Matching Theory for Future Wireless Networks: Fundamentals and Applications

The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this paper, the first comprehensive tutorial on the use of matching theory, a Nobelprize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then, the key solution concepts and algorithmic implementations of this framework are exposed. Then, the developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed

Offloading in Software Defined Network at Edge with Information Asymmetry: A Contract Theoretical Approach

The proliferation of highly capable mobile devices such as smartphones and tablets has significantly increased the demand for wireless access. Software defined network (SDN) at edge is viewed as one promising technology to simplify the traffic offloading process for current wireless networks. In this paper, we investigate the incentive problem in SDN-at-edge of how to motivate a third party access points (APs) such as WiFi and smallcells to offload traffic for the central base stations (BSs). The APs will only admit the traffic from the BS under the precondition that their own traffic demand is satisfied. Under the information asymmetry that the APs know more about own traffic demands, the BS needs to distribute the payment in accordance with the APs' idle capacity to maintain a compatible incentive. First, we apply a contract-theoretic approach to model and analyze the service trading between the BS and APs. Furthermore, other two incentive mechanisms: optimal discrimination contract and linear pricing contract are introduced to serve as the comparisons of the anti adverse selection contract. Finally, the simulation results show that the contract can effectively incentivize APs' participation and offload the cellular network traffic. Furthermore, the anti adverse selection contract achieves the optimal outcome under the information asymmetry scenario.Comment: 10 pages, 9 figure

Judging a video by its bitstream cover

Classifying videos into distinct categories, such as Sport and Music Video, is crucial for multimedia understanding and retrieval, especially in an age where an immense volume of video content is constantly being generated. Traditional methods require video decompression to extract pixel-level features like color, texture, and motion, thereby increasing computational and storage demands. Moreover, these methods often suffer from performance degradation in low-quality videos. We present a novel approach that examines only the post-compression bitstream of a video to perform classification, eliminating the need for bitstream. We validate our approach using a custom-built data set comprising over 29,000 YouTube video clips, totaling 6,000 hours and spanning 11 distinct categories. Our preliminary evaluations indicate precision, accuracy, and recall rates well over 80%. The algorithm operates approximately 15,000 times faster than real-time for 30fps videos, outperforming traditional Dynamic Time Warping (DTW) algorithm by six orders of magnitude

Does circulating progesterone mediate the associations of single nucleotide polymorphisms in progesterone receptor (PGR)-related genes with mammographic breast density in premenopausal women?

UNLABELLED: Progesterone is a proliferative hormone in the breast but the associations of genetic variations in progesterone-regulated pathways with mammographic breast density (MD) in premenopausal women and whether these associations are mediated through circulating progesterone are not clearly defined. We, therefore, investigated these associations in 364 premenopausal women with a median age of 44 years. We sequenced 179 progesterone receptor (PGR)-related single nucleotide polymorphisms (SNPs). We measured volumetric percent density (VPD) and non-dense volume (NDV) using Volpara. Linear regression models were fit on circulating progesterone or VPD/NDV separately. We performed mediation analysis to evaluate whether the effect of a SNP on VPD/NDV is mediated through circulating progesterone. All analyses were adjusted for confounders, phase of menstrual cycle and the Benjamini-Hochberg false discovery (FDR) adjusted p-value was applied to correct for multiple testing. In multivariable analyses, only PGR rs657516 had a direct effect on VPD (averaged direct effect estimate = - 0.20, 95%CI = - 0.38 ~ - 0.04, p-value = 0.02) but this was not statistically significant after FDR correction and the effect was not mediated by circulating progesterone (mediation effect averaged across the two genotypes = 0.01, 95%CI = - 0.02 ~ 0.03, p-value = 0.70). Five SNPs (PGR rs11571241, rs11571239, rs1824128, rs11571150, PGRMC1 rs41294894) were associated with circulating progesterone but these were not statistically significant after FDR correction. SNPs in PGR-related genes were not associated with VPD, NDV and circulating progesterone did not mediate the associations, suggesting that the effects, if any, of these SNPs on MD are independent of circulating progesterone. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-021-00438-1

Breast cancer incidence among US women aged 20 to 49 years by race, stage, and hormone receptor status

IMPORTANCE: Breast cancer in young women has a less favorable prognosis compared with older women. Yet, comprehensive data on recent trends and how period and cohort effects may affect these trends among young women are not well-known. OBJECTIVE: To evaluate breast cancer incidence among young women in the US over a 20-year period by race and ethnicity, hormone receptor status (estrogen receptor [ER] and progesterone receptor [PR]), tumor stage, and age at diagnosis, as well as how period and cohort effects may affect these trends. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used data from Surveillance, Epidemiology, and End Results 17 registries (2000-2019). Women aged 20 to 49 years with a primary invasive breast cancer were included. Data were analyzed between February and June 2023. MAIN OUTCOMES AND MEASURES: Age-standardized incidence rates (ASIR), incidence rate ratios (IRR), and average annual percent changes (AAPC) stratified by race and ethnicity, hormone receptor status, tumor stage, and age at diagnosis. RESULTS: Out of 217 815 eligible women (1485 American Indian or Alaska Native [0.7%], 25 210 Asian or Pacific Islander [11.6%], 27 112 non-Hispanic Black [12.4%], 37 048 Hispanic [17.0%], 126 960 non-Hispanic White [58.3%]), the majority were diagnosed with an ER+/PR+ tumor (134 024 [61.5%]) and were diagnosed with a stage I tumor (81 793 [37.6%]). Overall, invasive breast cancer incidence increased (AAPC, 0.79; 95% CI, 0.42 to 1.15), with increasing trends across almost all racial and ethnic groups. ASIR increased for ER+/PR+ (AAPC, 2.72; 95% CI, 2.34 to 3.12) and ER+/PR- tumors (AAPC, 1.43; 95% CI, 1.00 to 1.87), and decreased for ER-/PR+ (AAPC, -3.25; 95% CI, -4.41 to -2.07) and ER-/PR- tumors (AAPC, -0.55; 95% CI, -1.68 to 0.60). For women aged 20 to 29 and 30 to 39 years, ASIRs were highest among non-Hispanic Black women (age 20-29 years: IRR, 1.53; 95% CI, 1.43 to 1.65; age 30-39 years: IRR, 1.15; 95% CI, 1.12 to 1.18). For women aged 40 to 49 years, ASIR was lower for non-Hispanic Black women (IRR, 0.96; 95% CI, 0.94 to 0.97) compared with non-Hispanic White women. Incidence rates increased for stages I and IV tumors but decreased for stage II and III tumors. Age-period-cohort analysis demonstrated both cohort and period effects on breast cancer incidence (P \u3c .001). CONCLUSIONS AND RELEVANCE: In this population-based cross-sectional analysis, an increase in breast cancer incidence rates among young US women and age-related crossover between non-Hispanic White and Black women were observed. Prevention efforts in young women need to adopt a targeted approach to address racial disparities in incidence rates observed at different age phases

Family history of breast cancer and mammographic breast density in premenopausal women

Importance: Family history of breast cancer (FHBC) and mammographic breast density are independent risk factors for breast cancer, but the association of FHBC and mammographic breast density in premenopausal women is not well understood. Objectives: To investigate the association of FHBC and mammographic breast density in premenopausal women using both quantitative and qualitative measurements. Design, Setting, and Participants: This single-center cohort study examined 2 retrospective cohorts: a discovery set of 375 premenopausal women and a validation set of 14 040 premenopausal women. Data from women in the discovery set was collected between December 2015 and October 2016, whereas data from women in the validation set was collected between June 2010 and December 2015. Data analysis was performed between June 2018 and June 2020. Exposures: Family history of breast cancer (FHBC). Main Outcomes and Measures: The primary outcomes were mammographic breast density measured quantitatively as volumetric percent density using Volpara (discovery set) and qualitatively using BI-RADS (Breast Imaging Reporting and Data System) breast density (validation set). Multivariable regressions were performed using a log-transformed normal distribution for the discovery set and a logistic distribution for the validation set. Results: Of 14 415 premenopausal women included in the study, the discovery set and validation set had similar characteristics (discovery set with FHBC: mean [SD] age, 47.1 [5.6] years; 15 [17.2%] were Black or African American women and 64 [73.6%] were non-Hispanic White women; discovery set with no FHBC: mean [SD] age, 47.7 [4.5] years; 87 [31.6%] were Black or African American women and 178 [64.7%] were non-Hispanic White women; validation set with FHBC: mean [SD] age, 46.8 [7.3] years; 720 [33.4%] were Black or African American women and 1378 [64.0%] were non-Hispanic White women]; validation set with no FHBC: mean [SD] age, 47.5 [6.1] years; 4572 [38.5%] were Black or African American women and 6632 [55.8%] were non-Hispanic White women]). In the discovery set, participants who had FHBC were more likely to have a higher mean volumetric percent density compared with participants with no FHBC (11.1% vs 9.0%). In the multivariable-adjusted model, volumetric percent density was 25% higher (odds ratio [OR], 1.25 ;95% CI, 1.12-1.41) in women with FHBC compared with women without FHBC; and 24% higher (OR, 1.24; 95% CI, 1.10-1.40) in women who had 1 affected relative, but not significantly higher in women who had at least 2 affected relatives (OR, 1.40; 95% CI, 0.95-2.07) compared with women with no relatives affected. In the validation set, women with a positive FHBC were more likely to have dense breasts (BI-RADS 3-4) compared with women with no FHBC (BI-RADS 3: 41.1% vs 38.8%; BI-RADS 4: 10.5% vs 7.7%). In the multivariable-adjusted model, the odds of having dense breasts (BI-RADS 3-4) were 30% higher (OR, 1.30; 95% CI, 1.17-1.45) in women with FHBC compared with women without FHBC; and 29% higher (OR, 1.29; 95% CI, 1.14-1.45) in women who had 1 affected relative, but not significantly higher in women who had at least 2 affected relatives (OR, 1.38; 95% CI, 0.85-2.23) compared with women with no relatives affected. Conclusions and Relevance: In this cohort study, having an FHBC was positively associated with mammographic breast density in premenopausal women. Our findings highlight the heritable component of mammographic breast density and underscore the need to begin annual screening early in premenopausal women with a family history of breast cancer

Determinants of mammographic breast density by race among a large screening population

Background: Because of the mixed reports from smaller studies, we examined associations of race with mammographic breast density and evaluated racial differences in the determinants of breast density. Methods: Participants included 37 839 women (23 166 non-Hispanic white and 14 673 African American) receiving screening mammograms at the Joanne Knight Breast Health Center at Washington University School of Medicine from June 2010 to December 2015. Mammographic breast density was assessed using the Breast Imaging Reporting and Data System (5th edition). To determine the association of race and participant characteristics with mammographic breast density, we used multivariable polytomous logistic regression models (reference group: almost entirely fatty). Results: African American women had increased odds of extremely dense (adjusted odds ratio = 1.31, 95% confidence interval = 1.13 to 1.52) and reduced odds of heterogeneously dense breasts (adjusted odds ratio = 0.91, 95% confidence interval = 0.84 to 0.99) compared with non-Hispanic white women. Altogether, race, parity and age at first birth, current age, current body mass index (BMI), BMI at age 18 years, menarche, family history of breast cancer, oral contraceptive use, alcohol use, and menopausal status explained 33% of the variation in mammographic breast density. Among African American and non-Hispanic white women, these factors explained nearly 28.6% and 33.6% of the variation in mammographic density, respectively. Current BMI provided the greatest explanation of breast density (26.2% overall, 22.2% in African American, and 26.2% in non-Hispanic white women). Conclusions: The determinants of mammographic breast density were generally similar between African American women and non-Hispanic white women. After adjustments for confounders, African Americans had higher likelihood of extremely dense breasts but lower likelihood of heterogeneously dense breasts. The greatest explanation of breast density was provided by BMI, regardless of race

Palatine tonsillar metastasis of lung adenocarcinoma: An unusual immunohistochemical phenotype and a potential diagnostic pitfall

Metastasis rarely occurs to the palatine tonsils. Herein, we present an exceedingly rare case of palatine tonsillar metastasis from poorly differentiated lung adenocarcinoma with anaplastic lymphoma kinase (ALK) mutation in a 51-year-old woman. The patient manifested clinically as pharyngalgia without obvious respiratory symptoms, with swelling tonsil histomorphologically resembling lymphoma and partially expressing the markers of epithelial and squamous cell carcinoma (CK5/6, P63, and P40). Due to the non-specific immunohistochemical expression, it is easily misdiagnosed as a primary poorly differentiated squamous cell carcinoma of the tonsil. This case highlights the importance of a comprehensive assessment of suspicious tonsillar lesions, that may be a sign of a primary malignancy elsewhere in the body

P-CSREC: A New Approach for Personalized Cloud Service Recommendation

It is becoming a challenging issue for users to choose a satisfied service to fit their need due to the rapid growing number of cloud services and the vast amount of service type varieties. This paper proposes an effective cloud service recommendation approach, named personalized cloud service recommendation (P-CSREC), based on the characterization of heterogeneous information network, the use of association rule mining, and the modeling and clustering of user interests. First, a similarity measure is defined to improve the average similarity (AvgSim) measure by the inclusion of the subjective evaluation of users’ interests. Based on the improved AvgSim, a new model for measuring the user interest is established. Second, the traditional K-Harmonic Means (KHM) clustering algorithm is improved by means of involving multi meta-paths to avoid the convergence of local optimum. Then, a frequent pattern growth (FP-Growth) association rules algorithm is proposed to address the issue and the limitation of traditional association rule algorithms to offer personalization in recommendation. A new method to define a support value of nodes is developed using the weight of user’s score. In addition, a multi-level FP-Tree is defined based on the multi-level association rules theory to extract the relationship in higher level. Finally, a combined user interest with the improved KHM clustering algorithm and the improved FP-Growth algorithm is provided to improve accuracy of cloud services recommendation to target users. The experimental results demonstrated the effectiveness of the proposed approach in improving the computational efficiency and recommendation accuracy