Exact Image Research Articles

Comments on “Transient Magnetic Shielding of a Planar Conductive Thin Screen via Exact Image Theory”

Comments on “Transient Magnetic Shielding of a Planar Conductive Thin Screen via Exact Image Theory”

Reply to “Comments on Transient Magnetic Shielding of a Planar Conductive Thin Screen Via Exact Image Theory”

Reply to “Comments on Transient Magnetic Shielding of a Planar Conductive Thin Screen Via Exact Image Theory”

Minimally Distorted Adversarial Images with a Step-Adaptive Iterative Fast Gradient Sign Method

The safety and robustness of convolutional neural networks (CNNs) have raised increasing concerns, especially in safety-critical areas, such as medical applications. Although CNNs are efficient in image classification, their predictions are often sensitive to minor, for human observers, invisible modifications of the image. Thus, a modified, corrupted image can be visually equal to the legitimate image for humans but fool the CNN and make a wrong prediction. Such modified images are called adversarial images throughout this paper. A popular method to generate adversarial images is backpropagating the loss gradient to modify the input image. Usually, only the direction of the gradient and a given step size were used to determine the perturbations (FGSM, fast gradient sign method), or the FGSM is applied multiple times to craft stronger perturbations that change the model classification (i-FGSM). On the contrary, if the step size is too large, the minimum perturbation of the image may be missed during the gradient search. To seek exact and minimal input images for a classification change, in this paper, we suggest starting the FGSM with a small step size and adapting the step size with iterations. A few decay algorithms were taken from the literature for comparison with a novel approach based on an index tracking the loss status. In total, three tracking functions were applied for comparison. The experiments show our loss adaptive decay algorithms could find adversaries with more than a 90% success rate while generating fewer perturbations to fool the CNNs.

A Constraint Solving Approach to Parikh Images of Regular Languages

A common problem in string constraint solvers is computing the Parikh image, a linear arithmetic formula that describes all possible combinations of character counts in strings of a given language. Automata-based string solvers frequently need to compute the Parikh image of products (or intersections) of finite-state automata, in particular when solving string constraints that also include the integer data-type due to operations like string length and indexing. In this context, the computation of Parikh images often turns out to be both prohibitively slow and memory-intensive. This paper contributes a new understanding of how the reasoning about Parikh images can be cast as a constraint solving problem, and questions about Parikh images be answered without explicitly computing the product automaton or the exact Parikh image. The paper shows how this formulation can be efficiently implemented as a calculus, PC*, embedded in an automated theorem prover supporting Presburger logic. The resulting standalone tool Catra is evaluate on constraints produced by the Ostrich+ string solver when solving standard string constraint benchmarks involving integer operations. The experiments show that PC* strictly outperforms the standard approach by Verma et al. to extract Parikh images from finite-state automata, as well as the over-approximating method recently described by Janků and Turoňová by a wide margin, and for realistic timeouts (under 60 s) also the nuXmv model checker. When added as the Parikh image backend of Ostrich+ to the Ostrich string constraint solver’s portfolio, it boosts its results on the quantifier-free strings with linear integer algebra track of SMT-COMP 2023 (QF_SLIA) enough to solve the most Unsat instances in that track of all competitors.

Imaging n-Dimensional Spaces Within m-Dimensional Spaces: An Extension of Hinton’s Method

We derive a Euclidean manifold that is virtually cyclic using a simple equation based on a Euclidean geometry and related to Hinton’s method. The derived equation is simple to understand, but able to project n-dimensional spaces into m-dimensional spaces. In addition, the method produces exact images of rectangular cuboids as elements of a vector space, implying that information is a vector and not a scalar.

Enhanced Brain Tumor Diagnosis Through Differential and Canonical Quadri –Partitioned Neutrosophic Set Classification Methods:A Comparative Study

An early cancer diagnosis is carried out for adequate management of diseases. Magnetic resonance imaging (MRI) is most commonly preferred method for cancer diagnosis. Due to the uncontrolled and rapid growth of cells, brain tumor is occurred. If not treated at a preliminary phase, it may lead to death. Thus, a noteworthy prerequisite for a successful treatment outcome is an early and precise diagnosis.Many conventional methods are discussed for performing efficient tumor detection. But, conventional classification methods not distinguish MRI as primary and metastases tumors in an accurate manner. Therefore, the performance comparison of deep learning-based classification (i.e., Differential Quadri-Partitioned Neutrosophic Interval-valued Polynomial Attention-based Deep CNN (DQNI-PADCNN) method and Canonical Quadri-Partitioned Neutrosophic Set based Otsuka–Ochiai Deep Recurrent Neural Network (CQNS-ODRNN) method) is introduced to provide exact image classification results. The brain MRI images are considered as an input. MRI image classification is carried out through CNN and RNN to find the brain tumor disease. Before the classification process, input images are de-noised. The noise-removed images are get segmented to identify the region of interested regions. Later, the images are classified into four classes such as glioma, meningioma, no tumor, and pituitary classes to detect the brain tumor. Both classification methods use Quadri-Partitioned Neutrosophic set for categorizing the images. Depending on CNNs and RNNs achievement in handling intricate tasks, an optimal multi-class brain tumor diagnosis is carried out. Experimental evaluation is implemented using MATLAB 2017 for brain tumor detection with the Brain Tumor MRI dataset. To the total number of MRI images, the various performance metrics are calculated in terms of sensitivity, specificity, accuracy, and time for the detection of brain tumors.

A review: Community pharmacy practices depending on how to maintain the pharmacy, products, registers and medical record

There have been some significant developments in the world of drugstore over recent times; one of the most significant has been changes in the law to allow druggists to define. This has helped druggists to give further devoted care to their cases and to work more nearly with GPs and other health professionals. A community drugstore may retain needed records in a data processing system. All oral conventions shall be reduced to a hard dupe. All conventions must be retained for at least 2 times from the date of the last stuffing rather of storing the factual hard dupe of a tradition; an electronic imaging system may be used if it can reproduce the exact image of the tradition including the rear side if necessary. All records kept in a data processing system must be backed- up at least a week to assure no data is lost due to system failure. The system must be suitable to produce a periodic hard- dupe printout of all conventions filled or refilled. The printout must be produced or reported within 72 hours of the stuffing or refilling of the tradition. All druggists who filled or refilled conventions shall corroborate that the hard- dupe printout is correct by proper dates and signing of the document.

Avoid Misidentification in Eyewitness Identification Procedures

Misidentification is detrimental in judicial proceedings. People perceive memories as the reality that has happened, but they are not exact images in their minds, and thus memory can be distorted, and false memories can be implanted into people's minds. Numerous studies highlight the frequent adverse effects of eyewitness identification methods. In order to reduce misidentification in judicial trials, this study explored the influencing factors of misidentification. Based on the previous studies, the present study describes misidentification and the influencing factors, including suggestive information, repeated identification procedures, and the witnesses' confidence. Recommendations are made regarding suggestive information that can influence eyewitnesses decisions, the evidence is contaminated from repeated identification procedures, and only confidence in the initial identification can be considered reliable evidence. The significance of this study is beneficial for researchers to increase their insights into the influencing factors in misidentification, and it also has implications for reducing misidentification in judicial trials in the future.

Extended Cross-Calibration Analysis Using Data from the Landsat 8 and 9 Underfly Event

The Landsat 8 and 9 Underfly Event occurred in November 2021, during which Landsat 9 flew beneath Landsat 8 in the final stages before settling in its final orbiting path. An analysis was performed on the images taken during this event, which resulted in a cross-calibration with uncertainties estimated to be less than 0.5%. This level of precision was due, in part, to the near-identical sensors aboard each instrument, as well as the underfly event itself, which allowed the sensors to take nearly the exact same image at nearly the exact same time. This initial calibration was applied before the end of the on-orbit initial verification (OIV) period; this meant the analysis was performed in less than a month. While it was an effective and efficient first look at the data, a longer-term analysis was deemed prudent to obtain the most accurate cross-calibration with the smallest uncertainties. The three forms of uncertainty established in the initial analysis, dubbed “Phase 1”, were geometric, spectral, and angular. This paper covers Phase 2 of the underfly analysis; several modifications were made to the Phase 1 process to improve the cross-calibration results, including a spectral correction in the form of a spectral band adjustment factor (SBAF) and a more robust filtering system that used the statistics of the reflectance data to better include important data compared to the more aggressive filters used in Phase 1. A proper uncertainty analysis was performed to more accurately quantify the uncertainty associated with the underfly cross-calibration. The results of Phase 2 showed that the Phase 1 analysis was within its 0.5% uncertainty estimation, and the cross-calibration gain values in this paper were used by USGS EROS to update the Landsat 9 calibration at the end of 2022.

The Empowerment of the Classy Women in Kamila Shamsie’s Salt and Saffron

Western feminists have utilized the power they have acquired from their position in inactive discourses to cast third world women as victims who need the assistance of their western sisters. Structuring third world women in a category or one dimension of analysis as suppressed, savage, subjugated, pawn, helpless, and dependent, have outraged many non-western writers to enhance the exact image of women relying upon their different experiences and resistance methods to the gendered oppression of female and originating empowered examples of women in their literary narratives. The Pakistani-British author Kamila Shamsie offers a fresh perspective on third-world women by highlighting their struggles, contributions, and ordeals as well as their fight to live and fulfill their potential. By incorporating transnational feminist theory as a radical theoretical model to expose and define the west's hegemonic practices, transcending nation-state boundaries, and addressing inequalities globally, this study aims to demonstrate how Shamsie challenges western ideas and stereotypical images

Deep Adversarial Cascaded Hashing for Cross-Modal Vessel Image Retrieval

In recent years, cross-modal remote sensing image retrieval(CMRSIR) has attracted a lot of attention in remote sensing(RS) information processing. It's worth mentioning that land cover scenes, whether unimodal or cross-modal, are the primary research contents of remote sensing image retrieval (RSIR), and there are few studies on vessel images captured by RS satellites, let alone cross-modal retrieval tasks. Vessel images have smaller scale, lower resolution, and less detailed information than land cover images, so it's difficult to retrieve the exact images we want. In this paper, a hashing method called deep adversarial cascaded hashing (DACH) is proposed to address these problems. To extract the subtle and discriminative features contained in RS vessel images accurately, we build a deep cascaded network that fuses multilevel features boosted both in depth and width, and the self-attention mechanism can further enhance the fused features. Combined with hash learning, we also design a weighted quintuplet loss to supervise the transition of discrimination and similarity between different metric spaces, and reduce cross-modal discrepancy at the same time. In addition, we apply the deep adversarial constraint to both feature learning and hash learning, trying to bridge the modality gap and achieve a cross-modal retrieval as precise as unimodal retrieval. Comprehensive experiments on two public bimodal vessel image datasets compared with several excellent cross-modal retrieval methods are conducted to demonstrate the effectiveness of our DACH, and the results show that the proposed method is effective and competitive on cross-modal vessel image retrieval tasks, outperforming state-of-the-art methods.

Exciton Chirality Inversion in Dye Dimers Templated by DNA Holliday Junction.

While only one enantiomer of chiral biomolecules performs a biological function, access to both enantiomers (or enantiomorphs) proved to be advantageous for technology. Using dye covalent attachment to a DNA Holliday junction (HJ), we created two pairs of dimers of bis(chloroindolenine)squaraine dye that enabled strongly coupled molecular excitons of opposite chirality in solution. The exciton chirality inversion was achieved by interchanging single covalent linkers of unequal length tethering the dyes of each dimer to the HJ core. Dimers in each pair exhibited profound exciton-coupled circular dichroism (CD) couplets of opposite signs. Dimer geometries, modeled by simultaneous fitting absorption and CD spectra, were related in each pair as nonsuperimposable and nearly exact mirror images. The origin of observed exciton chirality inversion was explained in the view of isomerization of the stacked Holliday junction. This study will open new opportunities for creating excitonic DNA-based materials that rely on programmable system chirality.

A Non-Western Representation of the Third World Women in Nadia Hashimi’s A House without Windows

Third-world women in western literature have often been depicted as submissive, illiterate, vulnerable, and having no identity. They are cast from the outside discourses, always entangled in the gaze of the west as being primitive. Yet the world is open to change with viable feminist female practices to enhance the accurate and exact image oe third-world women. Nadia Hashimi’s A House Without Windows is a manifestation of deconstructing the false stereotyped image created by the West writers through skillfully presenting the real pressing contemporary issues of Afghan women in their society with diverse forms of ordeals. This paper aims to reflect upon the significant differences between stereotypical western depictions of third world women— are portrayed in mainstream western discourses as weak and in need of help— and their representation by Non-Western writers. It further explores how the female characters can cope with life hardships and pass the closures with no fear despite the difficulties of living within the nonwestern codes of patriarchy and culture. Investigating Hashmi’s novel is significant because it presents an accurate depiction of third-world women together with a variety of their inspirational tales, correcting and changing the stereotyped western representation. This study elucidates the Transnational Feminist Theory as a remarkable framework for comprehending the gap between the global north and south to address the representation of third-world women. Consequently, dismantling the stereotypical images of women in the third world and the phony universalization of global sisterhood is investigated by reflecting upon the vivid pictures of strong women.

Becoming Italian

Becoming Italian

Avoiding Halo Effect in Employee Selection: Implementing Effective Techniques in Hiring

The aim of this conceptual paper is to explore the issues affecting the importance of employee selection processes within the “Halo Effect” context. It’s so obvious that employee selection process will determine the quality of new hires and organizational success, or failure and it is also dependent on the talent pool of the candidates. First, if a total and exact image of a job isn’t developed beforehand, it will be so complex to choose and hire excellent employees. Therefore, if the modern and scientific principles in employee selection applied and the employee selection procedures are properly designed, it can save organizations a lot of money. However, differentiating between applicants to identify and hire those with a greater likelihood of success in a job by avoiding halo effect is very important for organizations for both in daily operations and organizational long-term success. In sum, in this paper, the process of employee selection will be outlined, effective interviewing and recruiting techniques and choosing the right candidate for the vacant job will be discussed.

Effects of Viral Infection on Endothelial Activation and Barrier Function

Endothelial cells (ECs) are in the inner layer of blood vessels, and it controls the transportation of nutrition and essential molecules from the extracellular matrix to the intracellular space making a homeostasis environment. Vascular endothelium can be disrupted with viruses, which are among the main reasons for epidemic and pandemic outbreaks, and it leads to health issues such as cardiovascular‐related diseases. Wide range of viruses can potentially target the endothelium including Dengue virus estimated to infect 390 million people per year. Zika virus leads to pregnancy complications such as preterm birth and miscarriage. Tick‐borne encephalic damaged the central nervous system and, Hantavirus has a mortality rate of 38% through hantavirus pulmonary syndrome. Reviewing these several diseases, including SARS‐CoV‐2, has given an in‐depth look at what the endothelial cells are going through when these viruses are infecting the host bodies. Following the virus infection, ECs are going through activation mode, which is accompanied with glycocalyx degradation, plasma leakage, phosphorylation of P120, and cadherin, increased proinflammatory responses and, disruption of barrier integrity.The SARS‐CoV‐2 pandemic has taken a global toll; With a total of 362 million cases, there are 5.63 million deaths occurring. With the usage of immunofluorescence and vascular permeability assay, we monitor how Human Umbilical Vascular Endothelial Cells (HUVEC) and Human Lung Microvascular Endothelial Cells (HLMVEC) are impacted when treated with UV‐inactivated & heat‐inactivated SARS‐CoV‐2, and tumor necrosis factor (TNF)‐α. TNF‐α is an inflammatory cytokine prominent during inflammation and its known to increase the permeability of endothelium. Like the TNF‐α, both cells treated with UV‐inactivated and heat‐inactivated SARS‐CoV2 demonstrated boosted permeability. With the considerable effect of SARS‐CoV2 on the permeability of ECs, it is essential to understand the mechanisms and pathways behind the boosted leak, including the impact of the virus on the expression of adhesion molecules like platelet endothelial cell adhesion molecule (PECAM‐1). One can see in the images of HUVEC and HLMVEC that SARS‐CoV‐2 diminish the localization of PECAM‐1. On a non‐treated negative control, cells are spaced evenly and are close to another since their junctions are not targeted. Once the cells are treated with either heat‐inactivated or UV‐inactivated SARS‐CoV‐2, PECAM‐1 localization is disrupted, which can be seen from the space between cells since the PECAM‐1 has been altered and is no longer holding the cells together. From those exact images, one can detect the enlarged and irregular shapes of nucleuses and change in morphology of these cells since the SARS‐CoV‐2 is not only altering the PECAM‐1 complex but it can also modify various proteins and mRNAs. By combining information of various viruses and knowledge from our studies, we can understand the mechanisms of ECs and take measurements to protect ECs from viruses. In future experiments, we will detect if the PECAM‐1 expression is lowered when treated with SARS‐CoV‐2 and TNF‐α or if PECAM‐1 is internalized in the cell. Moreover, we can do mRNA and immunoblotting analysis to elaborate the changes in protein pathways as a consequence of COVID‐19.

FastNet: Fast high-resolution network for human pose estimation

Aiming at the problem of developing efficient models for human pose estimation algorithms under computation-limited resources. In this paper, we proposed an efficient high-resolution network for human pose estimation, which named FastNet. First of all, The Lite-HRNet was constructed by using four parallel subnetworks. Each subnetwork contained multiple bottleneck block for feature extraction. The Lite-HRNet can effectively learn the high-resolution representation features of key points because of maintaining high-resolution. Secondly, instead of standard convolution, the asymmetric convolution was introduced to build an asymmetric bottleneck module. The asymmetric bottleneck module has different aspect ratios, and can be used to exact image features of keypoints with multi-scale characteristics and reduce the number of parameters. Thirdly, the waterfall module composed of multiple parallel convolutions were proposed to aggregates features with the same spatial size which can efficiently obtain delicate local representations. It retains rich spatial information and results in precise keypoint localization. Finally, the bottleneck blocks were replaced with asymmetric bottleneck modules in the third subnetwork of LiteHRNet. By which, The waterfall module is embedded into the structure of FastNet. Comprehensive experiments demonstrate that the proposed method achieves superior results on two benchmark datasets, MSCOCO and MPII. Moreover, FastNet demonstrates superior results on human pose estimation over popular lightweight networks.

Real-Time Super-Resolution ISAR Imaging Using Unsupervised Learning

Compressive sensing (CS) enables high-resolution inverse synthetic aperture radar (ISAR) imaging with limited measurements. However, these methods reconstruct images via iterative optimization, resulting in a high computational load. Recently, convolutional neural networks (CNNs) have been used to perform super-resolution ISAR imaging in real time, where high-resolution images are necessarily used as ground truth. However, the desired high-resolution images are not reliable in practice. This letter presents an unsupervised CNN-based framework for super-resolution ISAR imaging. The well-trained CNN can directly produce high-resolution ISAR images in real time. Moreover, the network is trained in an unsupervised manner, which is suitable for practical applications. Furthermore, a pseudo <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula> -norm has been used as the sparse constraint for the exact image reconstruction. The proposed approach has been used to process the real ISAR data, and the experimental results are convincing.

Design Feasibility Study of PET/CT Facility in Iraq

Positron Emission Tomography-Computed Tomography (PET/CT) framework is a cutting-edge imaging tool in nuclear medicine. The essential objective of PET/CT imaging is to create profoundly exact combination images with a good enrolment of both CT and PET images on the same platform. Such major specialized equipment requires careful structural designs, and planning considerations of the functional components to facilitate the traffic of patients, staff, and radioactive materials.

Rapid category selectivity for animals versus man-made objects: An N2pc study

Visual recognition occurs rapidly at multiple categorization levels, including the superordinate level (e.g., animal), basic level (e.g., cat), or exemplar level (e.g., my cat). Visual search for animals is faster than for man-made objects, even when the images from those categories have comparable gist statistics (i.e., low- or mid-level visual information), which suggests that higher-level, conceptual influences may support this search advantage for animals. However, it remains unclear whether the search advantage can be explained in part by early visual search processes via the N2pc ERP component, which emerges earlier than behavioral responses, across different categorization levels. Participants searched for 1) an exact image (e.g., a specific squirrel image, Exemplar-level Search), 2) any images of an item (e.g., any squirrels, Basic-level Search), or 3) any items in a category (e.g., any animals, Superordinate-level Search). In addition to Target Present trials, Foil trials measured involuntary attentional selection of task-irrelevant images related to the targets (e.g., other squirrel images when searching for a specific squirrel image, or other animals when searching for squirrels). ERP results revealed 1) a larger N2pc amplitude during Foil trials in Exemplar-level Search for animals than man-made objects, and 2) faster onset latencies for animal search than man-made object search across all categorization levels. These results suggest that the search advantage for animals over man-made objects emerges early, and that attentional selection is more biased toward the basic-level (e.g., squirrel) for animals than for man-made objects during visual search.