INDIVIDUALITY OR CONFORMITY: RECOMMENDATION EXPLOITING COMMUNITY-LEVEL SOCIAL INFLUENCE

With the increasing prevalence of online businesses and social networking services, a huge volume of data about transaction records and social connections between users is accumulated at an unprecedented speed, which enables us to take advantage of electronic word-of-mouth effect embedded in social networks for precision marketing and social recommendations. Different from existing works on social recommendations, our research focuses on discriminating the community-level social influence of different friend groups to enhance the quality of recommendation. To this end, we propose a novel probabilistic topic model integrating community detection with topic discovery to model user behaviors. Based on this model, a recommendation method taking both individual interests and conformity influence into consideration is developed. To evaluate the performance of the proposed model and method, experiments are conducted on two real recommendation applications, and the results demonstrate that the proposed recommendation method exhibits superior performance compared with the state-of-art recommendation methods, and the proposed topic model exhibits good explainablibity of topic semantics and community interests. Furthermore, as some people are more individual interest oriented and some are more conformity oriented demonstrated by the experiments, we explore factors that influence each individual’s conformity tendency, and obtain some meaningful findings

Performance of Spatial Diversity DCO-OFDM in a Weak Turbulence Underwater Visible Light Communication Channel

The performance of underwater visible light communication (UVLC) system is severely affected by absorption, scattering and turbulence. In this article, we study the performance of spectral efficient DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) in combination with the transceiver spatial diversity in turbulence channel. Based on the approximation of the weighted sum of lognormal random variables (RVs), we derived a theoretical exact bit error rate (BER) for DCO-OFDM systems with spatial diversity. The simulation results are compared with the analytical prediction, confirming the validity of the analysis. It is shown that spatial diversity can effectively reduce the turbulence-induced channel fading. The obtained results can be useful for designing, predicting, and evaluating the DCO-OFDM UVLC system in a weak oceanic turbulence condition

Towards Complex Backgrounds: A Unified Difference-Aware Decoder for Binary Segmentation

Binary segmentation is used to distinguish objects of interest from background, and is an active area of convolutional encoder-decoder network research. The current decoders are designed for specific objects based on the common backbones as the encoders, but cannot deal with complex backgrounds. Inspired by the way human eyes detect objects of interest, a new unified dual-branch decoder paradigm named the difference-aware decoder is proposed in this paper to explore the difference between the foreground and the background and separate the objects of interest in optical images. The difference-aware decoder imitates the human eye in three stages using the multi-level features output by the encoder. In Stage A, the first branch decoder of the difference-aware decoder is used to obtain a guide map. The highest-level features are enhanced with a novel field expansion module and a dual residual attention module, and are combined with the lowest-level features to obtain the guide map. In Stage B, the other branch decoder adopts a middle feature fusion module to make trade-offs between textural details and semantic information and generate background-aware features. In Stage C, the proposed difference-aware extractor, consisting of a difference guidance model and a difference enhancement module, fuses the guide map from Stage A and the background-aware features from Stage B, to enlarge the differences between the foreground and the background and output a final detection result. The results demonstrate that the difference-aware decoder can achieve a higher accuracy than the other state-of-the-art binary segmentation methods for these tasks

Constrained Clustering Based on the Link Structure of a Directed Graph

In many segmentation applications, data objects are often clustered based purely on attribute-level similarities. This practice has neglected the useful information that resides in the link structure among data objects and the valuable expert domain knowledge about the desirable cluster assignment. Link structure can carry worthy information about the similarity between data objects (e.g. citation), and we should also incorporate the existing domain information on preferred outcome when segmenting data. In this paper, we investigate the segmentation problem combining these three sources of information, which has not been addressed in the existing literature. We propose a segmentation method for directed graphs that incorporates the attribute values, link structure and expert domain information (represented as constraints). The proposed method combines these three types of information to achieve good quality segmentation on data which can be represented as a directed graph. We conducted comprehensive experiments to evaluate various aspects of our approach and demonstrate the effectiveness of our method

Reliability analysis of aged natural gas pipelines based on utility theory

Pipelines are of major importance for transport of natural gas, but a lot of the current in-service pipelines are in wear-out phase. Safe and reliable operations of these pipelines are related to economic development and social stability. It is of great importance and practical significance to study when the corroded pipelines will be retired and how to guarantee that these pipelines will be operating under safe and reliable conditions. The paper proposes a model for assessing risk in natural gas pipelines, and for classifying sections of pipeline into risk categories with utility theory. It aims to help transmission and distribution companies when engaged in risk integrated assessment and decision making consider multiple dimensions of risk from pipeline leakage accidents. Firstly, we analyze the corrosion leakage probability of pipeline remaining life using the exponential distribution; secondly, we evaluate the economic loss, loss of life and damage to the environment in terms of the utility function to get the corresponding risk value of external loss. Finally, we calculate the internal economic loss when in-service pipelines are replaced ahead of scheduled time and then schedule a most optimal date to exchange the aging pipelines containing corrosion. To verify the effectiveness of the proposed methods, a numerical application based on a real case study is presented

Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion

Point clouds scanned by real-world sensors are always incomplete, irregular, and noisy, making the point cloud completion task become increasingly more important. Though many point cloud completion methods have been proposed, most of them require a large number of paired complete-incomplete point clouds for training, which is labor exhausted. In contrast, this paper proposes a novel Reconstruction-Aware Prior Distillation semi-supervised point cloud completion method named RaPD, which takes advantage of a two-stage training scheme to reduce the dependence on a large-scale paired dataset. In training stage 1, the so-called deep semantic prior is learned from both unpaired complete and unpaired incomplete point clouds using a reconstruction-aware pretraining process. While in training stage 2, we introduce a semi-supervised prior distillation process, where an encoder-decoder-based completion network is trained by distilling the prior into the network utilizing only a small number of paired training samples. A self-supervised completion module is further introduced, excavating the value of a large number of unpaired incomplete point clouds, leading to an increase in the network's performance. Extensive experiments on several widely used datasets demonstrate that RaPD, the first semi-supervised point cloud completion method, achieves superior performance to previous methods on both homologous and heterologous scenarios

Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning

In the field of quantitative trading, it is common practice to transform raw historical stock data into indicative signals for the market trend. Such signals are called alpha factors. Alphas in formula forms are more interpretable and thus favored by practitioners concerned with risk. In practice, a set of formulaic alphas is often used together for better modeling precision, so we need to find synergistic formulaic alpha sets that work well together. However, most traditional alpha generators mine alphas one by one separately, overlooking the fact that the alphas would be combined later. In this paper, we propose a new alpha-mining framework that prioritizes mining a synergistic set of alphas, i.e., it directly uses the performance of the downstream combination model to optimize the alpha generator. Our framework also leverages the strong exploratory capabilities of reinforcement learning~(RL) to better explore the vast search space of formulaic alphas. The contribution to the combination models' performance is assigned to be the return used in the RL process, driving the alpha generator to find better alphas that improve upon the current set. Experimental evaluations on real-world stock market data demonstrate both the effectiveness and the efficiency of our framework for stock trend forecasting. The investment simulation results show that our framework is able to achieve higher returns compared to previous approaches.Comment: Accepted by KDD '23, ADS trac

The association of depression status with menopause symptoms among rural midlife women in China

Objective: This study aims to evaluate the association of depression with menopausal status and some menopause symptoms (vasomotor symptoms and poor sleep).Methods: A total of 743 participants aged 40-60 years were recruited. Depression status was evaluated by using Self-Rating Depression Scale (SDS). Sleep quality and vasomotor symptoms were evaluated by specific symptoms questionnaire.Results: The prevalence of depression among participants was 11.4%. Depression was found more likely to occur in participants with poor sleep (OR, 6.02; 95%CI, 3.61, 10.03) or with vasomotor symptoms (VMS) (OR, 2.03; 95%CI, 1.20, 3.44) after controlling for age, education level, marital status, menopause status, monthly family income and chronic diseases. Menopause status was not associated with depression. Stratification analysis showed a significant association between poor sleep and depression across different menopause stages, while VMS were associated with depression only in premenopausal status.Conclusion: The majority of Chinese rural midlife women do not experience depression. The relationship between depression, VMS and sleep disturbances tends to change with menopausal status in Chinese rural midlife women.Keywords: depression, poor sleep, vasomotor symptoms, menopause, rural wome

Human Pose Driven Object Effects Recommendation

In this paper, we research the new topic of object effects recommendation in micro-video platforms, which is a challenging but important task for many practical applications such as advertisement insertion. To avoid the problem of introducing background bias caused by directly learning video content from image frames, we propose to utilize the meaningful body language hidden in 3D human pose for recommendation. To this end, in this work, a novel human pose driven object effects recommendation network termed PoseRec is introduced. PoseRec leverages the advantages of 3D human pose detection and learns information from multi-frame 3D human pose for video-item registration, resulting in high quality object effects recommendation performance. Moreover, to solve the inherent ambiguity and sparsity issues that exist in object effects recommendation, we further propose a novel item-aware implicit prototype learning module and a novel pose-aware transductive hard-negative mining module to better learn pose-item relationships. What's more, to benchmark methods for the new research topic, we build a new dataset for object effects recommendation named Pose-OBE. Extensive experiments on Pose-OBE demonstrate that our method can achieve superior performance than strong baselines