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2. GAN-driven Electromagnetic Imaging of 2-D Dielectric Scatterers

Author

Naseer, Ehtasham, Sandhu, Ali Imran, Siddique, Muhammad Adnan, Ahmed, Waqas W., Farhat, Mohamed, and Wu, Ying

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Inverse scattering problems are inherently challenging, given the fact they are ill-posed and nonlinear. This paper presents a powerful deep learning-based approach that relies on generative adversarial networks to accurately and efficiently reconstruct randomly-shaped two-dimensional dielectric objects from amplitudes of multi-frequency scattered electric fields. An adversarial autoencoder (AAE) is trained to learn to generate the scatterer's geometry from a lower-dimensional latent representation constrained to adhere to the Gaussian distribution. A cohesive inverse neural network (INN) framework is set up comprising a sequence of appropriately designed dense layers, the already-trained generator as well as a separately trained forward neural network. The images reconstructed at the output of the inverse network are validated through comparison with outputs from the forward neural network, addressing the non-uniqueness challenge inherent to electromagnetic (EM) imaging problems. The trained INN demonstrates an enhanced robustness, evidenced by a mean binary cross-entropy (BCE) loss of $0.13$ and a structure similarity index (SSI) of $0.90$. The study not only demonstrates a significant reduction in computational load, but also marks a substantial improvement over traditional objective-function-based methods. It contributes both to the fields of machine learning and EM imaging by offering a real-time quantitative imaging approach. The results obtained with the simulated data, for both training and testing, yield promising results and may open new avenues for radio-frequency inverse imaging.

Published
2024

7. ChatGPT and Bard Responses to Polarizing Questions

Author

Goyal, Abhay, Siddique, Muhammad, Parekh, Nimay, Schwitzky, Zach, Broekaert, Clara, Michelotti, Connor, Wong, Allie, Cheung, Lam Yin, Hanlon, Robin O, De Choudhury, Munmun, Lee, Roy Ka-Wei, and Kumar, Navin

Subjects
Computer Science - Computation and Language
Abstract

Recent developments in natural language processing have demonstrated the potential of large language models (LLMs) to improve a range of educational and learning outcomes. Of recent chatbots based on LLMs, ChatGPT and Bard have made it clear that artificial intelligence (AI) technology will have significant implications on the way we obtain and search for information. However, these tools sometimes produce text that is convincing, but often incorrect, known as hallucinations. As such, their use can distort scientific facts and spread misinformation. To counter polarizing responses on these tools, it is critical to provide an overview of such responses so stakeholders can determine which topics tend to produce more contentious responses -- key to developing targeted regulatory policy and interventions. In addition, there currently exists no annotated dataset of ChatGPT and Bard responses around possibly polarizing topics, central to the above aims. We address the indicated issues through the following contribution: Focusing on highly polarizing topics in the US, we created and described a dataset of ChatGPT and Bard responses. Broadly, our results indicated a left-leaning bias for both ChatGPT and Bard, with Bard more likely to provide responses around polarizing topics. Bard seemed to have fewer guardrails around controversial topics, and appeared more willing to provide comprehensive, and somewhat human-like responses. Bard may thus be more likely abused by malicious actors. Stakeholders may utilize our findings to mitigate misinformative and/or polarizing responses from LLMs

Published
2023

8. How is Fatherhood Framed Online in Singapore?

Author

Van, Tran Hien, Goyal, Abhay, Siddique, Muhammad, Cheung, Lam Yin, Parekh, Nimay, Huang, Jonathan Y, McCrickerd, Keri, Tandoc Jr., Edson C, Chung, Gerard, and Kumar, Navin

Subjects
Computer Science - Computation and Language
Abstract

The proliferation of discussion about fatherhood in Singapore attests to its significance, indicating the need for an exploration of how fatherhood is framed, aiding policy-making around fatherhood in Singapore. Sound and holistic policy around fatherhood in Singapore may reduce stigma and apprehension around being a parent, critical to improving the nations flagging birth rate. We analyzed 15,705 articles and 56,221 posts to study how fatherhood is framed in Singapore across a range of online platforms (news outlets, parenting forums, Twitter). We used NLP techniques to understand these differences. While fatherhood was framed in a range of ways on the Singaporean online environment, it did not seem that fathers were framed as central to the Singaporean family unit. A strength of our work is how the different techniques we have applied validate each other.

Published
2023

9. LegoNet: Alternating Model Blocks for Medical Image Segmentation

Author

Sobirov, Ikboljon, Xie, Cheng, Siddique, Muhammad, Patel, Parijat, Chan, Kenneth, Halborg, Thomas, Kotanidis, Christos, Fatima, Zarqiash, West, Henry, Channon, Keith, Neubauer, Stefan, Antoniades, Charalambos, and Yaqub, Mohammad

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Since the emergence of convolutional neural networks (CNNs), and later vision transformers (ViTs), the common paradigm for model development has always been using a set of identical block types with varying parameters/hyper-parameters. To leverage the benefits of different architectural designs (e.g. CNNs and ViTs), we propose to alternate structurally different types of blocks to generate a new architecture, mimicking how Lego blocks can be assembled together. Using two CNN-based and one SwinViT-based blocks, we investigate three variations to the so-called LegoNet that applies the new concept of block alternation for the segmentation task in medical imaging. We also study a new clinical problem which has not been investigated before, namely the right internal mammary artery (RIMA) and perivascular space segmentation from computed tomography angiography (CTA) which has demonstrated a prognostic value to major cardiovascular outcomes. We compare the model performance against popular CNN and ViT architectures using two large datasets (e.g. achieving 0.749 dice similarity coefficient (DSC) on the larger dataset). We evaluate the performance of the model on three external testing cohorts as well, where an expert clinician made corrections to the model segmented results (DSC>0.90 for the three cohorts). To assess our proposed model for suitability in clinical use, we perform intra- and inter-observer variability analysis. Finally, we investigate a joint self-supervised learning approach to assess its impact on model performance. The code and the pretrained model weights will be available upon acceptance., Comment: 12 pages, 5 figures, 4 tables

Published
2023

16. Diversity and Conservation Status of Large Mammals in Ghamot National Park, Azad Jammu and Kashmir, Pakistan

Author

Muhammad Jahangeer, Siddique Muhammad, Muhammad Shakeel, Mir Muhammad Saleem, Ali Usman, and Hussain Abid

Subjects
Ghamot National Park, Diversity, Distributions, Large Mammals, Richness, Mathematics, QA1-939, Physics, QC1-999, Chemistry, QD1-999, Biotechnology, TP248.13-248.65
Abstract

We assessed the richness, diversity, composition of the large mammal community in Ghamot National Park (GNP), Neelum valley Azad Jammu and Kashmir, Pakistan, and how these characteristics differed between four habitat types: forest, riparian zone, scrubland, and wetland, as well as between seasons. Across all locales with 66 line transect surveys (53km), indirect observation had the highest sampling (n=131; 76.60%) while direct observation had the lowest (n=40; 23.39). Fecal dropping was the most common type of indirect evidence (n=73; 55.72%), followed by foot prints (n=29; 9.94%) and dens/latrine (n=19; 14.50%). The Scrubland zone had the most indirect evidences (n=37) of any zone (elevation ranges between 2600-3600m).The forest zone (elevation ranges between 2700-3450m) had the second highest sampling (n=28), followed by the Alpine Zone (n=28), Riparian Zone (n=21), and Alpine Pasture Zone (n=16) (elevation 4000-4400m; habitat type high alpine pastures). By using direct and indirect field evidence, 14 species from 6 families were identified. Unfortunately, the number of species encountered directly was low in this study; four species (28.57%) were directly observed, including Canis aureus, Vulpes vulpes, Macaca mulatta, and Semnopithecus ajax, while the remaining ten (71.42%) were observed indirectly. Summer had the most direct and indirect observations (n=86; 50.29%), followed by winter (n=85; 49.70%). (4.28). Season had little effect on species richness, diversity, and composition across or within habitat types. However, the richness of species varied greatly across seasons among the four habitat types. The forest and riparian zone had the greatest similarity in species composition, both between and among seasons. Finally, our findings show that GNP has an essential influence in Pakistan mammal conservation. Our findings will provide as a baseline for park management to make effective conservation decisions, as well as a baseline for researchers conducting similar ecological studies.

Published
2024

18. Centrifugal Pump Health Condition Identification Based on Novel Multi-filter Processed Scalograms and CNN

Author

Ahmad, Zahoor, Siddique, Muhammad Farooq, Ullah, Niamat, Kim, Jaeyoung, Kim, Jong-Myon, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Choi, Bong Jun, editor, Singh, Dhananjay, editor, Tiwary, Uma Shanker, editor, and Chung, Wan-Young, editor

Published
2024
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36. Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study

Author

Thomas, Sheena, Denton, Jon, Farral, Robyn, Taylor, Carolyn, Qin, Wendy, Kasongo, Mary, Anthony, Susan, Banning, Adrian, Xie, Cheng, Kharbanda, Rajesh K, Pritchard, Amy, Halborg, Thomas, Syed, Nigar, Fry, Sam, Mathers, Chris, Rose, Anne, Hudson, George, Bajaj, Amrita, Das, Intrajeet, Deshpande, Aparna, Rao, Praveen, Lawday, Dan, Mirsadraee, Saeed, Hudson, Benjamin, Berry, Colin, Marwan, Mohamed, Maurovich-Horvat, Pál, He, Guo-Wei, Lin, Wen-Hua, Fan, Li-Juan, Takahashi, Naohiko, Kondo, Hidekazu, Dai, Neng, Ge, Junbo, Koo, Bon-Kwon, Guglielmo, Marco, Pontone, Gianluca, Huck, Daniel, Benedek, Theodora, Rajani, Ronak, Vilic, Dijana, Aljazzaf, Haleema, Mun, Mak S, Benedetti, Giulia, Preston, Rebecca L, Raisi-Estabragh, Zahra, Connolly, Derek L, Sharma, Vinoda, Grenfell, Rebecca, Bradlow, William, Schmitt, Matthias, Serfaty, Fabiano, Gottlieb, Ilan, Neves, Mario FT, Newby, David E, Dweck, Marc R, Hatem, Stéphane, Redheuil, Alban, Benetos, Georgios, Beer, Meinrad, Granillo, Gastón AR, Selvanayagam, Joseph, Lopez-Jimenez, Francisco, De Bosscher, Ruben, Tavildari, Alain, Figtree, Gemma, Danad, Ibrahim, Shantouf, Ronney, Kietselaer, Bas, Tousoulis, Dimitris, Dangas, George, Mehta, Nehal N, Kontanidis, Christos, Kunadian, Vijay, Fairbairn, Timothy A, Chan, Kenneth, Wahome, Elizabeth, Tsiachristas, Apostolos, Antonopoulos, Alexios S, Patel, Parijat, Lyasheva, Maria, Kingham, Lucy, West, Henry, Oikonomou, Evangelos K, Volpe, Lucrezia, Mavrogiannis, Michail C, Nicol, Edward, Mittal, Tarun K, Kotronias, Rafail A, Adlam, David, Modi, Bhavik, Rodrigues, Jonathan, Screaton, Nicholas, Kardos, Attila, Greenwood, John P, Sabharwal, Nikant, De Maria, Giovanni Luigi, Munir, Shahzad, McAlindon, Elisa, Sohan, Yogesh, Tomlins, Pete, Siddique, Muhammad, Kelion, Andrew, Shirodaria, Cheerag, Pugliese, Francesca, Petersen, Steffen E, Blankstein, Ron, Desai, Milind, Gersh, Bernard J, Achenbach, Stephan, Libby, Peter, Neubauer, Stefan, Channon, Keith M, Deanfield, John, and Antoniades, Charalambos

Published
2024
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