Rayleigh-Schroedinger-Goldstone variational perturbation theory for many fermion systems

We present a Rayleigh-Schroedinger-Goldstone perturbation formalism for many fermion systems. Based on this formalism, variational perturbation scheme which goes beyond the Gaussian approximation is developed. In order to go beyond the Gaussian approximation, we identify a parent Hamiltonian which has an effective Gaussian vacuum as a variational solution and carry out further perturbation with respect to the renormalized interaction using Goldstone's expansion. Perturbation rules for the ground state wavefunctional and energy are found. Useful commuting relations between operators and the Gaussian wavefunctional are also found, which could reduce the calculational efforts substantially. As examples, we calculate the first order correction to the Gaussian wavefunctional and the second order correction to the ground state of an electron gas system with the Yukawa-type interaction.Comment: 11pages, 1figur

Erasing the Ephemeral: Joint Camera Refinement and Transient Object Removal for Street View Synthesis

Synthesizing novel views for urban environments is crucial for tasks like autonomous driving and virtual tours. Compared to object-level or indoor situations, outdoor settings present unique challenges, such as inconsistency across frames due to moving vehicles and camera pose drift over lengthy sequences. In this paper, we introduce a method that tackles these challenges on view synthesis for outdoor scenarios. We employ a neural point light field scene representation and strategically detect and mask out dynamic objects to reconstruct novel scenes without artifacts. Moreover, we simultaneously optimize camera pose along with the view synthesis process, and thus, we simultaneously refine both elements. Through validation on real-world urban datasets, we demonstrate state-of-the-art results in synthesizing novel views of urban scenes

An examination of IPO performance in Canada's manufacturing industry

1 online resource (v, 30 p.)Includes abstract and appendices.Includes bibliographical references (p. 24-25).This paper investigates the IPO price performance of Canada’s manufacturing firms. We examine the theory and evidence on IPO activities: in the manufacturing sector based on first day of trading, and the long run IPO performance with respect to different benchmarks. The result shows that IPOs are underpriced in the initial issue period. However, the IPO performance which relative to S&P/TSX composite index confirms that it is overpriced in the long run, while the performance which relative to Dow Jones Industrial average index shows the IPO is underprice

Coloring the Past: Neural Historical Buildings Reconstruction from Archival Photography

Historical buildings are a treasure and milestone of human cultural heritage. Reconstructing the 3D models of these building hold significant value. The rapid development of neural rendering methods makes it possible to recover the 3D shape only based on archival photographs. However, this task presents considerable challenges due to the limitations of such datasets. Historical photographs are often limited in number and the scenes in these photos might have altered over time. The radiometric quality of these images is also often sub-optimal. To address these challenges, we introduce an approach to reconstruct the geometry of historical buildings, employing volumetric rendering techniques. We leverage dense point clouds as a geometric prior and introduce a color appearance embedding loss to recover the color of the building given limited available color images. We aim for our work to spark increased interest and focus on preserving historical buildings. Thus, we also introduce a new historical dataset of the Hungarian National Theater, providing a new benchmark for the reconstruction method

Weight-Aware Implicit Geometry Reconstruction with Curvature-Guided Sampling

Neural surface implicit representations offer numerous advantages, including the ability to easily modify topology and surface resolution. However, reconstructing implicit geometry representation with only limited known data is challenging. In this paper, we present an approach that effectively interpolates and extrapolates within training points, generating additional training data to reconstruct a surface with superior qualitative and quantitative results. We also introduce a technique that efficiently calculates differentiable geometric properties, i.e., mean and Gaussian curvatures, to enhance the sampling process during training. Additionally, we propose a weight-aware implicit neural representation that not only streamlines surface extraction but also extend to non-closed surfaces by depicting non-closed areas as locally degenerated patches, thereby mitigating the drawbacks of the previous assumption in implicit neural representations.Comment: 9 page

Low-Mid Adversarial Perturbation against Unauthorized Face Recognition System

In light of the growing concerns regarding the unauthorized use of facial recognition systems and its implications on individual privacy, the exploration of adversarial perturbations as a potential countermeasure has gained traction. However, challenges arise in effectively deploying this approach against unauthorized facial recognition systems due to the effects of JPEG compression on image distribution across the internet, which ultimately diminishes the efficacy of adversarial perturbations. Existing JPEG compression-resistant techniques struggle to strike a balance between resistance, transferability, and attack potency. To address these limitations, we propose a novel solution referred to as \emph{low frequency adversarial perturbation} (LFAP). This method conditions the source model to leverage low-frequency characteristics through adversarial training. To further enhance the performance, we introduce an improved \emph{low-mid frequency adversarial perturbation} (LMFAP) that incorporates mid-frequency components for an additive benefit. Our study encompasses a range of settings to replicate genuine application scenarios, including cross backbones, supervisory heads, training datasets, and testing datasets. Moreover, we evaluated our approaches on a commercial black-box API, \texttt{Face++}. The empirical results validate the cutting-edge performance achieved by our proposed solutions.Comment: published in Information Science

Malicious Selling Strategies During Livestream Shopping: A Case Study of Alibaba's Taobao and ByteDance's TikTok

Due to the limitations imposed by the COVID-19 pandemic, many users have shifted their shopping patterns from offline to online. Livestream shopping has become popular as one of the online shopping media. However, many streamers' malicious selling behaviors have been reported. In this research, we sought to explore streamers' malicious selling strategies and understand how viewers perceive these strategies. First, we recorded 40 livestream shopping sessions from two popular livestream platforms in China -- Taobao and TikTok (or "Douyin" in Chinese). We identified four categories of malicious selling strategies (i.e., Restrictive, Deceptive, Covert, and Asymmetric) and found that platform designs enhanced these malicious selling strategies. Second, through an interview study with 13 viewers, we provide a rich description of viewers' awareness of malicious selling strategies and the challenges they encountered while trying to overcome malicious selling. We conclude by discussing the policy and design implications of countering malicious selling

Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction

Many works employed prompt tuning methods to automatically optimize prompt queries and extract the factual knowledge stored in Pretrained Language Models. In this paper, we observe that the optimized prompts, including discrete prompts and continuous prompts, exhibit undesirable object bias. To handle this problem, we propose a novel prompt tuning method called MeCoD. consisting of three modules: Prompt Encoder, Object Equalization and Biased Object Obstruction. Experimental results show that MeCoD can significantly reduce the object bias and at the same time improve accuracy of factual knowledge extraction