Feature Fusion for Online Mutual Knowledge Distillation

We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated from parallel neural networks. Specifically, we train a number of parallel neural networks as sub-networks, then we combine the feature maps from each sub-network using a fusion module to create a more meaningful feature map. The fused feature map is passed into the fused classifier for overall classification. Unlike existing feature fusion methods, in our framework, an ensemble of sub-network classifiers transfers its knowledge to the fused classifier and then the fused classifier delivers its knowledge back to each sub-network, mutually teaching one another in an online-knowledge distillation manner. This mutually teaching system not only improves the performance of the fused classifier but also obtains performance gain in each sub-network. Moreover, our model is more beneficial because different types of network can be used for each sub-network. We have performed a variety of experiments on multiple datasets such as CIFAR-10, CIFAR-100 and ImageNet and proved that our method is more effective than other alternative methods in terms of performance of both sub-networks and the fused classifier.Comment: International Conference on Pattern Recognitio

FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning

A User Next Location Prediction (UNLP) task, which predicts the next location that a user will move to given his/her trajectory, is an indispensable task for a wide range of applications. Previous studies using large-scale trajectory datasets in a single server have achieved remarkable performance in UNLP task. However, in real-world applications, legal and ethical issues have been raised regarding privacy concerns leading to restrictions against sharing human trajectory datasets to any other server. In response, Federated Learning (FL) has emerged to address the personal privacy issue by collaboratively training multiple clients (i.e., users) and then aggregating them. While previous studies employed FL for UNLP, they are still unable to achieve reliable performance because of the heterogeneity of clients' mobility. To tackle this problem, we propose the Federated Learning for Geographic Information (FedGeo), a FL framework specialized for UNLP, which alleviates the heterogeneity of clients' mobility and guarantees personal privacy protection. Firstly, we incorporate prior global geographic adjacency information to the local client model, since the spatial correlation between locations is trained partially in each client who has only a heterogeneous subset of the overall trajectories in FL. We also introduce a novel aggregation method that minimizes the gap between client models to solve the problem of client drift caused by differences between client models when learning with their heterogeneous data. Lastly, we probabilistically exclude clients with extremely heterogeneous data from the FL process by focusing on clients who visit relatively diverse locations. We show that FedGeo is superior to other FL methods for model performance in UNLP task. We also validated our model in a real-world application using our own customers' mobile phones and the FL agent system.Comment: Accepted at 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2023

Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation

This paper investigates Cross-Domain Sequential Recommendation (CDSR), a promising method that uses information from multiple domains (more than three) to generate accurate and diverse recommendations, and takes into account the sequential nature of user interactions. The effectiveness of these systems often depends on the complex interplay among the multiple domains. In this dynamic landscape, the problem of negative transfer arises, where heterogeneous knowledge between dissimilar domains leads to performance degradation due to differences in user preferences across these domains. As a remedy, we propose a new CDSR framework that addresses the problem of negative transfer by assessing the extent of negative transfer from one domain to another and adaptively assigning low weight values to the corresponding prediction losses. To this end, the amount of negative transfer is estimated by measuring the marginal contribution of each domain to model performance based on a cooperative game theory. In addition, a hierarchical contrastive learning approach that incorporates information from the sequence of coarse-level categories into that of fine-level categories (e.g., item level) when implementing contrastive learning was developed to mitigate negative transfer. Despite the potentially low relevance between domains at the fine-level, there may be higher relevance at the category level due to its generalised and broader preferences. We show that our model is superior to prior works in terms of model performance on two real-world datasets across ten different domains.Comment: Accepted at 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023

Diagnostic accuracy of a three-protein signature in women with suspicious breast lesions: a multicenter prospective trial

Background Mammography screening has been proven to detect breast cancer at an early stage and reduce mortality; however, it has low accuracy in young women or women with dense breasts. Blood-based diagnostic tools may overcome the limitations of mammography. This study assessed the diagnostic performance of a three-protein signature in patients with suspicious breast lesions. Findings This trial (MAST; KCT0004847) was a prospective multicenter observational trial. Three-protein signature values were obtained using serum and plasma from women with suspicious lesions for breast malignancy before tumor biopsy. Additionally, blood samples from women who underwent clear or benign mammography were collected for the assays. Among 642 participants, the sensitivity, specificity, and overall accuracy values of the three-protein signature were 74.4%, 66.9%, and 70.6%, respectively, and the concordance index was 0.698 (95% CI 0.656, 0.739). The diagnostic performance was not affected by the demographic features, clinicopathologic characteristics, and co-morbidities of the participants. Conclusions The present trial showed an accuracy of 70.6% for the three-protein signature. Considering the value of blood-based biomarkers for the early detection of breast malignancies, further evaluation of this proteomic assay is warranted in larger, population-level trials. This Multi-protein Assessment using Serum to deTermine breast lesion malignancy (MAST) was registered at the Clinical Research Information Service of Korea with the identification number of KCT0004847 (https://cris.nih.go.kr).This study was supported by the Bertis Inc. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication

Strongly enhanced Rashba splittings in an oxide heterostructure: A tantalate monolayer on BaHfO3

In the two-dimensional electron gas emerging at the transition metal oxide surface and interface, various exotic electronic ordering and topological phases can become experimentally more accessible with the stronger Rashba spin-orbit interaction. Here, we present a promising route to realize significant Rashba-type band splitting using a thin film heterostructure. Based on first-principles methods and analytic model analyses, a tantalate monolayer on BaHfO3 is shown to host two-dimensional bands originating from Ta t2g states with strong Rashba spin splittings, nearly 10% of the bandwidth, at both the band minima and saddle points. An important factor in this enhanced splitting is the significant t2g-eg interband coupling, which can generically arise when the inversion symmetry is maximally broken due to the strong confinement of the 2DEG on a transition metal oxide surface. Our results could be useful in realizing topological superconductivity at oxide surfaces. © 2016 American Physical Society1111sciescopu

Association between Dietary Intake of Flavonoids and Cancer Recurrence among Breast Cancer Survivors

Intake of flavonoids is associated with the incidence of breast cancer, but the association between the intake of flavonoids and cancer recurrence is unclear. This study aimed to investigate the hypothesis that intake of flavonoids and flavonoid-rich foods is negatively associated with cancer recurrence. Among 572 women who underwent breast cancer surgery, 66 patients had a cancer recurrence. Dietary data were collected using a structured 24-h dietary recall, and intake of flavonoids was calculated based on the Korea Rural Development Administration flavonoid database. Among overweight and obese patients, disease-free survival was associated with intake of flavonoids (p = 0.004) and flavonoid-rich foods (p = 0.003). Intake of flavonoids (hazard ratio (HR) = 0.249, 95% confidence interval (CI): 0.09–0.64) and flavonoid-rich foods (HR = 0.244, 95% CI: 0.09–0.66) was negatively associated with cancer recurrence after adjusting for confounding factors in overweight and obese patients. Consumption of flavonoids and flavonoid-rich foods was lower in overweight and obese patients with cancer recurrence than those without recurrence and in normal-weight patients. This study suggests that intake of flavonoids and flavonoid-rich foods could have beneficial effects on cancer recurrence in overweight and obese breast cancer survivors

Who loses more? Identifying the relationship between hospitalization and income loss: prediction of hospitalization duration and differences of gender and employment status

Abstract Background The major determinants of health and well-being include wider socio-economic and political responses to poverty alleviation. To data, however, South Korea has no related social protection policies to replace income loss or prevent non-preferable health conditions for workers. In particular, there are several differences in social protection policies by gender or occupational groups. This study aimed to investigate how hospitalization affects income loss among workers in South Korea. Methods The study sample included 4876 Korean workers who responded to the Korean Welfare Panel Study (KoWePS) for all eight years from 2009 to 2016. We conducted a receiver operating characteristics (ROC) analysis to determine the cut-off point for the length of hospitalization that corresponded to the greatest loss of income. We used panel multi-linear regression to examine the relationship between hospitalization and income loss by gender and employment arrangement. Results The greatest income loss for women in non-standard employment and self-employed men was observed when the length of hospitalization was seven days or less. When they were hospitalized for more than 14 days, income loss also occurred among men in non-standard employment. In addition, when workers were hospitalized for more than 14 days, the impact of the loss of income was felt into the subsequent year. Conclusion Non-standard and self-employed workers, and even female standard workers, are typically excluded from public insurance coverage in South Korea, and social security is insufficient when they are injured. To protect workers from the vicious circle of the poverty-health trap, national social protections such as sickness benefits are needed

Generation and characterization of FOLR1-CAR T cells.

<p>T cells were obtained from human PBMCs of three healthy donors and the FOLR1-CAR gene was cloned into the pLVX-IRES-GFP vector and the lentivirus infection efficiency was determined. (A) The growth rate of infected or untransduced T cells was measured by CellTiter-Glo assay for 12 days. (B) Lentivirus infection rate was analyzed by GFP fluorescence on day 12 using FACS analysis. (C) Expression of FOLR1-CAR in T cells was measured by western blot analysis. (D-E) The population of T cells was evaluated by FACS analysis using CD3, CD4, and CD8 antibodies on day 12. Experiments were repeated three times with similar results. The data are represented as the mean of luminescence ± SD and positive rate ± SD (%) from triplicate cultures. Statistical analysis was performed using the two-sided unpaired t-test.</p