Similar Description Research Articles

Virtual wave based phasor field projection for photoacoustic imaging

The phasor field technique has been demonstrated as a powerful tool to improve imaging performance and reduce computational complexity in time of flight imaging. In this work, we show a similar description and propose phasor field photoacoustics (PAs), a framework for forward acoustic propagation and backward source reconstruction, using phasor representations of acoustic pressure and back projection events. Based on the linear propagation mechanism, this method employs complex phasor filters to decompose the photoacoustic wave into pairs of orthogonal virtual waves, wherein each pair of orthogonal waves corresponds to the real and imaginary parts of the quasi-monochromatic phasor field contribution. By performing phasor field projection (PFP) and thereafter conjugated multiplication in the spatial domain, the complex envelope containing local amplitude and phase information can be faithfully retrieved, attaining rigorous “unipolar” photoacoustic images without ambiguous negative absorption artifacts. Theoretical and experimental results demonstrate that the orthogonal virtual wave based phasor field photoacoustic method can effectively eliminate negative artifacts and improve imaging signal-to-noise ratio (SNR) without excessively increasing computational complexity. This work may pave the way for high-fidelity quantitative imaging, e.g., multispectral and molecular imaging applications.

BEST: Building evidences from scattered templates for accurate contactless palmprint recognition

Contactless palmprint identification offers significantly improved hygiene and user convenience, making it highly attractive for a range of civilian applications, especially during the current pandemic. However, the accurate recognition of contactless palmprint images can be highly challenging, attributed to the significant variations in the intra-class similarity and limitations of conventional palmprint feature descriptors under involuntary or contactless imaging variations. State-of-the-art completely contactless palmprint matching algorithms in the literature cannot adequately address these challenges and are not sufficiently accurate and fast enough for such real-world applications. This paper proposes a novel approach that adaptively locates the local palmprint regions with high similarities between their corresponding feature representations or templates to address these challenges. We consider spatial localization of such highly similar feature representations from multiple local regions and consolidate them to generate a more reliable match score. This paper presents reproducible and comparative experimental results, using within-database, cross-database, and cross-sensor performance evaluation, on four publicly available contactless palmprint datasets, including a sizeable contactless palmprint database from 600 different subjects. The proposed method achieves outperforming results compared with three state-of-the-art deep learning-based methods and five widely used conventional methods. In addition, the proposed method is also significantly faster than all state-of-the-art baseline methods.

Few-Shot Remote Sensing Image Scene Classification Based on Metric Learning and Local Descriptors

Scene classification is a critical technology to solve the challenges of image search and image recognition. It has become an indispensable and challenging research topic in the field of remote sensing. At present, most scene classifications are solved by deep neural networks. However, existing methods require large-scale training samples and are not suitable for actual scenarios with only a few samples. For this reason, a framework based on metric learning and local descriptors (MLLD) is proposed to enhance the classification effect of remote sensing scenes on the basis of few-shot. Specifically, MLLD adopts task-level training that is carried out through meta-learning, and meta-knowledge is learned to improve the model’s ability to recognize different categories. Moreover, Manifold Mixup is introduced by MLLD as a feature processor for the hidden layer of deep neural networks to increase the low confidence space for smoother decision boundaries and simpler hidden layer representations. In the end, a learnable metric is introduced; the nearest category of the image is matched by measuring the similarity of local descriptors. Experiments are conducted on three public datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. Experimental results show that the proposed scene classification method can achieve the most advanced results on limited datasets.

An adaptive feature region-based line segment matching method for viewpoint-changed images with discontinuous parallax and poor textures

Many methods have been proposed to extract line segment (LS) correspondences for images with viewpoint variations. However, the matching performance on images with viewpoint variations is still limited due to the influence of discontinuous parallax and poor textures on images. Existing line segment matching (LSM) methods rarely consider the coexistence of these two situations. This study attempts to address this problem by proposing an adaptive feature region (FR)-based LSM method based on the observation that different FRs are suitable for different image scenes under image viewpoint change. In the proposed method, LSs are paired, and two types of FRs, symmetric FR centered on the line-pair intersection and asymmetric FR with the intersection as a vertex, are constructed for every line pair. The asymmetric FR of a line pair is a subregion of its symmetric FR. And the complement region of asymmetric FR in symmetric FR is called complement FR. To choose a suitable FR for the matching of each line pair adaptively, the asymmetric FR-based feature descriptor similarity and epipolar line-based constraints are combined to determine candidate line pair matches firstly. Then, the candidate matches with similar complement FR-based feature descriptors are considered initial matches and used to construct a topological constraint to check other candidates. Finally, we estimate an adaptive influence region-based local homography for each matched line pair to constrain the matching of unmatched individual LSs. The experimental results on the test images with viewpoint changes show that our approach outperforms the state-of-the-art methods in terms of Recall, matching precision (MP), and F-Measure (reflecting the overall matching performance of Recall and MP). Especially, it improves the average Recall and average F-Measure of the best method among the comparison methods by 45.20% and 25.83%, respectively.

Assessment of Reporting Quality of Drug-Related Randomized Controlled Trials Conducted in India and Published in MEDLINE-Indexed Indian Journals Over a Decade: A Systematic Review.

Poorly published trials may result in biased and erroneous healthcare decisions. We conducted this systematic review to evaluate the reporting quality of drug-related randomized controlled trials (RCTs) conducted in India and published in MEDLINE-indexed Indian journals over a decade (between January 1, 2011, and December 31, 2020), as per the Consolidated Standards of Reporting Trials (CONSORT) Checklist 2010. An extensive literature search was conducted using the terms "Randomized controlled trial AND India." The full-length papers were extracted for RCTs related to drugs. Two independent investigators assessed each article against the checklist containing 37 criteria. Each article was scored 1 or 0 against each criterion which was finally summed up and evaluated. None of the articles fulfilled all 37 criteria. A compliance rate of >75% was seen in only 15.5% of articles. More than 75% of articles fulfilled a minimum of 16 criteria. Major checklist points observed to be deficient were "important changes to methods after trial commencement" (7%), "interim analysis and stopping guidelines" (7%), and "description of similarity of interventions while blinding" (4%). There remains ample room for improvement regarding research methodology and manuscript preparation in India. Moreover, journals should stringently implement the CONSORT Checklist 2010 to enhance the standard and quality of publications.

Free comment as a valuable approach to characterize and identify the drivers of liking of high-protein flavored milk drink submitted to ohmic heating

Flavored milk drink is a popular dairy product traditionally processed by pasteurization, which is a safe and robust process. Still, it can imply a greater energy expenditure and a more significant sensorial alteration. Ohmic heating (OH) has been proposed as an alternative to dairy processing, including flavored milk drink. However, its impact on sensory characteristics needs to be evidenced. This study used Free Comment, an underexplored methodology in sensory studies, to characterize five samples of high-protein vanilla-flavored milk drink: PAST (conventional pasteurization 72 °C/15 s); OH6 (ohmic heating at 5.22 V/cm); OH8 (ohmic heating at 6.96 V/cm); OH10 (ohmic heating at 8.70 V/cm), and OH12 (ohmic heating at 10.43 V/cm). Free Comment raised similar descriptors to those found in studies that used more consolidated descriptive methods. The employed statistical approach allowed observation that pasteurization and OH treatment have different effects on the sensory profile of products, and the electrical field strength of OH also has a significant impact. PAST was slightly to moderately negatively associated with “acid taste,” “fresh milk taste,” “smoothness,” “sweet taste,” “vanilla flavor,” “vanilla aroma,” “viscous,” and “white color.” On the other hand, OH processing with more intense electric fields (OH10 and OH12) produced flavored milk drinks strongly associated with the “in natura” milk descriptors (“fresh milk aroma” and “fresh milk taste”). Furthermore, the products were characterized by the descriptors “homogeneous,” “sweet aroma,” “sweet taste,“ “vanilla aroma,” “white color,“ “vanilla taste,” and “smoothness.” In parallel, less intense electric fields (OH6 and OH8) produced samples more associated with a bitter taste, viscosity, and lumps presence. Sweet taste and fresh milk taste were the drivers of liking. In conclusion, OH with more intense electric fields (OH10 and OH12) was promising in flavored milk drink processing. Furthermore, the free comment was a valuable approach to characterize and identify the drivers of liking of high-protein flavored milk drink submitted to OH.

Efficient predictions of cytotoxicity of TiO2-based multi-component nanoparticles using a machine learning-based q-RASAR approach

The availability of experimental nanotoxicity data is in general limited which warrants both the use of in silico methods for data gap filling and exploring novel methods for effective modeling. Read-Across Structure-Activity Relationship (RASAR) is an emerging cheminformatic approach that combines the usefulness of a QSAR model and similarity-based Read-Across predictions. In this work, we have generated simple, interpretable, and transferable quantitative-RASAR (q-RASAR) models which can efficiently predict the cytotoxicity of TiO2-based multi-component nanoparticles. A data set of 29 TiO2-based nanoparticles with specific amounts of noble metal precursors was rationally divided into training and test sets, and the Read-Across-based predictions for the test set were generated. The optimized hyperparameters and the similarity approach, which yield the best predictions, were used to calculate the similarity and error-based RASAR descriptors. A data fusion of the RASAR descriptors with the chemical descriptors was done followed by the best subset feature selection. The final set of selected descriptors was used to develop the q-RASAR models, which were validated using the stringent OECD criteria. Finally, a random forest model was also developed with the selected descriptors, which could efficiently predict the cytotoxicity of TiO2-based multi-component nanoparticles superseding previously reported models in the prediction quality thus showing the merits of the q-RASAR approach. To further evaluate the usefulness of the approach, we have applied the q-RASAR approach also to a second cytotoxicity data set of 34 heterogeneous TiO2-based nanoparticles which further confirmed the enhancement of external prediction quality of QSAR models after incorporation of RASAR descriptors.

The Description of Tyche in Galen’s Treatise “Protrepticus”

This article discusses the extract from Galen’s treatise “Protrepticus” which contains a description of the goddess Tyche. In the extract, Galen contrasts Hermes (a male master of the arts) and Tyche (a capricious and irrational woman). The fragment is considered in the context of the entire treatise (“Protrepticus” was intended as a polemical statement against the empirical school and their method) and its purpose (exhortation to the study of arts). G. Kaibel has previously shown that many details in the description of Tyche coincide with similar descriptions in the treatise “Tabula Cebetis’’. As a result, the scholar came to the conclusion that Galen and the author of “Tabula’’ relied on the same source. The fragment from the poem by Pacuvius provides additional evidence. However, in addition to the similarities in the description, it is worth considering the differences that appear in Galen’s treatise. For example, Galen replaces the term μανία (μαινομένη in “Tabula Cebetis’’) with the term ἄνοια, depriving it of medical connotations. The translators, apparently, did not attach any importance to this replacement, and therefore we have different translations of the term ἄνοια: folie, mancanza di senno, inanity. Since the replacement of the term μανία is not commented on in the editions and the existing translations demonstrate different interpretations, the author of the article provides an additional commentary, including a comparison of “Protrepticus” with other texts of Galen and the works of Plato, and also draws attention to the parallel passage from Pacuvius.

Multi-Neighborhood Guided Kendall Rank Correlation Coefficient for Feature Matching

Seeking feature correspondences among two or more images is an important problem in computer vision and image processing. The putative matches constructed by the similarity of feature descriptors are often contaminated by many false matches. Typically, the local neighborhood points of a true match point have a rank order, which will be maintained in the corresponding image, and we call it rank consistency. In this paper, we design a number of sorting plans to obtain the neighborhood rank lists by taking full advantage of the local neighborhood geometry structure. In order to measure the differences between rank lists, we adopt the statistically famous Kendall rank correlation coefficient and generalize its definition for matching problem. We design a neighborhood common element guidance strategy and a multi-neighborhood strategy to improve the universality and robustness of our method. Our method has linear complexity and it has superiority over state-of-the-art methods on several challenging data sets. It also performs well in image registration and loop-closure detection tasks. The source code of our method is publicly available. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>¹</label> https://github.com/MnYangs/mGKRCC </fn>

A review of Stree sharira from classical Ayurvedic literature

Finding the proper etio-pathogenesis and treating gynaecological problems requires knowledge of stree-sharira (anatomy and physiology of the female reproductive system). The classics of Ayurveda have focussed on the practical application of knowledge and hence they discussed sharira rather than micro structural details. The name Yoni denotes the entire genital canal. Similar descriptions of the vagina are explained by modern science as a fibro-musculo-membranous sheath connecting the uterine chamber to the outside at the vulva. It contains the uterine secretory and menstrual blood excretory canal. Garbhashaya is located in the third avarta of the yoni, behind the bladder, between pittashaya and pakvashaya. Sushruta used the term plalamntargata, which is comparable to the ovaries or beeja granthi. Three doshas are in charge of the rutuchakra (menstrual cycle). The Rajasrava kala (menstrual cycle duration) can last anywhere between three and seven days. The mechanism of artava pravrutti has been clearly defined by Ayurveda. There is menstrual blood loss throughout the three to five or seven-day Rajahkala (menstrual phase), and vata dosha predominates. During this phase, the drava and sara gunas of the pitta dosha and the chala guna and kshepana karma of the vata dosha both function. Ritu-kala, or the proliferative phase, lasts for 12 or 16 days or the entire cycle. Even without menstruation, navina rajah (new cycles) are established at this time, and fertility chances are at their highest. These functions share similarities with the kapha dosha, indicating that kapha is the dominant dosha in this period.

Strings, branes, Schwarzian action and maximal chaos

In this article, we present explicit evidence that maximal chaos occurs for a generic, probe quark-like defect degrees of freedom, in a strongly coupled large Nc gauge theory. In holography, this corresponds to the dynamics of open string degrees of freedom, in the background of a closed string geometry. In this context, we explicitly show that a Schwarzian effective action for the soft sector emerges and couples with other modes, in the infra-red. This is manifest on an open string worldsheet, as well as a D1-brane world-volume, embedded in AdS3. The corresponding maximal chaos is governed by an intrinsic defect D-brane horizon, and an intrinsic non-linear description of the brane or the string. We also present explicit evidence of maximal chaos away from extremality on a D-brane horizon, by computing a four-point out-of-time order correlator of spin-one operators. This further suggests that a similar description of the soft sector physics of open string degrees of freedom may exist in general.

Szeged and Mostar root-indices of graphs

Various distance-based root-indices of graphs are introduced and studied in the present article. They are obtained as unique positive roots of modified graph polynomials. In particular, we consider the Szeged polynomial, the weighted-product Szeged polynomial, the weighted-plus Szeged polynomial, and the Mostar polynomial. We derive closed formulas of these polynomials for some basic families of graphs. Consequently, we provide closed formulas for some root-indices and examine the convergence of sequences of certain root-indices. Moreover, some general properties of studied root-indices are stated. Finally, numerical results related to discrimination power, correlations, structure sensitivity, and abruptness of root-indices are calculated, interpreted, and compared to already known similar descriptors.

High-Accuracy Relative Pose Measurement of Noncooperative Objects Based on Double-Constrained Intersurface Mutual Projections.

Relative pose measurement for noncooperative objects is an important part of 3D shape recognition and motion tracking. The methods based on scanning point clouds have better environmental adaptability and stability than image-based methods. However, the discrete points obtained from a continuous surface are sparse, which leads to point-to-point dislocations in the overlapping area and seriously reduces the accuracy. Therefore, this paper proposed a relative-pose-measurement algorithm based on double-constrained intersurface mutual projections. First, the initial corresponding set was constructed using mutual projections between the areas with similar feature descriptors, and then the final corresponding set was determined through the rigid-transformation-consistency constraint to improve the accuracy of the matchings and achieve a high-accuracy relative pose measurement. In the Stanford dataset, the rotation error and translation error were reduced by 19.3% and 13.4%, respectively. Furthermore, based on the proposed evaluation method, which separated the error of the pose-measurement algorithm from that of the instrument, the experiments were carried out with a self-made swept-frequency interferometer. The rotation error was reduced by 39.8%, and the surface deviation was reduced by 4.9%, which further proved the advancement of the method.

SAPENet: Self-Attention based Prototype Enhancement Network for Few-shot Learning

Few-shot learning considers the problem of learning unseen categories given only a few labeled samples. As one of the most popular few-shot learning approaches, Prototypical Networks have received considerable attention owing to their simplicity and efficiency. However, a class prototype is typically obtained by averaging a few labeled samples belonging to the same class, which treats the samples as equally important and is thus prone to learning redundant features. Herein, we propose a self-attention based prototype enhancement network (SAPENet) to obtain a more representative prototype for each class. SAPENet utilizes multi-head self-attention mechanisms to selectively augment discriminative features in each sample feature map, and generates channel attention maps between intra-class sample features to attentively retain informative channel features for that class. The augmented feature maps and attention maps are finally fused to obtain representative class prototypes. Thereafter, a local descriptor-based metric module is employed to fully exploit the channel information of the prototypes by searching k similar local descriptors of the prototype for each local descriptor in the unlabeled samples for classification. We performed experiments on multiple benchmark datasets: miniImageNet, tieredImageNet, and CUB-200-2011. The experimental results on these datasets show that SAPENet achieves a considerable improvement compared to Prototypical Networks and also outperforms related state-of-the-art methods.

Towards the experimental observation of turbulent regimes and the associated energy cascades with paraxial fluids of light

The propagation of light in nonlinear optical media has been widely used as a tabletop platform for emulating quantum-like phenomena due to their similar theoretical description to quantum fluids. These fluids of light are often used to study two-dimensional phenomena involving superfluid-like flows, yet turbulent regimes still remain underexplored. In this work, we study the possibility of creating two-dimensional turbulent phenomena and probing their signatures in the kinetic energy spectrum. To that end, we emulate and disturb a fluid of light with an all-optical defect using the propagation of two beams in a photorefractive crystal. Our experimental results show that the superfluid regime of the fluid of light breaks down at a critical velocity at which the defect starts to exert a drag force on the fluid, in accordance with the theoretical and numerical predictions. Furthermore, in this dissipative regime, nonlinear perturbations are excited on the fluid that can decay into vortex structures and thus precede a turbulent state. Using the off-axis digital holography method, we reconstructed the complex description of the output fluids and calculated the incompressible component of the kinetic energy. With these states, we observed the expected power law that characterizes the generated turbulent vortex dipole structures. The findings enclosed in this manuscript align with the theoretical predictions for the vortex structures of two-dimensional quantum fluids and thus may pave the way to the observation of other distinct hallmarks of turbulent phenomena, such as distinct turbulent regimes and their associated power laws and energy cascades.

Profiles of Children With Cortical Visual Impairment Who Use Augmentative and Alternative Communication: A Retrospective Examination.

Cortical visual impairment (CVI) is the most common cause of visual impairment in children today and can impact the outcomes of children who rely on augmentative and alternative communication (AAC). This study provides baseline data of 13 children with CVI who used AAC during their first year of participation in an integrated CVI program. One purpose was to describe similarities and differences in the student's demographic, functional vision, communication, and educational profiles. A second purpose was to examine differences in students described with different communicator profiles. Archived student records were de-identified and reviewed using a systematic coding scheme. Two researchers independently reviewed and coded all student records. Reliability was established. Measures included CVI Range scores; supports for positioning, mobility, vision, and writing; AAC systems, including modes, access methods, and language representation; communicative competence; self-determination; literacy; and mathematics. The study yielded a rich description of similarities and differences among students at baseline and led to careful consideration of differences among the participants with emergent communicator and context-dependent communicator profiles. Currently, limited evidence exists that informs practice regarding AAC assessment and intervention for children with CVI. This article describes a small sample of children with CVI who use AAC. Results underscore the need for educators and practitioners to ensure that vision functioning in students with CVI is evaluated carefully and regularly when conducting AAC assessment and intervention and formulating communication or education goals. https://doi.org/10.23641/asha.21357684.

Density-of-states similarity descriptor for unsupervised learning from materials data

We develop a materials descriptor based on the electronic density-of-states (DOS) and investigate the similarity of materials based on it. As an application example, we study the Computational 2D Materials Database (C2DB) that hosts thousands of two-dimensional materials with their properties calculated by density-functional theory. Combining our descriptor with a clustering algorithm, we identify groups of materials with similar electronic structure. We introduce additional descriptors to characterize these clusters in terms of crystal structures, atomic compositions, and electronic configurations of their members. This allows us to rationalize the found (dis)similarities and to perform an automated exploratory and confirmatory analysis of the C2DB data. From this analysis, we find that the majority of clusters consist of isoelectronic materials sharing crystal symmetry, but we also identify outliers, i.e., materials whose similarity cannot be explained in this way.

Taxonomy and Phylogenetic Appraisal of Dothideomycetous Fungi Associated with Magnolia, Lilium longiflorum and Hedychium coronarium.

This paper highlights the taxonomy of some interesting saprobic microfungi associated with dead plant materials of Hedychium coronarium, Lilium longiflorum, and Magnolia species. The taxa reported in this study belong to the orders Pleosporales and Kirschsteiniotheliales (Dothideomycetes). These taxa were identified based on multi-locus phylogeny of nuclear ribosomal DNA (rDNA) (LSU, SSU, and ITS) and protein-coding genes (tef1-α and rpb2), together with comprehensive morphological characterization. Two novel saprobic species, Leptoparies magnoliae sp. nov. and Neobambusicola magnoliae sp. nov., are introduced from Magnolia species in Thailand. Another new species, Asymmetrispora zingiberacearum sp. nov., is also described from dead stems of H. coronarium, which is the first asexual morph species of the genus Asymmetrispora. In addition, Ramusculicola thailandica and Kirschsteiniothelia thailandica are reported as new host records from dead twigs of Magnolia species. Sphaerellopsis paraphysata is reported as a new host record from L. longiflorum. Newly described taxa are compared with other similar species and detailed descriptions, micrographs, and phylogenetic trees to show the positions are provided.

Quantum Chemical Roots of Machine-Learning Molecular Similarity Descriptors.

In this work, we explore the quantum chemical foundations of descriptors for molecular similarity. Such descriptors are key for traversing chemical compound space with machine learning. Our focus is on the Coulomb matrix and on the smooth overlap of atomic positions (SOAP). We adopt a basic framework that allows us to connect both descriptors to electronic structure theory. This framework enables us to then define two new descriptors that are more closely related to electronic structure theory, which we call Coulomb lists and smooth overlap of electron densities (SOED). By investigating their usefulness as molecular similarity descriptors, we gain new insights into how and why Coulomb matrix and SOAP work. Moreover, Coulomb lists avoid the somewhat mysterious diagonalization step of the Coulomb matrix and might provide a direct means to extract subsystem information that can be compared across Born-Oppenheimer surfaces of varying dimension. For the electron density, we derive the necessary formalism to create the SOED measure in close analogy to SOAP. Because this formalism is more involved than that of SOAP, we review the essential theory as well as introduce a set of approximations that eventually allow us to work with SOED in terms of the same implementation available for the evaluation of SOAP. We focus our analysis on elementary reaction steps, where transition state structures are more similar to either reactant or product structures than the latter two are with respect to one another. The prediction of electronic energies of transition state structures can, however, be more difficult than that of stable intermediates due to multi-configurational effects. The question arises to what extent molecular similarity descriptors rooted in electronic structure theory can resolve these intricate effects.

Forgery detection in medical images with distinguished recognition of original and tampered regions using density-based clustering technique

Telemedicine is one of the most significant part of the medical field. It enables transfer of medical images over the internet which offers telediagnosis facility. Due to transfer of medical images from different channels, detection of their authenticity is a crucial research concern. Copy-move forgery is one of the most popular technique for image manipulation. We propose a method for copy-move forgery detection in medical images. Our method apply boundary extraction followed by Laplacian blob detection from the image to identify regions with similar properties. Further, we utilize Good Features To Track (GFTT) and BinBoost techniques for keypoint extraction and descriptor computation, respectively. Similar descriptors are identified using Hamming distance-based nearest neighbor search. Clustering over keypoints is performed using Ant Colony Density-based Clustering (ACDC) technique. We employ Fast Sample Consensus (FSC) technique for selection of correct matches and removal of imprecise keypoints. Further, we use correlation map generation for localization of manipulated regions. Experimental outcomes display that our technique can efficaciously identify tampered medical images even when manipulated images are sustaining various geometrical and post-processing attacks. Proposed technique can also efficaciously distinguish between original and tampered region within forged medical images using Haralick texture features.