Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structure, by Sanborn et al.

The two images below are from the article titled – Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structure, by Sanborn et al. (https://arxiv.org/abs/2407.09468v1)

The paper talk about how “the mathematics of topology, geometry and algebra provide a conceptual framework to categorize the nature of data found in machine learning.” The two figures, present a “graphical taxonomy, to categorize the structures of data.” The article the authors discuss two types of data that we generally encounter: “either data as coordinates in space—for example the coordinate of the position of an object in a 2D space; or data as signals over a space—for example, an image is a 3D (RGB) signal defined over a 2D space. In each case, the space can either be a Euclidean space or it can be equipped with topological, geometric and algebraic structures.”

The paper goes on to then “review a large and disparate body of literature of non-Euclidean generalizations of algorithms classically defined for data residing in Euclidean spaces.” The algorithms presented assume that certain topological, algebraic, or geometric structures of the data / problem are known. However, it does not go into discussion on methods where such structures are not known. For example, methods that fall into the category of topological data analysis, metric learning, or group learning are not covered.

First undergrad group complete their senior project from the HCI wing.

Congratulations to them 🎉

It should be mentioned that, during this project they published 2 conference papers and another article is in preparation.

CCDS student James selected for the CERN Summer Student Program 2024

The Centre for Computational and Data Sciences (CCDS), Independent University, Bangladesh (IUB), is proud to announce that James Peter Gomes, a dedicated undergraduate research student at CCDS and a Physics (Honors) major student in the Department of Physical Sciences, was selected earlier this year to participate in the prestigious CERN Summer Student Program 2024. He was the only Bangladeshi student amongst the 300 selected out of around 10,500 applicants worldwide. The fully funded 8 weeklong program was held at CERN’s Meyrin site in Geneva, Switzerland from 24th June to 16th August 2024.

🟦For those who are not aware, CERN, the European Organization for Nuclear Research, stands as one of the world’s foremost centers for scientific inquiry and collaboration. Located near Geneva, Switzerland, CERN is renowned for its groundbreaking research in particle physics and its role in advancing our understanding of the fundamental forces and particles that govern the universe. Participation in the CERN Summer Student Program provides aspiring physicists like James with a unique opportunity to immerse themselves in this vibrant scientific community, engage in cutting-edge research projects, and collaborate with leading experts in the field.

🟩Here’s what James has to say about his experience.

“During the internship, I worked as an associated personnel of the LHCb (Large Hadron Collider beauty) Detector Group (EP-LBD). This experimental collaboration mostly focuses on CP violation in nature, which distinguishes between particle and antiparticle in nature. This asymmetry is important for Cosmological observations. This very minute asymmetry requires very good statistics from the last dataset obtained from the collisions in the Large Hadron Collider (LHC) via the highly efficient detectors in the experimental setup. One also needs a good understanding of the detectors which is studied via simulations. My responsibilities included testing and exploring the integration of the GPU-based simulation prototype, AdePT (Accelerated demonstrator of electromagnetic Particle Transport), into the Gaussino simulation framework for the LHCb experiment. This initiative aimed at enhancement of the efficiency and accuracy of particle physics simulations, thereby advancing our understanding of fundamental particles and their interactions. My supervisors were incredibly supportive and patient throughout this project. They generously shared their expertise and guidance, always willing to answer my questions and clarify my doubts. Their mentorship was invaluable, as I was still learning to navigate the tools required for this work.

Furthermore, I participated in a series of lectures on physics, from the Standard Model to Beyond Standard Model to Quantum Gravity, delivered by CERN personnel, active researchers, and distinguished professors like David Tong, throughout the weekdays from July 2nd to August 2nd.

One of the program’s greatest benefits was the comprehensive support it provided to participants. We enjoyed CERN’s health insurance, a full travel allowance, and a daily stipend. Additionally, we had access to world-class facilities like laboratories, libraries, and computing resources. These resources were instrumental in fostering collaboration and advancing our research.

Quite interestingly, almost a third of the summer students were from Computer Science and Engineering background. In fact, I was one of the only 4 physics students out of the 11 summer students in the simulation team and the rest were from CSE relevant background. It seemed like my prior knowledge of specialized tools like ROOT, Pythia8, GEANT4, and FeynCalc proved invaluable in securing this internship. These skills are essential for the computing-intensive projects that CERN undertakes. CERN summer internship program, being computing-heavy, offers a valuable opportunity for students with strong programming and Linux skills.

I’m deeply grateful to my supervisor, Dr. Arshad Momen, for his invaluable guidance throughout my time at IUB. His patience and mentorship have been instrumental in helping me discover my passion for physics and choose the right path. I first met Arshad Sir in my first semester and have been fortunate to learn from his expertise ever since. It was thanks to his encouragement that I learned about the CERN Summer Student Program and developed the skills necessary to participate. I’m truly thankful for his support.”

One paper has been accepted in ECAI 2024

Congratulations to our senior project student Fahim Ahmed and research assistant Md Fahim for getting their paper accepted into the core rank A conference, European Conference on AI (ECAI) https://www.ecai2024.eu/ . The acceptance rate was very competitive (24%) this time for ECAI 2024. The title of the paper is, “Improving the Performance of Transformer-based Models Over Classical Baselines in Multiple Transliterated Languages”.

Here is a short description of the paper:

Online discourse, by its very nature, is rife with transliterated text along with code-mixing and code-switching. Transliteration is heavily featured due to the ease of inputting romanized text with standard keyboards over native scripts. Due to its ubiquity, it is a critical area of study to ensure NLP models perform well in real-world scenarios.

In this paper, we analyze the performance of various language model’s performance on classification of romanized/transliterated social media text. We chose the tasks of sentiment analysis and offensive language identification. We carried out experiments for three different languages, namely Bangla, Hindi, and Arabic (for six datasets). To our surprise, we discovered across multiple datasets that the classical machine learning methods (Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and XGBoost) perform very competitively with fine-tuned transformer-based mono / multilingual language models (BanglishBERT, HingBERT, and DarijaBERT, XLM-RoBERTa, mBERT, and mDeBERTa), tiny LLMs (Gemma-2B, and TinyLLaMa) and ChatGPT for classification tasks in transliterated text. Additionally, we investigated various mitigation strategies such as translation and augmentation via the use of ChatGPT, as well as Masked Language Modelling to dataset-specific pretraining for language models. Depending on the dataset and language, employing those mitigation techniques yields a 2-3% further improvement in accuracy and macro-F1 above baseline.

We demonstrate TF-IDF and BoW-based classifiers achieve performance within around 3% of fine-tuned LMs and thus could thus be considered as a strong baseline for transliterated text-based NLP tasks.

5 papers from CCDS has been accepted in ICPR 2024

1.

Dehan, Farhan Noor; Fahim, Md; Rahman, AKM Mahabubur; Amin, M Ashraful; Ali, Amin Ahsan

TinyLLM Efficacy in Low-Resource Language 

In: 27th International Conference on Pattern Recognition, ICPR IEEE, KolKata, India, 2024.

2.

Sultana, Faria; Fuad, Md Tahmid Hasan; Fahim, Md; Rahman, Rahat Rizvi; Hossain, Meheraj; Amin, M Ashraful; Rahman, AKM Mahabubur; Ali, Amin Ahsan

How Good are LM and LLMs in Bangla Newspaper Article Summarization? 

In: 27th International Conference on Pattern Recognition, ICPR IEEE, KolKata, India, 2024.

3.

Kim, Minha; Bhaumik, Kishor; Ali, Amin Ahsan; Woo, Simon

MIXAD: Memory-Induced Explainable Time Series Anomaly Detection 

In: 27th International Conference on Pattern Recognition, ICPR IEEE, KolKata, India, 2024.

4.

Bhaumik, Kishor; Kimb, Minha; Niloy, Fahim Faisal; Ali, Amin Ahsan; Woo, Simon

SSMT: Few-Shot Traffic Forecasting with Single Source Meta-Transfer Learning 

In: IEEE Int’l Conf on Image Processing, ICPR IEEE, Abu Dhabi, 2024.

5.

Hossain, Mir Sazzat; Rahman, AKM Mahbubur; Amin, Md. Ashraful; Ali, Amin Ahsan

Lightweight Recurrent Neural Network for Image Super-resolution 

In: IEEE Int’l Conf on Image Processing, IEEE IEEE, Abu Dhabi, 2024.

Self supervised Model for Domain Adaptation in Build Up Area Categorization  

The urban environment is a complex system comprising various elements such as buildings, roads, vegetation, and water bodies. Classifying urban cities from satellite images or images captured by UAVs is an important task for urban planning, disaster management, and environmental monitoring. Urban environments differ across various cities of the world, and existing models for urban classification struggle to adapt to these changes. This project aims to develop an adaptive model for classifying urban cities from satellite images. This model will have the capability to generalize across different urban environments and adapt to the changing urban environment in real time. The model will be trained on a large dataset of satellite images of urban cities from different parts of the world. In inference or test time, the parameters of the model will be updated based on the changes in the different urban environments it has been deployed. This work will be built on recent work on Test time domain adaptation methods and our earlier research on the categorization of urban buildup [1], land usage, and land cover [2,3].

Related Works:

  1. Cheng, Q.; Zaber, M.; Rahman, A.M.; Zhang, H.; Guo, Z.; Okabe, A.; Shibasaki, R. Understanding the Urban Environment from Satellite Images with New Classification Method—Focusing on Formality and Informality. Sustainability 202214, 4336. https://doi.org/10.3390/su14074336
  2. Rahman, A.K.M.M.; Zaber, M.; Cheng, Q.; Nayem, A.B.S.; Sarker, A.; Paul, O.; Shibasaki, R. Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World. Sensors 202121, 7469. https://doi.org/10.3390/s21227469
  3. Niloy, Fahim Faisal, et al. “Attention toward neighbors: A context aware framework for high resolution image segmentation.” 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. 

IP:

Co-IP:

Crop Identification and Yield Estimation using SAR Data

Accurate crop identification and yield estimation are crucial for policymakers to develop effective agricultural policies, allocate resources efficiently, and support farmers in adopting suitable technologies. However, optical remote sensing methods, commonly used for crop identification and yield estimation, face challenges due to cloud cover and adverse weather conditions. King et al. (2013) [4] estimated that approximately 67 percent of the Earth’s surface is often obscured by clouds, making it difficult to obtain high-quality optical remote sensing data. Additionally, humid and semi-humid climate zones with abundant water sources pose further challenges for remote sensing in agriculture. To overcome these limitations, this project aims to utilize Synthetic Aperture Radar (SAR) data for crop identification and yield estimation. It enables continuous data collection regardless of light and weather conditions by using microwaves that can penetrate clouds. As SAR is sensitive to both the dielectric and geometrical characteristics of plants, it captures information below the vegetation canopy cover and provides insights into crop structure and health. Furthermore, SAR provides flexibility in imaging parameters such as incident angles and polarization configurations, facilitating the extraction of diverse information about agricultural landscapes.

Related Works:

  1. D. Suchi, A. Menon, A. Malik, J. Hu and J. Gao, Crop Identification Based on Remote Sensing Data using Machine Learning Approaches for Fresno County, California, 2021 IEEE Seventh International Conference on Big Data Computing Service and Applications (BigDataService), Oxford, United Kingdom, 2021, pp. 115-124, doi: 10.1109/BigDataService52369.2021.00019.
  2. Liu, C., Chen, Z., Shao, Y., Chen, J., Hasi, T., & Pan, H. (2019). Research advances of SAR remote sensing for agriculture applications: A review. Journal of Integrative Agriculture, 18(3), 506-525.
  3.   J. Singh, U. Devi, J. Hazra and S. Kalyanaraman, Crop-Identification Using Sentinel-1 and Sentinel-2 Data for Indian Region, IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018, pp. 5312-5314, doi: 10.1109/IGARSS.2018.8517356.
  4.   King, M. D., Platnick, S., Menzel, W. P., Ackerman, S. A., & Hubanks, P. A. (2013). Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites. IEEE Transactions on Geoscience and Remote Sensing, 51(7), 3826–3852. doi:10.1109/tgrs.2012.2227333

Test-Time Domain Adaptation for Urban Categorization from Satellite Images

The urban environment is a complex system comprising various elements such as buildings, roads, vegetation, and water bodies. Classifying urban cities from satellite images or images captured by UAVs is an important task for urban planning, disaster management, and environmental monitoring. Urban environments differ across various cities of the world, and existing models for urban classification struggle to adapt to these changes. This project aims to develop an adaptive model for classifying urban cities from satellite images. This model will have the capability to generalize across different urban environments and adapt to the changing urban environment in real time. The model will be trained on a large dataset of satellite images of urban cities from different parts of the world. In inference or test time, the parameters of the model will be updated based on the changes in the different urban environments it has been deployed. This work will be built on recent work on Test time domain adaptation methods and our earlier research on the categorization of urban buildup [1], land usage, and land cover [2,3].

Related Works:

  1. Cheng, Q.; Zaber, M.; Rahman, A.M.; Zhang, H.; Guo, Z.; Okabe, A.; Shibasaki, R. Understanding the Urban Environment from Satellite Images with New Classification Method—Focusing on Formality and Informality. Sustainability 2022, 14, 4336. https://doi.org/10.3390/su14074336
  2. Rahman, A.K.M.M.; Zaber, M.; Cheng, Q.; Nayem, A.B.S.; Sarker, A.; Paul, O.; Shibasaki, R. Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World. Sensors 2021, 21, 7469. https://doi.org/10.3390/s21227469
  3. Niloy, Fahim Faisal, et al. “Attention toward neighbors: A context aware framework for high resolution image segmentation.” 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. 

Developing a Multi-Agent Framework for Multimodal Multi-Task Learning

This project is focused on enhancing the capabilities of large multimodal models. Multimodal learning is an area of machine learning where models are designed to process and correlate information from various input modalities, such as text, images, and audio. In this project, we are developing a multi-agent framework where each agent is specialized in understanding a specific modality and task. These agents work in tandem, the framework incorporates specific agents for the tasks they are specialized in dynamically, enabling the system to handle multiple tasks simultaneously. By integrating these multi-agent based ideas into large multi-modal models, our project aims to significantly improve performance in multi-task learning and generalization to new tasks.

Related publications:

  1. Large Multimodal Agents: A Survey
    Xie, J., Chen, Z., Zhang, R., Wan, X., & Li, G. (2024). Large Multimodal Agents: A Survey. arXiv:2402.15116. https://doi.org/10.48550/arXiv.2402.15116 
  2. AgentLite: ALightweightLibraryforBuildingandAdvancing Task-Oriented LLM Agent System
    Liu, Z., Yao, W., Zhang, J., Yang, L., Liu, Z., Tan, J., Choubey, P. K., Lan, T., Wu, J., Wang, H., Heinecke, S., Xiong, C., & Savarese, S. (2024). AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System. arXiv:2402.15538. https://doi.org/10.48550/arXiv.2402.155381
  3. MuLan: Multimodal-LLM Agent for Progressive Multi-Object Diffusion
    Li, S., Wang, R., Hsieh, C.-J., Cheng, M., & Zhou, T. (2024). MuLan: Multimodal-LLM Agent for Progressive Multi-Object Diffusion. arXiv:2402.12741. https://doi.org/10.48550/arXiv.2402.12741