119 research outputs found
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
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