Minimize Error Propagation Research Articles

Self-calibration strategies for reducing systematic slope measurement errors of autocollimators in deflectometric profilometry.

Deflectometric profilometers are used to precisely measure the form of beam shaping optics of synchrotrons and X-ray free-electron lasers. They often utilize autocollimators which measure slope by evaluating the displacement of a reticle image on a detector. Based on our privileged access to the raw image data of an autocollimator, novel strategies to reduce the systematic measurement errors by using a set of overlapping images of the reticle obtained at different positions on the detector are discussed. It is demonstrated that imaging properties such as, for example, geometrical distortions and vignetting, can be extracted from this redundant set of images without recourse to external calibration facilities. This approach is based on the fact that the properties of the reticle itself do not change - all changes in the reticle image are due to the imaging process. Firstly, by combining interpolation and correlation, it is possible to determine the shift of a reticle image relative to a reference image with minimal error propagation. Secondly, the intensity of the reticle image is analysed as a function of its position on the CCD and a vignetting correction is calculated. Thirdly, the size ofthe reticle image is analysed as a function of its position and an imaging distortion correction is derived. It is demonstrated that, for different measurement ranges and aperture diameters of the autocollimator, reductions in the systematic errors of up to a factor of four to five can be achieved without recourse to external measurements.

Cascaded framework for earthquake building damage detection combining spatial and frequency domain feature integration

Abstract. Building collapse is a major cause of casualties after an earthquake, so accurately extracting building damage information is critical for post-earthquake assessment and rescue. Currently, most deep learning methods focus on the end-to-end detection of building collapse. However, in real-world earthquake scenarios, the end-to-end computational process often lacks flexibility and struggles to meet the requirements of rapid emergency response. To address this issue, this paper proposes a cascaded framework that combines pre-earthquake building extraction and post-earthquake building damage classification. The proposed framework includes two sections: (1) Progressive building semantic segmentation model in the joint frequency domain. This model is designed to accurately extract buildings prior to an earthquake, with the goal of minimizing error propagation throughout the cascading process. The model addresses the spatial similarity of buildings under complicated backgrounds, as well as the high internal heterogeneity of buildings, by utilizing frequency domain techniques. It compensates for the shortcomings of traditional models in terms of incomplete information extraction through the effective integration of global and local information. Finally, the model employs edge priors for edge regularization. (2) Rapid building damage classification process. Based on the accurate building extraction results, a fast and efficient classification process is developed. This process uses a simple and lightweight classification network to effectively extract building damage information caused by the earthquake. The superiority of the proposed framework is validated through comparison with traditional cascading architectures and end-to-end models. The results show that the cascading framework not only provides accurate pre-earthquake building extraction, but also enables efficient and accurate post-earthquake damage classification, which meets the requirements of rapid post-earthquake emergency response. This balance of accuracy and speed is essential for effective disaster management and recovery.

High-Fidelity Diffusion-Based Image Editing

Diffusion models have attained remarkable success in the domains of image generation and editing. It is widely recognized that employing larger inversion and denoising steps in diffusion model leads to improved image reconstruction quality. However, the editing performance of diffusion models tends to be no more satisfactory even with increasing denoising steps. The deficiency in editing could be attributed to the conditional Markovian property of the editing process, where errors accumulate throughout denoising steps. To tackle this challenge, we first propose an innovative framework where a rectifier module is incorporated to modulate diffusion model weights with residual features from the original images, thereby providing compensatory information to bridge the fidelity gap. Furthermore, we introduce a novel learning paradigm aimed at minimizing error propagation during the editing process, which trains the editing procedure in a manner similar to denoising score-matching. Extensive experiments demonstrate that our proposed framework and training strategy achieve high-fidelity reconstruction and editing results across various levels of denoising steps, meanwhile exhibits exceptional performance in terms of both quantitative metric and qualitative assessments. Lastly, we explore our model's generalization though several applications like image-to-image translation and out-of-domain image editing.

Preprocessing methods for quantitative phase image stitching.

Quantitative phase imaging of cell cultures and histopathological slides often requires measurements in large fields of view which is realized through the stitching of multiple high resolution phase maps. Due to the characteristic properties of phase images, careful preprocessing is crucial for maintaining the metrological value of the stitched phase image. In this work, we present various methods that address those properties. Our efforts are focused on increasing robustness to minimize error propagation in consecutive preprocessing steps.

Leveraging Multimodal Out-of-Domain Information to Improve Low-Resource Speech Translation

Speech translation (ST) is a bimodal conversion task from source speech to the target text. Generally, deep learning-based ST systems require sufficient training data to obtain a competitive result, even with a state-of-the-art model. However, the training data is usually unable to meet the completeness condition due to the small sample problems. Most low-resource ST tasks improve data integrity with a single model, but this optimization has a single dimension and limited effectiveness. In contrast, multimodality is introduced to leverage different dimensions of data features for multiperspective modeling. This approach mutually addresses the gaps in the different modalities to enhance the representation of the data and improve the utilization of the training samples. Therefore, it is a new challenge to leverage the enormous multimodal out-of-domain information to improve the low-resource tasks. This paper describes how to use multimodal out-of-domain information to improve low-resource models. First, we propose a low-resource ST framework to reconstruct large-scale label-free audio by combining self-supervised learning. At the same time, we introduce a machine translation (MT) pretraining model to complement text embedding and fine-tune decoding. In addition, we analyze the similarity at the decoder side. We reduce multimodal invalid pseudolabels by performing random depth pruning in the similarity layer to minimize error propagation and use additional CTC loss in the nonsimilarity layer to optimize the ensemble loss. Finally, we study the weighting ratio of the fusion technique in the multimodal decoder. Our experiment results show that the proposed method is promising for low-resource ST, with improvements of up to +3.6 BLEU points compared to baseline low-resource ST models.

UIFDBC: Effective density based clustering to find clusters of arbitrary shapes without user input

Density-based clustering has the ability to detect arbitrary shaped clusters in any dataset. In recent years, several density peak clustering methods have been reported. Among these, a few need user input(s), but majority use cluster validity indices to provide the best results. In this paper, we propose a density-based user-input-free clustering method named UIFDBC, which is capable of detecting clusters of arbitrary shapes, without depending on any specific cluster validity index. The method is evaluated on 16 synthetic and 7 real-world datasets and compared with 8 recent density-based clustering methods. The results show our method is superior, in general, to its counterparts in terms of discovering arbitrary shaped clusters on tested datasets. The approach also has the ability to handle low-density instances in a special manner to minimize error propagation. Our method is available as an R package and can be downloaded by clicking the link https://sites.google.com/view/hussinchowdhury/software.

Error propagation dynamics of velocimetry-based pressure field calculations (2): on the error profile

This work investigates the propagation of error in a Velocimetry-based Pressure field reconstruction (VPressure) problem to determine and explain the effects of error profile of the data on the error propagation. The results discussed are an extension to those found in Pan et al. (2016). We first show how to determine the upper bound of the error in the pressure field, and that this worst scenario for error in the data field is unique and depends on the characteristics of the domain. We then show that the error propagation for a V-Pressure problem is analogous to elastic deformation in, for example, a Euler-Bernoulli beam or Kirchhoff-Love plate for one- and two-dimensional problems, respectively. Finally, we discuss the difference in error propagation near Dirichlet and Neumann boundary conditions, and explain the behavior using Green’s function and the solid mechanics analogy. The methods discussed in this paper will benefit the community in two ways: i) to give experimentalists intuitive and quantitative insights to design tests that minimize error propagation for a V-pressure problem, and ii) to create tests with significant error propagation for the benchmarking of V-Pressure solvers or algorithms. This paper is intended as a summary of recent research conducted by the authors, whereas the full work has been recently published (Faiella et al., 2021).

Maximizing the Inner Resilience of a Network-on-Chip through Router Controllers Design.

Reducing component size and increasing the operating frequency of integrated circuits makes the Systems-on-Chip (SoCs) more susceptible to faults. Faults can cause errors, and errors can be propagated and lead to a system failure. SoCs employing many cores rely on a Network-on-Chip (NoC) as the interconnect architecture. In this context, this study explores alternatives to implement the flow regulation, routing, and arbitration controllers of an NoC router aiming at minimizing error propagation. For this purpose, a router with Finite-State Machine (FSM)-based controllers was developed targeting low use of logical resources and design flexibility for implementation in FPGA devices. We elaborated and compared the synthesis and simulation results of architectures that vary their controllers on Moore and Mealy FSMs, as well as the Triple Modular Redundancy (TMR) hardening application. Experimental results showed that the routing controller was the most critical one and that migrating a Moore to a Mealy controller offered a lower error propagation rate and higher performance than the application of TMR. We intended to use the proposed router architecture to integrate cores in a fault-tolerant NoC-based system for data processing in harsh environments, such as in space applications.

A Novel TLM Analysis for Solar Cells

Despite its widespread usage, the linear regression method used to obtain contact resistivity and sheet resistance from the transmission line model (TLM) can lead to huge errors. The method assumes that contact parameters (both electric and geometric) are identical across the probed structure, such as a solar cell. In contrast, the contact parameters display various process-associated inhomogeneities. This paper analyzes the limitations of the linear fit method by using mathematical analysis and measurements on the front-side Ni–Cu plated contacts of a passivated emitter and rear contact (PERC) solar cell. It presents a new evaluation method based on the compartmentalization of contact resistance and emitter sheet resistance of a TLM network. The relative increase in the uncertainty of the extracted contact parameters with decreasing contact resistance is verified and linked to strong error coupling. A large error (20%–180%) is observed in the extracted values of the contact resistance for an assumed (5%–40%) scatter in the sheet resistance values, when a linear fit is used on simulated data with absolutely no pre-estimated error in the contact resistance. The presented method enables the detection and elimination of the influence of bad contacts along with minimal error propagation, which is imperative for contacts with small opening width and lower values of contact resistivity.

Scaling and Complexity in Landscape Ecology

Landscapes and the ecological processes they support are inherently complex systems, in that they have large numbers of heterogeneous interacting components, interact in multiple ways, and exhibit scale dependence, non-linear dynamics, and emergent properties. The emergent properties of landscapes encompass a broad range of processes that influence biodiversity, ecosystem processes, and human environments. These properties, such as nutrient cycling, dispersal, evolutionary adaptation of organisms to their environments, and the focus of this article, ecological disturbance regimes (including wildfire), operate at scales that are relevant to human societies, but these are also often the scales at which ecosystem dynamics are most difficult to understand and predict. Modeling processes and interactions at the landscape scale, including future states of biological communities and their interactions with each other and with processes such as landscape fire, requires quantitative metrics and algorithms that minimize error propagation across scales. We identify three intrinsic limitations to progress in landscape ecology, and ecology in general: (1) the problem of coarse-graining, or how to aggregate fine-scale information to larger scales in a statistically unbiased manner; (2) the middle-number problem, which describes systems with elements that are too few and too varied to be amenable to global averaging, but too numerous and varied to be computationally tractable; and (3) non-stationarity, in modeled relationships or parameter choices that are valid in one environment but may not hold when projected onto future environments such as a warming climate. We illustrate these challenges with examples drawn from the context of landscape ecology and wildfire. Quantitative scaling relationships are the key to moving forward, and we review recent progress and paths to developing scaling laws in landscape ecology. We incorporate concepts of compression of state spaces from complexity theory to suggest ways to overcome the problems presented by coarse-graining, the middle-number domain, and non-stationarity.

Rational approximations of f(R) cosmography through Pad'e polynomials

We consider high-redshift f(R) cosmography adopting the technique of polynomial reconstruction. In lieu of considering Taylor treatments, which turn out to be non-predictive as soon as z>1, we take into account the Pad'e rational approximations which consist in performing expansions converging at high redshift domains. Particularly, our strategy is to reconstruct f(z) functions first, assuming the Ricci scalar to be invertible with respect to the redshift z. Having the so-obtained f(z) functions, we invert them and we easily obtain the corresponding f(R) terms. We minimize error propagation, assuming no errors upon redshift data. The treatment we follow naturally leads to evaluating curvature pressure, density and equation of state, characterizing the universe evolution at redshift much higher than standard cosmographic approaches. We therefore match these outcomes with small redshift constraints got by framing the f(R) cosmology through Taylor series around 0z≃ . This gives rise to a calibration procedure with small redshift that enables the definitions of polynomial approximations up to z≃ 10. Last but not least, we show discrepancies with the standard cosmological model which go towards an extension of the ΛCDM paradigm, indicating an effective dark energy term evolving in time. We finally describe the evolution of our effective dark energy term by means of basic techniques of data mining.

Operational Check Source Recalibration.

A formal protocol whereby a segregated source is used for later quality control trending and recalibration of operational sources is described. The method is of potential use for any quality radioactive assay program where multiple sources may be in use such that long term loss in activity is possible and not readily detected and corrected using common statistical assessment criteria. The method relies upon a rigorous initial characterization effort that can later be credited to minimize error propagation during recalibration to monitor the overall uncertainty budget. A simple example attachment for use in written procedures is also provided.

Cine delayed hyper-enhancement CMR imaging without additional scan time

Background A typical CMR exam includes cine, tagging, first-pass, and delayed-hyper-enhancement (DHE) imaging. The DHE image is usually acquired at a single timeframe. Nevertheless, obtaining cine DHE images would provide both viability and functional information in single set of images without misregistration problems for better image interpretation. However, repeated breath-hold scans are required to obtain cine DHE images, which increases scan-time with varying level of tissue enhancement with time. In this work, we propose a new technique for generating cine DHE images based on the acquired DHE image and motion field derived from the corresponding tagged images. Methods A mid-diastolic short-axis DHE image and the corresponding set of tagged images were acquired on 3T-MRI scanner. The corresponding cine images were acquired for reference. Image analysis has two steps: 1) apply the Lucas-Kanade optical-flow algorithm to the tagged images to extract the motion field of the myocardium through the cardiac cycle; 2) apply the measured motion field to the known (acquired) DHE image to generate the unknown DHE images. The infarcted regions were identified in the DHE images using the full-width at halfmaximum method. Radial thickening was measured from both cine and DHE images to evaluate the generated images. Infarction transmurality, circumferential strain, and radial thickening were measured from the DHE, tagged, and cine images, respectively, and the relationships between the generated parameters were studied using regression and correlation analyses. Results Figure 1 shows tagged and DHE images. Figure 2 shows transmurality and strain at different segments. Peak infarction transmurality (circumferential strain) were 91% (0%) and 0% (-14%) in the infarcted and lateral regions, respectively. Myocardial thickening, calculated from the DHE images, showed good correlation with the results from the gold-standard cine images (r = 0.72). Further, there was an excellent agreement between ED and ES infarction transmurality calculated from the DHE images (r = 0.998). Infarction transmurality showed good correlation with strain (r = 0.81; y = 0.19x+11.27). Conclusions The developed technique generates composite images that show both viability and functional information in one set of images, which is important for prognostic evaluation of the patient’s condition, e.g. determining candidates for revascularization. The generated images could be also useful for differentiating between gadolinium recess in the muscle and myocardial scar, usually conducted by comparing cine and DHE images side-by-side. Future work includes further improvement of the optical-flow algorithm to minimize error propagation with time, and implementing the algorithm on large number of patients with different degrees of infarction.

EggNOG v4.0: nested orthology inference across 3686 organisms

With the increasing availability of various ‘omics data, high-quality orthology assignment is crucial for evolutionary and functional genomics studies. We here present the fourth version of the eggNOG database (available at http://eggnog.embl.de) that derives nonsupervised orthologous groups (NOGs) from complete genomes, and then applies a comprehensive characterization and analysis pipeline to the resulting gene families. Compared with the previous version, we have more than tripled the underlying species set to cover 3686 organisms, keeping track with genome project completions while prioritizing the inclusion of high-quality genomes to minimize error propagation from incomplete proteome sets. Major technological advances include (i) a robust and scalable procedure for the identification and inclusion of high-quality genomes, (ii) provision of orthologous groups for 107 different taxonomic levels compared with 41 in eggNOGv3, (iii) identification and annotation of particularly closely related orthologous groups, facilitating analysis of related gene families, (iv) improvements of the clustering and functional annotation approach, (v) adoption of a revised tree building procedure based on the multiple alignments generated during the process and (vi) implementation of quality control procedures throughout the entire pipeline. As in previous versions, eggNOGv4 provides multiple sequence alignments and maximum-likelihood trees, as well as broad functional annotation. Users can access the complete database of orthologous groups via a web interface, as well as through bulk download.

Fault-tolerant ancilla preparation and noise threshold lower bounds for the 23-qubit Golay code

In fault-tolerant quantum computing schemes, the overhead is often dominated by the cost of preparing codewords reliably. This cost generally increases quadratically with the block size of the underlying quantum error-correcting code. In consequence, large codes that are otherwise very efficient have found limited fault-tolerance applications. Fault-tolerant preparation circuits therefore are an important target for optimization. We study the Golay code, a $23$-qubit quantum error-correcting code that protects the logical qubit to a distance of seven. In simulations, even using a na{\"i}ve ancilla preparation procedure, the Golay code is competitive with other codes both in terms of overhead and the tolerable noise threshold. We provide two simplified circuits for fault-tolerant preparation of Golay code-encoded ancillas. The new circuits minimize error propagation, reducing the overhead by roughly a factor of four compared to standard encoding circuits. By adapting the malignant set counting technique to depolarizing noise, we further prove a threshold above $\threshOverlap$ noise per gate.

Optimal linear attitude estimator and its recursive algorithm via geometric analysis

A new optimal linear attitude estimator is proposed for single-point attitude estimation using geometric approach, and a recursive optimal linear attitude estimator is developed through filtering noisy measurements. Dot and cross products are taken in order to eliminate the unknown parameters of relationships between measurements and Gibbs vector. The optimality criterion, which does not coincide with Wahba’s constrained criterion, yields linear attitude estimate. A prior rotation is adopted to avoid singularity which occurs when the principal angle is close to π. The recursive algorithm is achieved for the purpose of improving attitude accuracy using all past measurements. For long-term space missions, memory fading concept is introduced into recursive optimal linear attitude estimator. The optimal relative weighting is obtained through minimizing error propagation, and an efficient modification is proposed to significantly reduce the sudden increase of attitude error of recursive optimal linear attitude estimator in special cases. Numerical simulations show that the estimate of optimal linear attitude estimator is almost identical with that of the famous QUaternion ESTimator, and the accuracy provided by recursive optimal linear attitude estimator is over an order magnitude higher than that of optimal linear attitude estimator or QUaternion ESTimator in most cases.

Improved provenance tracing of Asian dust sources using rare earth elements and selected trace elements for palaeomonsoon studies on the eastern Tibetan Plateau

The Asian Monsoon forms an important part of the earth’s climate system, yet our understanding of the past interactions between its different sub-systems, the East Asian and Indian monsoons, and between monsoonal winds and other prevailing wind currents such as the Westerly jet, is limited, particularly in central Asia. This in turn affects our ability to develop climate models capable of accurately predicting future changes in atmospheric circulation patterns and monsoon intensities in Asia. Provenance studies of mineral dust deposited in terrestrial settings such as peat bogs can address this problem directly, by offering the possibility to examine past deposition rates and wind direction, and hence reconstruct past atmospheric circulation patterns. However, such studies are challenged by several issues, most importantly the identification of proxies that unambiguously distinguish between the different potential dust sources and that are independent of particle size. In addition, a single analytical method that is suitable for sample preparation of both dust source (i.e. desert sand, soil) and receptor (i.e. dust archive such as peat or soil profiles) material is desirable in order to minimize error propagation derived from the experimental and analytical work. Here, an improved geochemical framework of provenance tracers to study atmospheric circulation patterns and palaeomonsoon variability in central Asia is provided, by combining for the first time mineralogical as well as major and trace elemental (Sc, Y, Th and the rare earth elements) information on Chinese (central Chinese loess plateau, northern Qaidam basin and Taklamakan, Badain Juran and Tengger deserts), Indian (Thar desert) and Tibetan (eastern Qinghai–Tibetan Plateau) dust sources.Quartz, feldspars and clay minerals are the major constituents of all studied sources, with highly variable calcite contents reflected in the CaO concentrations. Chinese and Tibetan dust sources are enriched in middle REE relative to the upper continental crust and average shale but the Thar desert has a REE signature distinctly different from all other dust sources. There are significant differences in major, trace and REE compositions between the coarse and fine fractions of the surface sands, with the finest <4μm fraction enriched in Al2O3, Fe2O3, MnO, MgO and K2O and the <32μm fractions in Sc, Y, Th and the REE relative to the coarse fractions. The <4μm fraction best represents the bulk REE geochemistry of the samples. The provenance tracers Y/∑REE, La/Er, La/Gd, Gd/Er, La/Yb, Y/Tb, Y/La, Y/Nd and to a certain extent the europium anomaly Eu/Eu∗ (all REE normalized to post-Archean Australian shale, PAAS) are particle size-independent tracers, of which combinations of Y/∑REE, La/Yb, Y/Tb, Y/La and Eu/Eu∗ can be used to distinguish the Thar desert, the Chinese deserts, the Chinese loess plateau and the Tibetan soils. Their independence upon grain size means that these tracers can be applied to the long-range provenance tracing of Asian dust even when only bulk samples are available in the source region. Combinations of La/Th, Y/Tb, Y/∑REE, Sc/La and Y/Er distinguish the Tibetan soils from the Chinese loess plateau and the Chinese deserts. La/Th and notably Th/∑REE isolate the signature of the Badain Juran desert and the combination of Sc/La and Y/Er that of the Taklamakan desert. The similarity in all trace and REE-based provenance tracers between the northern Qaidam basin and Tengger desert suggests that these two deposits may have a common aeolian source.

Multiview Registration of Surfaces Minimizing Error Propagation

The purpose of this paper is to describe a processing system on multi-view registration minimizing error propagation for 3D geometry modelling.In the geometry modelling stage, the algorithm based on graph and metaview method is used . In order to enhance its robustness, a method for judging bad pairwise registration is proposed based on the computation of two views’ overlapping percentage. In order to enrich its completeness, combined the graph analysis with the metaview method is used to deal with the measurement data for local details. Results show that the processing system can provide 3D geometry model with low distortion successfully.

Robust state-space realizations of digital filters against finite word length errors *

This article deals with two important issues in digital filter implementation: roundoff noise and limit cycles. A novel class of robust state-space realizations, called normal realizations, is derived and characterized. It is seen that these realizations are free of limit cycles. Another interesting property of the normal realizations is that they yield a minimal error propagation gain. The optimal realization problem, defined as to find those normal realizations that minimize roundoff noise gain, is formulated and solved analytically. A design example is presented to demonstrate the behavior of the optimal normal realizations and to compare them with several well-known digital filter realizations in terms of minimizing the roundoff noise and the error propagation.

On normal realizations of digital filters with minimum roundoff noise gain

It is well known that normal realizations are free of limit cycles and that a digital filter implemented with a state-space realization ( A , B , C , d ) has no limit cycles if there exists some diagonal matrix D > 0 such that D - A T DA ⩾ 0 . In this brief, a method is proposed to check the existence of such a D for any given realization. It is also shown that the normal realizations have a minimal error propagation gain. More interestingly, the normal realizations are characterized, the minimum roundoff noise normal realization problem is formulated and solved analytically. An example is presented to test the efficiency of the proposed method and to demonstrate the performance of the proposed optimal normal realizations.