Essays on Information Economics

This dissertation consists of three independent essays that examine how to improveinformation transmission and how to incentivize learning. In Chapter 2, I study the role of a recommender’s career concerns in his relation-ship with a consumer when the recommender has a private type in expertise. An informed type has valuable expertise for the consumer, whereas an uninformed type does not. The uninformed type cannot mimic the informed type, suggesting that the informed type can build a reputation for competence. However, I find that the relationship breaks down completely if the recommender is sufficiently patient. In Chapter 3, which is co-authored with Florian Ederer, we embed probabilisticlie detection in a standard model of Bayesian persuasion. We show that the Sender lies more when the lie detection probability increases. Moreover, the Sender’s and the Receiver’s equilibrium payoffs are unaffected by a weak lie detection technology because the Sender compensates by lying more. In Chapter 4, I analyze optimal contracting for experimentation when the agent who experiments and the principal who provides incentives agree to disagree over the quality of the project. If efforts are contractible, the principal prefers to reward good outcomes (efforts) exclusively for a more (less) optimistic agent. Moreover, longer experimentation is sustained with non-common prior. If efforts are not contractible, the optimal duration of experimentation is increasing in the agent’s confidence

Essays on Information Economics

This dissertation consists of three independent essays that examine how to improveinformation transmission and how to incentivize learning. In Chapter 2, I study the role of a recommender’s career concerns in his relation-ship with a consumer when the recommender has a private type in expertise. An informed type has valuable expertise for the consumer, whereas an uninformed type does not. The uninformed type cannot mimic the informed type, suggesting that the informed type can build a reputation for competence. However, I find that the relationship breaks down completely if the recommender is sufficiently patient. In Chapter 3, which is co-authored with Florian Ederer, we embed probabilisticlie detection in a standard model of Bayesian persuasion. We show that the Sender lies more when the lie detection probability increases. Moreover, the Sender’s and the Receiver’s equilibrium payoffs are unaffected by a weak lie detection technology because the Sender compensates by lying more. In Chapter 4, I analyze optimal contracting for experimentation when the agent who experiments and the principal who provides incentives agree to disagree over the quality of the project. If efforts are contractible, the principal prefers to reward good outcomes (efforts) exclusively for a more (less) optimistic agent. Moreover, longer experimentation is sustained with non-common prior. If efforts are not contractible, the optimal duration of experimentation is increasing in the agent’s confidence

Nasal Bacterial Microbiome: Probing a Healthy Porcine Family

Upper respiratory tract (URT) infection caused the leading and devastating diseases in pigs. It was believed that the normal microbiome of URT plays a vital role in health and disease development. As the entry point of the URT, little knowledge of bacterial microbiome in porcine nasal was known. A cultivation-independent approach directly to 16s ribosomal RNA genes enabled us to reveal the nasal bacterial community, structure and diversity. Here, we found that an unprecedented 207 phylotypes were characterized from 933 qualified clones, indicating the variable, species richness but particularly dominant bacterial microbiome. The dominant species were from genus Comamonas and Acinetobacter, which constitute core normal bacterial microbiome in porcine nasal. Moreover, a set of swine specific pathogens and zoonotic agents were detected in the swine nasal microbiome. Collectively, we provided a snapshot of our current knowledge of the community structure of porcine nasal bacterial ecosystem in a healthy family that will further enhance our view to understand URT infection and public health

Bayesian Persuasion with Lie Detection

We consider a model of Bayesian persuasion in which the Receiver can detect lies with positive probability. We show that the Sender lies more when the lie detection probability increases. As long as the lie detection probability is sufficiently small the Sender\u27s and the Receiver\u27s equilibrium payoffs are unaffected by the lie detection technology because the Sender simply compensates by lying more. When the lie detection probability is sufficiently high, the Sender\u27s (Receiver\u27s) equilibrium payoff decreases (increases) with the lie detection probability

Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation

Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation. Existing approaches typically assume a balanced class distribution for both labeled and unlabeled medical images. However, medical image data in reality is commonly imbalanced (i.e., multi-class label imbalance), which naturally yields blurry contours and usually incorrectly labels rare objects. Moreover, it remains unclear whether all negative samples are equally negative. In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised medical image segmentation. Specifically, we first develop an iterative contrastive distillation algorithm by softly labeling the negatives rather than binary supervision between positive and negative pairs. We also capture more semantically similar features from the randomly chosen negative set compared to the positives to enforce the diversity of the sampled data. Second, we raise a more important question: Can we really handle imbalanced samples to yield better performance? Hence, the key innovation in ACTION is to learn global semantic relationship across the entire dataset and local anatomical features among the neighbouring pixels with minimal additional memory footprint. During the training, we introduce anatomical contrast by actively sampling a sparse set of hard negative pixels, which can generate smoother segmentation boundaries and more accurate predictions. Extensive experiments across two benchmark datasets and different unlabeled settings show that ACTION significantly outperforms the current state-of-the-art semi-supervised methods.Comment: Accepted at Information Processing in Medical Imaging (IPMI 2023

Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts

Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation. Towards this end, recent deep learning-based medical segmentation methods have shown great promise in better modeling such information. However, convolution operators for medical segmentation typically operate on regular grids, which inherently blur the high-frequency regions, i.e., boundary regions. In this work, we propose MORSE, a generic implicit neural rendering framework designed at an anatomical level to assist learning in medical image segmentation. Our method is motivated by the fact that implicit neural representation has been shown to be more effective in fitting complex signals and solving computer graphics problems than discrete grid-based representation. The core of our approach is to formulate medical image segmentation as a rendering problem in an end-to-end manner. Specifically, we continuously align the coarse segmentation prediction with the ambiguous coordinate-based point representations and aggregate these features to adaptively refine the boundary region. To parallelly optimize multi-scale pixel-level features, we leverage the idea from Mixture-of-Expert (MoE) to design and train our MORSE with a stochastic gating mechanism. Our experiments demonstrate that MORSE can work well with different medical segmentation backbones, consistently achieving competitive performance improvements in both 2D and 3D supervised medical segmentation methods. We also theoretically analyze the superiority of MORSE.Comment: Accepted at International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2023

ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast

Medical data often exhibits long-tail distributions with heavy class imbalance, which naturally leads to difficulty in classifying the minority classes (i.e., boundary regions or rare objects). Recent work has significantly improved semi-supervised medical image segmentation in long-tailed scenarios by equipping them with unsupervised contrastive criteria. However, it remains unclear how well they will perform in the labeled portion of data where class distribution is also highly imbalanced. In this work, we present ACTION++, an improved contrastive learning framework with adaptive anatomical contrast for semi-supervised medical segmentation. Specifically, we propose an adaptive supervised contrastive loss, where we first compute the optimal locations of class centers uniformly distributed on the embedding space (i.e., off-line), and then perform online contrastive matching training by encouraging different class features to adaptively match these distinct and uniformly distributed class centers. Moreover, we argue that blindly adopting a constant temperature $\tau$ in the contrastive loss on long-tailed medical data is not optimal, and propose to use a dynamic $\tau$ via a simple cosine schedule to yield better separation between majority and minority classes. Empirically, we evaluate ACTION++ on ACDC and LA benchmarks and show that it achieves state-of-the-art across two semi-supervised settings. Theoretically, we analyze the performance of adaptive anatomical contrast and confirm its superiority in label efficiency.Comment: Accepted by International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2023

Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective

For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples. This is enabled by the observation that without accessing ground truth label, negative examples with truly dissimilar anatomical features, if sampled, can significantly improve the performance. In reality, however, these samples may come from similar anatomical features and the models may struggle to distinguish the minority tail-class samples, making the tail classes more prone to misclassification, both of which typically lead to model collapse. In this paper, we propose ARCO, a semi-supervised contrastive learning (CL) framework with stratified group sampling theory in medical image segmentation. In particular, we first propose building ARCO through the concept of variance-reduced estimation, and show that certain variance-reduction techniques are particularly beneficial in medical image segmentation tasks with extremely limited labels. Furthermore, we theoretically prove these sampling techniques are universal in variance reduction. Finally, we experimentally validate our approaches on three benchmark datasets with different label settings, and our methods consistently outperform state-of-the-art semi-supervised methods. Additionally, we augment the CL frameworks with these sampling techniques and demonstrate significant gains over previous methods. We believe our work is an important step towards semi-supervised medical image segmentation by quantifying the limitation of current self-supervision objectives for accomplishing medical image analysis tasks

Cell patterning via optimized dielectrophoretic force within hexagonal electrodes in vitro for skin tissue engineering

Abstract(#br)Tissue reconstruction through in vitro cell seeding is a popular method for tissue engineering. In this paper, we proposed a thin-layer structure consisting of multiple hexagons for the regeneration of skin tissue. Cells could be seeded and cultured within the structure via dielectrophoresis (DEP) actively. A thin layer of the structure was fabricated with biocompatible medical-grade stainless steel via precise laser cutting. The fabricated layers were stacked together to form a 3D electrode pair, which could be used to generate a 3D electric field. Thus, the suspended cells within the structure could be patterned via DEP manipulation. The input voltage was examined and optimized to ensure cell viability and patterning efficiency during the DEP manipulation process. As soon..

Two Candidate KH 15D-like Systems from the Zwicky Transient Facility

KH 15D contains a circumbinary disk that is tilted relative to the orbital plane of the central binary. The precession of the disk and the orbital motion of the binary together produce rich phenomena in the photometric light curve. In this work, we present the discovery and preliminary analysis of two objects that resemble the key features of KH 15D from the Zwicky Transient Facility. These new objects, Bernhard-1 and Bernhard-2, show large-amplitude ($>1.5\,$mag), long-duration (more than tens of days), and periodic dimming events. A one-sided screen model is developed to model the photometric behaviour of these objects, the physical interpretation of which is a tilted, warped circumbinary disk occulting the inner binary. Changes in the object light curves suggest potential precession periods over timescales longer than 10 years. Additional photometric and spectroscopic observations are encouraged to better understand the nature of these interesting systems.Comment: 10 pages, 5 figures, 2 tables, accepted to ApJ Letter