Mapping Of Descriptors Research Articles

Cross-modal and multimodal data analysis based on functional mapping of spectral descriptors and manifold regularization

Multimodal manifold modeling methods extend spectral geometry-aware data analysis to incorporate learning from multiple related and complementary modalities. However, most methods assume an equal number of homogeneous data samples in each modality and partial correspondences between modalities as prior knowledge. This work introduces two new multimodal modeling methods. The first method introduces a comprehensive framework for handling multimodal information in heterogeneous data without requiring specific prior knowledge. To achieve this, we begin by extracting local descriptors using spectral graph wavelet signatures (SGWS) to identify manifold localities. Then, we propose a manifold regularization framework that incorporates functional mapping between SGWS descriptors (FMBSD) to determine pointwise correspondences. The second method involves manifold regularized multimodal classification based on pointwise correspondences (M2CPC). This method addresses the challenge of multiclass classification in multimodal heterogeneous data by determining correspondences between modalities using the FMBSD method. Experimental results evaluating the FMBSD method on three widely used cross-modal retrieval datasets and evaluating the M2CPC method on three benchmark multimodal multiclass classification datasets demonstrate their effectiveness and superiority over state-of-the-art methods.

Implementing the Blowers-Masel Approximation to Scale Activation Energy Based on Reaction Enthalpy in Mean-Field Microkinetic Modeling for Catalytic Methane Partial Oxidation.

Mean-field microkinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a microkinetic model often need to be adjusted when changing species' binding energies to model different catalysts, when performing thermodynamic sensitivity analyses, and when fitting experimental data. When altering reaction enthalpies, the activation energies should also be reasonably altered to ensure realistic reaction rates. The Blowers-Masel approximation (BMA) relates the reaction barrier to the reaction enthalpy. Unlike the Brønsted-Evans-Polani relationship, the BMA requires less data because only one parameter, the intrinsic activation energy, needs to be determined. We validate this application of BMA relations to model surface reactions by comparing against density functional theory data taken from the literature. By incorporating the BMA rate description into the open-source Cantera software, we enable a new workflow, demonstrated herein, allowing rapid screening of catalysts using linear scaling relationships and BMA kinetics within the process simulation software. For demonstration purposes, a catalyst screening for catalytic methane partial oxidation on 81 hypothetical metals is conducted. We compared the results with and without BMA-corrected rates. The heat maps of various descriptors (e.g., CH4 conversion, syngas yield) show that using BMA rates instead of Arrhenius rates (with constant activation energies) changes which metals are most active. Heat maps of sensitivity analyses can help identify which reactions or species are the most influential in shaping the descriptor map patterns. Our findings indicate that while using BMA-adjusted rates did not markedly affect the most sensitive reactions, it did change the most influential species.

CMAT: ClinVar Mapping and Annotation Toolkit.

Semantic ontology mapping of clinical descriptors with disease outcome is essential. ClinVar is a key resource for human variation with known clinical significance. We present CMAT, a software toolkit and curation protocol for accurately enriching ClinVar releases with disease ontology associations and complex functional consequences. The software and ontology mappings can be obtained from: https://github.com/EBIvariation/CMAT.

ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation

Image keypoints and descriptors play a crucial role in many visual measurement tasks. In recent years, deep neural networks have been widely used to improve the performance of keypoint and descriptor extraction. However, the conventional convolution operations do not provide the geometric invariance required for the descriptor. To address this issue, we propose the Sparse Deformable Descriptor Head (SDDH), which learns the deformable positions of supporting features for each keypoint and constructs deformable descriptors. Furthermore, SDDH extracts descriptors at sparse keypoints instead of a dense descriptor map, which enables efficient extraction of descriptors with strong expressiveness. In addition, we relax the neural reprojection error (NRE) loss from dense to sparse to train the extracted sparse descriptors. Experimental results show that the proposed network is both efficient and powerful in various visual measurement tasks, including image matching, 3D reconstruction, and visual relocalization.

Three-dimensional Imaging Reveals Immune-driven Tumor-associated High Endothelial Venules as a Key Correlate of Tumor Rejection Following Depletion of Regulatory T Cells.

High endothelial venules (HEV) are specialized post capillary venules that recruit naïve T cells and B cells into secondary lymphoid organs (SLO) such as lymph nodes (LN). Expansion of HEV networks in SLOs occurs following immune activation to support development of an effective immune response. In this study, we used a carcinogen-induced model of fibrosarcoma to examine HEV remodeling after depletion of regulatory T cells (Treg). We used light sheet fluorescence microscopy imaging to visualize entire HEV networks, subsequently applying computational tools to enable topological mapping and extraction of numerical descriptors of the networks. While these analyses revealed profound cancer- and immune-driven alterations to HEV networks within LNs, these changes did not identify successful responses to treatment. The presence of HEV networks within tumors did however clearly distinguish responders from nonresponders. Finally, we show that a successful treatment response is dependent on coupling tumor-associated HEV (TA-HEV) development to T-cell activation implying that T-cell activation acts as the trigger for development of TA-HEVs which subsequently serve to amplify the immune response by facilitating extravasation of T cells into the tumor mass.

Systematic Mapping of Electrocatalytic Descriptors for Hybrid and Non-Hybrid Molybdenum Dichalcogenides with Graphene Support for Cathodic Hydrogen Generation

For the hydrogen evolution reaction (HER) from water electrolysis, electrocatalytic performances of nanocomposites consisting of hybrid and non-hybrid molybdenum dichalcogenides with graphene (MoCh2/Gr) support (MoS2/Gr, MoSe2/Gr, MoTe2/Gr, MoSSe/Gr, MoSTe/Gr, MoSeTe/Gr, and MoS0.67Se0.67Te0.67/Gr) have been investigated both computationally and experimentally. In the computational part, hydrogen binding energetics of various adsorption sites on hybrid and non-hybrid MoCh2/Gr are elucidated with plane-wave density functional theory to evaluate the catalytic performance according to Sabatier’s principle. Near-zero values of Gibbs free energy of binding (ΔGb ≈ 0) have been considered a criterion for establishing the most catalytically active site. Moreover, electrochemical measurements revealed that all the materials, which have been synthesized through a microwave-assisted heating approach, are highly active and durable for the HER with overpotentials in the range of 127–217 mV and have negligible activity loss for 96 h of constant potential tests. Among all the different molybdenum dichalcogenide/graphene nanocomposites, the materials containing a high (≥50%) stoichiometric proportion of tellurium (Te) exhibited better HER activities compared to other chalcogens. Most importantly, hybrid nanocomposites, except the highest-entropy alloy MoS0.67Se0.67Te0.67/Gr, exhibited improved performance compared to the non-hybrid MoCh2 compounds.

Texture-less surface reconstruction using shape-based image augmentation

The development of intelligent Robot-Assisted Minimally Invasive Surgery demands geometric reconstruction from endoscopic images. However, images of human tissue surfaces are commonly texture-less. Obtaining the dense depth map of a texture-less scene is very difficult because traditional feature-based 3D reconstruction methods cannot detect enough features to build dense correspondences for depth computation. Given this problem, this study proposes a novel reconstruction method based on our shape-based image augmentation method. The main contribution of this method is the provision of a novel means to resolve the texture-less problem mainly on the input data level. In our method, we first calculate two shape gradient maps using Shape-From-Shading (SFS) method and we build Fast Point Feature Histogram (FPFH) 3D descriptor map according to the shape. Second, a series of augmented images can be computed by combining shape gradient maps, FPFH map, and the original image with different weights. Finally, we detect features on the new augmented images. Based on feature calculated sparse depth information and SFS calculated dense shape information, we further integrate a rectified dense depth map. Experiments show that our method can reconstruct texture-less surfaces with good accuracy.

An invariant descriptor map for 3D objects matching

Meshes and point clouds are traditionally used to represent and match 3D shapes. The matching prob-lem can be formulated as finding the best one-to-one correspondence between featured regions of two shapes. This paper presents an efficient and robust 3D matching method using vertices descriptors de-tection to define feature regions and an optimization approach for regions matching. To do so, we compute an invariant shape descriptor map based on 3D surface patches calculated using Zernike coef-ficients. Then, we propose a multi-scale descriptor map to improve the measured descriptor map quali-ty and to deal with noise. In addition, we introduce a linear algorithm for feature regions segmentation according to the descriptor map. Finally, the matching problem is modelled as sub-graph isomorphism problem, which is a combinatorial optimization problem to match feature regions while preserving the geometric. Finally, we show the robustness and stability of our method through many experimental re-sults with respect to scaling, noise, rotation, and translation. Â

XMaP-An Interpretable Alignment-Free Four-Dimensional Quantitative Structure-Activity Relationship Technique Based on Molecular Surface Properties and Conformer Ensembles.

A novel alignment-free molecular descriptor called xMaP (flexible MaP descriptor) is introduced. The descriptor is the advancement of the previously published translationally and rotationally invariant three-dimensional (3D) descriptor MaP (mapping property distributions onto the molecular surface) to the fourth dimension (4D). In addition to MaP, xMaP is independent of the chosen starting conformation of the encoded molecules and is therefore entirely alignment-free. This is achieved by using ensembles of conformers, which are generated by conformational searches. This step of the procedure is similar to Hopfinger's 4D quantitative structure-activity relationship (QSAR). A five-step procedure is used to compute the xMaP descriptor. First, a conformational search for each molecule is carried out. Next, for each of the conformers an approximation to the molecular surface with equally distributed surface points is computed. Third, molecular properties are projected onto this surface. Fourth, areas of identical properties are clustered to so-called patches. Fifth, the spatial distribution of the patches is converted into an alignment-free descriptor that is based on the entire conformer ensemble. The resulting descriptor can be interpreted by superimposing the most important descriptor variables and the molecules of the data set. The most important descriptor variables are identified with chemometric regression tools. The novel descriptor was applied to several benchmark data sets and was compared to other descriptors and QSAR techniques comprising a binary fingerprint, a topological pharmacophore descriptor (Cats2D), and the field-based 3D-QSAR technique GRID/PLS which is alignment-dependent. The use of conformer ensembles renders xMaP very robust. It turns out that xMaP performs very well on (almost) all data sets and that the statistical results are comparable to GRID/PLS. In addition to that, xMaP can also be used to efficiently visualize the derived quantitative structure-activity relationships.

Identification of Acoustic Emission Signals Originating from the Core Magnetization of Power Oil Transformer

Abstract In this paper, the properties of AE signals originating from phenomena occurring during magnetization of ferromagnetic materials which are used to construct power transformer cores are presented. The AE signals in a selected power oil transformer were recorded and analyzed. The analysis included, i.e., time, frequency, and time-frequency analyses, calculations of amplitude distributions of the signals and defined AE descriptors, determination of the descriptor map on the side walls of transformers, as well as a detailed analysis of selected part of the signals. The maps of descriptors were analyzed in the frequency bands of 20–70 kHz, 70–100 kHz, and 100–200 kHz. The analysis of the properties of the signals was performed in time and frequency domains. Based on the analysis, there were identified the AE signals originating from the phenomena occurring during the core magnetization of a power oil transformer. To identify those phenomena, the maps of the ADC descriptor calculated in the band of 20–70 kHz when selecting the measurement points in which there were no AE sources from partial discharges were used. An analysis of magnetoacoustic emission signals in the bands of 70–100 kHz and 100–200 kHz was also performed. The analysis of the signal properties in such an extended frequency range allowed determining the properties of the magnetoacoustic signals coming from core sheets of power oil transformers.

Text String Recognition from Natural Scenes by Character Descriptor Map and Stroke Width

Text String Recognition from Natural Scenes by Character Descriptor Map and Stroke Width

Straightforward MIA‐QSTR evaluation of environmental toxicities of aromatic aldehydes to Tetrahymena pyriformis

Aldehydes are toxic environmental contaminants which cause severe health hazards. There is a growing need by industries and regulatory agencies for the development of tools able to assess the potential hazardous effects of chemicals on living organisms. In this background, multivariate image analysis combined with quantitative structure–toxicity relationships (MIA-QSTR) was used to evaluate the toxicity of aromatic aldehydes to Tetrahymena pyriformis. The techniques of genetic algorithm–partial least squares (GA-PLS) were applied effectively as MIA descriptor selection and mapping tools. In MIA-QSTR evaluation, pixels of 2D images of chemical structures could be used to recognize physicochemical information and predict changes in the toxicities. The resulting MIA-QSTR explains 90.3% leave-one-out predicted variance and 93.1% external predicted variance. The MIA-QSTR/GA-PLS performances were validated using various evaluation techniques such as cross-validation, applicability domain and Y-scrambling procedures, suggesting that the present methodology together with mechanistic interpretation may be useful to evaluate toxicity, safety and risk assessment of toxic environmental contaminants.

Identification of Deformation Processes within Power Oil Transformers Using Maps of ADC Descriptors in Selected Frequency Bands

The paper presents identi cation results of deformation processes within power oil transformers where according to dissolved gas analysis investigation results partial discharges took place. The original method worked out for analysis of acoustic emission signals recorded within tested transformer and particularly maps of ADC descriptors have been applied. Analysis has been carried out within chosen frequency bands in order to distinguish signals coming from di erent sources (among other things partial discharges, Barkhausen's e ect, circulation of the oil, and outer acoustic sources). One source of partial discharges has been identi ed within the tested transformer as a result of analysis of acoustic emission signals.

Topology maps of bond descriptors based on the kinetic energy density and the essence of chemical bonding

Analysis of the kinetic energy density within a molecule identifies patterns in its electronic structure that are intuitively linked to familiar concepts of chemical bonding. The function ν(r), termed localized-orbital locator and based on the positive-definite kinetic energy density τ+, is employed to characterize classes of covalent bonds in terms of its full topology of all critical points of rank three. Not only does ν(r) reveal patterns in chemical bonding, it also discloses features and the influence of extended electronic cores. Gradient paths define the extension of the valence space around an atomic centre; they separate various core level regions from bonding domains, and partition molecules in sensible bonded subunits. Location and ν(r)-values of critical points add a quantitative aspect to the bond characterization; profiles of unconventional chemical linkages such as charge-shift bonds emerge in a natural way.

Joint mapping of statistical streamflow descriptors

Summary An approach is presented for a joint mapping and regionalization of statistical descriptors of streamflow like the mean value and the coefficient of variation of daily data, the flow duration curve, moments and distributions of annual minima and maxima. Probability theory, and specifically extreme value theory, offers possible parametric relations to be tested, linking knowledge of the basic mean value and coefficient of variation with the other descriptors. This initial study is limited to theoretical results derived from the assumption that the original daily data follow the lognormal or the gamma distribution. With these standard distributions the statistical regularity found is surprisingly good when confronting the theory against the empirical data of daily streamflow from 35 catchments in the Moselle drainage basin in France. The lognormal distribution appears to be the best candidate for the flow duration curve, as well as maximum and minimum streamflow. The tail behaviour of streamflow maxima is well described, while for minima the results are more difficult to interpret. One problem might be the relative higher uncertainty in annual low flow data, and especially the sensitivity to outliers towards high values.

Self-organizing maps of molecular descriptors for sesquiterpene lactones and their application to the chemotaxonomy of the Asteraceae family.

The Asteraceae, one of the largest families among angiosperms, is chemically characterised by the production of sesquiterpene lactones (SLs). A total of 1,111 SLs, which were extracted from 658 species, 161 genera, 63 subtribes and 15 tribes of Asteraceae, were represented and registered in two dimensions in the SISTEMATX, an in-house software system, and were associated with their botanical sources. The respective 11 block of descriptors: Constitutional, Functional groups, BCUT, Atom-centred, 2D autocorrelations, Topological, Geometrical, RDF, 3D-MoRSE, GETAWAY and WHIM were used as input data to separate the botanical occurrences through self-organising maps. Maps that were generated with each descriptor divided the Asteraceae tribes, with total index values between 66.7% and 83.6%. The analysis of the results shows evident similarities among the Heliantheae, Helenieae and Eupatorieae tribes as well as between the Anthemideae and Inuleae tribes. Those observations are in agreement with systematic classifications that were proposed by Bremer, which use mainly morphological and molecular data, therefore chemical markers partially corroborate with these classifications. The results demonstrate that the atom-centred and RDF descriptors can be used as a tool for taxonomic classification in low hierarchical levels, such as tribes. Descriptors obtained through fragments or by the two-dimensional representation of the SL structures were sufficient to obtain significant results, and better results were not achieved by using descriptors derived from three-dimensional representations of SLs. Such models based on physico-chemical properties can project new design SLs, similar structures from literature or even unreported structures in two-dimensional chemical space. Therefore, the generated SOMs can predict the most probable tribe where a biologically active molecule can be found according Bremer classification.

Weighting Function-Based Mapping of Descriptors to Frequency-Gain Curves in Listeners With Hearing Loss

The frequency-gain curve (FGC) is among the most important parameters to consider when fitting a hearing aid. In practice, a prescriptive FGC, derived from the audiogram, is initially applied. In the subsequent fine-tuning stage, the patient often communicates their concerns about the sound quality using descriptors (e.g., "it sounds hollow") and the clinician modifies the FGC accordingly. In this study, we present and evaluate a method that could enhance this process by rapidly mapping descriptors to FGC shapes. In addition, we begin to use this method to examine the extent to which there is across-individual agreement in how descriptors map to FGC shapes. Ten listeners with hearing loss rated the extent to which each of a series of FGCs captured the meaning of a particular descriptor. Regression analyses were conducted to determine the degree to which these ratings were correlated with the gain values associated with each of 25 frequency bands. The array of slopes of these regression lines across frequency bands is termed the weighting function and was interpreted as the FGC shape that corresponded to the descriptor. We used this procedure to determine the FGC shapes associated with four of the most common descriptors used to describe hearing aid sound quality problems ("tinny," "sharp," "hollow," and "in a barrel, tunnel, or well"). The weighting function shape was highly replicable despite variable listener responses, reached asymptotic performance quickly (<20 ratings), and was predictive of listener responses. On the global level, there was some agreement across individuals about how common descriptors mapped to weighting function shape. However, considerable differences were apparent between individuals in terms of the specifics of that mapping. The current approach for descriptor-to-FGC mapping is a quick, reliable method for determining individualized changes to the FGC. Given the range of individual differences in the specifics of the descriptor-to-FGC mappings observed, this approach could be useful in a clinical setting to easily quantify these acoustic parameters. Implementation of such procedures could lead to more personalized fine-tuning of amplification devices.

TWO‐DIMENSIONAL MAPPING OF SENSORY TEXTURE DESCRIPTORS

ABSTRACT Although language is of great importance in sensory science, there is no reliable descriptor compilation for German language. To evaluate whether there is redundancy in existing descriptor collections, a two‐dimensional mapping technique was applied to texture descriptors within the firmness–brittleness space; this technique does not investigate texture attributes as such but focuses on the consumers' mental reconstruction of texture properties in terms of language. A total of 68 subjects, skilled and unskilled in sensory science, contributed to the tests. The statistics show that there are no significant differences between skilled and unskilled individuals with respect to the location of the descriptors in the firmness–brittleness map. Some descriptors formed pronounced clusters with a high degree of similarity, which, however, does not justify descriptor elimination. Consumers' mental representations of the texture descriptors can be mapped in the frame of mechanical texture attributes, but not in an exhaustive manner; they appear to be multimodal and multidimensional. PRACTICAL APPLICATIONSThe two‐dimensional descriptor mapping technique can be helpful in identifying potentially matching sensory descriptors and in making a set of sensory terms, which is used in descriptive analysis or in communication with consumers, more reliable and free of redundancy; however, aspects of multimodality and multidimensionality of texture descriptors have to be considered.

Mapping, indexing and querying of MPEG-7 descriptors in RDBMS with IXMDB

MPEG-7 is a promising standard for the description of multimedia content. A number of applications based on MPEG-7 media descriptions have been set up for research, commercial and industrial applications. Therefore, an efficient storage solution for large amounts of MPEG-7 descriptions is certainly desirable. As a kind of data-centric XML documents, MPEG-7 descriptions can be stored in the relational DBMS for efficient and effective management. The approaches of storing XML data in relational DBMS can be classified into two classes of storage model: schema-conscious and schema-oblivious. The schema-conscious model, however, cannot support complex XPath-based queries efficiently and the schema-oblivious approach lacks the flexibility in typed representation and access. Although the leading database systems have provided functionality for the XML document management, none of them can reach all the critical requirements for the MPEG-7 descriptions management. In this paper, we present a new storage approach, called IXMDB, for MPEG-7 documents storage solution. IXMDB integrates the advantages of both the schema-conscious method and the schema-oblivious method, and avoids the main drawbacks from each method. The design of IXMDB pays attention to both multimedia information exchange and multimedia data manipulation. Its features can reach the most critical requirements for the MPEG-7 documents storage and management. The translation mechanism for converting XQuery to SQL and the support of query from multimedia perspective are provided with IXMDB. Performance studies are conducted by performing a set of queries from the XML perspective and from the multimedia perspective. The experimental results are presented in the paper and initial results are encouraging.

Traffic descriptor mapping and traffic control for frame relay over ATM network

Frame relay (FR) service via an FR interface over a common asynchronous transfer mode (ATM) backbone network requires an FR-to-ATM interworking unit (IWU), the functionality of which directly impacts the engineering of the overall network in terms of the capacity required, quality of service (QoS) expected, and the interference among different types of services being offered on the same platform. In the absence of a traffic shaping function in the equipment at the customer's location, the issue of policing and bandwidth allocation becomes much more critical. This paper focuses on the issue of FR traffic descriptor mapping, the issue of traffic policing in the frame mode and the cell mode, and the implication on the QoS when the same bandwidth resources are shared by the ATM [cell relay (CR)] and FR virtual connections. Some major conclusions that we draw from the computer simulations are: (1) the performance is somewhat independent of the mode in which traffic policing is implemented; (2) from a cost standpoint it is more advantageous to use the cell mode for traffic policing; (3) the allocation of bandwidth in the multiplexer and the switching fabric is greatly dependent on the type of service interface used since the FR traffic becomes much burstier after frame-to-cell conversion process; and (4) it becomes necessary to provide a traffic shaping function after the conversion process in order to achieve high bandwidth utilization, fair allocation of bandwidth resources, and guaranteed QoS.