Predicting Airline Choices: A Decision Support Perspective and Alternative Approaches

The ability to predict the choices of prospective passengers allows airlines to alleviate the need for overbooking flights and subsequently bumping passengers, potentially leading to improved customer satisfaction. Past studies have typically focused on identifying the important factors that influence choice behaviors and applied discrete choice framework models to model passengers’ airline choices. Typical discrete choice models rely on two major assumptions: the existence of a utility function that represents the preferences over a choice set and the linearity of the utility function with respect to attributes of alternatives and decision makers. These assumptions allow the discrete choice models to be easily interpreted, as each unit change of an input attribute can be directly translated into change in utility that eventually affects the optimal choice. However, these restrictive assumptions might impede the ability of typical discrete choice models to deliver operational accurate prediction and forecasts. In this paper, we focus on developing operational models that are intended for supporting the actual prediction decisions of airlines. We propose two alternative approaches, pairwise preference learning using classification techniques and ranking function learning using evolutionary computation. We have empirically compared these approaches against the standard discrete choice framework models and report some promising results in this paper

Robust AN-Aided Beamforming Design for Secure MISO Cognitive Radio Based on a Practical Nonlinear EH Model

Energy harvesting techniques are promising in next generation wireless communication systems. However, most of the existing works are based on an ideal linear energy harvesting model. In this paper, a multiple-input single-output cognitive radio network is studies under a practical non-linear energy harvesting model. In order to improve the security of both the primary network and the secondary network, a cooperative jamming scheme is proposed. A robust artificial noise aided beamforming design problem is formulated under the bounded channel state information error model. The formulated problem is non-convex and challenging to be solved. Using S-procedure and the semidefinite relaxation method, a suboptimal beamforming can be obtained. Simulation results show that the performance achieved under the non-linear energy harvesting model may be better than that obtained under the linear energy harvesting model. It is also shown that the cooperation betwen the primary network and the secondary network can obtain a performance gain compared with that without this cooperation

Comparison of tooth movement and biological response resulting from different force magnitudes combined with osteoperforation in rabbits

Objective: To compare tooth movement rate and histological responses with three different force magnitude designs under osteoperforation in rabbit models. Methodology: 48 rabbits were divided into three groups: Group A, Group B, and Group C, with traction force of 50 g, 100 g, 150 g, respectively. Osteoperforation was performed at the mesial of the right mandibular first premolar, the left side was not affected. One mini-screw was inserted into bones between two central incisors. Coil springs were fixed to the first premolars and the mini-screw. Tooth movement distance was calculated, and immunohistochemical staining of PCNA, OCN, VEGF, and TGF-β1 was analyzed. Results: The tooth movement distance on the surgical side was larger than the control side in all groups (P<0.01). No significant intergroup difference was observed for the surgical side in tooth movement distance among the three groups (P>0.05). For the control side, tooth movement distance in Group A was significantly smaller than Groups B and C (P<0.001); no significant difference in tooth movement distance between Group B and Group C was observed (P>0.05). On the tension area of the moving premolar, labeling of PCNA, OCN, VEGF and TGF-β1 were confirmed in alveolar bone and periodontal ligament in all groups. PCNA, OCN, VEGF and TGF-β1 on the surgical side was larger than the control side in all groups (P<0.001). Conclusion: Osteoperforation could accelerate orthodontic tooth movement rate in rabbits. Fast osteoperforation-assisted tooth movement in rabbits was achieve with light 50 g traction

Research of Simulation in Character Animation Based on Physics Engine

Computer 3D character animation essentially is a product, which is combined with computer graphics and robotics, physics, mathematics, and the arts. It is based on computer hardware and graphics algorithms and related sciences rapidly developed new technologies. At present, the mainstream character animation technology is based on the artificial production of key technologies and capture frames based on the motion capture device technology. 3D character animation is widely used not only in the production of film, animation, and other commercial areas but also in virtual reality, computer-aided education, flight simulation, engineering simulation, military simulation, and other fields. In this paper, we try to study physics based character animation to solve these problems such as poor real-time interaction that appears in the character, low utilization rate, and complex production. The paper deeply studied the kinematics, dynamics technology, and production technology based on the motion data. At the same time, it analyzed ODE, PhysX, Bullet, and other variety of mainstream physics engines and studied OBB hierarchy bounding box tree, AABB hierarchical tree, and other collision detection algorithms. Finally, character animation based on ODE is implemented, which is simulation of the motion and collision process of a tricycle

Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation

Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding. Existing efforts to create a general vision model are limited in the scope of assessed tasks and offer no overarching framework to perform them holistically. We present a new comprehensive benchmark, General-purpose Visual Understanding Evaluation (G-VUE), covering the full spectrum of visual cognitive abilities with four functional domains \unicode{x2014} Perceive, Ground, Reason, and Act. The four domains are embodied in 11 carefully curated tasks, from 3D reconstruction to visual reasoning and manipulation. Along with the benchmark, we provide a general encoder-decoder framework to allow for the evaluation of arbitrary visual representation on all 11 tasks. We evaluate various pre-trained visual representations with our framework and observe that (1) Transformer-based visual backbone generally outperforms CNN-based backbone on G-VUE, (2) visual representations from vision-language pre-training are superior to those with vision-only pre-training across visual tasks. With G-VUE, we provide a holistic evaluation standard to motivate research toward building general-purpose visual systems via obtaining more general-purpose visual representations

Neural Kernel Surface Reconstruction

We present a novel method for reconstructing a 3D implicit surface from a large-scale, sparse, and noisy point cloud. Our approach builds upon the recently introduced Neural Kernel Fields (NKF) representation. It enjoys similar generalization capabilities to NKF, while simultaneously addressing its main limitations: (a) We can scale to large scenes through compactly supported kernel functions, which enable the use of memory-efficient sparse linear solvers. (b) We are robust to noise, through a gradient fitting solve. (c) We minimize training requirements, enabling us to learn from any dataset of dense oriented points, and even mix training data consisting of objects and scenes at different scales. Our method is capable of reconstructing millions of points in a few seconds, and handling very large scenes in an out-of-core fashion. We achieve state-of-the-art results on reconstruction benchmarks consisting of single objects, indoor scenes, and outdoor scenes.Comment: CVPR 202

Pulmonary alveolar type I cell population consists of two distinct subtypes that differ in cell fate.

Pulmonary alveolar type I (AT1) cells cover more than 95% of alveolar surface and are essential for the air-blood barrier function of lungs. AT1 cells have been shown to retain developmental plasticity during alveolar regeneration. However, the development and heterogeneity of AT1 cells remain largely unknown. Here, we conducted a single-cell RNA-seq analysis to characterize postnatal AT1 cell development and identified insulin-like growth factor-binding protein 2 (Igfbp2) as a genetic marker specifically expressed in postnatal AT1 cells. The portion of AT1 cells expressing Igfbp2 increases during alveologenesis and in post pneumonectomy (PNX) newly formed alveoli. We found that the adult AT1 cell population contains both Hopx+Igfbp2+ and Hopx+Igfbp2- AT1 cells, which have distinct cell fates during alveolar regeneration. Using an Igfbp2-CreER mouse model, we demonstrate that Hopx+Igfbp2+ AT1 cells represent terminally differentiated AT1 cells that are not able to transdifferentiate into AT2 cells during post-PNX alveolar regeneration. Our study provides tools and insights that will guide future investigations into the molecular and cellular mechanism or mechanisms underlying AT1 cell fate during lung development and regeneration