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Professor C.-C. Jay Kuo, Director of MCL, visited Singapore from June 17-23 and attended two events in his Asia trip.For the first event, Professor Kuo gave a seminar titled “Demystify Artificial Intelligence and Technology Outlook” at the Nanyang Technological University on June 20 (Wednesday). The abstract of his talk is given below.“The term “Artificial Intelligence (AI)” was coined in 1956. Although the field evolved slowly in the first 55 years, we have witnessed rapid advances in AI in the last decade (e.g., the ChatGPT service). Questions are raised about AI’s role in human society and civilization, e.g., whether AI will replace human intelligence (HI), how HI and AI complement each other, etc. I will shed light on them. Then, I will comment on the limitations of today’s deep-learning-based (DL) AI models. DL-based models are neither interpretable nor sustainable. An alternative methodology is desired. To this end, I have investigated a new statistically based AI/ML framework called “Green Learning” (GL). GL significantly reduces the model size and complexity of DL models while yielding competitive performance and allowing mathematical transparency. GL adopts the feedforward one-pass training pipeline, so all intermediate results are explainable. Finally, I will address the AI role in human society and issues with today’s chatbots, such as bias and fairness.”Professor Kuo’s talk was hosted by Professor Woon Seng Gan at NTU. He also had a reunion with Professor Jiaying Liu of Peking University. Professor Liu was a visiting PhD student at MCL from August 2007 to August 2008.For the 2nd activity, Professor Kuo delivered a keynote speech at the Workshop titled “Emerging Trends and Innovations in Machine Learning and AI” on June 21 (Friday) organized by APSIPA Singapore Chapter and quite a few [...]

Nuclei segmentation is a fundamental task required to analyze the underlying nuclei structure of an organ of interest. Cancer starts from the cells, and understanding the nuclei shapes, sizes and distribution can provide cues on whether or not a patient has cancer. Further analysis can also help in cancer grading and prognosis. However, studying whole slide images of biopsied tissues requires a large amount of time and effort. 

Such monotonous and laborious tasks can be simplified by using AI and ML, and can perhaps improve the accuracy of the detections as well. Some challenges in the nuclei segmentation task include inherent staining variations in the WSI, a wide variety of shapes and sizes in nuclei, and irregular boundaries which make it difficult to track the actual contours. Most of the current research in this area involves deep learning based architectures like the U-Net, R-CNN, and even Vision Transformer. These methods require a large number of training samples and high complexity to achieve generalization among the variations inherent in nuclei from different organs.

We propose to use a light-weight, interpretable, and simple Green Learning based approach to perform Nuclei Segmentation. Prior work on highly effective Unsupervised Nuclei Instance Segmentation (HUNIS) [1] forms the first stage of our current approach. To further improve HUNIS results, we now focus on the regions where HUNIS requires the help of labels. We divide our current task into two stages: (i) to identify those areas where we need help and (ii) to correct those areas towards their actual class. With the help of Saab Transform, our main task now is to perform feature engineering to identify the ideal features to implement the above two stages. 

References:

[1] V. Magoulianitis, Y. Yang, and C.-C. J. [...]

Surface reconstruction from point cloud scans plays a pivotal role in 3D vision and graphics, with diverse applications in areas such as AR/VR games, heritage preservation, and indoor/outdoor scene reconstruction. This task is inherently challenging due to the ill-posed nature of reconstructing continuous surfaces from discrete points. Furthermore, real-world point cloud scans introduce several obstacles, such as varying densities, sensor noise, and missing parts. These properties make the problem a long-standing one, continually driving researchers to seek more effective solutions.

Early research focused on constructing watertight objects using combinatorial methods [1][2], which inferred the connectivity between points directly. The mainstream of surface reconstruction adopts an implicit surface approach [3][4], where the surface is represented as an unknown continuous function solved by associated partial differential equations (PDEs). Although these methods offer good quality, they are constrained to predicting watertight objects and cannot handle highly distorted LiDAR scenes. Recently, indoor/outdoor scene reconstruction has gained more attention, with deep learning (DL) models [5] demonstrating success in solving this problem based on a supervised learning framework.

Despite their high reconstruction quality, DL models face challenges in generalizability and complexity. In scenarios such as point cloud compression, quality assessment, and dynamic point cloud processing, there is a growing need for low-complexity, low-latency surface reconstruction methods. However, existing DL-based methods often sacrifice simplicity for high reconstruction quality, leaving a gap for low-complexity solutions. We aim to develop an unsupervised few-shot learning method to achieve reconstruction for scenes with low complexity.

Building on our previous unsupervised framework (GPSR), we propose an enhanced version to handle non-watertight indoor/outdoor scenes, named Green Point Cloud Surface Reconstruction++ (GPSR++). The main idea involves building an unsigned distance field (UDF) through approximated heat diffusion and optimizing the surface using [...]

Three MCL members attended the Viterbi PhD hooding ceremony on Wednesday, May 8, 2024, in the Bovard Auditorium. They were Zhanxuan Mei, Chengwei Wei and Wei Wang. Congratulations to them for their accomplishments in completing their PhD program at USC!

Zhanxuan Mei received his Bachelor’s degree in Electrical Engineering from Beijing Institute of Technology, China, in June 2018. He joined the Media Communication Lab in the summer of 2020. His research interests include image processing and video processing.

Chengwei Wei received his bachelor’s degree at Central South University, China in Jun 2018. He joined the Media Communications Lab in the summer of 2019. His research interests include signal processing, natural language processing, and machine learning. His thesis is titled “Syntax-aware Natural Language Processing Techniques and Their Applications”. 

Wei Wang received her bachelors in Applied Physics from Northeastern University (CN), and her MS degree in Materials Engineering from the University of Southern California in 2014 and 2016, respectively.. Her research interests include deep learning and image processing.

Congratulations to them all! Let us wish them all the best in the future!

Syntactic parsing is a natural language processing technique used to analyze the grammatical structure of a sentence. There are typically two syntactic parsings, dependency parsing and constituency parsing. Fig. 1 shows the parse trees corresponding to dependency parsing and constituency parsing, respectively. Dependency parsing identifies the dependency relationships between the words in a sentence and creates a directed graph representing these dependency relationships. In dependency parsing, each word in the sentence is represented as a node in the graph, and the dependency relationships between the words are represented as edges. The edges are labeled with the type of dependency relationship between the words, such as subject, object, or modifier. The resulting graph is called a dependency tree or a dependency graph. Constituency parsing is the process of analyzing a sentence to identify its syntactic structure and hierarchical organization based on the grammatical rules of a language. In constituency parsing, a sentence is divided into a hierarchy of phrases, each of which has a specific grammatical structure and serves a particular function within the sentence. These phrases are called constituents, and they can include nouns, verbs, adjectives, prepositions, and other parts of speech.

In this project, we aim to propose a simple but effective constituency parsing construction method. The constituency parse tree is first converted to the binary tree where an example is shown in Fig. 2. The core idea behind the method is that once we know the interval height between adjacent words, the binarized constituency parse tree can be constructed [1]. Instead of directly predicting the height, necessitates a complex model for concise prediction, presently, we have trained a binary classifier to compare the height of the intervals pairwisely. Then the height of an [...]