Architecture of Afterlife: Future Cemetery in Metropolis

Do you believe soul never dies? Do you believe in afterlife? Think but do not answer. How do you remember a loved one who passed away? How would you like to remember a loved one who has passed away? Think but do not answer. Life and Death presents an eternal topic for human beings to study. Cemeteries, human being’s last stop and final dwellings, is the primary type of funerary architecture. Contemporary Cemetery is facing a severe challenge, namely the shortage of burial space, especially in Metropolis such as London, Tokyo, Hong Kong, and etc. This severe dearth of permanent burial grounds, coupled with the high cost of rental ash holding agencies has struck a near fatal blow to the contemporary cemetery development. In my opinion, the options for future cemeteries are either change, shrink or disappear. This doctor dissertation will mainly discuss the major problems of the conventional and contemporary cemetery and use the latest relevance case studies to explore and propose design guidelines for future cemetery design. The design research chapter will show an example of how to apply these guidelines into the architecture project in order to make the future cemetery carry on the culture and spirit aspect along with innovative technologies

DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration

We present DeepICP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods. Different from other keypoint based methods where a RANSAC procedure is usually needed, we implement the use of various deep neural network structures to establish an end-to-end trainable network. Our keypoint detector is trained through this end-to-end structure and enables the system to avoid the inference of dynamic objects, leverages the help of sufficiently salient features on stationary objects, and as a result, achieves high robustness. Rather than searching the corresponding points among existing points, the key contribution is that we innovatively generate them based on learned matching probabilities among a group of candidates, which can boost the registration accuracy. Our loss function incorporates both the local similarity and the global geometric constraints to ensure all above network designs can converge towards the right direction. We comprehensively validate the effectiveness of our approach using both the KITTI dataset and the Apollo-SouthBay dataset. Results demonstrate that our method achieves comparable or better performance than the state-of-the-art geometry-based methods. Detailed ablation and visualization analysis are included to further illustrate the behavior and insights of our network. The low registration error and high robustness of our method makes it attractive for substantial applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results updated, accepted by ICCV 201

First-principles Study of High-Pressure Phase Stability and Superconductivity of Bi4I4

Bismuth iodide Bi4I4 exhibits intricate crystal structures and topological insulating states that are highly susceptible to influence by environments, making its physical properties highly tunable by external conditions. In this work, we study the evolution of structural and electronic properties of Bi4I4 at high pressure using an advanced structure search method in conjunction with first-principles calculations. Our results indicate that the most stable ambient-pressure monoclinic α−Bi4I4 phase in C2/m symmetry transforms to a trigonal P31c structure (ɛ−Bi4I4) at 8.4 GPa, then to a tetragonal P4/mmm structure (ζ−Bi4I4) above 16.6 GPa. In contrast to the semiconducting nature of ambient-pressure Bi4I4, the two high-pressure phases are metallic, in agreement with reported electrical measurements. The ɛ−Bi4I4 phase exhibits distinct ionic states of Iδ− and (Bi4I3)δ + (δ=0.4123 e), driven by a pressure-induced volume reduction. We show that both ɛ- and ζ−Bi4I4 are superconductors, and the emergence of pressure-induced superconductivity might be intimately linked to the underlying structural phase transitions

Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models

The rise of deepfake images, especially of well-known personalities, poses a serious threat to the dissemination of authentic information. To tackle this, we present a thorough investigation into how deepfakes are produced and how they can be identified. The cornerstone of our research is a rich collection of artificial celebrity faces, titled DeepFakeFace (DFF). We crafted the DFF dataset using advanced diffusion models and have shared it with the community through online platforms. This data serves as a robust foundation to train and test algorithms designed to spot deepfakes. We carried out a thorough review of the DFF dataset and suggest two evaluation methods to gauge the strength and adaptability of deepfake recognition tools. The first method tests whether an algorithm trained on one type of fake images can recognize those produced by other methods. The second evaluates the algorithm's performance with imperfect images, like those that are blurry, of low quality, or compressed. Given varied results across deepfake methods and image changes, our findings stress the need for better deepfake detectors. Our DFF dataset and tests aim to boost the development of more effective tools against deepfakes.Comment: 8 pages, 5 figure

Human Papillomavirus Types 16 E6 and E7 Contribute Differently to Carcinogenesis

AbstractHigh-risk human papillomaviruses (HPVs) are etiologically implicated in human cervical cancer. Two viral genes, E6 and E7, are commonly found expressed in these cancer cells. We have previously shown that mice transgenic for the HPV-16 E6 gene or E7 gene, in which the E6 or E7 was expressed in the basal layer of epithelia, developed skin tumors. The spectrum of tumors derived from E6 and E7 mice differed, however; although most tumors derived from the E7-transgenic mice were benign, the majority of the tumors from the E6-transgenic mice were malignant. These findings led us to hypothesize that E6 and E7 play different roles in carcinogenesis. To assess at what stages in carcinogenesis E6 and E7 act, we treated the skin of K14E6- and K14E7-transgenic mice with chemical carcinogens known to contribute to distinct stages in carcinogenesis. Both E6 and E7 were found to synergize with chemical carcinogens in causing tumor formation. E6 was found to act weakly at the promotion stage of carcinogenesis in the formation of benign tumors but strongly at the progression stage which involves the malignant conversion of benign tumors. In contrast, E7 primarily affected the promotion stage of carcinogenesis. These results provide direct evidence that E6 and E7 contribute differently to carcinogenesis; E7 promotes the formation of benign tumors, and E6 acts primarily to accelerate progression of these benign tumors to the malignant stage. Consistent with this model, we found E6 and E7 to cooperate in inducing tumor formation in mice expressing both oncogenes