Option Pricing and Hedging with Regret Optimisation

This thesis focuses on the option pricing and hedging based on a regret optimisation problem in a discrete-time financial market model with proportional transaction costs. In such model, the no-arbitrage price interval can be very large. Such large interval makes it difficult for an investor to choose the “right” prices, which is a long standing difficulty in the field. We introduce an indifference pricing method based on minimising regret/disutility, and show that the spread between the buyer’s and seller’s prices can be much narrower than the no-arbitrage price interval. The regret optimisation problem allows possible fund injection/withdrawal at each time step, and in doing so it extends the classic utility maximisation problems in financial models. Moreover, by allowing the investor’s preference towards risk to be different at different time step, it also extends the optimal investment and consumption problem in financial market models with a finite horizon. In addition, the investor’s endowment that is considered in our setting is modelled by a portfolio flow which extends the notion of initial wealth. We prove that there exists a solution to the regret optimisation problem, and indifference prices are always within the no-arbitrage price interval. Under an exponential type regret function, we find a dynamic programming algorithm to construct a solution to a Lagrangian dual problem. By solving the dual problem, we can not only solve the regret optimisation problem but also calculate the option indifference prices. In binary models, we calculate the optimal injection/withdrawal strategy for various different values of given parameters, and also compute the indifference prices of various European options. The numerical results show that the bid-ask indifference price interval can be much narrower than the no-arbitrage price interval, and such smaller price interval can be used to guide the investor to choose the “right” prices

SimpleNet: A Simple Network for Image Anomaly Detection and Localization

We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anomalies. SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that transfers local features towards target domain, (3) a simple Anomaly Feature Generator that counterfeits anomaly features by adding Gaussian noise to normal features, and (4) a binary Anomaly Discriminator that distinguishes anomaly features from normal features. During inference, the Anomaly Feature Generator would be discarded. Our approach is based on three intuitions. First, transforming pre-trained features to target-oriented features helps avoid domain bias. Second, generating synthetic anomalies in feature space is more effective, as defects may not have much commonality in the image space. Third, a simple discriminator is much efficient and practical. In spite of simplicity, SimpleNet outperforms previous methods quantitatively and qualitatively. On the MVTec AD benchmark, SimpleNet achieves an anomaly detection AUROC of 99.6%, reducing the error by 55.5% compared to the next best performing model. Furthermore, SimpleNet is faster than existing methods, with a high frame rate of 77 FPS on a 3080ti GPU. Additionally, SimpleNet demonstrates significant improvements in performance on the One-Class Novelty Detection task. Code: https://github.com/DonaldRR/SimpleNet.Comment: Accepted to CVPR 202

Percutaneous Nephrolithotomy under Local Infiltration Anesthesia in Kneeling Prone Position for a Patient with Spinal Deformity

Urolithiasis, a common condition in patients with spinal deformity, poses a challenge to surgical procedures and anesthetic management. A 51-year-old Chinese male presented with bilateral complex renal calculi. He was also affected by severe kyphosis deformity and spinal stiffness due to ankylosing spondylitis. Dr. Li performed the percutaneous nephrolithotomy under local infiltration anesthesia with the patient in a kneeling prone position, achieving satisfactory stone clearance with no severe complications. We found this protocol safe and effective to manage kidney stones in patients with spinal deformity. Local infiltration anesthesia may benefit patients for whom epidural anesthesia and intubation anesthesia are difficult

CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model

Supervised crowd counting relies heavily on costly manual labeling, which is difficult and expensive, especially in dense scenes. To alleviate the problem, we propose a novel unsupervised framework for crowd counting, named CrowdCLIP. The core idea is built on two observations: 1) the recent contrastive pre-trained vision-language model (CLIP) has presented impressive performance on various downstream tasks; 2) there is a natural mapping between crowd patches and count text. To the best of our knowledge, CrowdCLIP is the first to investigate the vision language knowledge to solve the counting problem. Specifically, in the training stage, we exploit the multi-modal ranking loss by constructing ranking text prompts to match the size-sorted crowd patches to guide the image encoder learning. In the testing stage, to deal with the diversity of image patches, we propose a simple yet effective progressive filtering strategy to first select the highly potential crowd patches and then map them into the language space with various counting intervals. Extensive experiments on five challenging datasets demonstrate that the proposed CrowdCLIP achieves superior performance compared to previous unsupervised state-of-the-art counting methods. Notably, CrowdCLIP even surpasses some popular fully-supervised methods under the cross-dataset setting. The source code will be available at https://github.com/dk-liang/CrowdCLIP.Comment: Accepted by CVPR 202

Simultaneous Improvement and Genetic Dissection of Salt Tolerance of Rice (Oryza sativa L.) by Designed QTL Pyramiding

Breeding of multi-stress tolerant rice varieties with higher grain yields is the best option to enhance the rice productivity of abiotic stresses prone areas. It also poses the greatest challenge to plant breeders to breed rice varieties for such stress prone conditions. Here, we carried out a designed QTL pyramiding experiment to develop high yielding “Green Super Rice” varieties with significantly improved tolerance to salt stress and grain yield. Using the F4 population derived from a cross between two selected introgression lines, we were able to develop six mostly homozygous promising high yielding lines with significantly improved salt tolerance and grain yield under optimal and/or saline conditions in 3 years. Simultaneous mapping using the same breeding population and tunable genotyping-by-sequencing technology, we identified three QTL affecting salt injury score and leaf chlorophyll content. By analyzing 32M SNP data of the grandparents and graphical genotypes of the parents, we discovered 87 positional candidate genes for salt tolerant QTL. According to their functional annotation, we inferred the most likely candidate genes. We demonstrated that designed QTL pyramiding is a powerful strategy for simultaneous improvement and genetic dissection of complex traits in rice