Analysis of dose-response data from developmental toxicity studies

Ph.DDOCTOR OF PHILOSOPH

On the boundedness and nonmonotonicity of generalized score statistics

We show in the context of the linear regression model fitted by Gaussian quasi-likelihood estimation that the generalized score statistics of Boos and Hu and Kalbfleisch for individual parameters can be bounded and nonmonotone in the parameter, making i

On Reinforcement Learning for Full-length Game of StarCraft

StarCraft II poses a grand challenge for reinforcement learning. The main difficulties of it include huge state and action space and a long-time horizon. In this paper, we investigate a hierarchical reinforcement learning approach for StarCraft II. The hierarchy involves two levels of abstraction. One is the macro-action automatically extracted from expert's trajectories, which reduces the action space in an order of magnitude yet remains effective. The other is a two-layer hierarchical architecture which is modular and easy to scale, enabling a curriculum transferring from simpler tasks to more complex tasks. The reinforcement training algorithm for this architecture is also investigated. On a 64x64 map and using restrictive units, we achieve a winning rate of more than 99\% against the difficulty level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat model, we can achieve over 93\% winning rate of Protoss against the most difficult non-cheating built-in AI (level-7) of Terran, training within two days using a single machine with only 48 CPU cores and 8 K40 GPUs. It also shows strong generalization performance, when tested against never seen opponents including cheating levels built-in AI and all levels of Zerg and Protoss built-in AI. We hope this study could shed some light on the future research of large-scale reinforcement learning.Comment: Appeared in AAAI 201

Fuzzy automata system with application to target recognition based on image processing

AbstractIn order to get better image processing and target recognition, this paper presents a fuzzy automata system to target recognition. The system first performs image processing, and then accomplishes the target recognition. The system consists of four parts: image preprocessing, feature extraction, target matching and experiment. Compared with existing approaches, this paper uses both global features and local features of the target image, and carries out target recognition by using a fuzzy automata system. Simulation results show that the correct recognition rate based on the fuzzy automata system for target recognition is higher at 94.59%, an improvement on an average of 29.24%, compared to other existing approaches. Finally, some directions for future research are described

Online Platforms in Networked Markets: Transparency, Anticipation and Demand Management

The global economy has been transformed by the introduction of online platforms in the past two decades. These companies, such as Uber and Amazon, have benefited and undergone massive growth, and are a critical part of the world economy today. Understanding these online platforms, their designs and how participation change with anticipation and uncertainty can help us identify the necessary ingredients for successful implementation of online platforms in the future, especially for those with underlying network constraints, e.g., the electricity grid. This thesis makes three main contributions. First, we identify and compare common access and allocation control designs for online platforms, and highlight their trade-offs between transparency and control. We make these comparisons under a networked Cournot competition model and consider three popular designs: (i) open access, (ii) discriminatory access, and (iii) controlled allocation. Our findings reveal that designs that control over access are more efficient than designs that control over allocations, but open access designs are susceptible to substantial search costs. Next, we study the impact of demand management in a networked Stackelberg model considering network constraints and producer anticipation. We provide insights on limiting manipulation under these constrained networked marketplaces with nodal prices, and show that demand management mechanisms that traditionally aid system stability also help plays a vital role economically. In particular, we show that demand management empower consumers and give them "market power" to counter that of producers, limiting the impact of their anticipation and their potential for manipulation. Lastly, we study how participants (e.g., drivers on Uber) make competitive real-time production (driving) decisions. To that end, we design a novel pursuit algorithm for making online optimization under limited inventory constraints. Our analysis yields an algorithm that is competitive and applicable to achieve optimal results in the well known one-way trading problem, and new variants of the original problem.</p

Malicious Agent Detection for Robust Multi-Agent Collaborative Perception

Recently, multi-agent collaborative (MAC) perception has been proposed and outperformed the traditional single-agent perception in many applications, such as autonomous driving. However, MAC perception is more vulnerable to adversarial attacks than single-agent perception due to the information exchange. The attacker can easily degrade the performance of a victim agent by sending harmful information from a malicious agent nearby. In this paper, we extend adversarial attacks to an important perception task -- MAC object detection, where generic defenses such as adversarial training are no longer effective against these attacks. More importantly, we propose Malicious Agent Detection (MADE), a reactive defense specific to MAC perception that can be deployed by each agent to accurately detect and then remove any potential malicious agent in its local collaboration network. In particular, MADE inspects each agent in the network independently using a semi-supervised anomaly detector based on a double-hypothesis test with the Benjamini-Hochberg procedure to control the false positive rate of the inference. For the two hypothesis tests, we propose a match loss statistic and a collaborative reconstruction loss statistic, respectively, both based on the consistency between the agent to be inspected and the ego agent where our detector is deployed. We conduct comprehensive evaluations on a benchmark 3D dataset V2X-sim and a real-road dataset DAIR-V2X and show that with the protection of MADE, the drops in the average precision compared with the best-case "oracle" defender against our attack are merely 1.28% and 0.34%, respectively, much lower than 8.92% and 10.00% for adversarial training, respectively

Mechanism of long-term strength retrogression of silica-enriched Portland cement assessed by quantitative X-ray diffraction analysis

In order to clarify on the driving force of cement long-term strength retrogression, a comprehensive quantitative X-ray diffraction (XRD) analysis were conducted on silica-enriched (60%–80% by weight of cement) cement samples set and cured under the condition of 200°C and 50 MPa with a maximum duration of 180 days. The phase content evolution with time was determined by three different methods on the average of three specimens: the external standard method; the partial or no known crystal structure (PONKCS) method; and the hybrid method. Although the specific phase content estimated by different methods varied slightly, the overall trend of change of all phases were similar. The phase transformation in set cement at high temperature condition is dependent on the slurry composition. In silica-deficient system, tobermorite and amorphous C-S-H were transformed to xonotlite; while in silica-sufficient system, tobermorite and amorphous C-S-H were transformed to gyrolite. These phase transformations involve gradual structural changes of cement hydration products, which may be the driving force of long-term strength retrogression. However, such structural changes can only be detected by XRD once the transformation is complete

LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection

Most of current anomaly detection models assume that the normal pattern remains same all the time. However, the normal patterns of Web services change dramatically and frequently. The model trained on old-distribution data is outdated after such changes. Retraining the whole model every time is expensive. Besides, at the beginning of normal pattern changes, there is not enough observation data from the new distribution. Retraining a large neural network model with limited data is vulnerable to overfitting. Thus, we propose a Light and Anti-overfitting Retraining Approach (LARA) for deep variational auto-encoder based time series anomaly detection methods (VAEs). This work aims to make three novel contributions: 1) the retraining process is formulated as a convex problem and can converge at a fast rate as well as prevent overfitting; 2) designing a ruminate block, which leverages the historical data without the need to store them; 3) mathematically proving that when fine-tuning the latent vector and reconstructed data, the linear formations can achieve the least adjusting errors between the ground truths and the fine-tuned ones. Moreover, we have performed many experiments to verify that retraining LARA with even 43 time slots of data from new distribution can result in its competitive F1 Score in comparison with the state-of-the-art anomaly detection models trained with sufficient data. Besides, we verify its light overhead.Comment: Accepted by ACM Web Conference 2024 (WWW 24