Imaginary Data Research Articles

Comparison of Bayesian and frequentist monitoring boundaries motivated by the Multiplatform Randomized Clinical Trial.

The coronavirus disease 2019 pandemic highlighted the need to conduct efficient randomized clinical trials with interim monitoring guidelines for efficacy and futility. Several randomized coronavirus disease 2019 trials, including the Multiplatform Randomized Clinical Trial (mpRCT), used Bayesian guidelines with the belief that they would lead to quicker efficacy or futility decisions than traditional "frequentist" guidelines, such as spending functions and conditional power. We explore this belief using an intuitive interpretation of Bayesian methods as translating prior opinion about the treatment effect into imaginary prior data. These imaginary observations are then combined with actual observations from the trial to make conclusions. Using this approach, we show that the Bayesian efficacy boundary used in mpRCT is actually quite similar to the frequentist Pocock boundary. The mpRCT's efficacy monitoring guideline considered stopping if, given the observed data, there was greater than 99% probability that the treatment was effective (odds ratio greater than 1). The mpRCT's futility monitoring guideline considered stopping if, given the observed data, there was greater than 95% probability that the treatment was less than 20% effective (odds ratio less than 1.2). The mpRCT used a normal prior distribution that can be thought of as supplementing the actual patients' data with imaginary patients' data. We explore the effects of varying probability thresholds and the prior-to-actual patient ratio in the mpRCT and compare the resulting Bayesian efficacy monitoring guidelines to the well-known frequentist Pocock and O'Brien-Fleming efficacy guidelines. We also contrast Bayesian futility guidelines with a more traditional 20% conditional power futility guideline. A Bayesian efficacy and futility monitoring boundary using a neutral, weakly informative prior distribution and a fixed probability threshold at all interim analyses is more aggressive than the commonly used O'Brien-Fleming efficacy boundary coupled with a 20% conditional power threshold for futility. The trade-off is that more aggressive boundaries tend to stop trials earlier, but incur a loss of power. Interestingly, the Bayesian efficacy boundary with 99% probability threshold is very similar to the classic Pocock efficacy boundary. In a pandemic where quickly weeding out ineffective treatments and identifying effective treatments is paramount, aggressive monitoring may be preferred to conservative approaches, such as the O'Brien-Fleming boundary. This can be accomplished with either Bayesian or frequentist methods.

ASL MRI Denoising via Multi Channel Collaborative Low-Rank Regularization.

Arterial spin labeling (ASL) perfusion MRI is the only non-invasive imaging technique for quantifying regional cerebral blood flow (CBF), which is a fundamental physiological variable. ASL MRI has a relatively low signal-to-noise-ratio (SNR). In this study, we proposed a novel ASL denoising method by simultaneously exploiting the inter- and intra-receive channel data correlations. MRI including ASL MRI data have been routinely acquired with multi-channel coils but current denoising methods are designed for denoising the coil-combined data. Indeed, the concurrently acquired multi-channel images differ only by coil sensitivity weighting and random noise, resulting in a strong low-rank structure of the stacked multi-channel data matrix. In our method, this matrix was formed by stacking the vectorized slices from different channels. Matrix rank was then approximately measured through the logarithm-determinant of the covariance matrix. Notably, our filtering technique is applied directly to complex data, avoiding the need to separate magnitude and phase or divide real and imaginary data, thereby ensuring minimal information loss. The degree of low-rank regularization is controlled based on the estimated noise level, striking a balance between noise removal and texture preservation. A noteworthy advantage of our framework is its freedom from parameter tuning, distinguishing it from most existing methods. Experimental results on real-world imaging data demonstrate the effectiveness of our proposed approach in significantly improving ASL perfusion quality. By effectively mitigating noise while preserving important textural information, our method showcases its potential for enhancing the utility and accuracy of ASL perfusion MRI, paving the way for improved neuroimaging studies and clinical diagnoses.

A probabilistic solution to geophysical inverse problems in complex variables and its application to complex resistivity imaging

SUMMARY We introduce a novel probabilistic framework for the solution of non-linear geophysical inverse problems in complex variables. By using complex probability distributions, this approach can simultaneously account for individual errors of real and imaginary data parts, independently regularize real and imaginary parts of the complex model, and still take into account cross-sensitivities resulting from a complex forward calculation. The inverse problem is solved by means of optimization. An application of the framework to complex resistivity (CR) imaging demonstrates its advantages over the established inversion approach for CR measurements. We show that CR data, with real and imaginary parts being subject to different errors, can be fitted adequately, accounting for the individual errors and applying independent regularization to the real and imaginary part of the subsurface conductivity. The probabilistic framework itself serves as a basis for the future application of global sampling approaches, such as Markov chain Monte Carlo methods.

Assessment of Groundwater Resources in the Hard Basement Rocks of Yemen, Al Bayda City Case Study

The present study was carried out with the aim of assessing the availability of groundwater in a typical hard basement complex area in Yemen, the catchment area of Al-Bayda city catchment in the mountainous region of the country. The main objective of this study is to evaluate and quantify the groundwater resources available in this area. The available regional data on climate, topography, and geology as well as ASTER and Landsat imaginary data were collected, reviewed, and analyzed together with local hydrogeological studies and well inventories and then used to identify the general geohydrology of the catchment, characterize the local aquifer system, assess the groundwater situation and quantify the available groundwater in the study area. The results of this study show that the aquifer system accounts for only about 5 % of the entire catchment area and has a total thickness of 10 - 30 m. This aquifer has a small lateral extent and is interrupted by faults, dykes, and plutonic intrusions. Limited amounts of groundwater occur in isolated bodies along the main wadis at the base of the aquifer system. The groundwater level in these bodies has dropped by 19 m since 1983, on average by 0.5 m per year. Groundwater recharge for the current state is estimated at 1.95 million m3/year, which is only 3% of the total annual precipitation. The total amount of groundwater withdrawn is estimated at 2.2 million m3/year, which is 80% below the current (2023) total annual demand of around 11 million m3. The study underlines the need to implement an urgent action plan to improve groundwater recharge through water extraction techniques. The research results can be used for the improvement and development of water resources in this area in particular and in the Yemen region in general.

Indicators for Takt Production Performance Assessment—A Conceptual Study

This conceptual study aims to produce rough analysis methods and visualizations for production data (formatted in time, location, and work) that can be collected from construction sites that utilize takt production. The scope is on creating methods for evaluating the soundness of the takt plan and its execution. Relevant production literature regarding takt production management and data collection are utilized in the production of the methods and visualizations. However, only imaginary production data are utilized in this study to keep the indicators as simplified and clear as possible. A total of seven indicators with varying levels of novelty are provided in the study. The proposed indicators emphasize punctual adherence to the takt schedule, homogenous production pace, avoiding trade overlapping in locations, steady work in process, and coherent short and long-term production targets. Both as-planned and as-built perspectives are considered. The proposed indicators are argued to be valuable for production management and research and development processes since they provide status information and document the progression of the production for later indicators purposes. This study also acts as a foundation for further empirical studies regarding takt production data utilization.

All electron GW with linearized augmented plane waves for metals and semiconductors

GW approximation is one of the most popular parameter-free many-body methods that go beyond the limitations of the standard density functional theory (DFT) to determine the excitation spectra for moderately correlated materials and in particular the semiconductors. It is also the first step in developing the diagrammatic Monte Carlo method into an electronic structure tool, which would offer a numerically exact solution to the solid-state problem. While most electronic structure packages offer support for GW calculations for band-insulating materials, the level of support for metallic systems is somewhat limited. This limitation can be partly attributed to the relatively minor differences often observed between GW and DFT results in treating metallic systems, which is not expected to persist to higher orders in perturbation theory. Describing metals within the GW framework presents a challenge, as it requires accurate resolution of Fermi surface singularities, which, in turn, calls for a dense momentum mesh. Here we implement the GW algorithm within the all-electron Linear Augmented Plane Wave framework, where we pay special attention to the metallic systems, the convergence with respect to momentum mesh, and proper treatment of the deep laying core states, as needed for the future variational diagrammatic Monte Carlo implementation. Our improved algorithm for resolving Fermi surface singularities allows us a stable and accurate analytic continuation of imaginary axis data, which is carried out for GW excitation spectra throughout the Brillouin zone in both the metallic and insulating materials and is compared to numerically more stable contour deformation integration technique. We compute band structures for elemental metallic systems Li, Na, and Mg as well as for various narrow and wide bandgap insulators such as Si, BN, SiC, MgO, LiF, ZnS, and CdS and compare our results with previous GW calculations and available experiments data. Our results are in good agreement with the available literature. Thus our software allows users to compute full bandstructures for metals and insulators using all-electron potential without downfolding to Wannier orbital basis.

Novel multicomponent functionalized biopolymers with enhanced thermal and dielectric properties

A new functionalized biopolymer was obtained by adding ethylenediaminetetraacetic acid to chitin and magnetite nanoparticles (Fe3O4). Thermogravimetric analysis was performed, and dielectric properties were examined. The obtained biopolymer showed better thermal stability, as evidenced by a higher onset temperature. The activation energy calculated using imaginary impedance data and Nyquist plots was found to be consistent. Moreover, the dielectric permittivity decreased rapidly with increasing frequency. At high frequencies, there was no dependence of dielectric loss on temperature and frequency. The obtained biopolymer can be used in many applications such as microwave devices, deflection yoke, high-frequency capacitors, sensors, etc.

Deep learning for video game genre classification

In this paper, we propose a new multi-modal deep learning framework with a visual modality and a textual modality for video game genre classification. The proposed framework consists of three parts: two deeep networks for textual data and imaginary data, a feature concatenation algorithm, and then a softmax classifier. Video game covers and textual descriptions are usually the very first impression to its consumers and they often convey important information about the video games. Video game genre classification based on its cover and textual description would be utterly beneficial to many modern identification, collocation, and retrieval systems. At the same time, it is also an extremely challenging task due to the following reasons: First, there exists a wide variety of video game genres, many of which are not concretely defined. Second, video game covers vary in many different ways such as colors, styles, textual information, etc, even for games of the same genre. Third, cover designs and textual descriptions may vary due to many external factors such as country, culture, target reader populations, etc. With the growing competitiveness in the video game industry, the cover designers and typographers push the cover designs to its limit in the hope of attracting sales. The computer-based automatic video game genre classification systems become a particularly exciting research topic in recent years. The contribution of this paper is four-fold. First, we compiles a large dataset consisting of 50,000 video games from 21 genres made of cover images, description text, and title text and the genre information. Second, image-based and text-based, state-of-the-art models are evaluated thoroughly for the task of genre classification for video games. Third, we developed an efficient and scalable multi-modal framework based on both images and texts. Fourth, a thorough analysis of the experimental results is given and future works to improve the performance is suggested. The results show that the multi-modal framework outperforms the current state-of-the-art image-based or text-based models. Several challenges are outlined for this task. More efforts and resources are needed for this classification task in order to reach a satisfactory level.

Throughput Maximization Using Deep Complex Networks for Industrial Internet of Things

The high-density Industrial Internet of Things needs to meet the requirements of high-density device access and massive data transmission, which requires the support of multiple-input multiple-output (MIMO) antenna cognitive systems to keep high throughput. In such a system, spectral efficiency (SE) optimization based on dynamic power allocation is an effective way to enhance the network throughput as the channel quality variations significantly affect the spectral efficiency performance. Deep learning methods have illustrated the ability to efficiently solve the non-convexity of resource allocation problems induced by the channel multi-path and inter-user interference effects. However, current real-valued deep-learning-based power allocation methods have failed to utilize the representational capacity of complex-valued data as they regard the complex-valued channel data as two parts: real and imaginary data. In this paper, we propose a complex-valued power allocation network (AttCVNN) with cross-channel and in-channel attention mechanisms to improve the model performance where the former considers the relationship between cognitive users and the primary user, i.e., inter-network users, while the latter focuses on the relationship among cognitive users, i.e., intra-network users. Comparison experiments indicate that the proposed AttCVNN notably outperforms both the equal power allocation method (EPM) and the real-valued and the complex-valued fully connected network (FNN, CVFNN) and shows a better convergence rate in the training phase than the real-valued convolutional neural network (AttCNN).

Vegetation Change Detection using Remote Sensing and GIS, 2001-2021 – A Case Study of Nashik City, Maharashtra

This article provides an improved vegetation change detection technique for the analysis of satellite imagebased on normalized difference vegetation index (NDVI). The normalized difference vegetation indexmeasures the amount of vegetation on the earth’s surface across a large region. Remote sensing deliversspectral data from satellites in digital form without any direct interaction with the item. In the form ofimaging, this digital data is transformed into visual images. In different settings such as topography,biodiversity, land use, cultural elements, and so on, imagery is the best and most trustworthy source ofdata about the earth’s surface. In the hands of geographers, the geographic information system (GIS) is arelatively new technology. This index takes use of the disparity features of two bands a multispectral rasterdataset: chlorophyll pigment absorptions in the red band (Red) and high reflectivity of plant materials inthe near-infrared (NIR) band from a multispectral raster dataset. The NDVI for every pixel always yields afigure between -1 and +1, although no vegetation cover yields a value near to zero. The NDVI value around+1 represents the maximum area under the vegetation cover. The NDVI value near to -1 indicates barrenexpanses of rock, sand, or built-up areas. In urban area, vegetation plays essential role in enhancing themicroclimate. Natural vegetation has declined in many tropical cities as a result of urbanization. The Nashikcity is located to northwest region of the Maharashtra in India. The normalized difference vegetation indexwas analyzed using Landsat satellite imaginary data for the study period 2001-2021. The results showedthat the vegetation cover of the Nashik city declined due to urban sprawl. The class, very low densityvegetation explains that the area has increased which clearly indicates that the vegetation cover has beendecreased from 2001 to 2021, i.e. 25.51% to 55.87% per sq.km area. The class, very high density vegetationexplains that the area has decreased from 2001 to 2021, i.e. 11.66% to 5.54%% per sq.km area.

Continual Learning through Retrieval and Imagination

Continual learning is an intellectual ability of artificial agents to learn new streaming labels from sequential data. The main impediment to continual learning is catastrophic forgetting, a severe performance degradation on previously learned tasks. Although simply replaying all previous data or continuously adding the model parameters could alleviate the issue, it is impractical in real-world applications due to the limited available resources. Inspired by the mechanism of the human brain to deepen its past impression, we propose a novel framework, Deep Retrieval and Imagination (DRI), which consists of two components: 1) an embedding network that constructs a unified embedding space without adding model parameters on the arrival of new tasks; and 2) a generative model to produce additional (imaginary) data based on the limited memory. By retrieving the past experiences and corresponding imaginary data, DRI distills knowledge and rebalances the embedding space to further mitigate forgetting. Theoretical analysis demonstrates that DRI can reduce the loss approximation error and improve the robustness through retrieval and imagination, bringing better generalizability to the network. Extensive experiments show that DRI performs significantly better than the existing state-of-the-art continual learning methods and effectively alleviates catastrophic forgetting.

Long-term memory magnetic correlations in the Hubbard model: A dynamical mean-field theory analysis

We investigate the onset of a not-decaying asymptotic behavior of temporal magnetic correlations in the Hubbard model in infinite dimensions. This long-term memory feature of dynamical spin correlations can be precisely quantified by computing the difference between the zero-frequency limit of the Kubo susceptibility and the corresponding static isothermal one. Here, we present a procedure for reliably evaluating this difference starting from imaginary time-axis data, and apply it to the testbed case of the Mott-Hubbard metal-insulator transition (MIT). At low temperatures, we find long-term memory effects in the entire Mott regime, abruptly ending at the first order MIT. This directly reflects the underlying local moment physics and the associated degeneracy in the many-electron spectrum. At higher temperatures, a more gradual onset of an infinitely-long time-decay of magnetic correlations occurs in the crossover regime, not too far from the Widom line emerging from the critical point of the MIT. Our work has relevant algorithmic implications for the analytical continuation of dynamical susceptibilities in strongly correlated regimes and offers a new perspective for unveiling fundamental properties of the many-particle spectrum of the problem under scrutiny.

Fast aerial image model for EUV lithography using the adjoint fully convolutional network

The effects of thick-mask and oblique incidence in extreme ultraviolet (EUV) lithography system make the aerial image calculation a challenging task. This paper develops a fast EUV lithography aerial image model based on a new kind of deep learning framework called adjoint fully convolutional network (AFCN). The AFCN consists of two adjoint data paths to respectively recover the real part and imaginary part of the complex mask diffraction-near-field (DNF). The feature-swapping technique is introduced to exchange the information between the real and imaginary data paths. The AFCN is trained based on a pre-calculated rigorous thick-mask DNF dataset. Given the estimated thick-mask DNF, the Abbe's method is used to calculate the aerial image of the partially coherent lithography system. Compared to the traditional non-parametric kernel regression method, the proposed model reduces the error by more than 80% and achieves 25-fold improvement in computational efficiency.

Auto-Pattern Recognition for Diagnosis in Benign Paroxysmal Positional Vertigo Using Principal Component Analysis: A Preliminary Study

Objectives: The aim of this study was to develop a filtering algorithm for raw nystagmus images and a diagnostic assistive algorithm using a principal component analysis (PCA) to distinguish the different types of benign paroxysmal positional vertigo (BPPV).Methods: Fifteen video clips of clinical data with typical nystagmus patterns of BPPV (13 cases) and with normal nystamgmus (two cases) were preprocessed when applied the thresholding, morphology operation, residual noise filtering, and center point extraction stages. We analyzed multiple data clusters in a single frame via a PCA; in addition, we statistically analyzed the horizontal and vertical components of the main vector among the multiple data clusters in the canalolithiasis of the lateral semicircular canal (LSCC) and the posterior semicircular canal (PSCC).Results: We obtained a clear imaginary pupil and data on the fast phases and slow phases after preprocessing the images. For a normal patient, a round shape of clustered dots was observed. Patients with LSCC showed an elongated horizontal shape, whereas patients with PSCC showed an oval shape at the (x, y) coordinates. The scalar values (mm) of the horizontal component of the main vector when performing a PCA between the LSCC- and PSCC-BPPV were substantially different (102.08±20.11 vs. 32.36±12.52 mm, respectively; <i>p</i>=0.0012). Additionally, the salar ratio of horizontal to vertical components in LSCC and PSCC exhibited a significant difference (16.11±10.74 mm vs. 2.61±1.07 mm, respectively; <i>p</i>=0.0023).Conclusions: The data of a white simulated imaginary pupil without any background noise can be a separate monitoring option, which can aid clinicians in determining the types of BPPV exhibited. Therefore, this analysis algorithm will provide assistive information for diagnosis of BPPV to clinicians.

A full second-order statistical analysis of strictly linear and widely linear estimators with MSE and Gaussian entropy criteria

Novel physical insights are provided into second-order weight error bounds for both strictly linear (SL) and widely linear (WL) estimators for noncircular Gaussian data, under both mean square error (MSE) and Gaussian entropy criteria. This is achieved by first defining complementary weight error variances of these estimators and by further exploiting the so obtained additional degrees of freedom, related to complex noncircularity. Next, the strong uncorrelating transform (SUT) is employed for a joint diagonalization of both the input covariance matrix and the complementary covariance matrix, to allow for the boundedness of complementary weight error variances for different estimators to be addressed. Furthermore, a joint consideration of both the standard and complementary weight error variance analyses is shown to make it possible to measure weight error power distribution of these estimators over both real and imaginary data channels, an important finding which is not possible to obtain through the standard variance analysis only. Simulations in both system identification and channel estimation settings support the analysis.

Discriminative learning of imaginary data for few-shot classification

Humans can quickly learn new visual categories, because they can easily visualize or imagine what we need to recognize in an image with a complex background, and how it differs from other images. Incorporating this ability to hallucinate discriminative instances of novel classes might help machine vision systems perform better few-shot classification. In this paper, we propose a Discriminative Hallucination Learning Network based on attentional mechanism, and unify the GNN-based classifier with a “hallucinator” for few-shot classification. Firstly, we use a pre-trained saliency network to hallucinate the foreground image. Then, the hallucination feature network (FNet) and zoom network (ZNet) are designed to extract more fine-grained local images adaptively with intra-cluster similarity and the inter-cluster dissimilarity. The embedding network (Enet) initialize the node representation of the graph structure from a jointly trained convolutional neural network. Finally, the proposed method is evaluated extensively on three challenging ZSL benchmark datasets. It significantly outperforms state-of-the-art methods in both supervised and semi-supervised few-shot image classification experiments.

Electrical and Dielectric Characterization of Bismuth Holmium Nickel Titanate (BiHoNiTiO6)

A lead-free multiferroic double perovskite BiHoNiTiO6 (BHNT) was prepared at high temperature following mixed oxide solid state reaction mechanism. Structural evaluation by X-ray diffractogram suggested monoclinic crystal system for the BHNT. SEM images showed homogeneous morphology with almost similar and uniform grain distribution. The compound possessed low value of tanδ even at a high temperature (400oC) at 100 kHz. Evaluation of the dielectric and electrical plots with frequency and temperature obtained from the impedance spectroscopy revealed that the contributions of grains, grain boundaries and electrode has profound effect on the above properties. These plots are very much helpful to understand the conduction mechanism and microstructure characteristics of the electronic material. The experimental real and imaginary impedance data were modelled by designing an equivalent circuit with the capacitance and resistance parameter. The transport characteristics like σac (ac conductivity), electrical modulus and magneto-electric effect of the material were also reported in this paper.

Engineering of dielectric composites on electromagnetic and microwave absorbing properties for operation in the X-band

In this study, carbon black (CB) powder-loaded polyurethane (PU) composites (CB–PU composites) were prepared by melt mixing method with different volume percentages (45, 50, 55, 58 and 61 vol.%) of CB in the PU matrix. The prepared CB–PU composites had been further studied for surface morphology using the field-emission scanning electron microscopy (FESEM) technique. Dielectric properties in terms of real permittivity ([Formula: see text] and imaginary permittivity ([Formula: see text] of the fabricated composites were computed using an Agilent E8364B vector network analyzer in the frequency range of 8–12 GHz ([Formula: see text]-band). Dielectric loss factor of the prepared CB–PU composites was computed in terms of the dielectric loss tangent (tan [Formula: see text] = [Formula: see text]/[Formula: see text]. Microwave absorbing properties were appraised in terms of the reflection loss (RL) which in turn was calculated for varying thicknesses of the prepared composites from the measured real and imaginary permittivity data. The minimum RL was observed as −20.10 dB for the absorber with a thickness of 2.2 mm and the bandwidth achieved was 1.92 GHz for RL [Formula: see text]10 dB. Based on the above results these CB–PU composites have potential use as effective microwave absorbers in 8–12-GHz ([Formula: see text]-band) frequency range.

An efficient image encryption scheme using Fresnelet transform and elliptic curve based scrambling

This work introduces a novel and more efficient image encryption scheme, that deploys Fresnelet diffraction in the wave-propagation domain in conjunction with image scrambling effect, based on a specific elliptic curve group, that substantially reduces the computational cost for the desired outcome. Significantly elevated security of the encrypted image is guaranteed by highly complex algebraic structure of the elliptic curve over the Galois field $\mathbb {F}_{2}^{4}$ , in association with Fresnelet transform-based data-decomposition. At first stage, the proposed scheme, propagates confidential information, with selected wavelength at ceratin specific distance, using the Fresnelet transform. During this process confidential data decomposes into four complex sub-bands. We further separate these sub-bands into real and imaginary sub-band data. Then, at the second stage, elliptic curve group law is deployed to add confusion by scrambling the net sub-band data. The security and quality of the presented technique is examined through highly significant tools and we prove that when compared with other prevailing schemes, the proposed scheme offers coherent outcomes.

Extending the average spectrum method: Grid point sampling and density averaging

Analytic continuation of imaginary time or frequency data to the real axis is a crucial step in extracting dynamical properties from quantum Monte Carlo simulations. The average spectrum method provides an elegant solution by integrating over all non-negative spectra weighted by how well they fit the data. In a recent paper, we found that discretizing the functional integral as in Feynman's path-integrals, does not have a well-defined continuum limit. Instead, the limit depends on the discretization grid whose choice may strongly bias the results. In this paper, we demonstrate that sampling the grid points, instead of keeping them fixed, also changes the functional integral limit and rather helps to overcome the bias considerably. We provide an efficient algorithm for doing the sampling and show how the density of the grid points acts now as a default model with a significantly reduced biasing effect. The remaining bias depends mainly on the width of the grid density, so we go one step further and average also over densities of different widths. For a certain class of densities, including Gaussian and exponential ones, this width averaging can be done analytically, eliminating the need to specify this parameter without introducing any computational overhead.