malicious tampering during deployment. Regular checks are therefore necessary to detect and prevent such risks. Fragile watermarking is a technique used to identify tampering in AI models. However, previous methods have faced challenges including risks of omission, additional information transmission, and inability to locate tampering precisely. In this paper, we propose a method for detecting tampered parameters and bits, which can be used to detect, locate, and restore parameters that have been tampered with. We also propose an adaptive embedding method that maximizes information capacity while maintaining model accuracy. Our approach was tested on multiple neural networks subjected to attacks that modified weight parameters, and our results demonstrate that our method achieved great recovery performance when the modification rate was below 20%. Furthermore, for models where watermarking significantly affected accuracy, we utilized an adaptive bit technique to recover more than 15% of the accuracy loss of the model

Outage Performance of Uplink Rate Splitting Multiple Access with Randomly Deployed Users

With the rapid proliferation of smart devices in wireless networks, more powerful technologies are expected to fulfill the network requirements of high throughput, massive connectivity, and diversify quality of service. To this end, rate splitting multiple access (RSMA) is proposed as a promising solution to improve spectral efficiency and provide better fairness for the next-generation mobile networks. In this paper, the outage performance of uplink RSMA transmission with randomly deployed users is investigated, taking both user scheduling schemes and power allocation strategies into consideration. Specifically, the greedy user scheduling (GUS) and cumulative distribution function (CDF) based user scheduling (CUS) schemes are considered, which could maximize the rate performance and guarantee scheduling fairness, respectively. Meanwhile, we re-investigate cognitive power allocation (CPA) strategy, and propose a new rate fairness-oriented power allocation (FPA) strategy to enhance the scheduled users' rate fairness. By employing order statistics and stochastic geometry, an analytical expression of the outage probability for each scheduling scheme combining power allocation is derived to characterize the performance. To get more insights, the achieved diversity order of each scheme is also derived. Theoretical results demonstrate that both GUS and CUS schemes applying CPA or FPA strategy can achieve full diversity orders, and the application of CPA strategy in RSMA can effectively eliminate the secondary user's diversity order constraint from the primary user. Simulation results corroborate the accuracy of the analytical expressions, and show that the proposed FPA strategy can achieve excellent rate fairness performance in high signal-to-noise ratio region.Comment: 38 pages,8 figure

A constitutive model for gassy clay

Fine-grained marine sediments often contain gas bubbles that can cause many geotechnical problems. This soil has a composite structure with gas bubbles fitting within the saturated soil matrix. The gas cavity has a detrimental effect on the soil stiffness and strength when they are filled with undissolved gas only. The gas cavity can be filled with gas and pore water due to ‘bubble flooding’. Bubble flooding has a beneficial effect on the soil stiffness and undrained shear strength because it makes the saturated soil matrix partially drained under a globally undrained condition. A critical state constitutive model for gassy clay is presented which accounts for the composite structure of the soil and bubble flooding. The gas cavity is assumed to have a detrimental effect on the plastic hardening of the saturated soil matrix. Some of the bubbles can be flooded by pore water from the saturated soil matrix which leads to higher mean effective stress of the saturated soil matrix. Consequently, both soil stiffness and strength increase. Only one new parameter is introduced to model the detrimental effect of gas bubbles on plastic hardening. The model has been validated by the results of three gassy clays

Design, synthesis and antimycobacterial activity of novel nitrobenzamide derivatives

We report herein the design and synthesis of a series of novel nitrobenzamide derivatives. Results reveal that many of them display considerable in vitro antitubercular activity. Four N-benzyl or N-(pyridine-2-yl)methyl 3,5-dinitrobenzamides A6, A11, C1 and C4 have not only the same excellent MIC values of 1500), opening a new direction for further development

Study on coal seam physical characteristics and influence on stimulation: A case study of coal seams in zhengzhuang block

Coalbed Methane (CBM) is an unconventional form of natural gas which is self-generated and self-stored in coal seams. In order to realize the effective exploitation of CBM in Zhengzhuang block, microstructure, wettability, permeability, rock mechanics and in-situ stress of coal were studied in this research. It is found that high rank anthracite characterized by high vitrinite content and low inorganic mineral content, is abundant in CBM. More than 96% of inorganic minerals are clays dominated by kaolinite and illite. Various types of pores are developed on the coal. The wettability of coal differs from high to low to surface water, active water, and foam fracturing fluid; and contact angles of coal with active water and foam fracturing fluid decrease with the increase of burial depth. Gradients of fracture pressure and closure pressure in No.3 coal seam are higher than that of No.15 coal seam. The elastic modulus of coal is lower than that of sandstone. The construction curve of hydraulic fracturing shows that, when the construction flow rate and sand quantity are similar, the construction pressure of prepad in No.3 coal seam is lower than the pumping pressure of No.15 coal seam, but the propagated pressure is higher than that of No.15 coal seam. The drainage effect of No.3 coal seam with large pore volume, shallow burial depth and obvious fracture pressure is better than that of No.15 coal seam. The comprehensive understanding of coal physical properties and engineering practice in the block provide certain guiding significance to the CBM exploitation in Qinshui Basin

Integrative transcriptome and metabolome analysis reveals the mechanisms of light-induced pigmentation in purple waxy maize

IntroductionWaxy maize, mainly consumed at the immature stage, is a staple and vegetable food in Asia. The pigmentation in the kernel of purple waxy maize enhances its nutritional and market values. Light, a critical environmental factor, affects anthocyanin biosynthesis and results in pigmentation in different parts of plants, including in the kernel. SWL502 is a light-sensitive waxy maize inbred line with purple kernel color, but the regulatory mechanism of pigmentation in the kernel resulting in purple color is still unknown.MethodsIn this study, cyanidin, peonidin, and pelargonidin were identified as the main anthocyanin components in SWL502, evaluated by the ultra-performance liquid chromatography (UPLC) method. Investigation of pigment accumulation in the kernel of SWL502 was performed at 12, 17, and 22 days after pollination (DAP) under both dark and light treatment conditions via transcriptome and metabolome analyses.ResultsDark treatment affected genes and metabolites associated with metabolic pathways of amino acid, carbohydrate, lipid, and galactose, biosynthesis of phenylpropanoid and terpenoid backbone, and ABC transporters. The expression of anthocyanin biosynthesis genes, such as 4CL2, CHS, F3H, and UGT, was reduced under dark treatment. Dynamic changes were identified in genes and metabolites by time-series analysis. The genes and metabolites involved in photosynthesis and purine metabolism were altered in light treatment, and the expression of genes and metabolites associated with carotenoid biosynthesis, sphingolipid metabolism, MAPK signaling pathway, and plant hormone signal transduction pathway were induced by dark treatment. Light treatment increased the expression level of major transcription factors such as LRL1, myc7, bHLH125, PIF1, BH093, PIL5, MYBS1, and BH074 in purple waxy maize kernels, while dark treatment greatly promoted the expression level of transcription factors RVE6, MYB4, MY1R1, and MYB145.DiscussionThis study is the first report to investigate the effects of light on waxy maize kernel pigmentation and the underlying mechanism at both transcriptome and metabolome levels, and the results from this study are valuable for future research to better understand the effects of light on the regulation of plant growth