arXiv subjects

Yilin Li

Publications and source records attributed to Yilin Li.

47 records · Page 3Linked to original sources

arXiv2024

Unbiased Estimation for Total Treatment Effect Under Interference Using Aggregated Dyadic Data

In social media platforms, user behavior is often influenced by interactions with other users, complicating the accurate estimation of causal effects in traditional A/B experiments. This study investigates situations where an individual's outcome can be broken down into the sum of multiple pairwise outcomes, a reflection of user interactions. These outcomes, referred to as dyadic data, are prevalent in many social network contexts. Utilizing a Bernoulli randomized design, we introduce a novel unbiased estimator for the total treatment effect (TTE), which quantifies the difference in population mean when all individuals are assigned to treatment versus control groups. We further explore the bias of our estimator in scenarios where it is impractical to include all individuals in the experiment, a common constraint in online control experiments. Our numerical results reveal that our proposed estimator consistently outperforms some commonly used estimators, underscoring its potential for more precise causal effect estimation in social media environments.

cs.SI↗

arXiv2024

Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition

Skeleton-based action recognition has attracted much attention, benefiting from its succinctness and robustness. However, the minimal inter-class variation in similar action sequences often leads to confusion. The inherent spatiotemporal coupling characteristics make it challenging to mine the subtle differences in joint motion trajectories, which is critical for distinguishing confusing fine-grained actions. To alleviate this problem, we propose a Wavelet-Attention Decoupling (WAD) module that utilizes discrete wavelet transform to effectively disentangle salient and subtle motion features in the time-frequency domain. Then, the decoupling attention adaptively recalibrates their temporal responses. To further amplify the discrepancies in these subtle motion features, we propose a Fine-grained Contrastive Enhancement (FCE) module to enhance attention towards trajectory features by contrastive learning. Extensive experiments are conducted on the coarse-grained dataset NTU RGB+D and the fine-grained dataset FineGYM. Our methods perform competitively compared to state-of-the-art methods and can discriminate confusing fine-grained actions well.

cs.CV↗

arXiv2023

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance.

cs.CV↗

arXiv2022

Robust Direct Learning for Causal Data Fusion

In the era of big data, the explosive growth of multi-source heterogeneous data offers many exciting challenges and opportunities for improving the inference of conditional average treatment effects. In this paper, we investigate homogeneous and heterogeneous causal data fusion problems under a general setting that allows for the presence of source-specific covariates. We provide a direct learning framework for integrating multi-source data that separates the treatment effect from other nuisance functions, and achieves double robustness against certain misspecification. To improve estimation precision and stability, we propose a causal information-aware weighting function motivated by theoretical insights from the semiparametric efficiency theory; it assigns larger weights to samples containing more causal information with high interpretability. We introduce a two-step algorithm, the weighted multi-source direct learner, based on constructing a pseudo-outcome and regressing it on covariates under a weighted least square criterion; it offers us a powerful tool for causal data fusion, enjoying the advantages of easy implementation, double robustness and model flexibility. In simulation studies, we demonstrate the effectiveness of our proposed methods in both homogeneous and heterogeneous causal data fusion scenarios.

stat.ML↗

arXiv2022

A self-censoring model for multivariate nonignorable nonmonotone missing data

We introduce a self-censoring model for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome is affected by its own value and is associated with missingness indicators of other outcomes, while conditionally independent of the other outcomes. The self-censoring model complements previous graphical approaches for the analysis of multivariate nonignorable missing data. It is identified under a completeness condition stating that any variability in one outcome can be captured by variability in the other outcomes among complete cases. For estimation, we propose a suite of semiparametric estimators including doubly robust estimators that deliver valid inferences under partial misspecification of the full-data distribution. We evaluate the performance of the proposed estimators with simulations and apply them to analyze a study about the effect of highly active antiretroviral therapy on preterm delivery of HIV-positive mothers.

stat.ME↗

arXiv2022

An Extensive Study of Residential Proxies in China

We carry out the first in-depth characterization of residential proxies (RESIPs) in China, for which little is studied in previous works. Our study is made possible through a semantic-based classifier to automatically capture RESIP services. In addition to the classifier, new techniques have also been identified to capture RESIPs without interacting with and relaying traffic through RESIP services, which can significantly lower the cost and thus allow a continuous monitoring of RESIPs. Our RESIP service classifier has achieved a good performance with a recall of 99.7% and a precision of 97.6% in 10-fold cross validation. Applying the classifier has identified 399 RESIP services, a much larger set compared to 38 RESIP services collected in all previous works. Our effort of RESIP capturing lead to a collection of 9,077,278 RESIP IPs (51.36% are located in China), 96.70% of which are not covered in publicly available RESIP datasets. An extensive measurement on RESIPs and their services has uncovered a set of interesting findings as well as several security implications. Especially, 80.05% RESIP IPs located in China have sourced at least one malicious traffic flows during 2021, resulting in 52-million malicious traffic flows in total. And RESIPs have also been observed in corporation networks of 559 sensitive organizations including government agencies, education institutions and enterprises. Also, 3,232,698 China RESIP IPs have opened at least one TCP/UDP ports for accepting relaying requests, which incurs non-negligible security risks to the local network of RESIPs. Besides, 91% China RESIP IPs are of a lifetime less than 10 days while most China RESIP services show up a crest-trough pattern in terms of the daily active RESIPs across time.

cs.CR↗

arXiv2022

Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions

Promotions have been trending in the e-commerce marketplace to build up customer relationships and guide customers towards the desired actions. Since incentives are effective to engage customers and customers have different preferences for different types of incentives, the demand for personalized promotion decision making is increasing over time. However, research on promotion decision making has focused specifically on purchase conversion during the promotion period (the direct effect), while generally disregarding the enduring effect in the post promotion period. To achieve a better lift return on investment (lift ROI) on the enduring effect of the promotion and improve customer retention and loyalty, we propose a framework of multiple treatment promotion decision making by modeling each customer's direct and enduring response. First, we propose a customer direct and enduring effect (CDEE) model which predicts the customer direct and enduring response. With the help of the predictions of the CDEE, we personalize incentive allocation to optimize the enduring effect while keeping the cost under the budget. To estimate the effect of decision making, we apply an unbiased evaluation approach of business metrics with randomized control trial (RCT) data. We compare our method with benchmarks using two promotions in Mercari and achieve significantly better results.

cs.IR↗

arXiv2019

Integrated Access and Backhaul Optimization for Millimeter Wave Heterogeneous Networks

By allowing a large number of links to be simultaneously transmitted, directional antenna arrays with beamforming have been envisioned as a promising candidate to reach unprecedented levels of spatial isolation. To achieve the high efficiency of spatial reuse in improving system performance, an optimization problem that maximizes the achievable data rate of a multihop heterogeneous network, which incorporates the concept of integrated access and backhaul and supports both downlink and uplink transmissions on access and backhaul links, is formulated. The optimization problem is then systematically decomposed and demonstrated as NP-hard, and a heuristic joint scheduling and resource allocation algorithm is proposed to maximize the achievable data rate. In addition, an efficient dynamic routing algorithm is proposed to further enhance the data rate. With extensive system-level simulations, it is demonstrated that the proposed algorithms achieve significant gain over benchmark schemes, in terms of data rate, and closely approach the theoretical optimum, yet with lower latency. Besides, the proposed algorithms enable a flexible adjustment of downlink and uplink transmission duration allocation and support both half- and full-duplex modes with considerable performance enhancement. In particular, the proposed algorithms are capable of fulfilling different performance requirements for both point-to-point and point-to-multipoint communications.

cs.IT↗

arXiv2019

Multihop Routing for Data Delivery in V2X Networks

Data delivery relying on the carry-and-forward strategy of vehicle-to-vehicle (V2V) communications is of significant importance, however highly challenging due to frequent connection disruption. Fortunately, incorporating vehicle-to-infrastructure (V2I) communications, motivated by its availability in bridging long-range vehicular connectivity, dramatically improves delivery opportunity. Nevertheless, the cooperation of V2V and V2I communications, known as vehicular-to-everything (V2X) communications, necessitates a specific design of multihop routing for enhancing data delivery performance. To address this issue, this paper provides a mathematical framework to investigate the data delivery performance in V2X networks in terms of both delivery latency and data rate. With theoretical analysis, we formulate a global and a distributed optimization problem to maximize the weighted sum of delivery latency and data rate. The optimization problems are then solved by convex optimization theory and based on the solutions, we propose a global and a distributed multihop routing algorithm to select the optimal route for maximizing the weighted sum. The rigorousness of the proposed algorithms is validated by extensive simulation under a wide range of system parameters and simulation results shed insight on the design of multihop routing algorithm in V2X networks for minimizing latency and maximizing data rate.

cs.IT↗

arXiv2017

On the Beamformed Broadcast Signaling for Millimeter Wave Cell Discovery: Performance Analysis and Design Insight

Availability of abundant spectrum has enabled millimeter wave (mm-wave) as a prominent candidate solution for the next generation cellular networks. Highly directional transmissions are essential for exploitation of mm-wave bands to compensate high propagation loss and attenuation. The directional transmission, nevertheless, necessitates a specific design for mm-wave initial cell discovery, as conventional omni-directional broadcast signaling may fail in delivering the cell discovery information. To address this issue, this paper provides an analytical framework for mm-wave beamformed cell discovery based on an information theoretical approach. Design options are compared considering four fundamental and representative broadcast signaling schemes to evaluate discovery latency and signaling overhead. The schemes are then simulated under realistic system parameters. Analytical and simulation results reveals four key findings: (i) For cell discovery without knowledge of beacon timing, analog/hybrid beamforming performs as well as digital beamforming in terms of cell discovery latency; (ii) Single beam exhaustive scan optimize the latency, however leads to overhead penalty; (iii) Multi-beam simultaneous scan can significantly reduce the overhead, and provide the flexibility to achieve trade-off between the latency and the overhead; (iv) The latency and the overhead are relatively insensitive to extreme low block error rates.

cs.IT↗

arXiv2017

5G Radio Access above 6 GHz

Designing and developing a millimetre-wave(mmWave) based mobile Radio Access Technology (RAT) in the 6-100 GHz frequency range is a fundamental component in the standardization of the new 5G radio interface, recently kicked off by 3GPP. Such component, herein called the new mmWave RAT, will not only enable extreme mobile broadband (eMBB) services,but also support UHD/3D streaming, offer immersive applications and ultra-responsive cloud services to provide an outstanding Quality of Experience (QoE) to the mobile users. The main objective of this paper is to develop the network architectural elements and functions that will enable tight integration of mmWave technology into the overall 5G radio access network (RAN). A broad range of topics addressing mobile architecture and network functionalities will be covered-starting with the architectural facets of network slicing, multiconnectivity and cells clustering, to more functional elements of initial access, mobility, radio resource management (RRM) and self-backhauling. The intention of the concepts presented here is to lay foundation for future studies towards the first commercial implementation of the mmWave RAT above 6 GHz.

cs.IT↗