Multi-Objective Predictive Taxi Dispatch via Network Flow Optimization

In this paper, we discuss a large-scale fleet management problem in a multi-objective setting. We aim to seek a receding horizon taxi dispatch solution that serves as many ride requests as possible while minimizing the cost of relocating vehicles. To obtain the desired solution, we first convert the multi-objective taxi dispatch problem into a network flow problem, which can be solved using the classical minimum cost maximum flow (MCMF) algorithm. We show that a solution obtained using the MCMF algorithm is integer-valued; thus, it does not require any additional rounding procedure that may introduce undesirable numerical errors. Furthermore, we prove the time-greedy property of the proposed solution, which justifies the use of receding horizon optimization. For computational efficiency, we propose a linear programming method to obtain an optimal solution in near real time. The results of our simulation studies using real-world data for the metropolitan area of Seoul, South Korea indicate that the performance of the proposed predictive method is almost as good as that of the oracle that foresees the future.Comment: 28 pages, 12 figures, Published in IEEE Acces

Associations between maternal stress during pregnancy and offspring internalizing and externalizing problems in childhood

BACKGROUND: Maternal psychological health during pregnancy has been associated with offspring psychopathology. However, it is uncertain whether these associations are mediated by the postpartum depression and related child-rearing factors. Therefore, we examined the associations between prenatal and postnatal factors and internalizing and externalizing behavioral problems in childhood, focusing on maternal psychological health in school-aged children in Korea. FINDINGS: The current study included 1,003 children (580 boys, 423 girls, mean age 9.05 ± 0.70 years, age range 8–11 years) recruited from schools in five Korean cities. Children’s internalizing and externalizing problems were assessed by the Child Behavior Checklist (CBCL). The parents of the children completed structured questionnaires on perinatal factors. Among 1,003 children, 44 had internalizing problems (IP) and 30 had externalizing problems (EP). When comparing children with IP (n = 44) and without IP (n = 959), severe maternal stress during pregnancy (OR3.36, 95% CI 1.80-6.25) and postpartum depression (OR3.19, 95% CI 1.36-7.53) showed a significant association with the IP. When comparing children with EP (n = 30) and without EP (n = 973), low family income (OR2.19, 95% CI 1.05-4.56), unwanted pregnancy (OR2.76, 95% CI 1.28-5.95) and severe maternal stress during pregnancy (OR2.69, 95% CI 1.29-5.61) with the EP. Only maternal stress during pregnancy was significantly associated with the IP after controlling for postpartum depression and with the EP after controlling for family income and unwanted pregnancy. CONCLUSION: This study suggests the importance of maternal psychological health during perinatal period on children’s mental health. Further prospective studies in a larger sample are required to confirm our findings

A Three-Step Resolution-Reconfigurable Hazardous Multi-Gas Sensor Interface for Wireless Air-Quality Monitoring Applications

This paper presents a resolution-reconfigurable wide-range resistive sensor readout interface for wireless multi-gas monitoring applications that displays results on a smartphone. Three types of sensing resolutions were selected to minimize processing power consumption, and a dual-mode front-end structure was proposed to support the detection of a variety of hazardous gases with wide range of characteristic resistance. The readout integrated circuit (ROIC) was fabricated in a 0.18 ??m CMOS process to provide three reconfigurable data conversions that correspond to a low-power resistance-to-digital converter (RDC), a 12-bit successive approximation register (SAR) analog-to-digital converter (ADC), and a 16-bit delta-sigma modulator. For functional feasibility, a wireless sensor system prototype that included in-house microelectromechanical (MEMS) sensing devices and commercial device products was manufactured and experimentally verified to detect a variety of hazardous gases

A Study on the Research Trends of Big Data at Public Libraries : with a Focus on the Journal “Public Library Quarterly”

This study aims to analyze big data-related research trends in the public library field using the social network analysis method. One hundred seventeen articles published in the journal ‘Public Library Quarterly’ were analyzed with author keywords and the frequency, degree centrality, and betweenness centrality of the keywords were examined. The keywords “programs” and “community development” had the highest degree centrality. the keywords “programs,” “strategic planning,” “community development,” “future of libraries,” “outreach,” and “evaluation” post a high degree of centrality and betweenness centrality. The keywords “measurement,” “survey,” “community partnerships,” and “community engagement” showed a high degree of centrality but not high betweenness centrality. On the other hand, the keywords “planning,” “marketing,” “community needs,” and “community building” showed high betweenness centrality but not high degree centrality. Based on the results of this study, public libraries should focus on the following research directions when focusing on big data. First, as mentioned above, a more in-depth discussion is needed regarding COVID-19 and social media. Second, academic interest in big data education and training for public librarians and educational content is needed. Third, public libraries should think about ways to efficiently perform their role as a local data center, including cooperation with other organizations in the local community

Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming

Model-based reinforcement learning (MBRL) has been a primary approach to ameliorating the sample efficiency issue as well as to make a generalist agent. However, there has not been much effort toward enhancing the strategy of dreaming itself. Therefore, it is a question whether and how an agent can "dream better" in a more structured and strategic way. In this paper, inspired by the observation from cognitive science suggesting that humans use a spatial divide-and-conquer strategy in planning, we propose a new MBRL agent, called Dr. Strategy, which is equipped with a novel Dreaming Strategy. The proposed agent realizes a version of divide-and-conquer-like strategy in dreaming. This is achieved by learning a set of latent landmarks and then utilizing these to learn a landmark-conditioned highway policy. With the highway policy, the agent can first learn in the dream to move to a landmark, and from there it tackles the exploration and achievement task in a more focused way. In experiments, we show that the proposed model outperforms prior pixel-based MBRL methods in various visually complex and partially observable navigation tasks.Comment: First two authors contributed equall

Video Probabilistic Diffusion Models in Projected Latent Space

Despite the remarkable progress in deep generative models, synthesizing high-resolution and temporally coherent videos still remains a challenge due to their high-dimensionality and complex temporal dynamics along with large spatial variations. Recent works on diffusion models have shown their potential to solve this challenge, yet they suffer from severe computation- and memory-inefficiency that limit the scalability. To handle this issue, we propose a novel generative model for videos, coined projected latent video diffusion models (PVDM), a probabilistic diffusion model which learns a video distribution in a low-dimensional latent space and thus can be efficiently trained with high-resolution videos under limited resources. Specifically, PVDM is composed of two components: (a) an autoencoder that projects a given video as 2D-shaped latent vectors that factorize the complex cubic structure of video pixels and (b) a diffusion model architecture specialized for our new factorized latent space and the training/sampling procedure to synthesize videos of arbitrary length with a single model. Experiments on popular video generation datasets demonstrate the superiority of PVDM compared with previous video synthesis methods; e.g., PVDM obtains the FVD score of 639.7 on the UCF-101 long video (128 frames) generation benchmark, which improves 1773.4 of the prior state-of-the-art.Comment: Project page: https://sihyun.me/PVD

Re-investigation of Moment Direction in a Kitaev Material α\alpha-RuCl3_{3}

We report X-ray diffraction and resonant elastic X-ray scattering (REXS) studies on two $\alpha$-RuCl$_{3}$ crystals with distinct magnetic transition temperatures: T$_{N}$=7.3K and 6.5K. We find that the sample with T$_{N}$=6.5K exhibits a high degree of structural twinning at low temperature, whereas the T$_{N}$=7.3K sample primarily comprises a single domain of R$\bar{3}$. Notwithstanding, both samples exhibit an identical zigzag magnetic structure, with magnetic moments pointing away from the honeycomb plane by $\alpha=31(2)^{\circ}$. We argue that the identical ordered moment directions in these samples suggest that the intralayer magnetic Hamiltonian remains mostly unchanged regardless of T$_{N}$

Ultrafast dynamics of fractional particles in α\alpha-RuCl3_3

In a Kitaev spin liquid, electron spins can break into fractional particles known as Majorana fermions and Z$_2$ fluxes. Recent experiments have indicated the existence of such fractional particles in a two-dimensional Kitaev material candidate, $\alpha$-RuCl$_3$. These exotic particles can be used in topological quantum computations when braided within their lifetimes. However, the lifetimes of these particles, critical for applications in topological quantum computing, have not been reported. Here we study ultrafast dynamics of photoinduced excitations in single crystals of $\alpha$-RuCl$_3$ using pump-probe transient grating spectroscopy. We observe intriguing photoexcited nonequilibrium states in the Kitaev paramagnetic regime between $T_N$~7 K and $T_H$~100 K, where $T_N$ is the N\'eel temperature and $T_H$ is set by the Kitaev interaction. Two distinct lifetimes are detected: a longer lifetime of ~50 ps, independent of temperature; a shorter lifetime of 1-20 ps, with a strong temperature dependence, $T^{-1.40}$. We analyze the transient grating signals using coupled differential equations and propose that the long and short lifetimes are associated with fractional particles in the Kitaev paramagnetic regime, Z$_2$ fluxes and Majorana fermions, respectively

Machine Learning Based PCB/Package Stack-up Optimization For Signal Integrity

PCB/package stack-up design optimization is time-consuming and requiring a great deal of experience. Although some iterative optimization algorithms are applied to implement automatic stack-up design, evaluating the results of each iteration is still time-intensive. This paper proposes a combined Bayesian optimization-artificial neural network (BO-ANN) algorithm, utilizing a trained ANN-based surrogate model to replace a 2D cross-section analysis tool for fast PCB/package stack-up design optimization. With the acceleration of ANN, the proposed BO-ANN algorithm can finish 100 iterations in 40 seconds while achieving the target characteristic impedance. To better generalize the BO-ANN algorithm, a strategy of effective dielectric calculation is applied to multiple-dielectric stack-up optimization. the BO-ANN algorithm will be able to output optimized stack-up designs with dielectric layers chosen from the pre-defined library and the obtained designs are verified by 2D solver