“I think it is [the] mother who keeps things going”: The gendered division of labor in the transmission of memory of the Armenian Genocide

In this paper, we discuss what role gender plays in remembering, transmitting, and reframing memories of the Armenian Genocide in order to address the question of how young Armenian women negotiate their roles in this process. Centering the societal roles of memory transmission, we employ the specific sociological lens of gender to analyze 26 interviews conducted in Beirut during the week of the official commemorations of the Armenian Genocide in 2016. We define gender as the social construction of a stylized repetition of acts that reflect power relations. Accordingly, the examination of these power relations is necessary not only to understand the experiences and testimonies of men and women, but also the transmission of memory. While understanding Armenian youth as agents of the collective memory, gender allows us to discuss different patterns of remembrance and transmission. We therefore argue that gender influences how individuals remember the Armenian Genocide, as it underpins the (historically) assigned roles of memory and transmission.Peer Reviewe

Semi-supervised auto-encoder for facial attributes recognition

The particularity of our faces encourages many researchers to exploit their features in different domains such as user identification, behaviour analysis, computer technology, security, and psychology. In this paper, we present a method for facial attributes analysis. The work addressed to analyse facial images and extract features in the purpose to recognize demographic attributes: age, gender, and ethnicity (AGE). In this work, we exploited the robustness of deep learning (DL) using an updating version of autoencoders called the deep sparse autoencoder (DSAE). In this work we used a new architecture of DSAE by adding the supervision to the classic model and we control the overfitting problem by regularizing the model. The pass from DSAE to the semi-supervised autoencoder (DSSAE) facilitates the supervision process and achieves an excellent performance to extract features. In this work we focused to estimate AGE jointly. The experiment results show that DSSAE is created to recognize facial features with high precision. The whole system achieves good performance and important rates in AGE using the MORPH II databas

Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review

Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses. This article reviews the diverse applications across cancer types and the methodology to train and validate these models on whole slide images. From bottom-up to pathologist-driven to hybrid approaches, the leading trends include a variety of weakly supervised deep learning-based approaches, as well as mechanisms for training strongly supervised models in select situations. While results of these algorithms look promising, some challenges still persist, including small training sets, rigorous validation, and model explainability. Biomarker prediction models may yield a screening method to determine when to run molecular tests or an alternative when molecular tests are not possible. They also create new opportunities in quantifying intratumoral heterogeneity and predicting patient outcomes.Comment: 20 pages, 2 figure

Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient

A Mixed Methods Evaluation of Patient and Provider Perspectives of Chronic Illness Management Following Kidney Transplantation

Introduction: Inconsistent, fragmented care coordination in kidney transplant recipients (KTRs)—whose management requires long-term, complex care, and multiple handoffs among providers—has been shown to result in suboptimal care and higher costs. In order to move forward in improving long-term outcomes, it is necessary to fully assess current practice patterns with appropriate measures. With a full and accurate picture of how elements of the management plan influence both KTR and the health care provider (HCP), it will be possible to implement changes that improve long-term outcomes.Methods: The Chronic Care Model (CCM) was the framework for the study. A mixed method research approach was employed, integrating quantitative and qualitative methodologies in a single cross-sectional, correlational study with data collected from both KTRs and physicians. The 659 KTRs were selected from a list of KTRs who had received a kidney transplant at Methodist University Transplant Institute (MUTI). Physicians were recruited from a list of 96 referring nephrologists who practice in the region. The quantitative data were dichotomized results from Patient Assessment of Chronic Illness Care (PACIC) and Assessment of Chronic Illness Care (ACIC) questionnaires. Continuous data characteristics of the KTRs and HCPs were summarized, with means and standard deviations and medians and quartiles. Categorical data were reported as proportions. Chi-Square and Fisher’s Exact tests, as appropriate, were used to determine if any significant associations existed between categorical independent variables and the scale scores. Continuous variables were analyzed using t-tests and Wilcoxon Rank Sum, as appropriate.For qualitative data, NVivo 10 was used to organize the interviews and focus group discussion. Data were analyzed using five phase thematic content analysis.Results: There was variation in the perceptions of chronic illness management as assessed by the PACIC and the ACIC. The number of hospitalizations, time on dialysis and time with graft were the patient variables most associated with PACIC scores. Type of practice, embedded decision support, time in practice and age were the variables most associated with ACIC scores. Patients and providers recognized coordinated care/ follow- up, education, and community resources as barriers to chronic illness management.Discussion: The initial work presented here sought to clarify patient and provider perceptions of the influence of community resources and policies, as well as healthcare system organization using the CCM as a framework. An understanding of the perceptions and experiences of patients and providers will provide the foundation for future work that will address ways in which productive patient-provider interactions can be enhanced, thereby improving patient outcomes

Cyber Security

This open access book constitutes the refereed proceedings of the 17th International Annual Conference on Cyber Security, CNCERT 2021, held in Beijing, China, in AJuly 2021. The 14 papers presented were carefully reviewed and selected from 51 submissions. The papers are organized according to the following topical sections: ​data security; privacy protection; anomaly detection; traffic analysis; social network security; vulnerability detection; text classification