Consumer Coupon Redemption Behavior Prediction on B2C E-commerce

How to recognize the tendency of the coupons among the users who receive the coupons and then send the coupon reminder to improve the coupon redemption rate and reduce the marketing cost has become an important issue in the coupon decision-making process. Based on the log data and transaction data in enterprise database, this study combined with the demographics, past purchasing behavior, past coupon usage behavior and the visiting behavior during the coupon validity period to construct the e-coupon redemption behavior prediction model. The model is constructed to help e-commerce enterprises identify the target users who have the coupon proneness after the coupons are issued, so as to send coupon reminders in time and enhance the effectiveness of coupon marketing

A Wideband Receiver with Adaptive Strong Interference Suppression

In this paper, a wideband receiver with high dynamic range is proposed. At the front end of the proposed receiver, a sensing waveform is used to sense the input signal. And by adjusting the sensing waveform so as to project the interference to zero, the receiver can eliminate the strong interference signal adaptively before sampling. Both the theoretic analysis and simulation show that this method can suppress the interference signal effectively and improve the sampling accuracy of the weak desired signal when the instantaneous dynamic range of the input signal is larger than the dynamic range of the ADC's quantizer

RNA Sequencing Characterizes Transcriptomes Differences in Cold Response Between Northern and Southern Alternanthera philoxeroides and Highlight Adaptations Associated With Northward Expansion

Alternanthera philoxeroides recently expanded its range northwards in China. It is unknown if the range expansion has a genetic and/or epigenetic basis, or merely an environmental basis due to a warming climate. To test these possibilities, we used an RNAseq approach with a common greenhouse design to examine gene expression in individuals from the northern edge and central portion of alligator weed range from China to determine if there were differences in their responses to cold temperatures. We hypothesized that if the recent range expansion was primarily environmental, we would observe few differences or only differences unrelated to low-temperature adaptations. We assembled over 75,000 genes of which over 65,000 had long open reading frames with similarity to sequences from arabidopsis. Differences in expression between northern and southern populations that were both exposed to low temperatures showed similar expression among genes in the C-REPEAT/DRE BINDING FACTOR (CBF) regulon. However, gene set and sub-network enrichment analysis indicated differences in the response of photosynthetic processes and oxidative stress responses were different between the two populations and we relate these differences to cold adaptation. The transcriptome differences in response to cold between the individuals from the two populations is consistent with adaptations potentiating or resulting from selection after expansion into colder environments and may indicate that genetic changes have accompanied the recent northward expansion of A. philoxeroides in China. However, we cannot rule out the possibility of epigenetic changes may have a role in this expansion

Quantum Algorithm for Unsupervised Anomaly Detection

Anomaly detection, an important branch of machine learning, plays a critical role in fraud detection, health care, intrusion detection, military surveillance, etc. As one of the most commonly used unsupervised anomaly detection algorithms, the Local Outlier Factor algorithm (LOF algorithm) has been extensively studied. This algorithm contains three steps, i.e., determining the k-distance neighborhood for each data point x, computing the local reachability density of x, and calculating the local outlier factor of x to judge whether x is abnormal. The LOF algorithm is computationally expensive when processing big data sets. Here we present a quantum LOF algorithm consisting of three parts corresponding to the classical algorithm. Specifically, the k-distance neighborhood of x is determined by amplitude estimation and minimum search; the local reachability density of each data point is calculated in parallel based on the quantum multiply-adder; the local outlier factor of each data point is obtained in parallel using amplitude estimation. It is shown that our quantum algorithm achieves exponential speedup on the dimension of the data points and polynomial speedup on the number of data points compared to its classical counterpart. This work demonstrates the advantage of quantum computing in unsupervised anomaly detection