MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Pose

We propose a generalizable neural radiance fields - MonoNeRF, that can be trained on large-scale monocular videos of moving in static scenes without any ground-truth annotations of depth and camera poses. MonoNeRF follows an Autoencoder-based architecture, where the encoder estimates the monocular depth and the camera pose, and the decoder constructs a Multiplane NeRF representation based on the depth encoder feature, and renders the input frames with the estimated camera. The learning is supervised by the reconstruction error. Once the model is learned, it can be applied to multiple applications including depth estimation, camera pose estimation, and single-image novel view synthesis. More qualitative results are available at: https://oasisyang.github.io/mononerf .Comment: ICML 2023 camera ready version. Project page: https://oasisyang.github.io/mononer

FRED Navigation & Communication Subsystem

Clear Blue Sea (CBS), a non-profit organization, has focused on removing the plastic from the Great Pacific Garbage Patch by designing and piloting a Floating Robot for Eliminating Debris (FRED). The goal for this project is to design and prototype two subsystems; a navigation and communication subsystem and a power subsystem. The navigation and communication subsystem will allow for tracking location, remote control of the vehicle, operational status and environmental conditions monitoring. The power subsystem will use solar power to operate the overall FRED system. Our objective is to integrate these subsystems with the other USD Clear Blue Sea team?s final prototype. This report discusses our objectives, requirements and functions of our subsystems. After extensive research on different components, we decided on utilizing high-quality and low-cost autopilot hardware. Rather than build from scratch our subteam switched gears and unanimously decided on using a flight controller and open drone software. This flight controller would then manage all the sensors and motors on the FRED unit itself, as well as allow for communication between the FRED system, a computer, and a handheld controller for manual inputs. For the power subsystem, it consists of 3 main parts: a solar panel, a battery and two motors. Solar panel converts solar energy into electric current, then power the thruster and the motor. Part of the generated electric power is stored into the battery for later use

Topology optimization of microstructures with perturbation analysis and penalty methods

Topology optimization at the continuum nano/microscale is of wide interest in designing and developing more efficient micro/nano electromechanical systems. This paper presents a new methodology for topology optimization of microstructures that is based on perturbation analysis and the penalty methods. The homogenized material coefficients are numerically computed based on perturbation analysis, and periodic boundary conditions are imposed by the penalty methods. The sensitivity analysis is implemented directly without the adjoint method. The extension of the proposed method to the design of components for multi-field analysis is straightforward. The capability and performance of the presented methodology are demonstrated through several numerical examples

Recommendation Scheme Based on Converging Properties for Contents Broadcasting

Popular videos are often clicked by a mount of users in a short period. With content recommendation, the popular contents could be broadcast to the potential users in wireless network, to save huge transmitting resource. In this paper, the contents propagation model is analyzed due to users' historical behavior, location, and the converging properties in wireless data transmission, with the users' communication log in the Chinese commercial cellular network. And a recommendation scheme is proposed to achieve high energy efficiency.Comment: 6 pages. This work is present at 2015 International Workshop on Networking Issues in Multimedia Entertainment (NIME'15

Calibration of YSZ Sensors for the Measurement of Oxygen Concentration in Liquid Pb-Bi Eutectic

Although liquid lead-bismuth eutectic (LBE) is a good candidate for coolant in the subcritical transmutation blanket, it is known to be corrosive to stainless steel, the material of the carrying tubes and containers. Such longterm corrosion problem can be prevented by producing and maintaining a protective oxide layer on the exposed surface of stainless steel. For this purpose, it is required to accurately control the concentration of oxygen dissolved in LBE. Currently, YSZ (Yttria Stabilized Zirconia) oxygen sensors, based on an existing automotive oxygen sensor, with molten bismuth saturated with oxygen as the reference, have been selected for oxygen-concentration measurement. The oxygen concentration difference across the solid electrolyte and the resultant oxygen ion conduction inside the electrolyte establishes an electromagnetic force that is used to measure the ppb level concentration of oxygen dissolved in liquid LBE. A set of calibration curves of voltage vs. temperature ranging from 300 0C to 500 0C under various oxygen concentrations in liquid LBE for the YSZ oxygen sensor has been obtained and is presented in this paper. Although the current calibration strategy using the direct injection of hydrogen and oxygen is still inadequate to determine the oxygen concentration in the system, we have found a good candidate for our purpose, which is varying hydrogen to water steam ratio in the system

Accurate Reconstruction of Molecular Phylogenies for Proteins Using Codon and Amino Acid Unified Sequence Alignments (CAUSA)

Based on molecular clock hypothesis, and neutral theory of molecular evolution, molecular phylogenies have been widely used for inferring evolutionary history of organisms and individual genes. Traditionally, alignments and phylogeny trees of proteins and their coding DNA sequences are constructed separately, thus often different conclusions were drawn. Here we present a new strategy for sequence alignment and phylogenetic tree reconstruction, codon and amino acid unified sequence alignment (CAUSA), which aligns DNA and protein sequences and draw phylogenetic trees in a unified manner. We demonstrated that CAUSA improves both the accuracy of multiple sequence alignments and phylogenetic trees by solving a variety of molecular evolutionary problems in virus, bacteria and mammals. Our results support the hypothesis that the molecular clock for proteins has two pointers existing separately in DNA and protein sequences. It is more accurate to read the molecular clock by combination (additive) of these two pointers, since the ticking rates of them are sometimes consistent, sometimes different. CAUSA software were released as Open Source under GNU/GPL license, and are downloadable free of charge from the website www.dnapluspro.com

Correction to: Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials

In the original publication of the article, the author wanted to correct the authors group and affiliation as it was wrongly updated. The correct authors group and affiliation should be: Hongwei Guo1,2, Xiaoying Zhuang1,2, Xiaolong Fu3, Yunzheng Zhu4 and Timon Rabczuk5 1 Department of Geotechnical Engineering,Tongji University,Shanghai, 200092, P.R. China. 2 Chair of Computational Science and Simulation Technology, Leibniz Universitat Hannover, Hannover, Germany. 3 Xi’an Modern Chemistry Research Institute, Xi’an, China. 4 Department of Electrical and Computer Engineering, UCLA, 420 Westwood Plaza, Los Angeles, CA 90095, USA. 5 Institute of Structural Mechanics, Bauhaus Universität Weimar, Weimar, Germany Now, the original article has been updated

Differentially Private Learning with Per-Sample Adaptive Clipping

Privacy in AI remains a topic that draws attention from researchers and the general public in recent years. As one way to implement privacy-preserving AI, differentially private learning is a framework that enables AI models to use differential privacy (DP). To achieve DP in the learning process, existing algorithms typically limit the magnitude of gradients with a constant clipping, which requires carefully tuned due to its significant impact on model performance. As a solution to this issue, latest works NSGD and Auto-S innovatively propose to use normalization instead of clipping to avoid hyperparameter tuning. However, normalization-based approaches like NSGD and Auto-S rely on a monotonic weight function, which imposes excessive weight on small gradient samples and introduces extra deviation to the update. In this paper, we propose a Differentially Private Per-Sample Adaptive Clipping (DP-PSAC) algorithm based on a non-monotonic adaptive weight function, which guarantees privacy without the typical hyperparameter tuning process of using a constant clipping while significantly reducing the deviation between the update and true batch-averaged gradient. We provide a rigorous theoretical convergence analysis and show that with convergence rate at the same order, the proposed algorithm achieves a lower non-vanishing bound, which is maintained over training iterations, compared with NSGD/Auto-S. In addition, through extensive experimental evaluation, we show that DP-PSAC outperforms or matches the state-of-the-art methods on multiple main-stream vision and language tasks.Comment: To appear in AAAI 2023, Revised acknowledgments and citation

Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials

We present a physics-informed deep learning model for the transient heat transfer analysis of three-dimensional functionally graded materials (FGMs) employing a Runge–Kutta discrete time scheme. Firstly, the governing equation, associated boundary conditions and the initial condition for transient heat transfer analysis of FGMs with exponential material variations are presented. Then, the deep collocation method with the Runge–Kutta integration scheme for transient analysis is introduced. The prior physics that helps to generalize the physics-informed deep learning model is introduced by constraining the temperature variable with discrete time schemes and initial/boundary conditions. Further the fitted activation functions suitable for dynamic analysis are presented. Finally, we validate our approach through several numerical examples on FGMs with irregular shapes and a variety of boundary conditions. From numerical experiments, the predicted results with PIDL demonstrate well agreement with analytical solutions and other numerical methods in predicting of both temperature and flux distributions and can be adaptive to transient analysis of FGMs with different shapes, which can be the promising surrogate model in transient dynamic analysis