Emergence of scaling in human-interest dynamics

Human behaviors are often driven by human interests. Despite intense recent efforts in exploring the dynamics of human behaviors, little is known about human-interest dynamics, partly due to the extreme difficulty in accessing the human mind from observations. However, the availability of large-scale data, such as those from e-commerce and smart-phone communications, makes it possible to probe into and quantify the dynamics of human interest. Using three prototypical "big data" sets, we investigate the scaling behaviors associated with human-interest dynamics. In particular, from the data sets we uncover power-law scaling associated with the three basic quantities: (1) the length of continuous interest, (2) the return time of visiting certain interest, and (3) interest ranking and transition. We argue that there are three basic ingredients underlying human-interest dynamics: preferential return to previously visited interests, inertial effect, and exploration of new interests. We develop a biased random-walk model, incorporating the three ingredients, to account for the observed power-law scaling relations. Our study represents the first attempt to understand the dynamical processes underlying human interest, which has significant applications in science and engineering, commerce, as well as defense, in terms of specific tasks such as recommendation and human-behavior prediction

Anchoring Bias in Online Voting

Voting online with explicit ratings could largely reflect people's preferences and objects' qualities, but ratings are always irrational, because they may be affected by many unpredictable factors like mood, weather, as well as other people's votes. By analyzing two real systems, this paper reveals a systematic bias embedding in the individual decision-making processes, namely people tend to give a low rating after a low rating, as well as a high rating following a high rating. This so-called \emph{anchoring bias} is validated via extensive comparisons with null models, and numerically speaking, the extent of bias decays with interval voting number in a logarithmic form. Our findings could be applied in the design of recommender systems and considered as important complementary materials to previous knowledge about anchoring effects on financial trades, performance judgements, auctions, and so on.Comment: 5 pages, 4 tables, 5 figure

Averaging principle for SDEs with singular drifts driven by αα-stable processes

In this paper, we investigate the convergence rate of the averaging principle for stochastic differential equations (SDEs) with $β$-Hölder drift driven by $α$-stable processes. More specifically, we first derive the Schauder estimate for nonlocal partial differential equations (PDEs) associated with the aforementioned SDEs, within the framework of Besov-Hölder spaces. Then we consider the case where $(α,β)\in(0,2)\times(1-\tfracα{2},1)$. Using the Schauder estimate, we establish the strong convergence rate for the averaging principle. In particular, under suitable conditions we obtain the optimal rate of strong convergence when $(α,β)\in(\tfrac{2}{3},1]\times(2-\tfrac{3α}{2},1)\cup(1,2)\times(\tfracα{2},1)$. Furthermore, when $(α,β)\in(0,1]\times(1-α,1-\tfracα{2}]\cup(1,2)\times(\tfrac{1-α}{2},1-\tfracα{2}]$, we show the convergence of the martingale solutions of original systems to that of the averaged equation. When $α\in(1,2)$, the drift can be a distribution.30 page

Study on postoperative survival prediction model for non-small cell lung cancer: application of radiomics technology workflow based on multi-organ imaging features and various machine learning algorithms

ObjectiveThis study aims to construct an effective prediction model for the two-year postoperative survival probability of patients with non-small cell lung cancer (NSCLC). It particularly focuses on integrating radiomics features, including the erector spinae and whole-lung imaging features, to enhance the accuracy and stability of prognostic predictions.Materials and methodsThe study included 37 NSCLC patients diagnosed and surgically treated at the First Affiliated Hospital of Anhui Medical University from January 2020 to December 2021. The average age of the patients was 59 years, with the majority being female and non-smokers. Additionally, CT imaging data from 98 patients were obtained from The Cancer Imaging Archive (TCIA) public database. All imaging data were derived from preoperative chest CT scans and standardized using 3D Slicer software. The study extracted radiomic features from the tumor, whole lung, and erector spinae muscles of the patients and applied 11 machine learning algorithms to construct prediction models. Subsequently, the classification performance of all constructed models was compared to select the optimal prediction model.ResultsUnivariate Cox regression analysis showed no significant correlation between the collected clinical factors and patient survival time. In the external validation set, the K-Nearest Neighbors (KNN) model based on bilateral erector spinae features performed the best, with accuracy and AUC (Area Under the Curve) values consistently above 0.7 in both the training and external testing sets. Among the prognostic models based on whole-lung imaging features, the AdaBoost model also performed well, but its AUC value was below 0.6 in the external validation set, indicating overall classification performance still inferior to the KNN model based on erector spinae features.ConclusionThis study is the first to introduce erector spinae imaging features into lung cancer research, successfully developing a stable and well-performing prediction model for the postoperative survival of NSCLC patients. The research results provide new perspectives and directions for the application of radiomics in cancer research and emphasize the importance of incorporating multi-organ imaging features to improve the accuracy and stability of prediction models

Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest

Traffic Control via Connected and Automated Vehicles (CAVs): An Open-Road Field Experiment with 100 CAVs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. Also called "phantom jams"or "stop-and-go waves,"these instabilities are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system, referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment, the MegaVanderTest (MVT), leveraged a heterogeneous fleet of 100 longitudinally controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this article. The MegaController is a hierarchical control architecture that consists of two main layers. The upper layer is called the Speed Planner and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock onboard sensors. The Speed Planner ingests live data feeds provided by third parties as well as data from our own control vehicles and uses both to perform the speed assignment. The architecture of the Speed Planner allows for the modular use of standard control techniques, such as optimal control, model predictive control (MPC), kernel methods, and others. The architecture of the local controller allows for the flexible implementation of local controllers. Corresponding techniques include deep reinforcement learning (RL), MPC, and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers or only some. Likewise, control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars to electronic selection of adaptive cruise control (ACC) setpoints in others. The proposed architecture technically allows for the combination of all possible settings proposed previously, that is Speed Planner algorithms × local Vehicle Controller algorithms} × {full or partial sensing} × {torque or speed control}. Most configurations were tested throughout the ramp up to the MegaVandertest (MVT)

Zimo Cheng ENGG 390 Final Paper

This summer I worked at Galaxy digital as a Product Management Intern. Galaxy is a digital asset and blockchain pioneer that provides technology-driven financial services for digital assets in different spectrums including Trading, Lending, Asset Management, Investment Banking, Venture Capitals, Crypto Mining, and principal Investments. My project for the summer is to improve the workflow and create knowledge library in Confluence and Jira for the whole organization. I created a knowledge library inside confluence which includes the improvement procedures, documents of format and best practice, and methods in creating the end product

Cheng - ENGG 390 Project Abstract

This summer I worked at Galaxy digital as a Product Management Intern. Galaxy is a digital asset and blockchain pioneer that provides technology-driven financial services for digital assets in different spectrums including Trading, Lending, Asset Management, Investment Banking, Venture Capitals, Crypto Mining, and principal Investments. My project for the summer is to improve the workflow and create knowledge library in Confluence and Jira for the whole organization. I created a knowledge library inside confluence which includes the improvement procedures, documents of format and best practice, and methods in creating the end product

Strong and weak convergence for the averaging principle of DDSDE with singular drift

Cheng M, Hao Z, Röckner M. Strong and weak convergence for the averaging principle of DDSDE with singular drift. Bernoulli. 2024;30(2):1586-1610.In this paper, we study the averaging principle for distribution dependent stochastic differential equations with drift in localized Lp spaces. Using Zvonkin's transformation and estimates for solutions to Kolmogorov equations, we prove that the solutions of the original system strongly and weakly converge to the solution of the averaged system as the time scale epsilon goes to zero. Moreover, we obtain rates of the strong and weak convergence that depend on p