Partial Classification Research Articles

Nomenclature and taxonomy of the partial infrageneric classification systems proposed for Kalanchoe (Crassulaceae subfam. Kalanchooideae) by Boiteau and Boiteau & Mannoni in the mid-20th century

The partial infrageneric classification systems proposed for Kalanchoe (Crassulaceae subfam. Kalanchooideae) by Boiteau and Boiteau & Mannoni during the mid-20th century are analysed. The pertinent work of Boiteau published in 1947, as subsequently elaborated on by Boiteau & Mannoni in the late-1940s, is assessed to clarify the use of infrageneric ranks by these authors. From this analysis it is evident that, with the exception of two new combinations published by Boiteau in 1947 at the rank of section and two at the rank of subsection, and one unranked infrageneric name published by Boiteau & Mannoni in 1949, none of the other infrageneric designations that they proposed was validly published.

Signed (0,2)‐graphs with few eigenvalues and a symmetric spectrum

AbstractWe investigate properties of signed graphs that have few distinct eigenvalues together with a symmetric spectrum. Our main contribution is to determine all signed rectagraphs (triangle‐free signed ‐graphs) with vertex degree at most 6 that have precisely two distinct eigenvalues . Next, we consider to what extent induced subgraphs of signed graph with two distinct eigenvalues are determined by their spectra. Lastly, we classify signed rectagraphs that have a symmetric spectrum with three distinct eigenvalues and give a partial classification for signed ‐graphs with four distinct eigenvalues.

Partial Disaster Classification Based on Attention Mechanism and Balanced Softmax Loss Function

Partial Disaster Classification Based on Attention Mechanism and Balanced Softmax Loss Function

Fast online estimation of quail eggs freshness using portable NIR spectrometer and machine learning

Quail eggs are one of the main natural sources of essential nutrients, presenting high amounts of protein, antioxidants, calcium, iron and phosphorus. However, its quality assessment demands laborious methods and chemicals, and there is currently no standard method do quantify its freshness. This work aimed to investigate the performance of a portable NIR spectrometer, in combination with machine learning, to estimate the freshness of quail eggs. Since there is no standard index to classify quail eggs, we compared Haugh Unit (HU), Yolk Index (YI) and the Egg Quality Index (EQI) as reference methods. Partial Least Squares Regression (PLSR) and Support Vector Machine Regression (SVMR) were used to build prediction models, and Partial Least Squares-Discriminant Analysis (PLSDA) and Support Vector Machine Classification (SVMC) for the development of classification models. For the first time, we demonstrated that EQI, which is a parameter that measures egg freshness according to the quality of the yolk and the albumen, is the best way to express the freshness of quail eggs. The best prediction models were obtained for YI and EQI, using SVMR, with RPD = 2.0–2.5 and RER >10, indicating good predictive capacity. PLSDA and SVMC models showed similar performance, correctly classifying more than 80% of the samples. The results obtained demonstrate the potential of portable NIR spectrometer for monitoring quail eggs freshness during storage. • Portable NIR is a promising alternative to estimate quail egg freshness. • Egg Quality Index is the best parameter to express quail egg freshness. • A new scale for Haugh Unit was proposed to classify fresh and stale quail eggs. • One-point spectra and SVMR allowed predicting Egg Quality Index. • PLSDA and SVMC showed accuracies higher than 80% to classify fresh and stale eggs.

Representations of Ten-Dimensional Levi Decomposition Lie Algebras

The authors have recently completed a partial classification of the ten-dimensional real Lie algebras that have the non-trivial Levi decomposition, namely, for such algebras whose semi-simple factor is so(3). In the present paper, we obtain a matrix representation for each of these Lie algebras. We are able to find such representations by exploiting properties of the radical, principally, when it has a trivial center, in which case we can obtain such a representation by restricting the adjoint representation. Another important subclass of algebras is where the radical has a codimension one abelian nilradical and for which a representation can readily be found. In general, finding matrix representations for abstract Lie algebras is difficult and there is no algorithmic process, nor is it at all easy to program by computer, even for algebras of low dimension. The present paper represents another step in our efforts to find linear representations for all the low dimensional abstract Lie algebras.

Time Governed Multi-Objective Optimization

Multi-objective optimization (MOO) is an optimization involving minimization or maximization of several objective functions more than the conventional one objective optimization, which is useful in many fields. Many of the current methodologies addresses challenges and solutions that attempt to solve simultaneously several Objectives with multiple constraints subjoined to each. Often MOO are generally subjected to linear inequality, equality and or bounded constraint that prevent all objectives from being optimized at once. This paper reviews some recent articles in area of MOO and presents deep analysis of Random and Uniform Entry-Exit time of objectives. It further breaks down process into sub-process and then provide some new concepts for solving problems in MOO, which comes due to periodical objectives that do not stay for the entire duration of process lifetime, unlike permanent objectives which are optimized once for the entire process duration. A methodology based on partial optimization that optimizes each objective iteratively and weight convergence method that optimizes sub-group of objectives are given. Furthermore, another method is introduced which involve objective classification, ranking, estimation and prediction where objectives are classified based on their properties, and ranked using a given criteria and in addition estimated for an optimal weight point (pareto optimal point) if it certifies a coveted optimal weight point. Then finally predicted to find how far it deviates from the estimated optimal weight point. A Sample Mathematical Tri-Objectives and Real-world Optimization was analyzed using partial method, ranking and classification method, the result showed that an objective can be added or removed without affecting previous or existing optimal solutions. Therefore, suitable for handling time governed MOO. Although this paper presents concepts work only, it’s practical application are beyond the scope of this paper, however base on analysis and examples presented, the concept is worthy of igniting further research and application.

Second-Order PDEs in 3D with Einstein–Weyl Conformal Structure

Einstein–Weyl geometry is a triple ({mathbb {D}},g,omega ) where {mathbb {D}} is a symmetric connection, [g] is a conformal structure and omega is a covector such that bullet connection {mathbb {D}} preserves the conformal class [g], that is, {mathbb {D}}g=omega g; bullet trace-free part of the symmetrised Ricci tensor of {mathbb {D}} vanishes. Three-dimensional Einstein–Weyl structures naturally arise on solutions of second-order dispersionless integrable PDEs in 3D. In this context, [g] coincides with the characteristic conformal structure and is therefore uniquely determined by the equation. On the contrary, covector omega is a somewhat more mysterious object, recovered from the Einstein–Weyl conditions. We demonstrate that, for generic second-order PDEs (for instance, for all equations not of Monge–Ampère type), the covector omega is also expressible in terms of the equation, thus providing an efficient ‘dispersionless integrability test’. The knowledge of g and omega provides a dispersionless Lax pair by an explicit formula which is apparently new. Some partial classification results of PDEs with Einstein–Weyl characteristic conformal structure are obtained. A rigidity conjecture is proposed according to which for any generic second-order PDE with Einstein–Weyl property, all dependence on the 1-jet variables can be eliminated via a suitable contact transformation.

Application of Partial Differential Equation Image Classification Methods to the Aesthetic Evaluation of Images

The average accuracy of the fusion of color harmony and composition features is 75.17%, which is higher than that of a single feature. The classification accuracy of NP-DP-DCNN structure is about 1% higher than that of other methods and 1.77% higher than that of NP-DCNN. Traditional image aesthetic evaluation methods are only effective for specific image sets or specific style images and are not suitable for all types of images. Based on the introduction of the partial differential equation image filtering method, through the parallel supervised learning of aesthetic attribute labels, this paper extracts the global aesthetic depth features, adopts the partial differential equation to evolve the contour C constant, and constructs a convolution neural network. The structure of a convolution kernel learned by using parallel network structure achieves better classification performance. Through the aesthetic evaluation experiment, the overall test accuracy is improved by 0.58% and the average accuracy of the integration of color harmony and composition features is 75.17%, which is higher than that of a single feature. The classification accuracy of NP-DP-DCNN structure is about 1% higher than that of other methods and 1.83% higher than that of NP-DCNN. It has achieved better test accuracy than before in the seven subcategories with discrimination between high aesthetic and low aesthetic images. It has achieved better classification performance than the traditional feature extraction methods in the public dataset CUHK database, and it has excellent aesthetic reference value.

Integrable spin chains and cellular automata with medium-range interaction

We study integrable spin chains and quantum and classical cellular automata with interaction range ℓ≥3. This is a family of integrable models for which there was no general theory so far. We develop an algebraic framework for such models, generalizing known methods from nearest-neighbor interacting chains. This leads to a new integrability condition for medium-range Hamiltonians, which can be used to classify such models. A partial classification is performed in specific cases, including U(1)-symmetric three-site interacting models, and Hamiltonians that are relevant for interaction-round-a-face models. We find a number of models which appear to be new. As an application we consider quantum brickwork circuits of various types, including those that can accommodate the classical elementary cellular automata on light cone lattices. In this family we find that the so-called Rule150 and Rule105 models are Yang-Baxter integrable with three-site interactions. We present integrable quantum deformations of these models, and derive a set of local conserved charges for them. For the famous Rule54 model we find that it does not belong to the family of integrable three-site models, but we cannot exclude Yang-Baxter integrability with longer interaction ranges.

Design of Ensemble Fuzzy-RBF Neural Networks Based on Feature Extraction and Multi-feature Fusion for GIS Partial Discharge Recognition and Classification

Design of Ensemble Fuzzy-RBF Neural Networks Based on Feature Extraction and Multi-feature Fusion for GIS Partial Discharge Recognition and Classification

On Unitarizable Harish-Chandra Bimodules for Deformations of Type-A Kleinian Singularities

Abstract The notion of a Harish-Chandra bimodule, that is, finitely generated $U(\mathfrak {g})$-bimodule with locally finite adjoint action, was generalized to any filtered algebra in a work of Losev [9]. Similarly to the classical case we can define the notion of a unitarizable bimodule. We investigate a question when the regular bimodule, that is, the algebra itself, for a deformation of Kleinian singularity of type $A$ is unitarizable. We obtain a partial classification of unitarizable regular bimodules.

Semi-supervised partial multi-label classification with low-rank and manifold constraints

• A novel method for simultaneous processing of redundant and empty annotation in multi-label classification scenarios. • L ion is a unified framework for both estimating latent labels and training prediction models, simultaneously. • The low-rank constraint could capture local label correlations for recovering latent labels more accurately. • The manifold constraint could spread the supervision over linked neighboring instances even for unlabeled ones. Multi-Label Classification (MLC), processing data annotated with multiple labels simultaneously, is usually based on the assumption that the instances are all correctly labeled with the ground-truth labels. However this assumption is often not satisfied in real-world scenarios due to the high cost of manual labeling. In this paper, we consider a classification scenario in which only a part of the instances are redundantly annotated, and call this scenario a Semi-supervised Partial Multi-Label Classification (SPMLC). We propose a novel method L ion (semi-supervised partial multi-label classification with Low-rank and manIfOld coNstraints) to handle this particular scenario. Especially, the not exactly known ground-truth labels are regarded as latent labels, and the prediction is achieved by estimating the confidences of latent labels ( i.e., the latent ground-truth confidences) in future data. L ion jointly learns the latent ground-truth confidences and the prediction model. The low-rank and manifold constraints are utilized to capture local label correlations and neighboring structure of instances, so as to accurately estimate the latent ground-truth confidences. Extensive experimental results validate the effectiveness of L ion .

The revision partial knee classification system: understanding the causative pathology and magnitude of further surgery following partial knee arthroplasty.

AimsJoint registries classify all further arthroplasty procedures to a knee with an existing partial arthroplasty as revision surgery, regardless of the actual procedure performed. Relatively minor procedures, including bearing exchanges, are classified in the same way as major operations requiring augments and stems. A new classification system is proposed to acknowledge and describe the detail of these procedures, which has implications for risk, recovery, and health economics.MethodsClassification categories were proposed by a surgical consensus group, then ranked by patients, according to perceived invasiveness and implications for recovery. In round one, 26 revision cases were classified by the consensus group. Results were tested for inter-rater reliability. In round two, four additional cases were added for clarity. Round three repeated the survey one month later, subject to inter- and intrarater reliability testing. In round four, five additional expert partial knee arthroplasty surgeons were asked to classify the 30 cases according to the proposed revision partial knee classification (RPKC) system.ResultsFour classes were proposed: PR1, where no bone-implant interfaces are affected; PR2, where surgery does not include conversion to total knee arthroplasty, for example, a second partial arthroplasty to a native compartment; PR3, when a standard primary total knee prosthesis is used; and PR4 when revision components are necessary. Round one resulted in 92% inter-rater agreement (Kendall’s W 0.97; p < 0.005), rising to 93% in round two (Kendall’s W 0.98; p < 0.001). Round three demonstrated 97% agreement (Kendall’s W 0.98; p < 0.001), with high intra-rater reliability (interclass correlation coefficient (ICC) 0.99; 95% confidence interval 0.98 to 0.99). Round four resulted in 80% agreement (Kendall’s W 0.92; p < 0.001).ConclusionThe RPKC system accounts for all procedures which may be appropriate following partial knee arthroplasty. It has been shown to be reliable, repeatable and pragmatic. The implications for patient care and health economics are discussed.Cite this article: Bone Jt Open 2021;2(8):638–645.

GEOGRAPHICAL IDENTIFICATION OF DURIAN (CV. MONTHONG) FROM PRACHUAP KIRI KHAN AND CHANTHABURI PROVINCE BY USING NEAR INFRARED SPECTROSCOPY

Geographic origin of durian (Durio zibethinus Murray) can influence consumers purchasing decisions. A guaranteed geographical origin is extremely useful to maintain the credibility and popularity of this fruit. The objective of this work is to confirm geographical identification (GI) of durian Monthong from Prachuap Kiri Khan Province from south of Thailand which achieved the GI lable from the department of intellectual property by near infrared spectroscopy (NIRs). Total of 60 durian Monthong samples were acquired in the wavenumber range of 12,500-4,000 cm&lt;sup&lt;-1&lt;/sup&lt; by FT-NIR spectrometer. The sample presentation for scanning were 3 forms including intact peel (with thorns), cut-thorns peel and stem of the fruit. Soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and support vector machine classification (SVMC) were used to establish identification model using the full spectra. Overall classification accuracy of SVMC was the highest in training and validation set of stems which was 100% and 100%, respectively. This study indicated that NIRs might be a method to guaranteed geographic origin of durian Monthong from Prachuap Kiri Khan. However, more robust model should be developed by using more samples.

The Ethno-Linguistic Nature of Zoonymic Names in the Kazakh Language

Linguistics deals with the problem of onomastics, the object of research is zoonimic names. From the scientific point of view, the peculiarities of the study, partial classification of zoonimic names have been demonstrated. Several reasons for the insufficient study of this field in the language have been identified. Internal and external extralinguistic factors have been singled out in the consideration of zoonim names. The ethnolinguistic nature of zoonymic names in the Kazakh language, especially those related to animal names, has been analyzed. Zoonymic names are of great importance in our language as one of the ways of defining national identity and worldview. We can notice that the names of four cattle of our people contain certain ethno-cultural information. The basis of national peculiarities in zoonimic names originates from Turkic cognition and is also manifested in the language of modern Turkic peoples. By considering the semantic ethnocultural character of each of the zoonimic names, we can make a huge contribution to modern ethnolinguistic science. Today it is known that the science of language develops in an external extra-linguistic direction rather than an internal factor. By taking each name in a language as national-cultural information, we can look into the past and present of our language. Zoonymic names are also very rich in linguistic units that convey information from our national existence. Therefore, the study of this area complements the field of etymology, ethnolinguistics. The issues discussed in the article contribute to the future development of the ethnolinguistic branch of language science.

Analysis of Garnet by Laser-Induced Breakdown Spectroscopy—Two Practical Applications

Laser-induced breakdown spectroscopy (LIBS) is a simple and straightforward technique of atomic emission spectroscopy that can provide multi-element detection and quantification in any material, in-situ and in real time because all elements emit in the 200–900 nm spectral range of the LIBS optical emission. This study evaluated two practical applications of LIBS—validation of labels assigned to garnets in museum collections and discrimination of LCT (lithium-cesium-tantalum) and NYF (niobium, yttrium and fluorine) pegmatites based on garnet geochemical fingerprinting, both of which could be implemented on site in a museum or field setting with a handheld LIBS analyzer. Major element compositions were determined using electron microprobe analysis for a suite of 208 garnets from 24 countries to determine garnet type. Both commercial laboratory and handheld analyzers were then used to acquire LIBS broadband spectra that were chemometrically processed by partial least squares discriminant analysis (PLSDA) and linear support vector machine classification (SVM). High attribution success rates (&gt;98%) were obtained using PLSDA and SVM for the handheld data suggesting that LIBS could be used in a museum setting to assign garnet type quickly and accurately. LIBS also identifies changes in garnet composition associated with increasing mineral and chemical complexity of LCT and NYF pegmatites.

Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators

In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate several sources of distortions often observed in PRPDs obtained from fully operational hydroelectric generators. The capabilities of the denoising algorithm are the automatic removal of sparse noise and the suppression of non-dominant discharges, including those due to crosstalk. The input features are functions of PD distributions along amplitude and phase, which are calculated in a novel way to mitigate random effects inherent to PD measurements. The impact of the proposed contributions was statistically evaluated and compared to classification performance obtained using formerly published approaches. Higher recognition rates and reduced variances were obtained using the proposed methods, statistically outperforming autonomous classification techniques seen in earlier works. The values of the algorithm’s internal parameters are also validated by comparing the recognition performance obtained with different parameter combinations. All typical PD sources described in hydro-generators PD standards are considered and can be automatically detected.

Geometry and Automorphisms of Non-Kähler Holomorphic Symplectic Manifolds

AbstractWe consider the only one known class of non-Kähler irreducible holomorphic symplectic manifolds, described in the works by D. Guan and the 1st author. Any such manifold $Q$ of dimension $2n-2$ is obtained as a finite degree $n^2$ cover of some non-Kähler manifold $W_F$, which we call the base of $Q$. We show that the algebraic reduction of $Q$ and its base is the projective space of dimension $n-1$. Besides, we give a partial classification of submanifolds in $Q$, describe the degeneracy locus of its algebraic reduction and prove that the automorphism group of $Q$ satisfies the Jordan property.

Wronskian indices and rational conformal field theories

The classification scheme for rational conformal field theories, given by the Mathur-Mukhi-Sen (MMS) program, identifies a rational conformal field theory by two numbers: (n, l). n is the number of characters of the rational conformal field theory. The characters form linearly independent solutions to a modular linear differential equation (which is also labelled by (n, l)); the Wronskian index l is a non-negative integer associated to the structure of zeroes of the Wronskian.In this paper, we compute the (n, l) values for three classes of well-known CFTs viz. the WZW CFTs, the Virasoro minimal models and the mathcal{N} = 1 super-Virasoro minimal models. For the latter two, we obtain exact formulae for the Wronskian indices. For WZW CFTs, we get exact formulae for small ranks (upto 2) and all levels and for all ranks and small levels (upto 2) and for the rest we compute using a computer program. We find that any WZW CFT at level 1 has a vanishing Wronskian index as does the {hat{mathbf{A}}}_{mathbf{1}} CFT at all levels. We find intriguing coincidences such as: (i) for the same level CFTs with {hat{mathbf{A}}}_{mathbf{2}} and {hat{mathbf{G}}}_{mathbf{2}} have the same (n, l) values, (ii) for the same level CFTs with {hat{mathbf{B}}}_{mathbf{r}} and {hat{mathbf{D}}}_{mathbf{r}} have the same (n, l) values for all r ≥ 5.Classifying all rational conformal field theories for a given (n, l) is one of the aims of the MMS program. We can use our computations to provide partial classifications. For the famous (2, 0) case, our partial classification turns out to be the full classification (achieved by MMS three decades ago). For the (3, 0) case, our partial classification includes two infinite series of CFTs as well as fifteen “discrete” CFTs; except three all others have Kac-Moody symmetry.

A smart partial discharge classification SOM with optimized statistical transformation feature

Condition-based monitoring (CBM) has been a vital engineering method to assess high voltage (HV) equipment and power cables conditions or health levels. One of the effective CBM methods is partial discharge (PD) measurement or detection. PD event is the phenomenon that always associated with insulation healthiness. PD has been measured and evaluated in this paper to discriminate PD signals from a good signal. A mixed-signal being fed at an AI technique with statistical modified input data to do fast classification (less than five seconds) with nearly zero error. In this paper, an unsupervised neural network is applied for PD classification. The methods combine the self-organizing maps (SOMs) and feature statistical transformation. By the combination of these methods, the ‘range’ normalization method produced the best classification outcomes. This development decided that PD information was effectively correlated and grouped by means of MATLAB’s SOM Toolbox and transformation device to discriminate the normal signal from the PD signal.