Factorisation-Based Image Labelling

Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based labell propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labelling. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts.

Proactive Corporate Sustainability via Social Innovation—A Case Study of the Hennes & Mauritz Grand Challenge in Bangladesh

The purpose of the study is to explore how a multinational enterprise can use social innovations to drive change and solve grand challenges in an emerging market context. This paper brings market-shaping literature into a sustainability context, particularly by studying the implementation of social innovations in an emerging market context. Specifically, the study involves an in-depth qualitative study of H&M’s fair living wages program in Bangladesh. We find that H&M is tackling utterances of grand challenges revealed by orchestrating social innovation in collaborations with local stakeholders. Social innovation is carried out in ongoing projects involving multiple stakeholders. The study contributes to current literature by revealing that multinational enterprises indeed can use social innovation to drive change in emerging markets, although this requires long-term commitment, an ability and willingness to shape the surrounding business environment, and a prominent standing among key stakeholders.

Fast and Accurate U-Net Model for Fetal Ultrasound Image Segmentation

U-Net based algorithms, due to their complex computations, include limitations when they are used in clinical devices. In this paper, we addressed this problem through a novel U-Net based architecture that called fast and accurate U-Net for medical image segmentation task. The proposed fast and accurate U-Net model contains four tuned 2D-convolutional, 2D-transposed convolutional, and batch normalization layers as its main layers. There are four blocks in the encoder-decoder path. The results of our proposed architecture were evaluated using a prepared dataset for head circumference and abdominal circumference segmentation tasks, and a public dataset (HC18-Grand challenge dataset) for fetal head circumference measurement. The proposed fast network significantly improved the processing time in comparison with U-Net, dilated U-Net, R2U-Net, attention U-Net, and MFP U-Net. It took 0.47 seconds for segmenting a fetal abdominal image. In addition, over the prepared dataset using the proposed accurate model, Dice and Jaccard coefficients were 97.62% and 95.43% for fetal head segmentation, 95.07%, and 91.99% for fetal abdominal segmentation. Moreover, we have obtained the Dice and Jaccard coefficients of 97.45% and 95.00% using the public HC18-Grand challenge dataset. Based on the obtained results, we have concluded that a fine-tuned and a simple well-structured model used in clinical devices can outperform complex models.

Metallic Cu2N Monolayer with Planar Tetracoordinated Nitrogen as a Promising Catalyst for CO2 Electroreduction

The electrochemical carbon dioxide reduction reaction (CO2RR) holds great promise for mitigating CO2 emission and simultaneously generating high energy fuels. However, it remains a grand challenge to reduce CO2 to...

Confined in-situ polymerization in nanoscale porphyrinic metal-organic framework for fluorescence imaging-guided synergistic phototherapy

Engineering a versatile nanoplatform integrating imaging and therapeutic functions for efficient cancer treatment remains grand challenge. Herein, a type of metal-organic framework (MOF)-based hybrid material for fluorescence imaging-guided synergistic phototherapy...

Programming and Synthesis for Software-defined FPGA Acceleration: Status and Future Prospects

FPGA-based accelerators are increasingly popular across a broad range of applications, because they offer massive parallelism, high energy efficiency, and great flexibility for customizations. However, difficulties in programming and integrating FPGAs have hindered their widespread adoption. Since the mid 2000s, there has been extensive research and development toward making FPGAs accessible to software-inclined developers, besides hardware specialists. Many programming models and automated synthesis tools, such as high-level synthesis, have been proposed to tackle this grand challenge. In this survey, we describe the progression and future prospects of the ongoing journey in significantly improving the software programmability of FPGAs. We first provide a taxonomy of the essential techniques for building a high-performance FPGA accelerator, which requires customizations of the compute engines, memory hierarchy, and data representations. We then summarize a rich spectrum of work on programming abstractions and optimizing compilers that provide different trade-offs between performance and productivity. Finally, we highlight several additional challenges and opportunities that deserve extra attention by the community to bring FPGA-based computing to the masses.