Stan Z. Li

Bio: Stan Z. Li is an academic researcher from Westlake University. The author has contributed to research in topics: Facial recognition system & Face detection. The author has an hindex of 97, co-authored 532 publications receiving 41793 citations. Previous affiliations of Stan Z. Li include Microsoft & Macau University of Science and Technology.

LookHops: light multi-order convolution and pooling for graph classification.

Abstract: Convolution and pooling are the key operations to learn hierarchical representation for graph classification, where more expressive $k$-order($k>1$) method requires more computation cost, limiting the further applications. In this paper, we investigate the strategy of selecting $k$ via neighborhood information gain and propose light $k$-order convolution and pooling requiring fewer parameters while improving the performance. Comprehensive and fair experiments through six graph classification benchmarks show: 1) the performance improvement is consistent to the $k$-order information gain. 2) the proposed convolution requires fewer parameters while providing competitive results. 3) the proposed pooling outperforms SOTA algorithms in terms of efficiency and performance.

AutoMix: Unveiling the Power of Mixup.

Abstract: Mixup-based data augmentation has achieved great success as regularizer for deep neural networks. However, existing mixup methods require explicitly designed mixup policies. In this paper, we present a flexible, general Automatic Mixup (AutoMix) framework which utilizes discriminative features to learn a sample mixing policy adaptively. We regard mixup as a pretext task and split it into two sub-problems: mixed samples generation and mixup classification. To this end, we design a lightweight mix block to generate synthetic samples based on feature maps and mix labels. Since the two sub-problems are in the nature of Expectation-Maximization (EM), we also propose a momentum training pipeline to optimize the mixup process and mixup classification process alternatively in an end-to-end fashion. Extensive experiments on six popular classification benchmarks show that AutoMix consistently outperforms other leading mixup methods and improves generalization abilities to downstream tasks. We hope AutoMix will motivate the community to rethink the role of mixup in representation learning. The code will be released soon.

[Malignant pheochromocytoma: diagnosis and treatment].

Abstract: OBJECTIVE To review the experience with the diagnosis and treatment of malignant pheochromocytoma. METHODS Between 1986 and 1996, 7 patients with malignant pheochromocytoma were analysed. RESULTS Compared with benign pheochromocytoma of adrenal gland, the malignant one usually exceeded 7.0 cm in diameter, irregular in shape, invading the surrounding tissue and the normal structure of the effected adrenal gland disappeared. Bleeding and necrotic area were seen in tumor mass, even seemed as a cystic lesion with thick wall. In some patients, blood catecholamine and urine VMA significantly elevated without hypertension. The separating phenomenon and progressive weight loss, accelerated ESR were characteristics of malignant pheochromocytoma. CONCLUSIONS In some patients with malignant pheochromocytoma, histological findings are not consistent with their biological behaviors. Follow-up studies is mandatory in malignant pheochromocytoma as well as in benign one. Recurrence and/or metastasis after operation often occur in patients with malignant pheochromocytoma. In addition to early diagnosis and surgery, radiotherapy of tumor bed and systematic chemotherapy should be emphasized.

Correction of cardiac defects through a right minithoracotomy in children

Abstract: OBJECTIVE: To review the experience of correction of congenital cardiac defects through a right minithoracotomy METHOD: 319 patients underwent correction of congenital heart malformations through right lateral thoracotomy under cardiopulmonary bypass The average age was 344 +/- 159 years (range, 5 months-8 years) The average body weight was 1366 - 398 kg (range, 6 - 26 kg) Cardiac defects repaired included atrial septal defect in 87 patients (1 patient associated with left superior vena cava (LSVC), 6 pulmonary stenosis, 5 partial anomalous pulmonary venous connection), ventricular septal defect in 200 (7 patients with coexisting patent ductus arteriosus, 7 mitral insufficiency, 3 LSVC, 11 right ventricular outflow tract obstruction), Fallot's Tetralogy in 19 (3 patients associated with LSVC, 1 single coronary malformation), partial endocardial cushion defect in 2 and other defects in 11 The mean cardiopulmonary bypass time was 5607 +/- 2490 min (range, 20 - 176 min) and the mean aortic crossclamping time was 3297 +/- 2038 min (range, 6 - 140 min) The average mechanical ventilation time after operation was 1875 +/- 2457 hr (range, 2 - 14072 hr), and the mean postoperative hospital stay was 708 +/- 069 days (range, 7 - 17 days) RESULT: No operative mortality and severe postoperative complications were noted CONCLUSION: The right lateral thoracotomy is a safe and effective alternative to a median sternotomy for correction of cardiac defects Advantages of this approach include less injury, maintaining the continuity and the integrity of the bony thorax, and preventing postoperative pigeon breast The cosmetic result is superior to that of median sternotomy or bilateral submammary incision

A Survey on Protein Representation Learning: Retrospect and Prospect

Abstract: Proteins are fundamental biological entities that play a key role in life activities. The amino acid sequences of proteins can be folded into stable 3D structures in the real physicochemical world, forming a special kind of sequence-structure data. With the development of Artificial Intelligence (AI) techniques, Protein Representation Learning (PRL) has recently emerged as a promising research topic for extracting informative knowledge from massive protein sequences or structures. To pave the way for AI researchers with little bioinformatics background, we present a timely and comprehensive review of PRL formulations and existing PRL methods from the perspective of model architectures, pretext tasks, and downstream applications. We first briefly introduce the motivations for protein representation learning and formulate it in a general and unified framework. Next, we divide existing PRL methods into three main categories: sequence-based, structure-based, and sequence-structure co-modeling. Finally, we discuss some technical challenges and potential directions for improving protein representation learning. The latest advances in PRL methods are summarized in a GitHub repository https://github.com/LirongWu/awesome-protein-representation-learning.

You Only Look Once: Unified, Real-Time Object Detection

Abstract: We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Robust Face Recognition via Sparse Representation

Abstract: We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.

Integrating single-cell transcriptomic data across different conditions, technologies, and species.

Abstract: Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.