Temporal Learning and Sequence Modeling for a Job Recommender System

We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical observations and a hybrid matrix factorization over time re-weighted interactions. Second, we exploit sequence properties in user-items activities and develop a RNN-based recommendation model. Our solution achieved 5$^{th}$ place in the challenge among more than 100 participants. Notably, the strong performance of our RNN approach shows a promising new direction in employing sequence modeling for recommendation systems.Comment: a shorter version in proceedings of RecSys Challenge 201

Clinical analysis of rituximab for adult refractory primary membranous nephropathy

Objective·To evaluate clinical efficacy and safety of rituximab (RTX) in the patients with refractory primary membranous nephropathy (PMN).Methods·A retrospective analysis was carried out on the patients with refractory PMN, who had recurred or had not achieved clinical remission after more than 1 year of treatment with glucocorticoid and cyclosporine, cyclophosphamide or tacrolimus, admitted to the Department of Nephrology, Qingpu Branch of Zhongshan Hospital, Fudan University from July 2018 to April 2022. They received 2 doses of RTX. The single dose of RTX was 1 000 mg, and the interval between the two doses was 2 weeks. After 6 months, the numbers of peripheral blood B cells of the patients were detected. If the count of peripheral blood B cells were greater than 5 cell/µL, another 1 000 mg RTX would be added. The main indicators were serum albumin, serum creatinine, urinary protein/creatinine ratio (UPCR), blood antibody against phospholipase A2 receptor (PLA2R) antibody, peripheral blood B cell count, and serum total IgG level. The clinical efficacy and safety of the treatment regimen were evaluated by observing the change of the main indicators of patients and adverse reactions.Results·A total of 18 patients were included, with an average age of (58.17±16.73) years, including 11 males. The total dose of RTX was (2 222.22±485.07) mg, and the follow-up time after RTX treatment was (14.9±4.9) months. At the last follow-up, the serum albumin level was significantly higher than that before RTX treatment [(36.50±5.33) g/L vs (27.61±8.59) g/L, P=0.009]; the serum creatinine level was stable (P>0.05); the value of UPCR decreased significantly [863.30 (203.20, 2 291.75) mg/g vs 2 954.00 (1 458.00, 7 260.75) mg/g, P=0.047]; the PLA2R antibody decreased significantly [(44.32±33.71) RU/mL vs (168.40±88.40) RU/mL, P=0.015]; the peripheral blood B cell count decreased significantly [(37.89±12.43) cell/µL vs (246.40±239.98) cell/µL, P=0.009]; the total blood IgG level was stable (P>0.05). After RTX treatment, 8 patients achieved complete remission (44.4%), 7 patients achieved partial remission (38.9%), and the overall effective rate was 83.3%; only 3 patients were unrelieved (16.7%). In terms of adverse reactions, 1 patient had transfusion allergy reaction, and 1 patient had pulmonary infection.Conclusion·For the patients with refractory PMN who have relapse or do not relieve after traditional immunosuppressive therapy, RTX treatment can effectively induce clinically complete remission or partial remission with good safety

Driver distraction detection based on lightweight networks and tiny object detection

Real-time and efficient driver distraction detection is of great importance for road traffic safety and assisted driving. The design of a real-time lightweight model is crucial for in-vehicle edge devices that have limited computational resources. However, most existing approaches focus on lighter and more efficient architectures, ignoring the cost of losing tiny target detection performance that comes with lightweighting. In this paper, we present MTNet, a lightweight detector for driver distraction detection scenarios. MTNet consists of a multidimensional adaptive feature extraction block, a lightweight feature fusion block and utilizes the IoU-NWD weighted loss function, all while considering the accuracy gain of tiny target detection. In the feature extraction component, a lightweight backbone network is employed in conjunction with four attention mechanisms strategically integrated across the kernel space. This approach enhances the performance limits of the lightweight network. The lightweight feature fusion module is designed to reduce computational complexity and memory access. The interaction of channel information is improved through the use of lightweight arithmetic techniques. Additionally, CFSM module and EPIEM module are employed to minimize redundant feature map computations and strike a better balance between model weights and accuracy. Finally, the IoU-NWD weighted loss function is formulated to enable more effective detection of tiny targets. We assess the performance of the proposed method on the LDDB benchmark. The experimental results demonstrate that our proposed method outperforms multiple advanced detection models

A microporous six-fold interpenetrated hydrogen-bonded organic framework for highly selective separation of C 2

A unique six-fold interpenetrated hydrogen-bonded organic framework (HOF) has been developed, for the first time, for highly selective separation of C2H4/C2H6 at room temperature and normal pressure