arXiv subjects

Yao Yao

Publications and source records attributed to Yao Yao.

At least 181 records · Page 10Linked to original sources

arXiv2022

Critical Regularizations for Neural Surface Reconstruction in the Wild

Neural implicit functions have recently shown promising results on surface reconstructions from multiple views. However, current methods still suffer from excessive time complexity and poor robustness when reconstructing unbounded or complex scenes. In this paper, we present RegSDF, which shows that proper point cloud supervisions and geometry regularizations are sufficient to produce high-quality and robust reconstruction results. Specifically, RegSDF takes an additional oriented point cloud as input, and optimizes a signed distance field and a surface light field within a differentiable rendering framework. We also introduce the two critical regularizations for this optimization. The first one is the Hessian regularization that smoothly diffuses the signed distance values to the entire distance field given noisy and incomplete input. And the second one is the minimal surface regularization that compactly interpolates and extrapolates the missing geometry. Extensive experiments are conducted on DTU, BlendedMVS, and Tanks and Temples datasets. Compared with recent neural surface reconstruction approaches, RegSDF is able to reconstruct surfaces with fine details even for open scenes with complex topologies and unstructured camera trajectories.

cs.CV↗

arXiv2022

Spatially resolved mass-metallicity relation at z~0.26 from the MUSE-Wide Survey

Aims: There is a spatially resolved star-forming main sequence (rSFMS) and mass-metallicity relation (rMZR) of galaxies in local universe. We know that the global mass-metallicity relation (MZR) results from the integral of rMZR, and it will evolve with the redshift. However, the evolution of rMZR with redshift is still unclear due to the low spatial resolution and signal-to-noise ratio. There are currently too few observations beyond local universe, and only simulations can reproduce the evolution of rMZR with redshift. Methods: In this work, we select ten emission-line galaxies with an average redshift of $z\sim 0.26$ from MUSE-Wide DR1. We obtain the spatially resolved star formation rate (SFR) and metallicity from the integral field spectroscopy (IFS), as well as the stellar mass surface density from the 3D-HST photometry. We derive the rSFMS and rMZR at $z\sim 0.26$ and compare them with local galaxies. Results: We find the rSFMS of galaxies at $z\sim 0.26$ has a slope of $\sim$0.771. The rMZR exists at $z\sim 0.26$, showing a similar shape to the local universe but a lower average metallicity about $\sim$0.11 dex than the local one. In addition, we also study their spatially resolved fundamental metallicity relation (rFMR). However, there is no obvious evidence that rFMR exists at $z\sim$0.26 and it is not an extension of rMZR at a high SFR. Conclusions: Similar to their global versions, the rSFMS and rMZR of galaxies also evolve with redshift. Given the fixed stellar mass, galaxies at higher redshift show higher SFR and lower metallicity. These suggest that the evolution of the global galaxy properties with redshift may result from integrating the evolution of spatially resolved properties of galaxies.

astro-ph.GA↗

arXiv2022

Strolling in Room-Scale VR: Hex-Core-MK1 Omnidirectional Treadmill

The natural locomotion interface is critical to the development of many VR applications. For household VR applications, there are two basic requirements: natural immersive experience and minimized space occupation. The existing locomotion strategies generally do not simultaneously satisfy these two requirements well. This paper presents a novel omnidirectional treadmill (ODT) system, named Hex-Core-MK1 (HCMK1). By implementing two kinds of mirror symmetrical spiral rollers to generate the omnidirectional velocity field, this proposed system is capable of providing real walking experiences with a full-degree of freedom in an area as small as 1.76 m^2, while delivering great advantages over several existing ODT systems in terms of weight, volume, latency and dynamic performance. Compared with the sizes of Infinadeck and HCP, the two best motor-driven ODTs so far, the 8 cm height of HCMK1 is only 20% of Infinadeck and 50% of HCP. In addition, HCMK1 is a lightweight device weighing only 110 kg, which provides possibilities of further expanding VR scenarios, such as terrain simulation. The latency of HCMK1 is only 23ms. The experiments show that HCMK1 can deliver on a starting acceleration of 16.00 m/s^2 and a braking acceleration of 30.00 m/s^2.

cs.HC↗

arXiv2022

NeILF: Neural Incident Light Field for Physically-based Material Estimation

We present a differentiable rendering framework for material and lighting estimation from multi-view images and a reconstructed geometry. In the framework, we represent scene lightings as the Neural Incident Light Field (NeILF) and material properties as the surface BRDF modelled by multi-layer perceptrons. Compared with recent approaches that approximate scene lightings as the 2D environment map, NeILF is a fully 5D light field that is capable of modelling illuminations of any static scenes. In addition, occlusions and indirect lights can be handled naturally by the NeILF representation without requiring multiple bounces of ray tracing, making it possible to estimate material properties even for scenes with complex lightings and geometries. We also propose a smoothness regularization and a Lambertian assumption to reduce the material-lighting ambiguity during the optimization. Our method strictly follows the physically-based rendering equation, and jointly optimizes material and lighting through the differentiable rendering process. We have intensively evaluated the proposed method on our in-house synthetic dataset, the DTU MVS dataset, and real-world BlendedMVS scenes. Our method is able to outperform previous methods by a significant margin in terms of novel view rendering quality, setting a new state-of-the-art for image-based material and lighting estimation.

cs.CV↗

arXiv2022

IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control

Agriculture is the foundation of human civilization. However, the rapid increase of the global population poses a challenge on this cornerstone by demanding more food. Modern autonomous greenhouses, equipped with sensors and actuators, provide a promising solution to the problem by empowering precise control for high-efficient food production. However, the optimal control of autonomous greenhouses is challenging, requiring decision-making based on high-dimensional sensory data, and the scaling of production is limited by the scarcity of labor capable of handling this task. With the advances of artificial intelligence (AI), the internet of things (IoT), and cloud computing technologies, we are hopeful to provide a solution to automate and smarten greenhouse control to address the above challenges. In this paper, we propose a smart agriculture solution named iGrow, for autonomous greenhouse control (AGC): (1) for the first time, we formulate the AGC problem as a Markov decision process (MDP) optimization problem; (2) we design a neural network-based simulator incorporated with the incremental mechanism to simulate the complete planting process of an autonomous greenhouse, which provides a testbed for the optimization of control strategies; (3) we propose a closed-loop bi-level optimization algorithm, which can dynamically re-optimize the greenhouse control strategy with newly observed data during real-world production. We not only conduct simulation experiments but also deploy iGrow in real scenarios, and experimental results demonstrate the effectiveness and superiority of iGrow in autonomous greenhouse simulation and optimal control. Particularly, compelling results from the tomato pilot project in real autonomous greenhouses show that our solution significantly increases crop yield (+10.15\%) and net profit (+92.70\%) with statistical significance compared to planting experts.

cs.AI↗

arXiv2022

CLS: Cross Labeling Supervision for Semi-Supervised Learning

It is well known that the success of deep neural networks is greatly attributed to large-scale labeled datasets. However, it can be extremely time-consuming and laborious to collect sufficient high-quality labeled data in most practical applications. Semi-supervised learning (SSL) provides an effective solution to reduce the cost of labeling by simultaneously leveraging both labeled and unlabeled data. In this work, we present Cross Labeling Supervision (CLS), a framework that generalizes the typical pseudo-labeling process. Based on FixMatch, where a pseudo label is generated from a weakly-augmented sample to teach the prediction on a strong augmentation of the same input sample, CLS allows the creation of both pseudo and complementary labels to support both positive and negative learning. To mitigate the confirmation bias of self-labeling and boost the tolerance to false labels, two different initialized networks with the same structure are trained simultaneously. Each network utilizes high-confidence labels from the other network as additional supervision signals. During the label generation phase, adaptive sample weights are assigned to artificial labels according to their prediction confidence. The sample weight plays two roles: quantify the generated labels' quality and reduce the disruption of inaccurate labels on network training. Experimental results on the semi-supervised classification task show that our framework outperforms existing approaches by large margins on the CIFAR-10 and CIFAR-100 datasets.

cs.CV↗

arXiv2022

RunnerDNA: Interpretable indicators and model to characterize human activity pattern and individual difference

Human activity analysis based on sensor data plays a significant role in behavior sensing, human-machine interaction, health care, and so on. The current research focused on recognizing human activity and posture at the activity pattern level, neglecting the effective fusion of multi-sensor data and assessing different movement styles at the individual level, thus introducing the challenge to distinguish individuals in the same movement. In this study, the concept of RunnerDNA, consisting of five interpretable indicators, balance, stride, steering, stability, and amplitude, was proposed to describe human activity at the individual level. We collected smartphone multi-sensor data from 33 volunteers who engaged in physical activities such as walking, running, and bicycling and calculated the data into five indicators of RunnerDNA. The indicators were then used to build random forest models and recognize movement activities and the identity of users. The results show that the proposed model has high accuracy in identifying activities (accuracy of 0.679) and is also effective in predicting the identity of running users. Furthermore, the accuracy of the human activity recognition model has significant improved by combing RunnerDNA and two motion feature indicators, velocity, and acceleration. Results demonstrate that RunnerDNA is an effective way to describe an individual's physical activity and helps us understand individual differences in sports style, and the significant differences in balance and amplitude between men and women were found.

cs.CY↗

arXiv2021

Critical region of topological trivial and nontrivial phases in interacting Kitaev chain with spatially varying potentials

By using the variational matrix product state method, we numerically study the interacting Kitaev chain with spatially varying periodic and quasi-periodic potentials and the latter follows the Fibonacci sequence. The edge correlation functions of Majorana fermions and low-lying ground states are computed to explore the robustness of topological superconducting phase. It is found that the original topological nontrivial phase is separated into to two branches by an emergent topological trivial phase, as a result of the competition among spatially varying potential, electronic Coulomb interaction and chemical potential. The analysis of energy gap and occupation number together suggests that the spontaneous symmetry breaking and the lift of degeneracy in the topological trivial phase are enabled by a potential-induced Fracton mechanism, namely the pairing of four Majorana fermions. It can be broken by further enhancing the interaction, and then the nontrivial phase reemerges. The evolution from the emergent fractal structure of population outside the critical region to the original structure of charge density wave is investigated as well.

cond-mat.str-el↗

arXiv2021

Comprehending robust quantum effects in organic semiconductors: Charge-transfer excitons, aggregate phonons and fractons

In organic semiconductors working under ambient circumstance, there are remarkable quantum effects lack of comprehensive understanding, and an exotic composite particle named charge transfer (CT) exciton is normally regarded as the key ingredient. Another essential substance is the phonons stemming from intra- and inter-molecular vibrations, and the relevant diagonal electron-phonon couplings give rise to spatial localization and the off-diagonal couplings refer to the dispersion of electron wavefunctions. In this work, we first propose a toy model based upon the fracton physics to phenomenologically unveil the coherent motion of CT excitons followed by resonant phonons in the aggregates. Based on this model, a generic scenario of hierarchical quantum effects is described in various material systems. To examine whether the fracton model can be applicable in practice, we calculate the out-of-time-ordered correlator (OTOC), a quantum dynamic measurement of the entanglement entropy, of CT excitons by the adaptive time-dependent density matrix renormalization group algorithm. On the basis of three commonly-used realistic models in organic materials with two competing interactions taking into account, we investigate the irreducible roles of CT excitons and aggregate phonons in the ultrafast charge transfer, the coherent polaron hopping, and the dissociation of triplet pairs, respectively. Our theory unifies the charge, exciton, spin and phonons into a single framework, which may help clarify the complicated and diverse quantum effects in organic semiconductors.

cond-mat.mes-hall↗

arXiv2021

Sub-galactic scaling relations with T$_{\rm e}$-based metallicity of low metallicity regions in galaxies: metal-poor gas inflow may have important effects?

The scaling relationship is a fundamental probe of the evolution of galaxies. Using the integral field spectroscopic data from the Mapping Nearby Galaxies at Apache Point Observatory survey, we select 1698 spaxels with significant detection of the auroral emission line \oiii$λ$4363 from 52 galaxies to investigate the scaling relationships at the low-metallicity end. We find that our sample's star formation rate is higher and its metallicity is lower in the scaling relationship than the star-forming sequence after removing the contribution of the Fundamental Metallicity Relation.We also find that the stellar ages of our sample are younger ($<$ 1 Gyr) and the stellar metallicities are also lower. Morphological parameters from Deep Learning catalog indicate that our galaxies are more likely to be merger. These results suggest that their low metallicity regions may be related to interaction, the inflow of metal-poor gas may dilute the interstellar medium and form new metal-poor stars in these galaxies during interaction.

astro-ph.GA↗

arXiv2021

Authentication of optical physical unclonable functions based on single-pixel detection

Physical unclonable function (PUF) has been proposed as a promising and trustworthy solution to a variety of cryptographic applications. Here we propose a non-imaging based authentication scheme for optical PUFs materialized by random scattering media, in which the characteristic fingerprints of optical PUFs are extracted from stochastical fluctuations of the scattered light intensity with respect to laser challenges which are detected by a single-pixel detector. The randomness, uniqueness, unpredictability, and robustness of the extracted fingerprints are validated to be qualified for real authentication applications. By increasing the key length and improving the signal to noise ratio, the false accept rate of a fake PUF can be dramatically lowered to the order of 10^-28. In comparison to the conventional laser-speckle-imaging based authentication with unique identity information obtained from textures of laser speckle patterns, this non-imaging scheme can be implemented at small speckle size bellowing the Nyquist--Shannon sampling criterion of the commonly used CCD or CMOS cameras, offering benefits in system miniaturization and immunity against reverse engineering attacks simultaneously.

cs.CR↗

arXiv2021

Robust Model-based Reinforcement Learning for Autonomous Greenhouse Control

Due to the high efficiency and less weather dependency, autonomous greenhouses provide an ideal solution to meet the increasing demand for fresh food. However, managers are faced with some challenges in finding appropriate control strategies for crop growth, since the decision space of the greenhouse control problem is an astronomical number. Therefore, an intelligent closed-loop control framework is highly desired to generate an automatic control policy. As a powerful tool for optimal control, reinforcement learning (RL) algorithms can surpass human beings' decision-making and can also be seamlessly integrated into the closed-loop control framework. However, in complex real-world scenarios such as agricultural automation control, where the interaction with the environment is time-consuming and expensive, the application of RL algorithms encounters two main challenges, i.e., sample efficiency and safety. Although model-based RL methods can greatly mitigate the efficiency problem of greenhouse control, the safety problem has not got too much attention. In this paper, we present a model-based robust RL framework for autonomous greenhouse control to meet the sample efficiency and safety challenges. Specifically, our framework introduces an ensemble of environment models to work as a simulator and assist in policy optimization, thereby addressing the low sample efficiency problem. As for the safety concern, we propose a sample dropout module to focus more on worst-case samples, which can help improve the adaptability of the greenhouse planting policy in extreme cases. Experimental results demonstrate that our approach can learn a more effective greenhouse planting policy with better robustness than existing methods.

cs.AI↗

arXiv2021

Bionic Optical Physical Unclonable Functions for Authentication and Encryption

Information security is of great importance for modern society with all things connected. Physical unclonable function (PUF) as a promising hardware primitive has been intensively studied for information security. However, the widely investigated silicon PUF with low entropy is vulnerable to various attacks. Herein, we introduce a concept of bionic optical PUFs inspired from unique biological architectures, and fabricate four types of bionic PUFs by molding the surface micro-nano structures of natural plant tissues with a simple, low-cost, green and environmentally friendly manufacturing process. The laser speckle responses of all bionic PUFs are statistically demonstrated to be random, unique, unpredictable and robust enough for cryptographic applications, indicating the broad applicability of bionic PUFs. On this ground, the feasibility of implementing bionic PUFs as cryptographic primitives in entity authentication and encrypted communication is experimentally validated, which shows its promising potential in the application of future information security.

cs.CR↗

arXiv2021

Fast random number generator based on optical physical unclonable functions

We propose an approach for fast random number generation based on homemade optical physical unclonable functions (PUFs). The optical PUF is illuminated with input laser wavefront of continuous modulation to obtain different speckle patterns. Random numbers are fully extracted from speckle patterns through a simple post-processing algorithm. Our proof-of-principle experiment achieves total random number generation rate of 0.96 Gbit/s with verified randomness, which is far faster than previous optical-PUF-based schemes. Our results demonstrate that the presented random number generator (RNG) proposal has great potential to achieve ultrafast random number generation rate up to several hundreds of Gbit/s.

cs.CR↗

arXiv2021

Learning Signed Distance Field for Multi-view Surface Reconstruction

Recent works on implicit neural representations have shown promising results for multi-view surface reconstruction. However, most approaches are limited to relatively simple geometries and usually require clean object masks for reconstructing complex and concave objects. In this work, we introduce a novel neural surface reconstruction framework that leverages the knowledge of stereo matching and feature consistency to optimize the implicit surface representation. More specifically, we apply a signed distance field (SDF) and a surface light field to represent the scene geometry and appearance respectively. The SDF is directly supervised by geometry from stereo matching, and is refined by optimizing the multi-view feature consistency and the fidelity of rendered images. Our method is able to improve the robustness of geometry estimation and support reconstruction of complex scene topologies. Extensive experiments have been conducted on DTU, EPFL and Tanks and Temples datasets. Compared to previous state-of-the-art methods, our method achieves better mesh reconstruction in wide open scenes without masks as input.

cs.CV↗

arXiv2021

Several classes of bent functions over finite fields

Let $\mathbb{F}_{p^{n}}$ be the finite field with $p^n$ elements and $\operatorname{Tr}(\cdot)$ be the trace function from $\mathbb{F}_{p^{n}}$ to $\mathbb{F}_{p}$, where $p$ is a prime and $n$ is an integer. Inspired by the works of Mesnager (IEEE Trans. Inf. Theory 60(7): 4397-4407, 2014) and Tang et al. (IEEE Trans. Inf. Theory 63(10): 6149-6157, 2017), we study a class of bent functions of the form $f(x)=g(x)+F(\operatorname{Tr}(u_1x),\operatorname{Tr}(u_2x),\cdots,\operatorname{Tr}(u_τx))$, where $g(x)$ is a function from $\mathbb{F}_{p^{n}}$ to $\mathbb{F}_{p}$, $τ\geq2$ is an integer, $F(x_1,\cdots,x_n)$ is a reduced polynomial in $\mathbb{F}_{p}[x_1,\cdots,x_n]$ and $u_i\in \mathbb{F}^{*}_{p^n}$ for $1\leq i \leq τ$. As a consequence, we obtain a generic result on the Walsh transform of $f(x)$ and characterize the bentness of $f(x)$ when $g(x)$ is bent for $p=2$ and $p>2$ respectively. Our results generalize some earlier works. In addition, we study the construction of bent functions $f(x)$ when $g(x)$ is not bent for the first time and present a class of bent functions from non-bent Gold functions.

cs.IT↗

arXiv2021

Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation

Model-based deep reinforcement learning has achieved success in various domains that require high sample efficiencies, such as Go and robotics. However, there are some remaining issues, such as planning efficient explorations to learn more accurate dynamic models, evaluating the uncertainty of the learned models, and more rational utilization of models. To mitigate these issues, we present MEEE, a model-ensemble method that consists of optimistic exploration and weighted exploitation. During exploration, unlike prior methods directly selecting the optimal action that maximizes the expected accumulative return, our agent first generates a set of action candidates and then seeks out the optimal action that takes both expected return and future observation novelty into account. During exploitation, different discounted weights are assigned to imagined transition tuples according to their model uncertainty respectively, which will prevent model predictive error propagation in agent training. Experiments on several challenging continuous control benchmark tasks demonstrated that our approach outperforms other model-free and model-based state-of-the-art methods, especially in sample complexity.

cs.LG↗

arXiv2021

Asymptotic simplification of Aggregation-Diffusion equations towards the heat kernel

We give sharp conditions for the large time asymptotic simplification of aggregation-diffusion equations with linear diffusion. As soon as the interaction potential is bounded and its first and second derivatives decay fast enough at infinity, then the linear diffusion overcomes its effect, either attractive or repulsive, for large times independently of the initial data, and solutions behave like the fundamental solution of the heat equation with some rate. The potential $W(x) \sim \log |x|$ for $|x| \gg 1$ appears as the natural limiting case when the intermediate asymptotics change. In order to obtain such a result, we produce uniform-in-time estimates in a suitable rescaled change of variables for the entropy, the second moment, Sobolev norms and the $C^α$ regularity with a novel approach for this family of equations using modulus of continuity techniques.

math.AP↗