Shadow Algorithm Research Articles

Error-mitigated fermionic classical shadows on noisy quantum devices

Efficiently estimating fermionic Hamiltonian expectation values is vital for simulating various physical systems. Classical shadow (CS) algorithms offer a solution by reducing the number of quantum state copies needed, but noise in quantum devices poses challenges. We propose an error-mitigated CS algorithm assuming gate-independent, time-stationary, and Markovian (GTM) noise. For n-qubit systems, our algorithm, which employs the easily prepared initial state 0n0n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left\\vert {0}^{n}\\right\\rangle \\,\\left\\langle {0}^{n}\\right\\vert$$\\end{document} assumed to be noiseless, efficiently estimates k-RDMs with Õ(knk)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\widetilde{{{{\\mathcal{O}}}}}(k{n}^{k})$$\\end{document} state copies and Õ(n)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\widetilde{{{{\\mathcal{O}}}}}(\\sqrt{n})$$\\end{document} calibration measurements for GTM noise with constant fidelities. We show that our algorithm is robust against noise types like depolarizing, damping, and X-rotation noise with constant strengths, showing scalings akin to prior CS algorithms for fermions but with better noise resilience. Numerical simulations confirm our algorithm’s efficacy in noisy settings, suggesting its viability for near-term quantum devices.

Optimal design of residential balcony based on environmental benefit: A case study in hot summer and cold winter area of China

The aggravation of the greenhouse gas emission-resulted climate change poses a significant challenge to the world. In response to the urgent need for significantly more sustainable societies, this paper develops a protocol for the optimal design of residential balcony based on environmental benefits in the case study of China’s hot summer and cold winter area. The study utilized ROI and AY as environmental performance indicators for decision-making scheme investment and EnergyPlus to simulate the A/C energy-saving potential of considered balcony design schemes. The literature and experiment validation results show that EnergyPlus with Shadow Algorithm is reliable for studying the energy consumption of residence with balcony. The added embodied environmental impact of the different design schemes was estimated, and the idea of life cycle assessment was used for quantitative evaluation of the carbon emissions of the balcony. The results show the depth of 1.2 m suits the south/north-facing balcony, and a big window-wall ratio of 0.6 was the most beneficial in the environmental performance of the balcony investment. When the balcony is attached to different rooms, the environmental profitability is influenced by the factors such as occupancy behaviour and balcony environmental investment. The attached room type of residence on a case-by-case basis should be analyzed.

Morpho-Statistical Description of Networks Through Graph Modelling and Bayesian Inference

Collaboration graphs are relevant sources of information to understand behavioural tendencies of groups of individuals. The study of these graphs enables figuring out factors that may affect the efficiency and the sustainability of cooperative work. For example, such a collaboration involves researchers who develop relationships with their external counterparts to address scientific challenges. As relations and projects change over time, the evolution of social structures must be tackled. We propose a statistical approach considering different structural collaboration patterns and captures the dynamic of the relational structures over the years. Our approach combines spatial processes modelling and Exponential Random Graph Models used to analyse social processes. Since the normalising constant involved in classical Markov Chain Monte Carlo (MCMC) approaches is intractable, the inference remains challenging. To overcome this issue, we propose a Bayesian tool that relies on the recent ABC Shadow algorithm. The method is illustrated on real data sets from an open archive of scholarly documents. Through a simple formalism, our approach highlights the interactions between the different types of social relations at stake in the collaboration network.

DARCLOS: a cloud shadow detection algorithm for TROPOMI

Abstract. Cloud shadows are observed by the TROPOMI satellite instrument as a result of its high spatial resolution compared to its predecessor instruments. These shadows contaminate TROPOMI's air quality measurements, because shadows are generally not taken into account in the models that are used for aerosol and trace gas retrievals. If the shadows are to be removed from the data, or if shadows are to be studied, an automatic detection of the shadow pixels is needed. We present the Detection AlgoRithm for CLOud Shadows (DARCLOS) for TROPOMI, which is the first cloud shadow detection algorithm for a spaceborne spectrometer. DARCLOS raises potential cloud shadow flags (PCSFs), actual cloud shadow flags (ACSFs), and spectral cloud shadow flags (SCSFs). The PCSFs indicate the TROPOMI ground pixels that are potentially affected by cloud shadows based on a geometric consideration with safety margins. The ACSFs are a refinement of the PCSFs using spectral reflectance information of the PCSF pixels and identify the TROPOMI ground pixels that are confidently affected by cloud shadows. Because we find indications of the wavelength dependence of cloud shadow extents in the UV, the SCSF is a wavelength-dependent alternative for the ACSF at the wavelengths of TROPOMI's air quality retrievals. We validate the PCSF and ACSF with true-colour images made by the VIIRS instrument on board Suomi NPP orbiting in close proximity to TROPOMI on board Sentinel-5P. We find that the cloud evolution during the overpass time difference between TROPOMI and VIIRS complicates this validation strategy, implicating that an alternative cloud shadow detection approach using co-located VIIRS observations could be problematic. We conclude that the PCSF can be used to exclude cloud shadow contamination from TROPOMI data, while the ACSF and SCSF can be used to select pixels for the scientific analysis of cloud shadow effects.

Experimental Investigation of Quantum Uncertainty Relations With Classical Shadows

The quantum component in uncertainty relation can be naturally characterized by the quantum coherence of a quantum state, which is of paramount importance in quantum information science. Here, we experimentally investigate quantum uncertainty relations construed with relative entropy of coherence, l1 norm of coherence, and coherence of formation. Instead of quantum state tomographic technology, we employ the classical shadow algorithm for the detection of lower bounds in quantum uncertainty relations. With an all-optical setup, we prepare a family of quantum states whose purity can be fully controlled. We experimentally explore the tightness of various lower bounds in different reference bases on the prepared states. Our results indicate that the tightness of quantum coherence lower bounds depends on the reference bases and the purity of the quantum state.

Morphostatistical characterization of the spatial galaxy distribution through Gibbs point processes

ABSTRACT This paper proposes a morphostatistical characterization of the galaxy distribution through spatial statistical modelling based on inhomogeneous Gibbs point processes. The galaxy distribution is supposed to exhibit two components. The first one is related to the major geometrical features exhibited by the observed galaxy field, here, its corresponding filamentary pattern. The second one is related to the interactions exhibited by the galaxies. Gibbs point processes are statistical models able to integrate these two aspects in a probability density, controlled by some parameters. Several such models are fitted to real observational data via the ABC shadow algorithm. This algorithm provides simultaneous parameter estimation and posterior-based inference, hence allowing the derivation of the statistical significance of the obtained results.

A real-time system for vehicle detection with shadow removal and vehicle classification based on vehicle features at urban roads

Monitoring traffic in urban areas is an important task for intelligent transport applications to alleviate the traffic problems like traffic jams and long trip times. The traffic flow in urban areas is more complicated than the traffic flow in highway, due to the slow movement of vehicles and crowded traffic flows in urban areas. In this paper, a vehicle detection and classification system at intersections is proposed. The system consists of three main phases: vehicle detection, vehicle tracking and vehicle classification. In the vehicle detection, the background subtraction is utilized to detect the moving vehicles by employing mixture of Gaussians (MoGs) algorithm, and then the removal shadow algorithm is developed to improve the detection phase and eliminate the undesired detected region (shadows). After the vehicle detection phase, the vehicles are tracked until they reach the classification line. Then the vehicle dimensions are utilized to classify the vehicles into three classes (cars, bikes, and trucks). In this system, there are three counters; one counter for each class. When the vehicle is classified to a specific class, the class counter is incremented by one. The counting results can be used to estimate the traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The system is applied to videos obtained by stationary cameras. The results obtained demonstrate the robustness and accuracy of the proposed system.

Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN)

Cloud detection algorithms are crucial in many remote-sensing applications to allow an optimized processing of the acquired data, without the interference of the cloud fields above the surfaces of interest (e.g., land, coral reefs, etc.). While this is a well-established area of research, replete with a number of cloud detection methodologies, many issues persist for detecting clouds over areas of high albedo surfaces (snow and sand), detecting cloud shadows, and transferring a given algorithm between observational platforms. Particularly for the latter, algorithms are often platform-specific with corresponding rule-based tests and thresholds inherent to instruments and applied corrections. Here, we present a convolutional neural network (CNN) algorithm for the detection of cloud and cloud shadow fields in multi-channel satellite imagery from World-View-2 (WV-2) and Sentinel-2 (S-2), using their Red, Green, Blue, and Near-Infrared (RGB, NIR) channels. This algorithm is developed within the NASA NeMO-Net project, a multi-modal CNN for global coral reef classification which utilizes imagery from multiple remote sensing aircraft and satellites with heterogeneous spatial resolution and spectral coverage. Our cloud detection algorithm is novel in that it attempts to learn deep invariant features for cloud detection utilizing both the spectral and the spatial information inherent in satellite imagery. The first part of our work presents the CNN cloud and cloud shadow algorithm development (trained using WV-2 data) and its application to WV-2 (with a cloud detection accuracy of 89%) and to S-2 imagery (referred to as augmented CNN). The second part presents a new domain adaptation CNN-based approach (domain adversarial NN) that allows for better adaptation between the two satellite platforms during the prediction step, without the need to train for each platform separately. Our augmented CNN algorithm results in better cloud prediction rates as compared to the original S-2 cloud mask (81% versus 48%), but still, clear pixels prediction rate is lower than S-2 (81% versus 91%). Nevertheless, the application of the domain adaptation approach shows promise in better transferring the knowledge gained from one trained domain (WV-2) to another (S-2), increasing the prediction accuracy of both clear and cloudy pixels when compared to a network trained only by WV-2. As such, domain adaptation may offer a novel means of additional augmentation for our CNN-based cloud detection algorithm, increasing robustness towards predictions from multiple remote sensing platforms. The approach presented here may be further developed and optimized for global and multi-modal (multi-channel and multi-platform) satellite cloud detection capability by utilizing a more global dataset.

A BIG DATA APPROACH FOR COMPREHENSIVE URBAN SHADOW ANALYSIS FROM AIRBORNE LASER SCANNING POINT CLOUDS

Abstract. Because of the importance of access to sunlight, shadow analysis is a common consideration in urban design, especially for dense urban developments. As shadow computation is computationally expensive, most urban shadow analysis tools have to date circumvented the high computational costs by representing urban complexity only through simplified geometric models. The simplification process removes details and adversely affects the level of realism of the ultimate results. In this paper, an alternative approach is presented by utilizing the highest level of detail and resolution captured in the geometric input data source, which is an extremely high-resolution airborne laser scanning point cloud (300 points/m2). To cope with the high computational demand caused by the use of this dense and detailed input data set, the Comprehensive Urban Shadow algorithm is introduced to distribute the computation for parallel processing on a Hadoop cluster. The proposed comprehensive urban shadow analysis solution is scalable, reasonably fast, and capable of preserving the original resolution and geometric detail of the original point cloud data.

Revectorization‐Based Soft Shadow Mapping

AbstractIn this paper, we present revectorization‐based soft shadow mapping, an algorithm that enables the rendering of visually plausible anti‐aliased soft shadows in real time. In revectorization‐based shadow mapping, shadow silhouettes are anti‐aliased and filtered on the basis of a discontinuity space. By replacing the filtering step of the theoretical framework of the percentage‐closer soft shadow algorithm by a revectorization‐based filtering algorithm, we are able to provide anti‐aliasing mainly for near contact shadows or small penumbra sizes generated from low‐resolution shadow maps. Moreover, we present a screen‐space variant of our technique that generates visually plausible soft shadows with an overhead of only in processing time, when compared to the fastest soft shadow algorithms proposed in the literature, but that introduces shadow overestimation artefacts in the final rendering.

Scalable Real-Time Shadows using Clustering and Metric Trees

Real-time shadow algorithms based on geometry generally produce high quality shadows. Recent works have considerably improved their efficiency. However, scalability remains an issue because these methods strongly depend on the geometric complexity. This paper focuses on this problem. We present a new real-time shadow algorithm for non-deformable models that scales the geometric complexity. Our method groups triangles into clusters by precomputing bounding spheres or bounding capsules (line-swept spheres). At each frame, we build a ternary metric tree to partition the spheres and capsules according to their apparent distance from the light. Then, this tree is used as an acceleration data structure to determine the visibility of the light for each image point. While clustering allows to scale down the geometric complexity, metric trees allow to encode the bounding volumes of the clusters in a hierarchical data structure. Our experiments show that our approach remains efficient, including with models with over 70 million triangles.

Online VM Auto-Scaling Algorithms for Application Hosting in a Cloud

We consider the auto-scaling problem for application hosting in a cloud, where applications are elastic and the number of requests changes over time. The application requests are serviced by Virtual Machines (VMs), which reside on Physical Machines (PMs) in a cloud. We aim to minimize the number of hosting PMs by intelligently packing VMs into PMs, while the VMs are auto-scaled, i.e., dynamically acquired and released, to accommodate varying application needs. We consider a shadow routing based approach for this problem. The proposed shadow algorithm employs a specially constructed virtual queueing system to dynamically produce an optimal solution that guides the VM auto-scaling and the VM-to-PM packing. The proposed algorithm runs continuously without the need to re-solve the underlying optimization problem “from scratch”, and adapts automatically to the changes in the application demands. We prove the asymptotic optimality of the shadow algorithm. The simulation experiments further demonstrate the algorithm's good performance and high adaptivity.

Automatic shadow elimination in a high-density scene

This paper presents an automatic system for monitoring high-density images to estimate parameters from video sequences using a single camera. Unlike traditional methods, which can detect and monitor this behaviour under the right conditions, the proposed method has a good ability to segment more specific objects (vehicles and people) by introducing a new shadow elimination algorithm. In addition, the proposed system may well address the problem of vehicle occlusions caused by shadows, which often results in failure of vehicle counting and grading. This problem is solved by a new linear shadow algorithm that uses simple tools to eliminate all undesirable shadows. Therefore, an automatic algorithm for detecting shadows is also proposed. The experimental results show that the proposed method is more robust, more precise and more powerful than other traditional methods.

Improving shadows detection for solar radiation numerical models

Solar radiation numerical models need the implementation of an accurate method for determining cast shadows on the terrain or on solar collectors. The aim of this work is the development of a new methodology to detect the shadows on a particular terrain. The paper addresses the detection of self and cast shadows produced by the orography as well as those caused by clouds. The paper presents important enhancements on the methodology proposed by the authors in previous works, to detect the shadows caused by the orography. The domain is the terrain surface discretised using an adaptive mesh of triangles. A triangle of terrain will be under cast shadows when, looking at the mesh from the Sun, you can find another triangle that covers all or partially the first one. For each time step, all the triangles should be checked to see if there are cast or self shadows on it. The computational cost of this procedure eventually resulted unaffordable when dealing with complex topography such as that in Canary Islands thus, a new methodology was developed. This one includes a filtering system to identify which triangles are those likely to be shadowed. If there are no self shadowed triangles, the entire mesh will be illuminated and there will not be any shadows. Only triangles that have their backs towards the Sun will be able to cast shadows on other triangles. Detection of shadows generated by clouds is achieved by a shadow algorithm using satellite images. In this paper, Landsat 8 images have been used. The code was done in python programming language. Finally, the outputs of both approaches, shadows generated by the topography and generated by clouds, can be combined in one map. The whole problem has been tested in Gran Canaria and Tenerife Island (Canary Islands – Spain), and in the Tatra Mountains (Poland and Slovakia).

Soft bilateral filtering shadows using multiple image-based algorithms

This study introduces Soft Bilateral Filtering Shadows method of dynamic scenes, which uses multi-matrices of the light sample points due to lack realism in soft shadows generation in real time. While geometry-based shadow algorithm requires one pass per polygon for rendering shadow that requires time-consuming, the adopted shadow map algorithm needs a single rendering pass for each sample point of the light source to generate shadow at low cost. This method renders a complex scenes and accurately eliminating the inherent deficiencies in shadow maps. In order to compute shadow maps, view matrices were used for each sample point of the extended light source. Then penumbra region was used for interpolation based on bilateral filtering to create the soft shadows. They depend on multiple shadow maps which provide antialiasing shadow maps. The method uses fragment shader for rendering multiple shadow maps with penumbra and umbra regions. The main contribution of this article is introducing interpolation bilaterally of image-based shadows. This method makes the most effect of the computation significantly appear at the edges of the penumbra region. Furthermore, the filtering allows to obtain on the soft shadow marvelously at the lowest number possible of the light sample points. The generated soft shadows have good performance and high quality therefore, they are suitable for interactive applications.

In-Class Exercises for Shadow Volume Algorithms

This article describes a simple course exercise to deepen the understanding of real-time shadow volume algorithms. The exercise takes less than 10 minutes to perform during lecture time, leads to a profounder understanding of the topic, and serves as a basis for further discussions of improvements for the shadow mapping algorithm. Using this exercise, the authors noticed an increase in students implementing shadow algorithms for later real-time graphics projects.

Partitioned Shadow Volumes

AbstractReal‐time shadows remain a challenging problem in computer graphics. In this context, shadow algorithms generally rely either on shadow mapping or shadow volumes. This paper rediscovers an old class of algorithms that build a binary space partition over the shadow volumes. For almost 20 years, such methods have received little attention as they have been considered lacking of both robustness and efficiency. We show that these issues can be overcome, leading to a simple and robust shadow algorithm. Hence we demonstrate that this kind of approach can reach a high level of performance. Our algorithm uses a new partitioning strategy which avoids any polygon clipping. It relies on a Ternary Object Partitioning tree, a new data structure used to find if an image point is shadowed. Our method works on a triangle soup and its memory footprint is fixed. Our experiments show that it is efficient and robust, including for finely tessellated models.

In-class exercise for shadow mapping algorithms.

This article describes a simple course exercise to deepen the understanding of real-time shadow mapping algorithms. The exercise takes less than 10 minutes to perform during lecture time, leads to a profounder understanding of the topic, and serves as a basis for further discussions of improvements for the shadow mapping algorithm. Using this exercise, the authors noticed an increase in implementing shadow algorithms for later real-time graphics projects.

Comparison of color and grayscale simulated TV-graphics in rendezvous and docking

Subjective evaluation is one of the most important methods of assessing the fidelity of a virtual dynamic scene, whose results could be seen as a reference to improve a rendering method. In the present study, we apply a rendering method to the color rendezvous and docking (RVD) scene, where one shadow algorithm and three tone mapping operator algorithms are used to process the scene based on the distance between two spacecrafts. The grayscale RVD scene is transformed from the color RVD scene, and each RVD scene is desired to make comparisons with the real RVD-video recorded in SZ-10 mission. In addition, we perform an evaluation on 36 subjects to compare the results. The results show that shadow effects have important roles in RVD virtual scenes, where they enhance the sense of realism and immersion. Shadows, high brightness, contrast and luminance are highly correlated with the overall preference, and the grayscale scene receives better evaluations than the color scene. The analysis of these conclusions is vital for improving the algorithms used to render color virtual space scenes when virtual scenes for astronaut training in Chinese space station mission are prepared.

Position-Dependent Importance Sampling of Light Field Luminaires

The possibility to use real world light sources (aka luminaires) for synthesizing images greatly contributes to their physical realism. Among existing models, the ones based on light fields are attractive due to their ability to represent faithfully the near-field and due to their possibility of being directly acquired. In this paper, we introduce a dynamic sampling strategy for complex light field luminaires with the corresponding unbiased estimator. The sampling strategy is adapted, for each 3D scene position and each frame, by restricting the sampling domain dynamically and by balancing the number of samples between the different components of the representation. This is achieved efficiently by simple position-dependent affine transformations and restrictions of Cumulative Distributive Functions that ensure that every generated sample conveys energy and contributes to the final result. Therefore, our approach only requires a low number of samples to achieve almost converged results. We demonstrate the efficiency of our approach on modern hardware by introducing a GPU-based implementation. Combined with a fast shadow algorithm, our solution exhibits interactive frame rates for direct lighting for large measured luminaires.