467 research outputs found
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
Synthesis and analysis of separation processes for extracellular chemicals generated from microbial conversions
Recent advances in metabolic engineering have enabled the production of chemicals via bio-conversion using microbes. However, downstream separation accounts for 60–80% of the total production cost in many cases. Previous work on microbial production of extracellular chemicals has been mainly restricted to microbiology, biochemistry, metabolomics, or techno-economic analysis for specific product examples such as succinic acid, xanthan gum, lycopene, etc. In these studies, microbial production and separation technologies were selected apriori without considering any competing alternatives. However, technology selection in downstream separation and purification processes can have a major impact on the overall costs, product recovery, and purity. To this end, we apply a superstructure optimization based framework that enables the identification of critical technologies and their associated parameters in the synthesis and analysis of separation processes for extracellular chemicals generated from microbial conversions. We divide extracellular chemicals into three categories based on their physical properties, such as water solubility, physical state, relative density, volatility, etc. We analyze three major extracellular product categories (insoluble light, insoluble heavy and soluble) in detail and provide suggestions for additional product categories through extension of our analysis framework. The proposed analysis and results provide significant insights for technology selection and enable streamlined decision making when faced with any microbial product that is released extracellularly. The parameter variability analysis for the product as well as the associated technologies and comparison with novel alternatives is a key feature which forms the basis for designing better bioseparation strategies that have potential for commercial scalability and can compete with traditional chemical production methods
Validating quantum-supremacy experiments with exact and fast tensor network contraction
The quantum circuits that declare quantum supremacy, such as Google Sycamore
[Nature \textbf{574}, 505 (2019)], raises a paradox in building reliable result
references. While simulation on traditional computers seems the sole way to
provide reliable verification, the required run time is doomed with an
exponentially-increasing compute complexity. To find a way to validate current
``quantum-supremacy" circuits with more than qubits, we propose a
simulation method that exploits the ``classical advantage" (the inherent
``store-and-compute" operation mode of von Neumann machines) of current
supercomputers, and computes uncorrelated amplitudes of a random quantum
circuit with an optimal reuse of the intermediate results and a minimal memory
overhead throughout the process. Such a reuse strategy reduces the original
linear scaling of the total compute cost against the number of amplitudes to a
sublinear pattern, with greater reduction for more amplitudes. Based on a
well-optimized implementation of this method on a new-generation Sunway
supercomputer, we directly verify Sycamore by computing three million exact
amplitudes for the experimentally generated bitstrings, obtaining an XEB
fidelity of which closely matches the estimated value of .
Our computation scales up to cores with a sustained
single-precision performance of Pflops, which is accomplished within
days. Our method has a far-reaching impact in solving quantum many-body
problems, statistical problems as well as combinatorial optimization problems
where one often needs to contract many tensor networks which share a
significant portion of tensors in common.Comment: 7 pages, 4 figures, comments are welcome
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