Due Process in Civil Forfeiture Cases in Washington after \u3ci\u3eTellevik v. Real Property\u3c/i\u3e

In Tellevik v. Real Property, the Washington Supreme Court held that the government\u27s seizure of real property through an ex parte proceeding complied with the due process requirements of the federal Constitution. This Note examines the Tellevik decision in light of United States Supreme Court case law on procedural due process and lower federal court rulings in real property forfeiture cases. It argues that the Tellevik court, in reaching its decision, misapplied federal case law and concludes that due process requires an opportunity for a full hearing before the government can deprive an owner of real property

HollowNeRF: Pruning Hashgrid-Based NeRFs with Trainable Collision Mitigation

Neural radiance fields (NeRF) have garnered significant attention, with recent works such as Instant-NGP accelerating NeRF training and evaluation through a combination of hashgrid-based positional encoding and neural networks. However, effectively leveraging the spatial sparsity of 3D scenes remains a challenge. To cull away unnecessary regions of the feature grid, existing solutions rely on prior knowledge of object shape or periodically estimate object shape during training by repeated model evaluations, which are costly and wasteful. To address this issue, we propose HollowNeRF, a novel compression solution for hashgrid-based NeRF which automatically sparsifies the feature grid during the training phase. Instead of directly compressing dense features, HollowNeRF trains a coarse 3D saliency mask that guides efficient feature pruning, and employs an alternating direction method of multipliers (ADMM) pruner to sparsify the 3D saliency mask during training. By exploiting the sparsity in the 3D scene to redistribute hash collisions, HollowNeRF improves rendering quality while using a fraction of the parameters of comparable state-of-the-art solutions, leading to a better cost-accuracy trade-off. Our method delivers comparable rendering quality to Instant-NGP, while utilizing just 31% of the parameters. In addition, our solution can achieve a PSNR accuracy gain of up to 1dB using only 56% of the parameters.Comment: Accepted to ICCV 202

Effective Real Image Editing with Accelerated Iterative Diffusion Inversion

Despite all recent progress, it is still challenging to edit and manipulate natural images with modern generative models. When using Generative Adversarial Network (GAN), one major hurdle is in the inversion process mapping a real image to its corresponding noise vector in the latent space, since its necessary to be able to reconstruct an image to edit its contents. Likewise for Denoising Diffusion Implicit Models (DDIM), the linearization assumption in each inversion step makes the whole deterministic inversion process unreliable. Existing approaches that have tackled the problem of inversion stability often incur in significant trade-offs in computational efficiency. In this work we propose an Accelerated Iterative Diffusion Inversion method, dubbed AIDI, that significantly improves reconstruction accuracy with minimal additional overhead in space and time complexity. By using a novel blended guidance technique, we show that effective results can be obtained on a large range of image editing tasks without large classifier-free guidance in inversion. Furthermore, when compared with other diffusion inversion based works, our proposed process is shown to be more robust for fast image editing in the 10 and 20 diffusion steps' regimes.Comment: Accepted to ICCV 2023 (Oral

Smooth and Stepwise Self-Distillation for Object Detection

Distilling the structured information captured in feature maps has contributed to improved results for object detection tasks, but requires careful selection of baseline architectures and substantial pre-training. Self-distillation addresses these limitations and has recently achieved state-of-the-art performance for object detection despite making several simplifying architectural assumptions. Building on this work, we propose Smooth and Stepwise Self-Distillation (SSSD) for object detection. Our SSSD architecture forms an implicit teacher from object labels and a feature pyramid network backbone to distill label-annotated feature maps using Jensen-Shannon distance, which is smoother than distillation losses used in prior work. We additionally add a distillation coefficient that is adaptively configured based on the learning rate. We extensively benchmark SSSD against a baseline and two state-of-the-art object detector architectures on the COCO dataset by varying the coefficients and backbone and detector networks. We demonstrate that SSSD achieves higher average precision in most experimental settings, is robust to a wide range of coefficients, and benefits from our stepwise distillation procedure.Comment: Accepted by International Conference on Image Processing (ICIP) 202

Tailoring single-atom FeN4 moieties as a robust heterogeneous catalyst for high-performance electro-Fenton treatment of organic pollutants

An iron single-atom catalyst, composed of robust FeN4 moieties anchored on a nitrogen-doped porous carbón matrix (Fe-SAC/NC), has been developed via a surfactant-coordinated metal-organic framework (MOF) approach for application in heterogeneous electro-Fenton (HEF) process. The cohesive interaction between the surfactant and MOF precursor enabled the formation of abundant and stable FeN4 moieties. The Fe-SAC/NC-catalyzed HEF allowed the complete degradation of 2,4-dichlorophenol with low iron leaching (1.2 mg L-1), being superior to nanoparticle catalyst synthesized without surfactant. The experiments and density functional theory (DFT) calculations demonstrated the dominant role of single-atom FeN4 sites to activate the electrogenerated H2O2 yielding ¿OH. The dense FeN4 moieties allowed harnessing the modulated electronic structure of the SAC to facilitate the electron transfer, whereas the adjacent pyrrolic N enhanced the adsorption of target organic pollutants. Moreover, the excellent catalysis, recyclability and viability of the Fe-SAC/NC were verified by successfully treating several organic pollutants even in urban wastewater

Ultra-uniform MIL-88B(Fe)/Fe3S4 hybrids engineered by partial sulfidation to boost catalysis in electro-Fenton treatment of micropollutants: Experimental and mechanistic insights

Fe-based metal-organic frameworks are promising catalysts for water treatment, although their viability is hampered by the slow regeneration of active Fe(II) sites. A facile sulfidation strategy is proposed to boost the catalytic activity of MIL-88B(Fe) in heterogeneous electro-Fenton (HEF) treatment of organic micropollutants at mild pH. The synthesized MIL-88B(Fe)/Fe3S4 hybrids possessed numerous and durable unsaturated iron sites, acting the S2- atoms as electron donors that enhanced the Fe(II) recycling. The sulfidated catalyst outperformed the MIL-88B(Fe), as evidenced by the 7-fold faster degradation of antibiotic trimethoprim by HEF and the fast destruction of micropollutants in urban wastewater. The hybrid catalyst was reused, obtaining >90% drug removal after four runs and, additionally, its inherent magnetism facilitated the post-treatment recovery. Electrochemical tests and DFT calculations provided mechanistic insights to explain the enhanced catalysis, suggesting that the accelerated Fe(III)/Fe(II) cycling and the enhanced mass transport and electron transfer accounted for the efficient trimethoprim degradation

The analysis of barriers to bim implementation for industrialized building construction: a China study

The emerging Building Information Modeling (BIM) can better promote the development of building industrialization, with data integration between information-rich building models and business processes. However, the practical implementation of BIM still faces barriers. Existing studies have discussed these barriers extensively, but the research on the barriers to the implementation of BIM amid building industrialization in China is inadequate. In this study, 23 barriers were identified through literature review. A questionnaire survey approach was used to collect data from various parties. Factor analysis methods were used to process and rank barrier factors for BIM applications in the context of industrialized building. Based on the analysis of each factor, analytic hierarchy process was adopted to identify the key barriers to the implementation of BIM for industrialized building construction. The study concluded that the main barriers for BIM implementation for industrialized building were capital-related factors and the lack of support from owners. This study proposes that in addition to governmental policy support for BIM and multi-stakeholder engagement, companies should also organize experts to effectively evaluate the risks of applying BIM. Overall, this study provides suggestions on construction organizational transformations in the roadmap of moving towards digital-driven building industrialization