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Seismic data processing involves detecting earthquake signals and picking seismic phases from the diverse types of signals recorded by seismographs. During a seismic event, energy radiates from the focus (or hypocenter) as waves travel in all directions. These waves are categorized into body waves and surface waves. Understanding and accurately detecting these waves are crucial for rapid response and seismic hazard assessment.

Types of Seismic Waves

Body Waves: These waves travel through the Earth’s interior and are divided into two types:

P Waves (Primary Waves): The fastest waves, P waves are the first to be recorded on seismographs. They can travel through both solid and liquid media, causing the ground to move forward and backward.

S Waves (Secondary Waves): Following the P waves, S waves travel more slowly and cause a swinging motion that moves the ground up and down. S waves only travel through solid materials.

Surface Waves: Generated when body waves reach the Earth’s surface, surface waves spread out over the Earth’s surface. They are typically more destructive and damaging than body waves due to their larger amplitude and longer duration.

Importance of Seismic Phase Picking

Modern seismic networks continuously generate vast amounts of data. Manual analysis of this data is impractical due to the need for rapid response. Additionally, seismic data often contain significant noise and ambiguous signals, complicating interpretation. Accurate and efficient detection and phase picking are vital for reliable seismic event characterization, crucial for understanding seismic hazards and responding to potentially damaging earthquakes.

Deep Learning Approaches

Several deep learning (DL) models have been developed for seismic phase picking, including:

Generalized Phase Detector [1]

PhaseNet [2]

Earthquake Transformer [3]

These models achieve high accuracy in P-phase picking and can accurately identify the arrival of S waves, even when overlapped with the coda of [...]

Congratulations to Chengwei Wei for passing his defense. Chengwei’s thesis is titled “Syntax-aware natural language processing techniques and their applications.” His Dissertation Committee includes Jay Kuo (Chair), Antonio Ortega, and Swabha Swayamdipta (Outside Member). The Committee members were impressed by the wide range of topics conducted in Chengwei’s Ph.D. research.  Many thanks to our lab members for participating in his rehearsal and providing valuable feedback. The MCL News team invited Chengwei for a short talk on his thesis and PhD experience. Here is the summary. We thank Chengwei for his kind sharing and wish him all the best on his next journey. A high-level abstract of Chengwei’s thesis is given below:

”Syntax in language processing controls the structure of textual data, playing a crucial role in textual data understanding and generation. For example, syntax in natural language sentences governs the relationships between words, which is crucial for grasping the sentence’s overall meaning. In this thesis, we focus on two primary objectives: 1) Develop efficient methods for constructing syntactic structures. 2) Investigate the significance of syntax and integrate syntax-aware techniques into various Natural Language Processing (NLP) applications, spanning from word-level, sentence-level, document-level, and structured-data-level tasks.”

Chengwei shared his Ph.D. experience at MCL as follows :

I would first like to express my gratitude to Prof. Kuo for his guidance, patience, and unwavering support throughout this journey. His passion for research has been truly inspiring, leading me to join the lab in the summer of 2019 and driving me to pursue excellence in my academic endeavors. I am also thankful for his visionary insights into future research directions, exemplified by our collaborative effort on a survey paper on language models in the summer of 2022. This collaboration proved prescient, [...]

We are so happy to welcome a new MCL member, Dingyi Nie joining MCL this semester. Here is a quick interview with Dingyi:

1. Could you briefly introduce yourself and your research interests?

My name is Dingyi Nie. I am a current Master of Science student in Computer Science at USC. I am joining MCL as a research intern starting from April 2024. My research interests mainly include digital signal processing and machine learning, particularly real-world AI. In my spare time, I enjoy music and sports. I’m a keyboard and drum player, and I play soccer and volleyball.

2. What is your impression about MCL and USC?

I got to know Professor Jay Kuo and his MCL from my professor who teaches multimedia systems. I personally have a long-standing interest in multimedia, DSP and machine learning. I find MCL’s focus on Green Learning particularly fascinating because it seamlessly integrates these areas. I am very excited to explore its potential as an emerging tool. I love LA as a city of culture and diversity, and I feel that USC is a good reflection of it. I am excited to interact with all the creative people here.

3. What are your future expectations and plans in MCL?

Starting from the fall semester, I will be working with Yixing Wu on a project exploring Green Learning solutions for irregularly sampled time series modeling. I look forward to building connections with all the members in the lab.

Camouflage object detection (COD)is a challenging task that aims to identify targets “seamlessly” concealed within their surrounding environment, presenting a more challenging task compared to traditional object detection[1]. While under the Video camouflage object detection (VCOD), the intrinsic high variations and increased complexity in the scene poses new obstacles for the detection with videos. We proposed a green method, termed GreenCOD, that leverages gradient boosting and deep features extracted from pre-trained Deep Neural Networks (DNNs), efficiently detects the camouflage objects without back-propagation. In this quarter, based on the GreenCOD model, we further move on to explore how to deal with object detection in camouflage videos in a light-weighted, explainable way.

Inspired by the GreenCOD pipeline, our architecture integrates the EfficientNetB4 backbone in the feature extraction module for each frame. Initially, the input frames are first reshaped into a standard size of 672x672x3 and the processed through the 8-block EfficientNetB4 backbone for feature extraction under different size of reception fields. To well consider the information across different reception spatial sizes, all the features are resized and concatenated to form a rich set of features. A hierarchical architecture is implemented in decision learning module.

Then XGBoost is trained based on the initial prediction maps and temporal information. The short term temporal information is considered by extracting the motion among consecutive frames. The motion flow maps are extracted at a higher resolution, then followed by the utilization of neighborhood reconstruction. This approach ensures that each prediction location takes into account the information from a corresponding 4×4 window in the motion map. The initial result show a satisfying result on VCOD problems.

[1] Fan, Deng-Ping, et al. “Camouflaged object detection.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. [...]

Understanding and retrieving information in 3D scenes poses a significant challenge in artificial intelligence (AI) and machine learning (ML), particularly in grasping complex spatial relationships and detailed properties of objects in 3D spaces. While large foundational models such as CLIP [1] have made impressive progress in the 2D domain, their direct application to 3D scene understanding is not straightforward. Therefore, we aim to leverage existing large foundation models to understand 3D scenes and extract 3D information, avoiding building an entirely new 3D model from scratch.
The advancement of large foundation models has inspired recent studies to establish connections among images, text, and 3D data. The work in [2] extracts features from 3D objects with a 3D encoder and aligns them with features extracted by a visual encoder and a text encoder from corresponding rendered 2D images and text, enabling information retrieval of 3D objects from different modalities. However, the model mainly focuses on 3D objects and is limited in understanding complicated 3D spaces like 3D indoor scenes.
[3] Reconstructed compact 3D indoor scenes from multi-view images using DVGO and aligned them with semantic segmentation extracted from corresponding 2D multiview images using the CLIP-LSeg model. A visual reasoning pipeline is applied for reasoning tasks in 3D spaces.
Presently, we are going to develop a ML model that leverages the existing large foundation models to get a better understanding of 3D scenes with lower computational complexity.

Reference:
[1]Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. “Learning transferable visual models from natural language supervision,” In International Conference on Machine Learning, pages 8748–8763. PMLR, 2021.
[2] Hegde, Deepti, Jeya Maria Jose Valanarasu, and Vishal [...]