Search

Following the risk-taking model of Seel and Strack, $n$ players decide when to stop privately observed Brownian motions with drift and absorption at zero. They are then ranked according to their level of stopping and paid a rank-dependent reward. We study the problem of a principal who aims to induce a desirable equilibrium performance of the players by choosing how much reward is attributed to each rank. Specifically, we determine optimal reward schemes for principals interested in the average performance and the performance at a given rank. While the former can be related to reward inequality in the Lorenz sense, the latter can have a surprising shape., To appear in SIAM Journal on Financial Mathematics

In this work we present an efficient numerical algorithm for the solution of molecular density functional theory (DFT) in cylindrical geometry to facilitate the study of how curvature affects the m...

We introduce a mean field game with rank-based reward: competing agents optimize their effort to achieve a goal, are ranked according to their completion time, and paid a reward based on their relative rank. First, we propose a tractable Poissonian model in which we can describe the optimal effort for a given reward scheme. Second, we study the principal--agent problem of designing an optimal reward scheme. A surprising, explicit design is found to minimize the time until a given fraction of the population has reached the goal., 33 pages, 3 figures

The design of colormaps can help tomography operators obtain accurate visual comprehension, thereby assisting safety-critical decisions. The research presented here is about deploying colormaps that promote the best affective responses for industrial microwave tomography (MWT). To answer the two research questions related to our study, we firstly conducted a quantitative analysis of 11 frequently-used colormaps on a segmentation task. Secondly, we presented the same colormaps within a crowdsourced study comprising two parts to verify the quantitative outcomes. The first part encoded affective responses from participants into a prevailing four-quadrant valence–arousal grid, the second part recorded participant ratings towards the accuracy of each colormap on MWT segmentation. We concluded that three colormaps are the best suited in the context of MWT tasks. We also found that the colormaps triggering emotions in the positive–exciting quadrant can facilitate more accurate visual comprehension than other affect-related quadrants. A synthetic colormap design guideline was consequently proposed.

Industrial process tomography (IPT) based process control is an advisable approach in industrial heating processes for improving system efficiency and quality. When using it, appropriate dataflow pipelines and visualizations are key for domain users to implement precise data acquisition and analysis. In this article, we propose a complete data processing and visualizing workflow regarding a specific case—microwave tomography (MWT) controlled industrial microwave drying system. Furthermore, we present the up-to-date augmented reality (AR) technique to support the corresponding data visualization and on-site analysis. As a pioneering study of using AR to benefit IPT systems, the proposed AR module provides straightforward and comprehensible visualizations pertaining to the process data to the related users. Inside the dataflow of the case, a time reversal imaging algorithm, a post-imaging segmentation, and a volumetric visualization module are included. For the time reversal algorithm, we exhaustively introduce each step for MWT image reconstruction and then present the simulated results. For the post-imaging segmentation, an automatic tomographic segmentation algorithm is utilized to reveal the significant information contained in the reconstructed images. For volumetric visualization, the 3D generated information is displayed. Finally, the proposed AR system is integrated with the on-going process data, including reconstructed, segmented, and volumetric images, which are used for facilitating interactive on-site data analysis for domain users. The central part of the AR system is implemented by a mobile app that is currently supported on iOS/Android platforms.

In this paper we present an extension of the statistical associating fluid theory (SAFT) for branched molecules with a Lennard-Jones dimer reference fluid (SAFTD-LJ-Branch). The theory successfully predicts how branched architecture affects the attraction and repulsion between molecules. SAFTD-LJ-Branch takes a form similar to SAFTD-LJ with an additional parameter NB introduced to account for the branching effect. We propose an approach relating NB to the number of different types of articulation segments. The theory is used to study the effect of chain architecture on the thermodynamic properties of isomeric alkanes. SAFTD-LJ-Branch accurately predicts the phase diagram of pure butane, pentane or hexane isomers. Further, vapor pressures of n-triacontane and squalane are predicted without further fitting and shown to be in semiquantitative agreement with experimental data. Finally, SAFTD-LJ-Branch is demonstrated to be well applicable to mixtures as we model the vapor–liquid coexistence of binary alkane m...

This paper studies competitions with rank-based reward among a large number of teams. Within each sizable team, we consider a mean-field contribution game in which each team member contributes to the jump intensity of a common Poisson project process; across all teams, a mean field competition game is formulated on the rank of the completion time, namely the jump time of Poisson project process, and the reward to each team is paid based on its ranking. On the layer of teamwise competition game, three optimization problems are introduced when the team size is determined by: (i) the team manager; (ii) the central planner; (iii) the team members' voting as partnership. We propose a relative performance criteria for each team member to share the team's reward and formulate some special cases of mean field games of mean field games, which are new to the literature. In all problems with homogeneous parameters, the equilibrium control of each worker and the equilibrium or optimal team size can be computed in an explicit manner, allowing us to analytically examine the impacts of some model parameters and discuss their economic implications. Two numerical examples are also presented to illustrate the parameter dependence and comparison between different team size decision making., Comment: Final version, forthcoming in Applied Mathematics and Optimization

Inspired by the high performance in image-based medical analysis, this paper explores the use of advanced segmentation techniques for industrial Microwave Tomography (MWT). Our context is the visual analysis of moisture levels in porous foams undergoing microwave drying. We propose an automatic segmentation technique—MWT Segmentation based on K-means (MWTS-KM) and demonstrate its efficiency and accuracy for industrial use. MWTS-KM consists of three stages: image augmentation, grayscale conversion, and K-means implementation. To estimate the performance of this technique, we empirically benchmark its efficiency and accuracy against two well-established alternatives: Otsu and K-means. To elicit performance data, three metrics (Jaccard index, Dice coefficient and false positive) are used. Our results indicate that MWTS-KM outperforms the well-established Otsu and K-means, both in visually observable and objectively quantitative evaluation.

Inspired by its high performance in image-based medical analysis, this poster paper explores the use of advanced segmentation techniques for industrial Microwave Tomography (MWT). Our context is the visual analysis of moisture levels in porous foams undergoing microwave drying. We propose an automatic segmentation technique---MWT Segmentation based on K-means (MWTS-KM) and demonstrate its efficiency and accuracy for industrial use. MWTS-KM consists of three stages: image augmentation, grey-scale conversion, and K-means implementation. To estimate the performance of this technique, we empirically benchmark its efficiency and accuracy against two well-established alternatives: Otsu and K-means. To elicit performance data, three metrics (Jaccard index, Dice coefficient and false positive) are used. Based on our experiments, our results indicate that MWTS-KM outperforms the well-established Otsu and K-means.

Inspired by the freezing tolerance performances found in living creatures, an effect approach is presented to develop novel antifreezing polymeric organohydrogel actuators. Through construction of a bilayer hydrogel including a nonresponsive polyacrylamide (PAAm) layer and a pH-responsive polyacrylic acid (PAA) layer in the presence of a mixed solvent of water and glycerol, organohydrogel actuators that could produce various shape deformations at subzero temperatures have been achieved, and the actuating speed could be tuned by adjusting the temperature and the ratio between glycerol and water. Moreover, a series of application demonstrations including a weightlifting robot, artificial valve, and robotic arm have been displayed. In addition, by introducing the ionic compound KI into the glycerol-based organogel, flexible conductors that could perform stable sensing performance over a wide range of temperatures from -30°C to 60°C have been developed.

We analyze a mean field tournament: a mean field game in which the agents receive rewards according to the ranking of the terminal value of their projects and are subject to cost of effort. Using Schr\"{o}dinger bridges we are able to explicitly calculate the equilibrium. This allows us to identify the reward functions which would yield a desired equilibrium and solve several related mechanism design problems. We are also able to identify the effect of reward inequality on the players' welfare as well as calculate the price of anarchy., Comment: To appear in Mathematics of Operations Research

The design space of social drones, where autonomous flyers operate in close proximity to human users or bystanders, is distinct from use cases involving a remote human operator and/or an uninhabited environment; and warrants foregrounding human-centered design concerns. Recently, research on social drones has followed a trend of rapid growth. This paper consolidates the current state of the art in human-centered design knowledge about social drones through a review of relevant studies, scaffolded by a descriptive framework of design knowledge creation. Our analysis identified three high-level themes that sketch out knowledge clusters in the literature, and twelve design concerns which unpack how various dimensions of drone aesthetics and behavior relate to pertinent human responses. These results have the potential to inform and expedite future research and practice, by supporting readers in defining and situating their future contributions. The materials and results of our analysis are also published in an open online repository that intends to serve as a living hub for a community of researchers and designers working with social drones.

Detection of a toxic formaldehyde (HCHO) pollutant in aqueous solutions is of significant importance, because HCHO is widely found in aquatic food because of illicit addition or improper storage. Many small-molecule-based fluorescent probes, which rely on HCHO-specific formaldehyde-amine condensation or the aza-Cope rearrangement reaction, have been developed in terms of facile operation and high selectivity. However, some primary challenging issues are the restricted sensitivity and long equilibrium response time caused by the slow chemical reaction between these small-molecule-based sensors and low-concentration HCHO pollutant in testing samples. Herein, a robust hydrophilic hydrazino-naphthalimide-functionalized chitosan (HN-Chitosan)-based polymeric probe is reported, which takes advantage of specific chemical reaction between HCHO and grafted hydrazino-naphthalimide groups to trigger a "turn-on" fluorescence response. Superior to its small-molecule analogs, HN-Chitosan is based on random coil polymer chains of biopolymeric chitosan, which is thus capable of employing the cooperative binding effect of multiple hydrazino-naphthalimide recognition sites and adjacent hydroxyl groups to "enrich" the low-concentration HCHO pollutant around the polymer chains via weak supramolecular interactions. Therefore, the HCHO-specific chemical reaction with grafted hydrazino-naphthalimide groups is significantly accelerated, resulting in the unprecedented ultrafast equilibrium fluorescence response (less than 1 min) and high sensitivity. Encouraged by its satisfying sensitivity, selectivity, fast response, and wide linear detection range, we successfully expand its application to real-world food and water analysis. In view of the modular design principle of our polymeric probe, the proposed strategy could be generally applicable to construct powerful polymeric probes for ultrafast detection of other important pollutants.

Although shape memory polymers have been highlighted widely and developed rapidly, it is still a challenging task to realize complex temporary shapes automatically in practical applications. Herein, a novel shape memory hydrogel with the ability of self-deformation is presented. Through constructing an anisotropic poly(acrylic acid)-polyacrylamide (PAAc-PAAm) structure, the obtained hydrogel exhibits stable self-deformation behavior in response to pH stimulus, and the shapes that formed automatically can be fixed by the coordination between carboxylic groups and Fe3+ ; therefore, self-deformation and shape memory behaviors are integrated in one system. Moreover, the magnitude of auto-deformation and shape memory could be adjusted with the concentration of corresponding ions, leading to programmable shape memory and shape recovery processes.

Some industrial processes take place in confined settings only observable by sensors, e.g. infrared (IR) cameras. Drying processes take place while a material is transported by means of a conveyor through a "black box" equipped with internal IR cameras. While such sensors deliver data at high rates, this is beyond what human operators can analyze and calls for automation. Inspired by numerous implementations monitoring techniques that analyse IR images using deep learning, this paper shows how they can be applied to the confined microwave drying of porous foams, with benchmarking their effectiveness at condition monitoring to conduct fault detection. Convolutional neural networks, derived transfer learning, and deep residual neural network methods are already regarded as cutting-edge and are studied here, using a set of conventional approaches for comparative evaluation. Our comparison shows that state-of-the-art deep learning techniques significantly benefit condition monitoring, providing an increase in fault finding accuracy of up to 48% over conventional methods. Nevertheless, we also found that derived transfer learning and deep residual network techniques do not in our case yield increased performance over normal convolutional neural networks.