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    MCL Genealogical Ancestry Series – Abraham Gotthelf Kastner

MCL Genealogical Ancestry Series – Abraham Gotthelf Kastner

Abraham Gotthelf Kastner (27 September 1719 – 20 June 1800) was a German mathematician and epigrammatist.

Kästner was the son of law professor Abraham Kästner. Starting from 1731 (aged 12), he studied law, philosophy, physics, mathematics and metaphysics in Leipzig. He was appointed a Notary in 1733, when he was 14. He gained his habilitation (PhD degree in Europe) from the University of Leipzig in 1739 at age 20.  In his early careers, he lectured mathematics, philosophy, logic and law in University of Leipzig after his habilitation. He soon became an associate professor in 1746, at age 27. In 1751 he was elected a member of the Royal Swedish Academy of Sciences, which have famous fellows such as Isaac Newton, Charles Darwin, Alan Turing, Stephen Hawking, etc. In 1756, at his 37, he took up a position as full professor of natural philosophy and geometry at the University of Göttingen. His notable doctoral students include Johann Pfaff, who is the doctoral advisor of Carl Friedrich Gauss). Kästner died in 1800 in Göttingen, at age 81.

Kästner has numerous mathematical writings, including Anfangsgründe der Mathematik (“Foundations of Mathematics”) (Göttingen 1758-69, 4 volumes; 6th edition 1800) and Geschichte der Mathematik (“History of Mathematics”) (Göttingen 1796-1800, 4 volumes). Geschichte der Mathematik is considered an astute work, but lacks a comprehensive overview of all subsections of mathematics. Besides his contribution in math, he is more well-known for his poems, which were notable for their biting humour and sharp irony on different contemporary personalities.

As his descendants, we know Kästner as a talented scholar and devoted researcher in mathematics, philosophy, logic and law. His rich contributions shall be remembered the same as himself.

By |April 16th, 2023|News|Comments Off on MCL Genealogical Ancestry Series – Abraham Gotthelf Kastner|
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    Professor Kuo Delivered Keynote Speech in A Workshop Held by IEEE Hong Kong Section

Professor Kuo Delivered Keynote Speech in A Workshop Held by IEEE Hong Kong Section

Professor C.-C. Jay Kuo, Director of MCL, was invited by the IEEE Hong Kong Chapter to deliver a keynote speech titled “On the 2nd AI wave: toward interpretable, reliable, and sustainable AI” in the 2-Day IEEE Workshop on Deep Learning held at the Hong Kong Science Park, Pak Shek Kok, New Territories, Hong Kong, on April 3, 2023. In his keynote, Professor Kuo pointed out some concerns with the deep learning methodology commonly employed by AI researchers and presented a new green learning paradigm as an alternative that can achieve comparable performance at a lower computational cost.
During his visit to Hong Kong from April 2-7, Professor Kuo visited several universities and gave the following talks:

“Green learning models for point cloud analysis,” Chinese University of Hong Kong.
“Green learning models for point cloud analysis,” Hong Kong University of Science and Technology.
“A lightweight blind image quality assessment (BIQA) method,” Hong Kong Polytechnic University.
“A lightweight blind image quality assessment (BIQA) method,” City University of Hong Kong.

He used “point cloud analysis” and “blind image quality assessment” as examples, explained how to use the green learning methodology to solve them, and compared the pros and cons of deep learning and green learning.
Furthermore, Professor Kuo gave a special seminar on “How to conduct high-quality research and manage large research groups” to faculty members at the Caritas Institute of Higher Education.

By |April 9th, 2023|News|Comments Off on Professor Kuo Delivered Keynote Speech in A Workshop Held by IEEE Hong Kong Section|
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Congratulations to Pranav Kadam for Passing His Defense

Congratulations to Pranav Kadam for passing his defense on Mar. 22. Pranav’s thesis is titled “Green Learning for 3D Point Cloud Data Processing”. His Dissertation Committee includes Jay Kuo (Chair), Antonio Ortega, and Aiichiro Nakano (Outside Member). Here we invite Pranav to share about his PhD thesis and his PhD experience.

 

Thesis Abstract: 
3D Point Cloud processing and analysis has attracted a lot of attention in present times due to the numerous applications such as in autonomous driving, computer graphics, and robotics. In this dissertation, we focus on the problems of point cloud registration, pose estimation, rotation invariant classification, odometry and scene flow estimation. These tasks are important in the realization of a 3D vision system. Rigid registration aims at finding a 3D transformation consisting of rotation and translation that optimally aligns two point clouds. The next two tasks focus on object-level analysis. For pose estimation, we predict the 6-DOF pose of an object with respect to a chosen frame of reference. Rotation invariant classification aims at classifying 3D objects which are arbitrarily rotated. The latter two problems are for outdoor environments. In odometry, we want to estimate the incremental motion of an object using the point cloud scans captured by it at every instance. While the scene flow estimation task aims at determining the point-wise flow between two consecutive point clouds.
3D perception using point clouds is dominated by deep learning methods nowadays. However, large scale learning on point clouds with deep learning techniques has several issues which are often overlooked. This research is based on the green learning (GL) paradigm and focuses on interpretability, smaller training times and smaller model size. Using GL, we separate the feature learning process from the decision. Features are [...]

By |April 6th, 2023|News|Comments Off on Congratulations to Pranav Kadam for Passing His Defense|
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MCL Research on Point-Cloud-based 3D Scene Flow Estimation

3D scene flow aims at finding the point-wise 3D displacement between consecutive point cloud scans. It finds applications in areas such as dynamic scene segmentation and may also guide inter-prediction in compression of dynamically acquired point clouds. We propose a green and interpretable 3D scene flow estimation method for the autonomous driving scenario and name it “PointFlowHop” [1]. We decompose our solution into vehicle ego-motion and object motion components.
The vehicle ego-motion is first compensated using the GreenPCO method which was recently proposed for the task of point cloud odometry estimation. Then, we divide the scene points into two classes – static and moving. The static points do not have any motion and can be assigned only the ego-motion component. The motion of the moving points is analyzed later. For classification, we use a lightweight XGBoost classifier with a 5-dimensional shape and motion feature as the input. Later, moving points are grouped into moving objects using DBSCAN clustering algorithm. Furthermore, the moving objects from the two point clouds are associated using the nearest centroids algorithm. An additional refinement step ensures reclassification of previously misclassified moving points. A rigid flow model is established for each object. Finally, the flow in local regions is refined assuming local scene rigidity.
PointFlowHop method adopts the green learning (GL) paradigm. The task-agnostic nature of the feature learning process in GL enables scene flow estimation through seamless modification and extension of prior related GL methods like R-PointHop and GreenPCO. Furthermore, a large number of operations in PointFlowHop are not performed during training. The ego-motion and object-level motion is optimized in inference only. Similarly, the moving points are grouped into objects only during inference. This makes the training process much faster [...]

By |March 28th, 2023|News, Research|Comments Off on MCL Research on Point-Cloud-based 3D Scene Flow Estimation|
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    Toward Interpretable, Reliable, and Sustainable AI – the 2nd AI Wave

Toward Interpretable, Reliable, and Sustainable AI – the 2nd AI Wave

Rapid advances in artificial intelligence (AI) in the last decade have been primarily attributed to the applications of deep learning (DL) technologies. DL technologies have been widely applied to speech, audio, image, video, computer graphics, 3D models, and chatbots (e.g., ChatGPTs). These advances are viewed as the first AI wave. There are concerns with the first AI wave. DL solutions are a black box (i.e., not interpretable) and vulnerable to adversarial attacks (i.e., unreliable). Besides, the high carbon footprint yielded by large DL networks is a threat to our environment (i.e., not sustainable).

Many researchers are looking for an alternative solution that is interpretable, reliable, and sustainable. This is expected to be the second AI wave. To this end, MCL members have conducted research on green learning (GL) since 2015. Since GL was inspired by DL, the two share some similarities. As time goes by, the two become more and more different. Low carbon footprints, small model sizes, low computational complexity, and mathematical transparency characterize GL. It offers energy-effective solutions in cloud centers and mobile/edge devices. It has three main modules: 1) unsupervised representation learning, 2) supervised feature learning, and 3) decision learning. GL has been successfully applied to a few applications.

The fundamental ideas of GL solution, its demonstrated examples, and its technical outlook were detailed in a recent overview paper by Kuo and Madni, “Green learning: Introduction, examples, and outlook.” Journal of Visual Communication and Image Representation (2022): 103685. We expect to see more GL solutions and applications emerging. All MCL members will be fully devoted to this field in the next decade.

By |March 19th, 2023|News|Comments Off on Toward Interpretable, Reliable, and Sustainable AI – the 2nd AI Wave|
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MCL Genealogical Ancestry Series: Johann Friedrich Pfaff

Zhiruo Zhou studied Johann Friedrich Pfaff and shared her study with MCL members in the pre-seminar sharing on Mar 6th, 2023. Johann Friedrich Pfaff was born 22 December 1765 in Stuttgart, Württemberg (now Germany), and died 21 April 1825 in Halle, Saxony (now Germany). He was an influential German mathematician and well known for his work on systems of partial differential equations.

Johann Friedrich was the second son in the family and had six brothers. Although he was probably the one that gained the most fame among his siblings, his brothers were also talented in various fields. His youngest brother Johann Wilhelm Pfaff was a mathematician, and his second youngest brother Christoph Heinrich Pfaff had experiences working on chemistry, medicine, pharmacy and electricity on animals.

Johann Friedrich was born in a family with the tradition of working for the government and therefore took the school named Hohe Karlsschule to receive training for government officials’ sons when he was nine years old. He spent around eleven years there and left when he had completed studies in law which was supposed to be a fitting subject for a civil servant. Driven by the interest in mathematics and encouragement from academy, Johann Friedrich decided to move toward scientific topics and studied mathematics and physics at the University of Göttingen for two years. Then he moved to Berlin to study astronomy and wrote his first paper on astronomy there.

In 1788, Johann Friedrich accepted the election as a professor of mathematics at the University of Helmstedt and stay there until 1810. He spent a lot of efforts on teaching and increasing the number of students of mathematics. He met the student Gauss there and had a good relationship with Gauss. However, [...]

By |March 12th, 2023|News|Comments Off on MCL Genealogical Ancestry Series: Johann Friedrich Pfaff|
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    MCL Research on Mask-Guided Image Synthesis Presented at AAAI-23

MCL Research on Mask-Guided Image Synthesis Presented at AAAI-23

Dr. Rouhsedaghat, a MCL alumna graduated last Summer, recently presented a work[1] on image synthesis related to her PhD thesis in AAAI-23. Here is the presentation summary from Dr. Rouhsedaghat:

We offer a method for one-shot mask-guided image synthesis that allows controlling manipulations of a single image by inverting a quasi-robust classifier equipped with strong regularizers. Our proposed method, entitled MAGIC , leverages structured gradients from a pre-trained quasi-robust classifier to better preserve the input semantics while preserving its classification accuracy, thereby guaranteeing credibility in the synthesis. Unlike current methods that use complex primitives to supervise the process or use attention maps as a weak supervisory signal, MAGIC aggregates gradients over the input, driven by a guide binary mask that enforces a strong, spatial prior. MAGIC implements a series of manipulations with a single framework achieving shape and location control, intense non-rigid shape deformations, and copy/move operations in the presence of repeating objects and gives users firm control over the synthesis by requiring to simply specify binary guide masks. Our study and findings are supported by various qualitative comparisons with the state-of-the-art on the same images sampled from ImageNet and quantitative analysis using machine perception along with a user survey of 100+ participants that endorse our synthesis quality.

[1]Rouhsedaghat, Mozhdeh, et al. “MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-Robust Classifier.” arXiv preprint arXiv:2209.11549 (2022).

By |March 5th, 2023|News, Research|Comments Off on MCL Research on Mask-Guided Image Synthesis Presented at AAAI-23|
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MCL Research on Advanced Knowledge Graph Embedding

Knowledge graph embedding has been intensively studied in recent years. TransE, RotatE, and PairRE are the most representative and effective among distance-based KG embeddings that make use of simple translation, rotation, and scaling operations respectively to represent relations between entities. These models use simple geometric transformations yet achieve relatively good performance in link prediction. However, it is unclear how to leverage the strength of these individual operations and produce a better embedding model. It turns out that we can treat each operation as a basic building block, from which we can build the most effective KG embedding scoring functions. The composition of geometric operators is a well-established tool for image manipulation. It is natural to apply the same strategy to combine translation, rotation, and scaling operators in the 3D space. Our previous work CompoundE has demonstrated that the composition of affine operations can be an effective strategy in designing KG embedding. We are interested to know whether we can achieve even better results by cascading 3D operators.

We propose to extend our previous work by including more affine operations other than only translation, rotation, and scaling in our framework, and moving from 2D transformations to 3D transformations. In addition, we propose to enhance our CompoundE by addressing two critical issues. First, although Compounding operations lead to plenty of model variants, it is still unclear how to systematically search for a scoring function that performs the best for an individual dataset. We propose to use beam search to gradually build more complex scoring functions from simple but effective variants. To speed up the model tuning during the search, we use the pre-trained entity embedding from base models as initialization. Second, although ensemble learning is a popular [...]

By |February 26th, 2023|News|Comments Off on MCL Research on Advanced Knowledge Graph Embedding|
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Celebration of SIPI 50th Anniversary

The SIPI 50th Anniversary Celebration was held on the USC Campus on Saturday, February 18th. The USC Signal and Image Processing Institute (SIPI) was one of the first research organizations in the world dedicated to image processing. Image processing work began at USC in 1962, and the Institute was founded in 1972 by William K. Pratt and Harry C. Andrews with support from the Defense Advanced Research Projects Agency (DARPA). SIPI has graduated around 500 PhDs over the last 50 years. As part of the SIPI 50th Anniversary celebration program, the inaugural SIPI Distinguished Alumni Awards have been established to honor USC SIPI graduates who have made professional and technical contributions that bring extraordinary distinction to themselves, the Institute, and the University.

Among the eight awardees, there are three MCL alumnus Ioannis Katsavounidis, Shan Liu and Chao Yang. Dr. Ioannis Katsavounidis won the senior academia award for outstanding contributions to objective video quality metrics and perceptual optimization of streaming videos. Dr. Shan Liu won the mid-career industry award for contributions to and leadership in multimedia technologies, standardization, and applications. Dr. Chao Yang won the junior industry award for contributions to computer vision, machine learning, and artificial intelligence in research publications and in commercial products and services. Congratulations to them!

Many MCL alumnus and current lab members joined the celebration and re-union. We had a great time together refreshing our memories on the past time and bridging different generations which composed of an important part of the MCL family. Happy 50th anniversary to SIPI and look forward to the brighter future for everyone!

 

Some information credit to:

SIPI 50th Anniversary