Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities

A new geometric shaping method is proposed, leveraging unsupervised machine learning to optimize the constellation design. The learned constellation mitigates nonlinear effects with gains up to 0.13 bit/4D when trained with a simplified fiber channel model.Comment: 3 pages, 6 figures, submitted to ECOC 201

Determination of Natural Radioactivity and Hazard in Soil and Rock Samples in a Mining Area in Ghana

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Inverse association between diabetes and altitude: a cross-sectional study in the adult population of the United States.

ObjectiveTo determine whether geographical elevation is inversely associated with diabetes, while adjusting for multiple risk factors.MethodsThis is a cross-sectional analysis of publicly available online data from the Behavioral Risk Factor Surveillance System, 2009. Final dataset included 285,196 US adult subjects. Odds ratios were obtained from multilevel mixed-effects logistic regression analysis.ResultsAmong US adults (≥20 years old), the odds ratio for diabetes was 1.00 between 0 and 499 m of altitude (reference), 0.95 (95% confidence interval, 0.90-1.01) between 500 and 1,499 m, and 0.88 (0.81-0.96) between 1,500 and 3,500 m, adjusting for age, sex, body mass index, ethnicity, self-reported fruit and vegetable consumption, self-reported physical activity, current smoking status, level of education, income, health status, employment status, and county-level information on migration rate, urbanization, and latitude. The inverse association between altitude and diabetes in the US was found among men [0.84 (0.76-0.94)], but not women [1.09 (0.97-1.22)].ConclusionsAmong US adults, living at high altitude (1,500-3,500 m) is associated with lower odds of having diabetes than living between 0 and 499 m, while adjusting for multiple risk factors. Our findings suggest that geographical elevation may be an important factor linked to diabetes

Food safety regulation: Perspectives of food service operators in the cape coast metropolis

Global reports since the 2000s suggest that food safety is an important public health concern that attracts the attention of governments, food producers and consumers. Governments all over the world try to prioritise the safety of their food because it is a major driver of food security. Nonetheless, foodborne illnesses continue to occur daily basis. Ghana has a legal framework, institutions and agencies at different levels of government for food safety management. Yet, Cape Coast in the Central Region grapples with foodborne related diseases, thus identified by UNICEF as a hotspot for foodborne related outbreaks. This study set out to explore the views and familiarity of food service operators on the regulation of their operations. Three hundred food service operators from the 16 communities were selected using purposive sampling method for the study. The findings showed that food service operators had functional knowledge of the rules and regulations, just about enough to guide their daily operations. Regulators were generally perceived to be friendly and accommodating but inadequate and irregular in their supervisory roles. It was recommended that regulators should have regular encounter with food service operators to enhance compliance and achieve the food safety goal

TinyReptile: TinyML with Federated Meta-Learning

Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask whether TinyML applications can benefit from aggregating their knowledge. Federated learning (FL) enables decentralized agents to jointly learn a global model without sharing sensitive local data. However, a common global model may not work for all devices due to the complexity of the actual deployment environment and the heterogeneity of the data available on each device. In addition, the deployment of TinyML hardware has significant computational and communication constraints, which traditional ML fails to address. Considering these challenges, we propose TinyReptile, a simple but efficient algorithm inspired by meta-learning and online learning, to collaboratively learn a solid initialization for a neural network (NN) across tiny devices that can be quickly adapted to a new device with respect to its data. We demonstrate TinyReptile on Raspberry Pi 4 and Cortex-M4 MCU with only 256-KB RAM. The evaluations on various TinyML use cases confirm a resource reduction and training time saving by at least two factors compared with baseline algorithms with comparable performance.Comment: Accepted by The International Joint Conference on Neural Network (IJCNN) 202

Statistics Oriented Preprocessing of Document Image

Old printed documents represent an important part of our cultural heritage. Their digitalization plays an important role in creating data and metadata. The paper proposed an algorithm for estimation of the global text skew. First, document image is binarized reducing the impact of noise and uneven illumination. The binary image is statistically analyzed and processed. Accordingly, redundant data have been excluded. Furthermore, the convex hulls are established encircling each text object. They are joined establishing connected components. Then, the connected components in complementary image are enlarged with morphological dilation. At the end, the biggest connected component is extracted. Its orientation is similar to the global orientation of text document which is calculated by the moments. Efficiency and correctness of the algorithm are verified by testing on a custom dataset