Tenders with Different Risk Preferences in Construction Industry

Underlying the fact that different tenderers have different preferences on risk-taking, this study investigates the different tenderers' behaviors in one-shot construction bid auctions. Our model extends the preconditions of previous assumption that all tenderers are characterized by neutral risk-taking in the original tendering model for lowest-price sealed tender. A general tendering model for the lowest-price sealed tender is established to explain the behavior of tenderers during the tendering. The results indicate that construction estimate is affected by the degree of uncertainties in the construction industry. Therefore, in a lowest-price sealed tender, risk-averse tenders would tender a higher price and conversely risk-seeking tenderers would tender a lower price when risk-neutral tenderers would tender a middle price. However, the risk-seeking tenderers are more likely to win the bid.Auction, tender, uncertainty, preference, construction industry

ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylization

The radiance fields style transfer is an emerging field that has recently gained popularity as a means of 3D scene stylization, thanks to the outstanding performance of neural radiance fields in 3D reconstruction and view synthesis. We highlight a research gap in radiance fields style transfer, the lack of sufficient perceptual controllability, motivated by the existing concept in the 2D image style transfer. In this paper, we present ARF-Plus, a 3D neural style transfer framework offering manageable control over perceptual factors, to systematically explore the perceptual controllability in 3D scene stylization. Four distinct types of controls - color preservation control, (style pattern) scale control, spatial (selective stylization area) control, and depth enhancement control - are proposed and integrated into this framework. Results from real-world datasets, both quantitative and qualitative, show that the four types of controls in our ARF-Plus framework successfully accomplish their corresponding perceptual controls when stylizing 3D scenes. These techniques work well for individual style inputs as well as for the simultaneous application of multiple styles within a scene. This unlocks a realm of limitless possibilities, allowing customized modifications of stylization effects and flexible merging of the strengths of different styles, ultimately enabling the creation of novel and eye-catching stylistic effects on 3D scenes

Business Strategy, State-Owned Equity and Cost Stickiness: Evidence from Chinese Firms

This paper investigates the relationship between business strategy and cost stickiness under different ownership. Using the data from listed firms in China from 2002 to 2015, we find that first, firms with different strategies exhibit different cost behavior. The cost stickiness of choosing a differentiation strategy is higher than that of choosing a low-cost strategy. Second, management expectations will affect cost stickiness. Optimistic expectations will increase cost stickiness, while pessimistic expectations will reduce cost stickiness. Third, management expectations can adjust the relationship between business strategy and cost stickiness in terms of government-created advantages (GCAs). If management expectations tend to be optimistic, the cost stickiness is higher with a differentiation strategy than with a low-cost strategy. If management expectations tend to be pessimistic, then cost stickiness is higher with a low-cost strategy than with a differentiation strategy. Finally, the state-owned equity affects the extent of the effect of a differentiation strategy on cost stickiness. State-owned firms, which receive more GCAs than non-state-owned firms, have stronger cost stickiness than non-state-owned firms, even if both categories of firms use more differentiation strategy

D2^2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video

Given a monocular video, segmenting and decoupling dynamic objects while recovering the static environment is a widely studied problem in machine intelligence. Existing solutions usually approach this problem in the image domain, limiting their performance and understanding of the environment. We introduce Decoupled Dynamic Neural Radiance Field (D$^2$NeRF), a self-supervised approach that takes a monocular video and learns a 3D scene representation which decouples moving objects, including their shadows, from the static background. Our method represents the moving objects and the static background by two separate neural radiance fields with only one allowing for temporal changes. A naive implementation of this approach leads to the dynamic component taking over the static one as the representation of the former is inherently more general and prone to overfitting. To this end, we propose a novel loss to promote correct separation of phenomena. We further propose a shadow field network to detect and decouple dynamically moving shadows. We introduce a new dataset containing various dynamic objects and shadows and demonstrate that our method can achieve better performance than state-of-the-art approaches in decoupling dynamic and static 3D objects, occlusion and shadow removal, and image segmentation for moving objects

Neural Fields with Hard Constraints of Arbitrary Differential Order

While deep learning techniques have become extremely popular for solving a broad range of optimization problems, methods to enforce hard constraints during optimization, particularly on deep neural networks, remain underdeveloped. Inspired by the rich literature on meshless interpolation and its extension to spectral collocation methods in scientific computing, we develop a series of approaches for enforcing hard constraints on neural fields, which we refer to as Constrained Neural Fields (CNF). The constraints can be specified as a linear operator applied to the neural field and its derivatives. We also design specific model representations and training strategies for problems where standard models may encounter difficulties, such as conditioning of the system, memory consumption, and capacity of the network when being constrained. Our approaches are demonstrated in a wide range of real-world applications. Additionally, we develop a framework that enables highly efficient model and constraint specification, which can be readily applied to any downstream task where hard constraints need to be explicitly satisfied during optimization.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Path from Photorealism to Perceptual Realism

Photorealism in computer graphics — rendering images that appear as realistic as photographs — has matured to the point that it is now widely used in industry. With emerging 3D display technologies, the next big challenge in graphics is to achieve Perceptual Realism — producing virtual imagery that is perceptually indistinguishable from real-world 3D scenes. Such a significant upgrade in the level of realism offers highly immersive and engaging experiences that have the potential to revolutionise numerous aspects of life and society, including entertainment, social networks, education, business, research, engineering, and design. While perceptual realism puts strict requirements on the quality of reproduction, the virtual scene does not have to be identical in light distributions to its physical counterpart to be perceptually realistic, providing that it is visually indistinguishable to human eyes. Due to the limitations of human vision, a significant improvement in perceptual realism can, in principle, be achieved by fulfilling the essential visual requirements with sufficient qualities and without having to reconstruct the physically accurate distribution of lights. In this dissertation, we start by discussing the capabilities and limits of the human visual system, which serves as a basis for the analysis of the essential visual requirements for perceptual realism. Next, we introduce a Perceptually Realistic Graphics (PRG) pipeline consisting of the acquisition, representation, and reproduction of the plenoptic function of a 3D scene. Finally, we demonstrate that taking advantage of the limits and mechanisms of the human visual system can significantly improve this pipeline. Specifically, we present three approaches to push the quality of virtual imagery towards perceptual realism. First, we introduce DiCE, a real-time rendering algorithm that exploits the binocular fusion mechanism of the human visual system to boost the perceived local contrast of stereoscopic displays. The method was inspired by an established model of binocular contrast fusion. To optimise the experience of binocular fusion, we proposed and empirically validated a rivalry-prediction model that better controls rivalry. Next, we introduce Dark Stereo, another real-time rendering algorithm that facilitates depth perception from binocular depth cues for stereoscopic displays, especially those under low luminance. The algorithm was designed based on a proposed model of stereo constancy that predicts the precision of binocular depth cues for a given contrast and luminance. Both DiCE and Dark Stereo have been experimentally demonstrated to be effective in improving realism. Their real-time performance also makes them readily integrable into any existing VR rendering pipeline. Nonetheless, only improving rendering is not sufficient to meet all the visual requirements for perceptual realism. The overall fidelity of a typical stereoscopic VR display is still confined by its limited dynamic range, low spatial resolution, optical aberrations, and vergence-accommodation conflicts. To push the limits of the overall fidelity, we present a High-Dynamic-Range Multi-Focal Stereo display (HDR-MF-S display) with an end-to-end imaging and rendering system. The system can visually reproduce real-world 3D objects with high resolution, accurate colour, a wide dynamic range and contrast, and most depth cues, including binocular disparity and focal depth cues, and permits a direct comparison between real and virtual scenes. It is the first work that achieves a close perceptual match between a physical 3D object and its virtual counterpart. The fidelity of reproduction has been confirmed by a Visual Turing Test (VTT) where naive participants failed to discern any difference between the real and virtual objects in more than half of the trials. The test provides insights to better understand the conditions necessary to achieve perceptual realism. In the long term, we foresee this system as a crucial step in the development of perceptually realistic graphics, for not only a quality unprecedentedly achieved but also a fundamental approach that can effectively identify bottlenecks and direct future studies for perceptually realistic graphics

CLIP-PAE: Projection-Augmentation Embedding to Extract Relevant Features for a Disentangled, Interpretable, and Controllable Text-Guided Face Manipulation

Recently introduced Contrastive Language-Image Pre-Training (CLIP) bridges images and text by embedding them into a joint latent space. This opens the door to ample literature that aims to manipulate an input image by providing a textual explanation. However, due to the discrepancy between image and text embeddings in the joint space, using text embeddings as the optimization target often introduces undesired artifacts in the resulting images. Disentanglement, interpretability, and controllability are also hard to guarantee for manipulation. To alleviate these problems, we propose to define corpus subspaces spanned by relevant prompts to capture specific image characteristics. We introduce CLIP Projection-Augmentation Embedding (PAE) as an optimization target to improve the performance of text-guided image manipulation. Our method is a simple and general paradigm that can be easily computed and adapted, and smoothly incorporated into any CLIP-based image manipulation algorithm. To demonstrate the effectiveness of our method, we conduct several theoretical and empirical studies. As a case study, we utilize the method for text-guided semantic face editing. We quantitatively and qualitatively demonstrate that PAE facilitates a more disentangled, interpretable, and controllable image manipulation with state-of-the-art quality and accuracy

Alternating Current Discharge Characteristics and Simulation Analysis of Rod-Plane Short Air Gaps under Salt Fog Conditions

In this paper, smog meteorological conditions in the natural environment is simulated by the salt fog method. The study of the alternating current (AC) discharge characteristics of rod-plane short air gaps in salt fog environments has important guiding significance for how to strengthen the external insulation strength of ultra-high voltage (UHV) transmission lines and electrical equipment in smog environments. The rod-plane short air gap is selected as the model to simulate the extremely uneven electric field. The AC discharge test is carried out in the salt fog environment with different conductivity, and the finite element method (FEM) is used to simulate the distribution of electric field in air gap under salt fog environment conditions. The results show that under clean fog conditions the AC discharge voltage in the air gap increased by 15.1% to 35.5% compared to that under dry conditions. With the increased conductivity of salt fog, the AC discharge voltage in air gap decreased by 4.1% to 9.2% compared to that under clean fog conditions, and the reduction is within 10%. The distortion of the electric field and the adsorption of free electrons in the gap by droplets lead to the decrease of the electric field intensity in the air gap. With the increase of the conductivity, the electric field intensity in the air gap increases slightly. Meanwhile, the influence of salt fog and its conductivity on the AC discharge voltage of rod-plane short air gap is examined, becoming saturated with the increase of the gap distance and the conductivity of salt fog