Strategies in TOEFL Reading Comprehension for Chinese Students

With China’s development, an increasing number of students choose to further their studies abroad. However, many Chinese students do not know how to prepare for the TOEFL test by themselves.Thus, there are three sections in my literature review, which are the introduction of TOEFL, TOEFL Reading Comprehension Strategies for Native Chinese Speakers, and TOEFL test preparation for Chinese students.After that, this project includes original materials so that beginning level students can get a preliminary understanding of the TOEFL reading comprehension tests. The material is deliberately based on Chinese culture, which makes it relevant and easier for beginners to understand. This project can improve students’ motivation to study for the TOEFL. There are four units in this project, and three of them introduce types of TOEFL reading questions. Each of the units contains one reading passage and several questions. The last unit is grammar-based and it presents the adjective clause system. The materials can be used by students who want to improve their English proficiency abilities while increasing their motivation for learning English. Furthermore, students can increase their confidence by being better prepared for the TOEFL

A Virtual 4D CT Scanner

4D CT scan is widely used in medical imaging. Images are acquired through phases. In this case, we can track the motion of organs such as heart. However, it also introduces motion artifacts. A lot of research focuses on remove these artifacts. It is difficult to acquire artifact data by a real CT scanner. In this project, we implement a virtual CT machine to simulate the real 4D CT scan. we also conduct experi- ments to check its clinical reality with respect to respiratory and heart motion parameters

Statistics-dependent quantum co-walking of two particles in one-dimensional lattices with nearest-neighbor interactions

We investigate continuous-time quantum walks of two indistinguishable particles [bosons, fermions or hard-core bosons (HCBs)] in one-dimensional lattices with nearest-neighbor interactions. The results for two HCBs are well consistent with the recent experimental observation of two-magnon dynamics [Nature 502, 76 (2013)]. The two interacting particles can undergo independent- and/or co-walking depending on both quantum statistics and interaction strength. Two strongly interacting particles may form a bound state and then co-walk like a single composite particle with statistics-dependent walk speed. Analytical solutions for the scattering and bound states, which appear in the two-particle quantum walks, are obtained by solving the eigenvalue problem in the two-particle Hilbert space. In the context of degenerate perturbation theory, an effective single-particle model for the quantum co-walking is analytically derived and the walk seep of bosons is found to be exactly three times of the ones of fermions/HCBs. Our result paves the way for experimentally exploring quantum statistics via two-particle quantum walks.Comment: 9 pages, 5 figures, an extension with more new results for our unpublished arXiv:1402.334

RD-DPP: Rate-Distortion Theory Meets Determinantal Point Process to Diversify Learning Data Samples

In some practical learning tasks, such as traffic video analysis, the number of available training samples is restricted by different factors, such as limited communication bandwidth and computation power; therefore, it is imperative to select diverse data samples that contribute the most to the quality of the learning system. One popular approach to selecting diverse samples is Determinantal Point Process (DPP). However, it suffers from a few known drawbacks, such as restriction of the number of samples to the rank of the similarity matrix, and not being customizable for specific learning tasks (e.g., multi-level classification tasks). In this paper, we propose a new way of measuring task-oriented diversity based on the Rate-Distortion (RD) theory, appropriate for multi-level classification. To this end, we establish a fundamental relationship between DPP and RD theory, which led to designing RD-DPP, an RD-based value function to evaluate the diversity gain of data samples. We also observe that the upper bound of the diversity of data selected by DPP has a universal trend of phase transition that quickly approaches its maximum point, then slowly converges to its final limits, meaning that DPP is beneficial only at the beginning of sample accumulation. We use this fact to design a bi-modal approach for sequential data selection

Learning on Bandwidth Constrained Multi-Source Data with MIMO-inspired DPP MAP Inference

This paper proposes a distributed version of Determinant Point Processing (DPP) inference to enhance multi-source data diversification under limited communication bandwidth. DPP is a popular probabilistic approach that improves data diversity by enforcing the repulsion of elements in the selected subsets. The well-studied Maximum A Posteriori (MAP) inference in DPP aims to identify the subset with the highest diversity quantified by DPP. However, this approach is limited by the presumption that all data samples are available at one point, which hinders its applicability to real-world applications such as traffic datasets where data samples are distributed across sources and communication between them is band-limited. Inspired by the techniques used in Multiple-Input Multiple-Output (MIMO) communication systems, we propose a strategy for performing MAP inference among distributed sources. Specifically, we show that a lower bound of the diversity-maximized distributed sample selection problem can be treated as a power allocation problem in MIMO systems. A determinant-preserved sparse representation of selected samples is used to perform sample precoding in local sources to be processed by DPP. Our method does not require raw data exchange among sources, but rather a band-limited feedback channel to send lightweight diversity measures, analogous to the CSI message in MIMO systems, from the center to data sources. The experiments show that our scalable approach can outperform baseline methods, including random selection, uninformed individual DPP with no feedback, and DPP with SVD-based feedback, in both i.i.d and non-i.i.d setups. Specifically, it achieves 1 to 6 log-difference diversity gain in the latent representation of CIFAR-10, CIFAR-100, StanfordCars, and GTSRB datasets

The causal effects of global supply chain disruptions on macroeconomic outcomes: evidence and theory

We study the causal effects and policy implications of global supply chain disruptions. We construct a new index of supply chain disruptions from the mandatory automatic identification system data of container ships, developing a novel spatial clustering algorithm that determines real-time congestion from the position, speed, and heading of container ships in major ports around the globe. We develop a model with search frictions between producers and retailers that links spare productive capacity with congestion in the goods market and the responses of output and prices to supply chain shocks. The co-movements of output, prices, and spare capacity yield unique identifying restrictions for supply chain disturbances that allow us to study the causal effects of such disruptions. We document how supply chain shocks drove inflation during 2021 but that, in 2022, traditional demand and supply shocks also played an important role in explaining inflation. Finally, we show how monetary policy is more effective in taming inflation after a global supply chain shock than in regular circumstances

MTS-LOF: Medical Time-Series Representation Learning via Occlusion-Invariant Features

Medical time series data are indispensable in healthcare, providing critical insights for disease diagnosis, treatment planning, and patient management. The exponential growth in data complexity, driven by advanced sensor technologies, has presented challenges related to data labeling. Self-supervised learning (SSL) has emerged as a transformative approach to address these challenges, eliminating the need for extensive human annotation. In this study, we introduce a novel framework for Medical Time Series Representation Learning, known as MTS-LOF. MTS-LOF leverages the strengths of contrastive learning and Masked Autoencoder (MAE) methods, offering a unique approach to representation learning for medical time series data. By combining these techniques, MTS-LOF enhances the potential of healthcare applications by providing more sophisticated, context-rich representations. Additionally, MTS-LOF employs a multi-masking strategy to facilitate occlusion-invariant feature learning. This approach allows the model to create multiple views of the data by masking portions of it. By minimizing the discrepancy between the representations of these masked patches and the fully visible patches, MTS-LOF learns to capture rich contextual information within medical time series datasets. The results of experiments conducted on diverse medical time series datasets demonstrate the superiority of MTS-LOF over other methods. These findings hold promise for significantly enhancing healthcare applications by improving representation learning. Furthermore, our work delves into the integration of joint-embedding SSL and MAE techniques, shedding light on the intricate interplay between temporal and structural dependencies in healthcare data. This understanding is crucial, as it allows us to grasp the complexities of healthcare data analysis

Knowledge Distillation Under Ideal Joint Classifier Assumption

Knowledge distillation is a powerful technique to compress large neural networks into smaller, more efficient networks. Softmax regression representation learning is a popular approach that uses a pre-trained teacher network to guide the learning of a smaller student network. While several studies explored the effectiveness of softmax regression representation learning, the underlying mechanism that provides knowledge transfer is not well understood. This paper presents Ideal Joint Classifier Knowledge Distillation (IJCKD), a unified framework that provides a clear and comprehensive understanding of the existing knowledge distillation methods and a theoretical foundation for future research. Using mathematical techniques derived from a theory of domain adaptation, we provide a detailed analysis of the student network's error bound as a function of the teacher. Our framework enables efficient knowledge transfer between teacher and student networks and can be applied to various applications