This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasting and trading results. It presented the findings of the follow-up project of the research "Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting". First, the project included an application of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks to provide a novel way of predicting stock option trends. Additionally, it examined the dependence of the ML models by evaluating the experimental method of combining multiple ML models to improve prediction results and decision-making. Lastly, two improved trading strategies and simulated investing results were presented. The Binomial Asset Pricing Model with discrete time stochastic process analysis and portfolio hedging was applied and suggested an optimized investment expectation. These results can be utilized in real-life trading strategies to optimize stock option investment results based on historical data.

This paper presents a novel way to apply mathematical finance and machine learning (ML) to forecast stock options prices. Following results from the paper Quasi-Reversibility Method and Neural Network Machine Learning to Solution of Black-Scholes Equations (appeared on the AMS Contemporary Mathematics journal), we create and evaluate new empirical mathematical models for the Black-Scholes equation to analyze data for 92,846 companies. We solve the Black-Scholes (BS) equation forwards in time as an ill-posed inverse problem, using the Quasi-Reversibility Method (QRM), to predict option price for the future one day. For each company, we have 13 elements including stock and option daily prices, volatility, minimizer, etc. Because the market is so complicated that there exists no perfect model, we apply ML to train algorithms to make the best prediction. The current stage of research combines QRM with Convolutional Neural Networks (CNN), which learn information across a large number of data points simultaneously. We implement CNN to generate new results by validating and testing on sample market data. We test different ways of applying CNN and compare our CNN models with previous models to see if achieving a higher profit rate is possible.

In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment. We optimize on the basis of the previous PP-YOLOv2, using anchor-free paradigm, more powerful backbone and neck equipped with CSPRepResStage, ET-head and dynamic label assignment algorithm TAL. We provide s/m/l/x models for different practice scenarios. As a result, PP-YOLOE-l achieves 51.4 mAP on COCO test-dev and 78.1 FPS on Tesla V100, yielding a remarkable improvement of (+1.9 AP, +13.35% speed up) and (+1.3 AP, +24.96% speed up), compared to the previous state-of-the-art industrial models PP-YOLOv2 and YOLOX respectively. Further, PP-YOLOE inference speed achieves 149.2 FPS with TensorRT and FP16-precision. We also conduct extensive experiments to verify the effectiveness of our designs. Source code and pre-trained models are available at https://github.com/PaddlePaddle/PaddleDetection.

嘈杂的标签损坏了深网络的性能。为了稳健的学习，突出的两级管道在消除可能的不正确标签和半监督培训之间交替。然而，丢弃观察到的标签的部分可能导致信息丢失，尤其是当腐败不是完全随机的时，例如依赖类或实例依赖。此外，从代表性两级方法Dividemix的训练动态，我们确定了确认偏置的统治：伪标签未能纠正相当大量的嘈杂标签，因此累积误差。为了充分利用观察到的标签和减轻错误的校正，我们提出了强大的标签翻新（鲁棒LR）-a新的混合方法，该方法集成了伪标签和置信度估计技术来翻新嘈杂的标签。我们表明我们的方法成功减轻了标签噪声和确认偏差的损害。结果，它跨数据集和噪声类型实现最先进的结果。例如，强大的LR在真实世界嘈杂的数据集网络VIVION上以前最好的绝对高度提高了4.5％的绝对顶级精度改进。

更好的准确性和效率权衡在对象检测中是一个具有挑战性的问题。在这项工作中，我们致力于研究对象检测的关键优化和神经网络架构选择，以提高准确性和效率。我们调查了无锚策略对轻质对象检测模型的适用性。我们增强了骨干结构并设计了颈部的轻质结构，从而提高了网络的特征提取能力。我们改善标签分配策略和损失功能，使培训更稳定和高效。通过这些优化，我们创建了一个名为PP-Picodet的新的实时对象探测器系列，这在移动设备的对象检测上实现了卓越的性能。与其他流行型号相比，我们的模型在准确性和延迟之间实现了更好的权衡。 Picodet-s只有0.99m的参数达到30.6％的地图，它是地图的绝对4.8％，同时与yolox-nano相比将移动CPU推理延迟减少55％，并且与Nanodet相比，MAP的绝对改善了7.1％。当输入大小为320时，它在移动臂CPU上达到123个FPS（使用桨Lite）。Picodet-L只有3.3M参数，达到40.9％的地图，这是地图的绝对3.7％，比yolov5s更快44％ 。如图1所示，我们的模型远远优于轻量级对象检测的最先进的结果。代码和预先训练的型号可在https://github.com/paddlepaddle/paddledentions提供。

Passive monitoring of acoustic or radio sources has important applications in modern convenience, public safety, and surveillance. A key task in passive monitoring is multiobject tracking (MOT). This paper presents a Bayesian method for multisensor MOT for challenging tracking problems where the object states are high-dimensional, and the measurements follow a nonlinear model. Our method is developed in the framework of factor graphs and the sum-product algorithm (SPA). The multimodal probability density functions (pdfs) provided by the SPA are effectively represented by a Gaussian mixture model (GMM). To perform the operations of the SPA in high-dimensional spaces, we make use of Particle flow (PFL). Here, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance even in challenging multisensor MOT scenarios with single sensor measurements that have a lower dimension than the object positions. We perform a numerical evaluation in a passive acoustic monitoring scenario where multiple sources are tracked in 3-D from 1-D time-difference-of-arrival (TDOA) measurements provided by pairs of hydrophones. Our numerical results demonstrate favorable detection and estimation accuracy compared to state-of-the-art reference techniques.

Location-aware networks will introduce new services and applications for modern convenience, surveillance, and public safety. In this paper, we consider the problem of cooperative localization in a wireless network where the position of certain anchor nodes can be controlled. We introduce an active planning method that aims at moving the anchors such that the information gain of future measurements is maximized. In the control layer of the proposed method, control inputs are calculated by minimizing the traces of approximate inverse Bayesian Fisher information matrixes (FIMs). The estimation layer computes estimates of the agent states and provides Gaussian representations of marginal posteriors of agent positions to the control layer for approximate Bayesian FIM computations. Based on a cost function that accumulates Bayesian FIM contributions over a sliding window of discrete future timesteps, a receding horizon (RH) control is performed. Approximations that make it possible to solve the resulting tree-search problem efficiently are also discussed. A numerical case study demonstrates the intelligent behavior of a single controlled anchor in a 3-D scenario and the resulting significantly improved localization accuracy.

Source-free domain adaptation aims to adapt a source model trained on fully-labeled source domain data to a target domain with unlabeled target domain data. Source data is assumed inaccessible due to proprietary or privacy reasons. Existing works use the source model to pseudolabel target data, but the pseudolabels are unreliable due to data distribution shift between source and target domain. In this work, we propose to leverage an ImageNet pre-trained feature extractor in a new co-learning framework to improve target pseudolabel quality for finetuning the source model. Benefits of the ImageNet feature extractor include that it is not source-biased and it provides an alternate view of features and classification decisions different from the source model. Such pre-trained feature extractors are also publicly available, which allows us to readily leverage modern network architectures that have strong representation learning ability. After co-learning, we sharpen predictions of non-pseudolabeled samples by entropy minimization. Evaluation on 3 benchmark datasets show that our proposed method can outperform existing source-free domain adaptation methods, as well as unsupervised domain adaptation methods which assume joint access to source and target data.

As a neural network compression technique, post-training quantization (PTQ) transforms a pre-trained model into a quantized model using a lower-precision data type. However, the prediction accuracy will decrease because of the quantization noise, especially in extremely low-bit settings. How to determine the appropriate quantization parameters (e.g., scaling factors and rounding of weights) is the main problem facing now. Many existing methods determine the quantization parameters by minimizing the distance between features before and after quantization. Using this distance as the metric to optimize the quantization parameters only considers local information. We analyze the problem of minimizing local metrics and indicate that it would not result in optimal quantization parameters. Furthermore, the quantized model suffers from overfitting due to the small number of calibration samples in PTQ. In this paper, we propose PD-Quant to solve the problems. PD-Quant uses the information of differences between network prediction before and after quantization to determine the quantization parameters. To mitigate the overfitting problem, PD-Quant adjusts the distribution of activations in PTQ. Experiments show that PD-Quant leads to better quantization parameters and improves the prediction accuracy of quantized models, especially in low-bit settings. For example, PD-Quant pushes the accuracy of ResNet-18 up to 53.08% and RegNetX-600MF up to 40.92% in weight 2-bit activation 2-bit. The code will be released at https://github.com/hustvl/PD-Quant.

As more and more artificial intelligence (AI) technologies move from the laboratory to real-world applications, the open-set and robustness challenges brought by data from the real world have received increasing attention. Data augmentation is a widely used method to improve model performance, and some recent works have also confirmed its positive effect on the robustness of AI models. However, most of the existing data augmentation methods are heuristic, lacking the exploration of their internal mechanisms. We apply the explainable artificial intelligence (XAI) method, explore the internal mechanisms of popular data augmentation methods, analyze the relationship between game interactions and some widely used robustness metrics, and propose a new proxy for model robustness in the open-set environment. Based on the analysis of the internal mechanisms, we develop a mask-based boosting method for data augmentation that comprehensively improves several robustness measures of AI models and beats state-of-the-art data augmentation approaches. Experiments show that our method can be widely applied to many popular data augmentation methods. Different from the adversarial training, our boosting method not only significantly improves the robustness of models, but also improves the accuracy of test sets. Our code is available at \url{https://github.com/Anonymous_for_submission}.