ABSTRACT
BACKGROUND: The Rohingya ethnic minority population in northern Rakhine state, Myanmar, have experienced some of the most protracted situations of persecution. Government-led clearance operations in August 2017 were one of many, but notably one of the most devastating, attacks on the population. The study aimed to conduct a multiphase mixed-methods assessment of the prevalence and contexts of violence and mortality across affected hamlets in northern Rakhine State during the August 2017 attacks. This publication describes qualitative accounts by Rohingya community leaders from affected hamlets, with a focus on the events and environment leading up to and surrounding the attacks. METHODS: Qualitative in-depth interviews were conducted with Rohingya community leaders representing 88 northern Rakhine state hamlets across three townships affected by the August 2017 attacks (Maungdaw, n = 34; Buthidaung, n = 42; Rathedaung, n = 12). Prior quantitative surveys conducted among representative hamlet leaders allowed for preliminary screening and identification of interview candidates: interviewees were then selected based on prior reports of 10 or more deaths among Rohingya hamlet community members, mass rape, and/or witness of mass graves in a hamlet or during displacement. Recorded interviews were transcribed, translated, and thematically coded. RESULTS: Rohingya leaders reported that community members were subjected to systematic civil oppression characterized by severe restrictions on travel, marriage, education, and legal rights, regular denial of citizenship rights, and unsubstantiated accusations of terrorist affiliations in the months prior to August 2017. During the attacks, Rohingya civilians (inclusive of women, men, children, and elderly) reportedly suffered severe, indiscriminate violence perpetrated by Myanmar security forces. Crimes against children and sexual violence were widespread. Bodies of missing civilians were discovered in mass graves and, in some cases, desecrated by armed groups. Myanmar Armed Forces (Tatmadaw), consisting of the Army, Navy, and Border Guard Police continued to pursue, assault, and obstruct civilians in flight to Bangladesh. CONCLUSIONS: Qualitative findings corroborate previously published evidence of widespread and systematic violence by the Myanmar security forces against the Rohingya. The accounts describe intentional oppression of Rohingya civilians leading up to the August 2017 attacks and coordinated and targeted persecution of Rohingya by state forces spanning geographic distances, and ultimately provide supporting evidence for investigations of crimes against humanity and acts of genocide.
ABSTRACT
Fisheries depredation by marine mammals is an economic concern worldwide. We combined questionnaires, acoustic monitoring, and participatory experiments to investigate the occurrence of bottlenose dolphins in the fisheries of Northern Cyprus, and the extent of their conflict with set-nets, an economically important metier of Mediterranean fisheries. Dolphins were present in fishing grounds throughout the year and were detected at 28% of sets. Net damage was on average six times greater where dolphins were present, was correlated with dolphin presence, and the associated costs were considerable. An acoustic deterrent pinger was tested, but had no significant effect although more powerful pingers could have greater impact. However, our findings indicate that effective management of fish stocks is urgently required to address the overexploitation that is likely driving depredation behaviour in dolphins, that in turn leads to net damage and the associated costs to the fisheries.
ABSTRACT
OBJECTIVE: Electronic health records possess critical predictive information for machine-learning-based diagnostic aids. However, many traditional machine learning methods fail to simultaneously integrate textual data into the prediction process because of its high dimensionality. In this paper, we present a supervised method using Laplacian Eigenmaps to enable existing machine learning methods to estimate both low-dimensional representations of textual data and accurate predictors based on these low-dimensional representations at the same time. MATERIALS AND METHODS: We present a supervised Laplacian Eigenmap method to enhance predictive models by embedding textual predictors into a low-dimensional latent space, which preserves the local similarities among textual data in high-dimensional space. The proposed implementation performs alternating optimization using gradient descent. For the evaluation, we applied our method to over 2000 patient records from a large single-center pediatric cardiology practice to predict if patients were diagnosed with cardiac disease. In our experiments, we consider relatively short textual descriptions because of data availability. We compared our method with latent semantic indexing, latent Dirichlet allocation, and local Fisher discriminant analysis. The results were assessed using four metrics: the area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC), specificity, and sensitivity. RESULTS AND DISCUSSION: The results indicate that supervised Laplacian Eigenmaps was the highest performing method in our study, achieving 0.782 and 0.374 for AUC and MCC, respectively. Supervised Laplacian Eigenmaps showed an increase of 8.16% in AUC and 20.6% in MCC over the baseline that excluded textual data and a 2.69% and 5.35% increase in AUC and MCC, respectively, over unsupervised Laplacian Eigenmaps. CONCLUSIONS: As a solution, we present a supervised Laplacian Eigenmap method to embed textual predictors into a low-dimensional Euclidean space. This method allows many existing machine learning predictors to effectively and efficiently capture the potential of textual predictors, especially those based on short texts.
