A Dual Method For Backward Stochastic Differential Equations with Application to Risk Valuation

We propose a numerical recipe for risk evaluation defined by a backward stochastic differential equation. Using dual representation of the risk measure, we convert the risk valuation to a stochastic control problem where the control is a certain Radon-Nikodym derivative process. By exploring the maximum principle, we show that a piecewise-constant dual control provides a good approximation on a short interval. A dynamic programming algorithm extends the approximation to a finite time horizon. Finally, we illustrate the application of the procedure to financial risk management in conjunction with nested simulation and on an multidimensional portfolio valuation problem

The Neural Networks Based Needle Detection for Medical Retinal Surgery

In recent years, deep learning technology has developed rapidly, and the application of deep neural networks in the medical image processing field has become the focus of the spotlight. This paper aims to achieve needle position detection in medical retinal surgery by adopting the target detection algorithm based on YOLOv5 as the basic deep neural network model. The state-of-the-art needle detection approaches for medical surgery mainly focus on needle structure segmentation. Instead of the needle segmentation, the proposed method in this paper contains the angle examination during the needle detection process. This approach also adopts a novel classification method based on the different positions of the needle to improve the model. The experiments demonstrate that the proposed network can accurately detect the needle position and measure the needle angle. The performance test of the proposed method achieves 4.80 for the average Euclidean distance between the detected tip position and the actual tip position. It also obtains an average error of 0.85 degrees for the tip angle across all test sets

Optical Coherence Tomography for Polymer Film Evaluation

Development of functional polymer films and film stacks has been under increasing demand to create new generations of novel, compact, light-weight optics. Optical coherence tomography (OCT) is capable of evaluating all the important optical properties of a film or film stack, including topology of surfaces or layer-to-layer interfaces, the refractive index and thickness, and polarization property. By engineering the scanning architecture of an OCT system, high-precision metrology of films of either flat or spherical geometry is achieved. In this chapter, the system design, metrology methodologies, and examples of OCT for film metrology are discussed to provide both the knowledge foundation and the engineering perspectives. The advanced film metrology capabilities offered by OCT play a key role in the manufacturing process maturity of newly developed films. Rapid advancement in the field of OCT is foreseen to drive the application toward in-line film metrology and facilitate the rapid growth of innovative films in the industry

Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability

Out-of-distribution (OOD) detection is an indispensable aspect of secure AI when deploying machine learning models in real-world applications. Previous paradigms either explore better scoring functions or utilize the knowledge of outliers to equip the models with the ability of OOD detection. However, few of them pay attention to the intrinsic OOD detection capability of the given model. In this work, we generally discover the existence of an intermediate stage of a model trained on in-distribution (ID) data having higher OOD detection performance than that of its final stage across different settings, and further identify one critical data-level attribution to be learning with the atypical samples. Based on such insights, we propose a novel method, Unleashing Mask, which aims to restore the OOD discriminative capabilities of the well-trained model with ID data. Our method utilizes a mask to figure out the memorized atypical samples, and then finetune the model or prune it with the introduced mask to forget them. Extensive experiments and analysis demonstrate the effectiveness of our method. The code is available at: https://github.com/tmlr-group/Unleashing-Mask.Comment: accepted by ICML 202

Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation

Out-of-distribution (OOD) detection is important for deploying reliable machine learning models on real-world applications. Recent advances in outlier exposure have shown promising results on OOD detection via fine-tuning model with informatively sampled auxiliary outliers. However, previous methods assume that the collected outliers can be sufficiently large and representative to cover the boundary between ID and OOD data, which might be impractical and challenging. In this work, we propose a novel framework, namely, Diversified Outlier Exposure (DivOE), for effective OOD detection via informative extrapolation based on the given auxiliary outliers. Specifically, DivOE introduces a new learning objective, which diversifies the auxiliary distribution by explicitly synthesizing more informative outliers for extrapolation during training. It leverages a multi-step optimization method to generate novel outliers beyond the original ones, which is compatible with many variants of outlier exposure. Extensive experiments and analyses have been conducted to characterize and demonstrate the effectiveness of the proposed DivOE. The code is publicly available at: https://github.com/tmlr-group/DivOE.Comment: accepted by NeurIPS 202

A Robust Nanoparticle Platform for RNA Interference in Macrophages to Suppress Tumor Cell Migration

Macrophages are one of the most abundant immune cells in the solid tumor and their increased density is associated with the specific pathological features of cancers, including invasiveness, metastasis, immunosuppression, neovascularization, and poor response to therapy. Therefore, reprogramming macrophage behavior is emerging as a promising therapeutic modality for cancer treatment. RNA interference (RNAi) technology is one of the powerful strategies for the regulation of macrophage activities by silencing specific genes. However, as polyanionic biomacromolecules, RNAi therapeutics such as small interfering RNA (siRNA) cannot readily cross cell membrane and thus specific delivery vehicles are required to facilitate the cytosolic siRNA delivery. Herein, we developed a robust nanoparticle (NP) platform for efficient siRNA delivery and gene silencing in macrophages. This NP platform is composed of biodegradable poly (ethylene glycol)-b-poly (-caprolactone) (PEG-b-PCL), poly (-caprolactone)-b-poly (2-aminoethyl ethylene phosphate) (PCL-b-PPEEA), and PCL homopolymer. We chose CC-chemokine ligand 18 (CCL-18) as a proof of concept therapeutic target and our results demonstrate that the CCL-18 silencing in macrophages can significantly inhibit the migration of breast cancer cells. The successful regulation of the macrophage behavior demonstrated herein shows great potential as an effective strategy for cancer therapy

Exploring Model Dynamics for Accumulative Poisoning Discovery

Adversarial poisoning attacks pose huge threats to various machine learning applications. Especially, the recent accumulative poisoning attacks show that it is possible to achieve irreparable harm on models via a sequence of imperceptible attacks followed by a trigger batch. Due to the limited data-level discrepancy in real-time data streaming, current defensive methods are indiscriminate in handling the poison and clean samples. In this paper, we dive into the perspective of model dynamics and propose a novel information measure, namely, Memorization Discrepancy, to explore the defense via the model-level information. By implicitly transferring the changes in the data manipulation to that in the model outputs, Memorization Discrepancy can discover the imperceptible poison samples based on their distinct dynamics from the clean samples. We thoroughly explore its properties and propose Discrepancy-aware Sample Correction (DSC) to defend against accumulative poisoning attacks. Extensive experiments comprehensively characterized Memorization Discrepancy and verified its effectiveness. The code is publicly available at: https://github.com/tmlr-group/Memorization-Discrepancy.Comment: accepted by ICML 202