CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems

In large-scale recommender systems, retrieving top N relevant candidates accurately with resource constrain is crucial. To evaluate the performance of such retrieval models, Recall@N, the frequency of positive samples being retrieved in the top N ranking, is widely used. However, most of the conventional loss functions for retrieval models such as softmax cross-entropy and pairwise comparison methods do not directly optimize Recall@N. Moreover, those conventional loss functions cannot be customized for the specific retrieval size N required by each application and thus may lead to sub-optimal performance. In this paper, we proposed the Customizable Recall@N Optimization Loss (CROLoss), a loss function that can directly optimize the Recall@N metrics and is customizable for different choices of N. This proposed CROLoss formulation defines a more generalized loss function space, covering most of the conventional loss functions as special cases. Furthermore, we develop the Lambda method, a gradient-based method that invites more flexibility and can further boost the system performance. We evaluate the proposed CROLoss on two public benchmark datasets. The results show that CROLoss achieves SOTA results over conventional loss functions for both datasets with various choices of retrieval size N. CROLoss has been deployed onto our online E-commerce advertising platform, where a fourteen-day online A/B test demonstrated that CROLoss contributes to a significant business revenue growth of 4.75%.Comment: 9 pages, 5 figures. Accepted by by CIKM 202

Geochemical characteristics of dissolved heavy metals in Zhujiang River, Southwest China: spatial-temporal distribution, source, export flux estimation, and a water quality assessment

To investigate the sources and spatial-temporal distribution of dissolved heavy metals in river water, and to evaluate the water quality, a total of 162 water samples were collected from 81 key sampling points in high and low flow seasons separately in the Zhujiang River, Southwest China. Ten dissolved heavy metals (V, Cr, Mn, Co, Ni, Cu, Mo, Cd, Ba, and Pb) in the Zhujiang River water exhibit little variation at temporal scale, but vary with a significant spatial heterogeneity. Furthermore, different metals present different variation trends along the main channel of the Zhujiang River. Our results suggest that Ba (14.72 μg L−1 in low flow season and 12.50 μg L−1 in high flow season) and Cr (6.85 μg L−1 in low flow season and 7.52 μg L−1 in high flow season) are consistently the most abundant metals in the two sampling periods. According to the water quality index (WQI values ranged from 1.3 to 43.9) and health risk assessment, metals investigated in Zhujiang River are below the hazard level (all hazard index (HI) < 1). Application of statistical approaches, including correlation matrix and principal component analysis (PCA), identify three principal components that account for 61.74% of the total variance, the results conclude that the anthropogenic heavy metals (V, Cr, Ni, and Cu) are greatly impacted by the dilution effect, and the heavy metals in Zhujiang River are mainly presented a natural sources signature from the perspective of entire basin. Moreover, our results reveal that the estimated export budget of several heavy metals including V (735.6 t year−1), Cr (1,561.1 t year−1), Ni (498.2 t year−1), and Mo (118.9 t year−1) to the ocean are higher than the world average

Stock Volatility Prediction Based on Transformer Model Using Mixed-Frequency Data

With the increasing volume of high-frequency data in the information age, both challenges and opportunities arise in the prediction of stock volatility. On one hand, the outcome of prediction using tradition method combining stock technical and macroeconomic indicators still leaves room for improvement; on the other hand, macroeconomic indicators and peoples' search record on those search engines affecting their interested topics will intuitively have an impact on the stock volatility. For the convenience of assessment of the influence of these indicators, macroeconomic indicators and stock technical indicators are then grouped into objective factors, while Baidu search indices implying people's interested topics are defined as subjective factors. To align different frequency data, we introduce GARCH-MIDAS model. After mixing all the above data, we then feed them into Transformer model as part of the training data. Our experiments show that this model outperforms the baselines in terms of mean square error. The adaption of both types of data under Transformer model significantly reduces the mean square error from 1.00 to 0.86.Comment: Accepted by the 7th APWeb-WAIM International Joint Conference on Web and Big Data. (APWeb 2023

CD23 expression in mantle cell lymphoma is associated with CD200 expression, leukemic non-nodal form, and a better prognosis

Mantle cell lymphoma (MCL) is usually CD23 negative, a feature helpful in distinguishing MCL from chronic lymphocytic leukemia/small lymphocytic lymphoma. However, a subset of MCL cases can be CD23+. Limited data are available regarding the clinicopathological features and prognosis of patients with CD23+ MCL. In this study, we reviewed 798 cases of MCL and identified 103 (13%) that were CD23+ by flow cytometry, all of which were positive for cyclin D1 and/or associated with CCND1/IGH. In all cases of CD23+ MCL, CD23 expression was dim partial or dim, unlike moderate to bright CD23 expression observed in chronic lymphocytic leukemia/small lymphocytic lymphoma. The clinicopathological features and outcome of patients with CD23+ MCL were compared with 240 patients with typical MCL negative for CD23. Patients with CD23+ MCL more often had an elevated leukocyte count (33% versus 18%, P = .009), bone marrow involvement (89% versus 78%, P = .02), stage 4 disease (87% versus 77%, P = .03), and a leukemic presentation (42% versus 11%, P = .0001). CD23+ MCL was also more often positive for CD200 (17% versus. 4.6%, P = .0005) and less commonly positive for SOX11 (55% versus. 74%, P = .027). All other clinicopathological features were similar. With similar treatment regimens and observation times, patients with CD23+ MCL had a significant better overall survival (P = .02) and progression-free survival (P = .029). In conclusion, CD23 expression was observed in 13% of MCL cases and is associated with a better prognosis in patients with MCL. CD23 is associated with leukocytosis, a leukemic presentation, bone marrow involvement, CD200 expression, and a lower frequency of SOX11 positivity

The deubiquitinase USP6 affects memory and synaptic plasticity through modulating NMDA receptor stability

人类与其他动物相比的重要区别在于人类拥有高等认知能力，这种能力集中体现在学习记忆和语言表达方面。厦门大学医学院神经科学研究所王鑫教授团队发现人科动物特异性基因USP6作为一个新的NMDA受体调控因子，可通过去泛素化途径调节NMDA型谷氨酸受体的降解和稳定性，进而调控突触可塑性和学习记忆能力。 本研究工作由王鑫教授指导完成，博士生曾凡伟、马学海与硕士生朱琳为共同第一作者，王鑫教授为通讯作者。Ubiquitin-specific protease (USP) 6 is a hominoid deubiquitinating enzyme previously implicated in intellectual disability and autism spectrum disorder. Although these findings link USP6 to higher brain function, potential roles for USP6 in cognition have not been investigated. Here, we report that USP6 is highly expressed in induced human neurons and that neuron-specific expression of USP6 enhances learning and memory in a transgenic mouse model. Similarly, USP6 expression regulates N-methyl-D-aspartate-type glutamate receptor (NMDAR)-dependent long-term potentiation and long-term depression in USP6 transgenic mouse hippocampi. Proteomic characterization of transgenic USP6 mouse cortex reveals attenuated NMDAR ubiquitination, with concomitant elevation in NMDAR expression, stability, and cell surface distribution with USP6 overexpression. USP6 positively modulates GluN1 expression in transfected cells, and USP6 down-regulation impedes focal GluN1 distribution at postsynaptic densities and impairs synaptic function in neurons derived from human embryonic stem cells. Together, these results indicate that USP6 enhances NMDAR stability to promote synaptic function and cognition.This work was partially supported by the National Natural Science Foundation of China (31871077, 81822014, 81571176 to XW; 81701349 to Hongfeng Z.; 81701130 to QZ; and 81471160 to HS), the National Key R&D Program of China (2016YFC1305900 to XW and HS), the Natural Science Foundation of Fujian Province of China (2017J06021 to XW), the Fundamental Research Funds for the Chinese Central Universities (20720150061 to XW and 20720180040 to ZS), Open Research Fund of State Key Laboratory of Cellular Stress Biology, Xiamen University (SKLCSB2019KF012 to QZ), and China Postdoctoral Science Foundation (2017M612130 to QZ).该研究得到了国家自然科学基金面上项目和优秀青年基金项目的支持