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RETRACTED: ELrokh et al. On Cubic Roots Cordial Labeling for Some Graphs. Symmetry 2023, 15, 990
The journal Symmetry retracts the article titled “On Cubic Roots Cordial Labeling for Some Graphs” [...]
Journal Article
Correction to: Multiple selective sweeps of ancient polymorphisms in and around LTalpha located in the MHC class III region on chromosome 6
After publication of our article [1] we were notified that a few duplicate sentences were included on Figure 3 and Figure 4 legends.
Journal Article
IL/I-Labeling Halin Graphs with Maximum Degree Eight
Suppose that T is a plane tree without vertices of degree 2 and with at least one vertex of at least degree 3, and C is the cycle obtained by connecting the leaves of T in a cyclic order. Set G=T∪C, which is called a Halin graph. A k-L(2,1)-labeling of a graph G=(V,E) is a mapping f:V(G)→0,1,...,k such that, for any x[sub.1],x[sub.2]∈V(G), it holds that |f(x[sub.1])−f(x[sub.2])|≥2 if x[sub.1]x[sub.2]∈E(G), and |f(x[sub.1])−f(x[sub.2])|≥1 if the distance between x[sub.1] and x[sub.2] is 2 in G. The L(2,1)-labeling number, denoted λ(G), of G is the least k for which G is k-L(2,1)-labelable. In this paper, we prove that every Halin graph G with Δ=8 has λ(G)≤10. This improves a known result, which states that every Halin graph G with Δ≥9 satisfies λ(G)≤Δ+2. This result, together with some known results, shows that every Halin graph G satisfies λ(G)≤Δ+6.
Journal Article
Bacteria Detection: From Powerful SERS to Its Advanced Compatible Techniques
The rapid, highly sensitive, and accurate detection of bacteria is the focus of various fields, especially food safety and public health. Surface‐enhanced Raman spectroscopy (SERS), with the advantages of being fast, sensitive, and nondestructive, can be used to directly obtain molecular fingerprint information, as well as for the on‐line qualitative analysis of multicomponent samples. It has therefore become an effective technique for bacterial detection. Within this progress report, advances in the detection of bacteria using SERS and other compatible techniques are discussed in order to summarize its development in recent years. First, the enhancement principle and mechanism of SERS technology are briefly overviewed. The second part is devoted to a label‐free strategy for the detection of bacterial cells and bacterial metabolites. In this section, important considerations that must be made to improve bacterial SERS signals are discussed. Then, the label‐based SERS strategy involves the design strategy of SERS tags, the immunomagnetic separation of SERS tags, and the capture of bacteria from solution and dye‐labeled SERS primers. In the third part, several novel SERS compatible technologies and applications in clinical and food safety are introduced. In the final part, the results achieved are summarized and future perspectives are proposed. This progress report focuses on the progress for bacterial detection by surface‐enhanced Raman spectroscopy (SERS) and other compatible techniques, and the contents mainly include the following parts: the enhancement principle and mechanism of SERS, the progress of label‐free strategy and label‐based strategy for SERS detection of bacteria, several novel developed SERS compatible technologies and the application in clinical and food safety.
Journal Article
Bonsai: diverse and shallow trees for extreme multi-label classification
Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousands or even millions of labels. In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees. We show three concrete realizations of this label representation space including: (i) the input space which is spanned by the input features, (ii) the output space spanned by label vectors based on their co-occurrence with other labels, and (iii) the joint space by combining the input and output representations. Furthermore, the constraint-free multi-way partitions learnt iteratively in these spaces lead to shallow trees. By combining the effect of shallow trees and generalized label representation, Bonsai achieves the best of both worlds—fast training which is comparable to state-of-the-art tree-based methods in XMC, and much better prediction accuracy, particularly on tail-labels. On a benchmark Amazon-3M dataset with 3 million labels, Bonsai outperforms a state-of-the-art one-vs-rest method in terms of prediction accuracy, while being approximately 200 times faster to train. The code for Bonsai is available at https://github.com/xmc-aalto/bonsai.
Journal Article
A Benchmark Dataset for Performance Evaluation of Multi-Label Remote Sensing Image Retrieval
Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels are required for more complex problems, such as RSIR. This motivated us to present a new benchmark dataset termed \"MLRSIR\" that was labeled from an existing single-labeled remote sensing archive. MLRSIR contained a total of 17 classes, and each image had at least one of 17 pre-defined labels. We evaluated the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep-learning-based ones on MLRSIR. More specifically, we compared the performances of RSIR methods from both single-label and multi-label perspectives. These results presented the advantages of multiple labels over single labels for interpreting complex remote sensing images, and serve as a baseline for future research on multi-label RSIR.
Journal Article
Multi-label classification with label clusters
Multi-label classification is the task of simultaneously predicting a set of labels for an instance, with global and local being the two predominant approaches. The global approach trains a single classifier to handle all classes simultaneously, while the local approach breaks down the problem into multiple binary problems. Despite extensive research, effectively capturing label correlations remains a challenge in both methods. In this paper, we introduce an approach that clusters the label space to create hybrid partitions (disjoint correlated label clusters), striking a balance between global and local strategies while leveraging both advantages. Our approach consists of (i) clustering the label space based on correlations, (ii) generating and validating the resulting hybrid partitions, (iii) selecting the best partitions, and (iv) evaluating their performance. We also compare our approach against an oracle, exhaustive search, and random search to assess how closely our hybrid partitions approximate the best possible partitions. The oracle selects the best partition using the test set, while the exhaustive approach relies on validation data. Experiments conducted on multiple multi-label datasets demonstrate that our method, along with random partitions, achieves results that are superior or competitive compared to traditional global and local approaches, as well as the state-of-the-art Ensemble of Classifier Chains. These findings suggest that conventional methods may not fully capture label correlations, and clustering the label space offers a promising solution.
Journal Article
Ultrasensitive Materials for Electrochemical Biosensor Labels
Since the fabrication of the first electrochemical biosensor by Leland C. Clark in 1956, various labeled and label-free sensors have been reported for the detection of biomolecules. Labels such as nanoparticles, enzymes, Quantum dots, redox-active molecules, low dimensional carbon materials, etc. have been employed for the detection of biomolecules. Because of the absence of cross-reaction and highly selective detection, labeled biosensors are advantageous and preferred over label-free biosensors. The biosensors with labels depend mainly on optical, magnetic, electrical, and mechanical principles. Labels combined with electrochemical techniques resulted in the selective and sensitive determination of biomolecules. The present review focuses on categorizing the advancement and advantages of different labeling methods applied simultaneously with the electrochemical techniques in the past few decades.
Journal Article