Rewriting a Deep Generative Model

A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a rule could be changed. In this paper, we introduce a new problem setting: manipulation of specific rules encoded by a deep generative model. To address the problem, we propose a formulation in which the desired rule is changed by manipulating a layer of a deep network as a linear associative memory. We derive an algorithm for modifying one entry of the associative memory, and we demonstrate that several interesting structural rules can be located and modified within the layers of state-of-the-art generative models. We present a user interface to enable users to interactively change the rules of a generative model to achieve desired effects, and we show several proof-of-concept applications. Finally, results on multiple datasets demonstrate the advantage of our method against standard fine-tuning methods and edit transfer algorithms.Comment: ECCV 2020 (oral). Code at https://github.com/davidbau/rewriting. For videos and demos see https://rewriting.csail.mit.edu

A self-adaptive segmentation method for a point cloud

The segmentation of a point cloud is one of the key technologies for three-dimensional reconstruction, and the segmentation from three-dimensional views can facilitate reverse engineering. In this paper, we propose a self-adaptive segmentation algorithm, which can address challenges related to the region-growing algorithm, such as inconsistent or excessive segmentation. Our algorithm consists of two main steps: automatic selection of seed points according to extracted features and segmentation of the points using an improved region-growing algorithm. The benefits of our approach are the ability to select seed points without user intervention and the reduction of the influence of noise. We demonstrate the robustness and effectiveness of our algorithm on different point cloud models and the results show that the segmentation accuracy rate achieves 96%

The antiproliferative and antimicrobial effects of cultivated anabaena circinalis rabenhorts ex bornet and flahault and nostoc entophytum bornet and flahault

Purpose: To investigate the antiproliferative and antimicrobial effects of cultivated Anabaena circinalis Rabenhorst Ex Bornet and Flahault [Synonym: Dolichospermum sigmoideum (Nygaard) Wacklin, L. Hoffmann and Komarek) and Nostoc entophytum Bornet and Flahault (Synonym: Nostoc paludosum Kützing ex Bornet and Flahault).Methods: The algae extracts were prepared using 0.5 M Tris-HCL pH: 8.00, N-butanol, Ethanol and Dimethyl sulfoxide, and then tested on Staphylococcus aureus ATCC 25923, Bacillus subtilis ATCC 6633, Listeria monocytogenes ATCC 7644, Escherichia coli O 157:H7, Pseudomonas aeruginosa ATCC 27853, Salmonella typhimurium CCM 5445, Candida albicans ATCC 10239 using the disc diffusion method in order to determine their antimicrobial effects. The anti-cancer activities of these two algae were tested against cancerous cell lines using BrdU cell proliferation ELISA method. The inhibition of these algae ethanol and butanol extracts were tested against Vero and HeLa carcinoma cells in concentrations of 100, 250 and 500 μg/mL.Results: Anabaena circinalis (AC) and Nostoc entophytum (NE) antimicrobial properties against the tes organisms. The buffer extract obtained from AC showed the highest level of antimicrobial activity against L. monocytogenes ATCC 7644 while the buffer extract from NE displayed the highest antimicrobial activity against E. coli O 157:H7. The anti-proliferative data indicate that NE has effective anti-cancer properties. Furthermore, the results showed that cyanobacteria species were superior to DMSO and control groups in terms of anti-cancer activity in tumor cells. NE exhibited significant (p < 0.05) anti-proliferative effects at all three concentrations (100, 250, 500 μg/mL), compared to DMSO while AC exhibited significant anti-proliferative activity only at 500 μg/mL concentration (p < 0.01).Conclusion: The results indicate that extracts possess antimicrobial and antiproliferative antivities. However, further studies are required to ascertain their clinical efficacy.Keywords: Anabaena circinalis, Nostoc entophytum, Cyanobacteria, Antiproliferative, Antimicrobia

ESTIMATION OF POPULATION NUMBER VIA LIGHT ACTIVITIES ON NIGHT-TIME SATELLITE IMAGES

Estimation and accurate assessment regarding population gets harder and harder day by day due to growth of world population in a fast manner. Estimating tendencies to settlements in cities and countries, socio-cultural development and population numbers is quite difficult. In addition to them, selection and analysis of parameters such as time, work-force and cost seems like another difficult issue. In this study, population number is guessed by evaluating light activities in İstanbul via night-time images of Turkey. By evaluating light activities between 2000 and 2010, average population per pixel is obtained. Hence, it is used to estimate population numbers in 2011, 2012 and 2013. Mean errors are concluded as 4.14 % for 2011, 3.74 % for 2012 and 3.04 % for 2013 separately. As a result of developed thresholding method, mean error is concluded as 3.64 % to estimate population number in İstanbul for next three years