Distinguished Boundary Research Articles

A new characterization for the distinguished boundaries of a few domains related to $$\varvec{\mu }$$-synthesis

A new characterization for the distinguished boundaries of a few domains related to $$\varvec{\mu }$$-synthesis

A Ranking-Based Cross-Entropy Loss for Early Classification of Time Series.

Early classification tasks aim to classify time series before observing full data. It is critical in time-sensitive applications such as early sepsis diagnosis in the intensive care unit (ICU). Early diagnosis can provide more opportunities for doctors to rescue lives. However, there are two conflicting goals in the early classification task-accuracy and earliness. Most existing methods try to find a balance between them by weighing one goal against the other. But we argue that a powerful early classifier should always make highly accurate predictions at any moment. The main obstacle is that the key features suitable for classification are not obvious in the early stage, resulting in the excessive overlap of time series distributions in different time stages. The indistinguishable distributions make it difficult for classifiers to recognize. To solve this problem, this article proposes a novel ranking-based cross-entropy () loss to jointly learn the feature of classes and the order of earliness from time series data. In this way, can help classifier to generate probability distributions of time series in different stages with more distinguishable boundary. Thus, the classification accuracy at each time step is finally improved. Besides, for the applicability of the method, we also accelerate the training process by focusing the learning process on high-ranking samples. Experiments on three real-world datasets show that our method can perform classification more accurately than all baselines at all moments.

Intelligent Gas Detection: g-C3N4/Polypyrrole Decorated Alginate Paper as Smart Selective NH3/NO2 Sensors at Room Temperature.

Chemiresistive NH3/NO2 sensors are attracting considerable attention for use in air-conditioning systems. However, the existing sensors suffer from cross-sensitivity, detection limit, and power consumption, owing to the inadequate charge-transfer ability of gas-sensing materials. Herein, we develop a flexible NH3/NO2 sensor based on graphitic carbon nitride/polypyrrole decorated alginate paper (AP@g-CN/PPy). The flexible sensor can work at room temperature and exhibits a positive response of 23-246% and a negative response of 37-262% toward 0.1-5 ppm of NH3 and NO2, which is ∼4.5 times and ∼7.0 times higher than a pristine PPy sensor. Moreover, the sensor exhibits flexibility, reproducibility, long-term stability, anti-interference, and high resilience to humidity, indicating its promising potential in real applications. Using the 9 feature parameters extracted from the transient response, a matched deep learning model was developed to achieve qualitative recognition of different types of gases with distinguished decision boundaries. This work not only provides an alternative gas-sensing material for dual NH3/NO2 sensing but also establishes an intelligent strategy to identify hazardous gases under an interfering atmosphere.

A Novel Mis-Seg-Focus Loss Function Based on a Two-Stage nnU-Net Framework for Accurate Brain Tissue Segmentation.

Brain tissue segmentation plays a critical role in the diagnosis, treatment, and study of brain diseases. Accurately identifying these boundaries is essential for improving segmentation accuracy. However, distinguishing boundaries between different brain tissues can be challenging, as they often overlap. Existing deep learning methods primarily calculate the overall segmentation results without adequately addressing local regions, leading to error propagation and mis-segmentation along boundaries. In this study, we propose a novel mis-segmentation-focused loss function based on a two-stage nnU-Net framework. Our approach aims to enhance the model's ability to handle ambiguous boundaries and overlapping anatomical structures, thereby achieving more accurate brain tissue segmentation results. Specifically, the first stage targets the identification of mis-segmentation regions using a global loss function, while the second stage involves defining a mis-segmentation loss function to adaptively adjust the model, thus improving its capability to handle ambiguous boundaries and overlapping anatomical structures. Experimental evaluations on two datasets demonstrate that our proposed method outperforms existing approaches both quantitatively and qualitatively.

Phytochemical Screening, In vitro Antioxidant, Antibacterial Activities on Skimmia laureola Leaves Extract: A Research

Background: Skimmia laureola owned by the Rutaceae family is one of the plants that might use the Indian traditional treatment system in the Indian subcontinent and act against many diseases. So, this new research was carried out to scientifically confirm the various solvent fractions and separate the active fraction from the plant against antioxidants and bacterial and microbial growth.
 Objectives: The main objective of the study involves the Phytochemical screening and research of invitro activities i.e. antioxidant and antibacterial from the extracts of the leaves part of the Skimmia laureola plant.
 Methods: Phytochemical screening was carried out of various extracts by the tests that involved it. Invitro antibacterial and antioxidant activities were examined of the plant extract through various methods DPPH, Metal chelating activity, Reducing power ability, Hydrogen peroxide scavenging activity, and Nitric oxide scavenging activity. After completion of the research, all the activity is shown graphically.
 Results: Invitro antioxidant activity shows that methanolic extract shows more antioxidant activity than other extracts. The antibacterial investigations show that the ethanolic extract of the plant removal was very compelling for bacterias Streptococcus aureus, Bacillus subtilis, and Escherichia coli at 25 μg/ml, 50 μg/ml, 75 μg/ml, 100 μg/ml, 200 μg/ml and 300 μg/ml individually.
 Conclusion: Auxiliary metabolites (phenol, flavonoid, tannin, and content) have been quantified. In antioxidant tests such as DPPH activity, the methanolic extract of the plant outperforms other extracts such as chloroform, petroleum ether, ethanol, and aqueous extract. The antibacterial investigations show that the ethanolic extract of the plant removed was very compelling for bacterias Streptococcus aureus, Bacillus subtilis, and E.coli. The current study may be useful in improving data regarding its distinguishing proof boundaries expected primarily in the method of the adequacy of homegrown medications in the current situation lacking administrative laws to control the nature of natural medications, as well as discovering antioxidants and antibacterial action.

Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining

The calculation of volumes for irregular bodies holds significant relevance across various production processes. This spans tasks such as evaluating the growth status of crops and fruits, conducting morphological analyses of spatial objects based on volume parameters, and estimating quantities for earthwork and excavation. While methods like drainage, surface reconstruction, and triangulation suffice for smaller irregular bodies, larger ones introduce heightened complexity. Technological advancements, such as UAV photogrammetry and LiDAR, have introduced efficient point cloud data acquisition methods, bolstering precision and efficiency in calculating volumes for substantial irregular bodies. Notably, open-pit mines, characterized by their dynamic surface alterations, exemplify the challenges posed by large irregular bodies. Ensuring accurate excavation quantity calculations in such mines is pivotal, impacting operational considerations, acceptance, as well as production cost management and project oversight. Thus, this study employs UAV-acquired point cloud data from open-pit mines as a case study. In practice, calculating volumes for substantial irregular bodies often relies on the point cloud slicing method. However, this approach grapples with distinguishing multi-contour boundaries, leading to inaccuracies. To surmount this hurdle, this paper introduces an enhanced point cloud slicing method. The methodology involves segmenting point cloud data at fixed intervals, followed by the segmentation of slice contours using the Euclidean clustering method. Subsequently, the concave hull algorithm extracts the contour polygons of each slice. The final volume calculation involves multiplying the area of each polygon by the spacing and aggregating these products. To validate the efficacy of our approach, we employ model-derived volumes as benchmarks, comparing errors arising from both the traditional slicing method and our proposed technique. Experimental outcomes underscore the superiority of our point cloud volume calculation method, manifesting in an average relative error of 1.17%, outperforming the conventional point cloud slicing method in terms of accuracy.

Some linguistic criteria for the relative dating of the primary sources within the complex text of the «Basic Annals of the first emperor of Qin» in the Sima Qian’s «Records of the Grand Historian»

This paper examines the division of the complex heterogeneous source text, namely the 6th chapter of the Basic Annals in the Sima Qians Records of the Grand Historian, into parts according to linguistic criteria. Also the paper analyzes the problem of distinguishing boundaries and differences between the primary sources included in the text of the examined source. We employed the identification of the grammatical characters in the text as a way of linguistic analysis based on the A.M. Karapetiants method, compiling the frequency dictionaries of grammatical characters and selecting the fixed set of the most frequent ones. Comparison of the figures obtained for the Records of the Grand Historian 6th chapter with the similar figures obtained for some epigraphic texts from Shuihudi (that is, the grammatical characters percentage within the whole text and the set of the most frequent ones) allows us to propose a hypothesis that explains the differences between the data due to the fact that authors of the texts come from different cultural fields. Thus, the results of the study allow us to distinguish the differences in the cultural fields producing the historical sources from chronological differences indicating dissimilar (or similar) times of texts creation.

A Schwarz Lemma for the Pentablock

In this paper, we prove a Schwarz lemma for the pentablock. The pentablock mathcal {P} is defined by P={(a21,trA,detA):A=[aij]i,j=12∈B2×2}\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\begin{aligned} \\mathcal {P}=\\{(a_{21}, {\ ext {tr}}A, \\det A) : A=[a_{ij}]_{i,j=1}^2 \\in \\mathbb {B}^{2\ imes 2}\\} \\end{aligned}$$\\end{document}where mathbb {B}^{2times 2} denotes the open unit ball in the space of 2times 2 complex matrices. The pentablock is a bounded non-convex domain in mathbb {C}^3 which arises naturally in connection with a certain problem of mu -synthesis. We develop a concrete structure theory for the rational maps from the unit disc mathbb {D} to the closed pentablock overline{mathcal {P}} that map the unit circle mathbb {T} to the distinguished boundary boverline{mathcal {P}} of overline{mathcal {P}}. Such maps are called rational {overline{mathcal {P}}}-inner functions. We give relations between {overline{mathcal {P}}}-inner functions and inner functions from mathbb {D} to the symmetrized bidisc. We describe the construction of rational {overline{mathcal {P}}}-inner functions x = (a, s, p) : mathbb {D} rightarrow overline{mathcal {P}} of prescribed degree from the zeroes of a, s and s^2-4p. The proof of this theorem is constructive: it gives an algorithm for the construction of a family of such functions x subject to the computation of Fejér–Riesz factorizations of certain non-negative trigonometric functions on the circle. We use properties and the construction of rational {overline{mathcal {P}}}-inner functions to prove a Schwarz lemma for the pentablock.

Rational Penta-Inner Functions and the Distinguished Boundary of the Pentablock

In this note, we give a description of rational maps from the open unit disc $$\mathbb {D}$$ to the pentablock that map the boundary of $$\mathbb {D}$$ to the distinguished boundary of the pentablock. We also obtain a new characterization of the distinguished boundary of the pentablock.

The symmetrization map and $$\Gamma$$-contractions

The symmetrization map $$\pi :{\mathbb{C}}^2\rightarrow {\mathbb{C}}^2$$ is defined by $$\pi (z_1,z_2)=(z_1+z_2,z_1z_2).$$ The closed symmetrized bidisc $$\Gamma$$ is the symmetrization of the closed unit bidisc $$\overline{{\mathbb{D}}^2}$$ , that is, $$\begin{aligned} \Gamma = \pi (\overline{{\mathbb{D}}^2})=\{ (z_1+z_2,z_1z_2)\,:\, |z_i|\le 1, i=1,2 \}. \end{aligned}$$ A pair of commuting Hilbert space operators (S, P) for which $$\Gamma$$ is a spectral set is called a $$\Gamma$$ -contraction. Unlike the scalars in $$\Gamma$$ , a $$\Gamma$$ -contraction may not arise as a symmetrization of a pair of commuting contractions, even not as a symmetrization of a pair of commuting bounded operators. We characterize all $$\Gamma$$ -contractions which are symmetrization of pairs of commuting contractions. We show by constructing a family of examples that even if a $$\Gamma$$ -contraction $$(S,P)=(T_1+T_2,T_1T_2)$$ for a pair of commuting bounded operators $$T_1,T_2$$ , no real number less than 2 can be a bound for the set $$\{ \Vert T_1\Vert ,\Vert T_2\Vert \}$$ in general. Then we prove that every $$\Gamma$$ -contraction (S, P) is the restriction of a $$\Gamma$$ -contraction $$({{\widetilde{S}}}, {{\widetilde{P}}})$$ to a common reducing subspace of $${{\widetilde{S}}}, {{\widetilde{P}}}$$ and that $$({{\widetilde{S}}}, {{\widetilde{P}}})=(A_1+A_2,A_1A_2)$$ for a pair of commuting operators $$A_1,A_2$$ with $$\max \{\Vert A_1\Vert , \Vert A_2\Vert \} \le 2$$ . We find new characterizations for the $$\Gamma$$ -unitaries and describe the distinguished boundary of $$\Gamma$$ in a different way. We also show some interplay between the fundamental operators of two $$\Gamma$$ -contractions (S, P) and $$(S_1,P)$$ .

Mutual gain adaptive network for segmenting brain stroke lesions

Brain stroke, an acute vascular disease, can lead to brain damage. As the incidence rate continues to increase, it becomes urgent and yet significantly to develop automatic tools for segmenting brain stroke lesions, for helping radiologists to detect brain stroke both effectively and accurately. Deep learning models, though have advanced this task, they are still suffering from the huge size variation and ambiguous boundaries of stroke lesions, both substantially degrading the segmentation performance. In this paper, we proposed an effective and generic learning network, called mutual gain adaptive network, to enhance the network’s ability on dealing with size variation and distinguishing ambiguous boundaries, for advancing deep neural networks in brain stroke lesion segmentation. The main contribution is that 1) a mutual gain adaptive similarity (MGAS) block and 2) a global context-awareness (GCA) block. MGAS block aims at capturing both short- and long-range spatial dependencies, for better encoding global contexts by leveraging feature similarity, to enhance feature activation for segmenting stroke lesions with different sizes. GCA block is designed to supply global context for low-semantic features, for facilitating the restoration of pixel localization to distinguish ambiguous boundaries. Our network is assessed in a publicly available dataset, e.g. Anatomical Tracings of Lesions After Stroke (ATLAS). Extensive experimental results show that it is better than state-of-the-art methods.

Weakly-Supervised Learning With Complementary Heatmap for Retinal Disease Detection.

There are many types of retinal disease, and accurately detecting these diseases is crucial for proper diagnosis. Convolutional neural networks (CNNs) typically perform well on detection tasks, and the attention module of CNNs can generate heatmaps as visual explanations of the model. However, the generated heatmap can only detect the most discriminative part, which is problematic because many object regions may exist in the region beside the heatmap in an area known as a complementary heatmap. In this study, we developed a method specifically designed multi-retinal diseases detection from fundus images with the complementary heatmap. The proposed CAM-based method is designed for 2D color images of the retina, rather than MRI images or other forms of data. Moreover, unlike other visual images for disease detection, fundus images of multiple retinal diseases have features such as distinguishable lesion region boundaries, overlapped lesion regions between diseases, and specific pathological structures (e.g. scattered blood spots) that lead to mis-classifications. Based on these considerations, we designed two new loss functions, attention-explore loss and attention-refine loss, to generate accurate heatmaps. We select both "bad" and "good" heatmaps based on the prediction score of ground truth and train them with the two loss functions. When the detection accuracy increases, the classification performance of the model is also improved. Experiments on a dataset consisting of five diseases showed that our approach improved both the detection accuracy and the classification accuracy, and the improved heatmaps were closer to the lesion regions than those of current state-of-the-art methods.

EVALUATION OF ANTIOXIDANT AND ANTIBACTERIAL ACTIVITY OF ZANTHOXYLUM ARMATUM: A RESEARCH

Introduction: The genus Zanthoxylum of the family Rutaceae is economically important due to its nutritional, industrial, and therapeutic values. Z. armatum is a short tree or a shrub that grows under dry conditions. Objective: The current examination manages the full scale concentrates just as investigations of assessment of pharmacognostical, phytochemical, antioxidants and antibacterial movement of Zanthoxylum armatum leaves some particular characters were noticed while contemplating their cross over areas. Materials and Methods: Physiochemical and primer phytochemical investigations of the leaves of the plant Zanthoxylum armatum were additionally done. The antioxidants assay is performed by various methods like DPPH, nitric oxide, and hydrogen peroxide. Results and Discussion: The extract of the plant shows various effects as antioxidants on various concentrations and showed graphically. The antibacterial examinations affirmed that the ethanolic removal was very compelling for Staphylococcus aureus, Bacillus subtilis, E. coli, and Pseudomonas aeruginosa at 25 μg/ml, 50 μg/ml, 100 μg/ml, 200 μg/ml, 300 μg/ml, 500 μg/ml and tetracycline (100 μg/ml) individually. The current investigation may be helpful to enhance data with respect to its distinguishing proof boundaries expected essentially in the method of the adequacy of home grown medications in the current situation lacking administrative laws to control nature of natural medications and furthermore to discover the antioxidants and antibacterial action.

PROBABILISTIC SILHOUETTE-BASED CLOSE-RANGE PHOTOGRAMMETRY USING A NOVEL 3D OCCUPANCY-BASED RECONSTRUCTION

Abstract. Digital three-dimensional (3D) reconstruction of objects has many applications in computer vision, archaeology, and the entertainment industry. Digital 3D reconstruction can be used to preserve the appearance of valuable historical artifacts; it can be used to track the pose of an object in the images, and it can facilitate object modelling. 3D reconstruction of objects in the past has been achieved using many sensors such as cameras and laser-strip scanners. Monocular camera-based object 3D modelling can be categorized into sparse feature detector/descriptor-based and dense silhouette-based approaches. Feature-based methods identify distinctive features on the objects (captured from many images). In contrast, silhouette-based methods only require a distinguishable boundary between the object and the background. Silhouette-based methods have the advantage that in the controlled setups, a special background can be designed to be distinguishable from the object of interest; therefore, uniquely identifiable textures on the object’s surface are not required. Despite their advantages, silhouette-based probabilistic reconstruction remains a challenge. This article proposes a new probabilistic approach using 3D occupancy grids for the silhouette-based digital reconstruction of an object. The proposed method is designed to be usable with monocular cameras and achieves an accurate reconstruction using only sixteen images. Compared to similar silhouette-based volumetric approaches, the voxels are not discarded immediately during the reconstruction, and the occupancy grid mapping continuously changes the occupancy probability of the voxels with each new image included.

REGULARITY OF THE SZEGÖ PROJECTION ON THE SYMMETRISED POLYDISC

AbstractWe consider the $L^{p}$ -regularity of the Szegö projection on the symmetrised polydisc $\mathbb {G}_{n}$ . In the setting of the Hardy space corresponding to the distinguished boundary of the symmetrised polydisc, it is shown that this operator is $L^{p}$ -bounded for $p\in (2-{1}/{n}, 2+{1}/{(n-1)})$ .

Developing a diceCT brain atlas: Comparison of diceCT neuroanatomy and brain histology in a rat model

Significant advancements and refinement of neurological imaging modalities can enable new understanding in nervous system organization. One recent example, diffusible iodine‐based contrast‐enhanced computed tomography (diceCT), is an iodine staining and X‐ray μCT‐imaging technique that allows for the differentiation of myelinated from unmyelinated nervous tissues at finer scales of spatial resolution than standard three‐dimensional (3D) brain‐imaging tools allow. DiceCT is versatile, capable of imaging specimens across several orders of magnitude in size. It enables large and complex structures such as human brains to be studied alongside those of less complex nervous systems (e.g., fish, frogs, rodents, birds). One key feature is that the staining agent can be removed, allowing the subsequent application of acetate, fluorescent, and immunofluorescent histology following diceCT imaging. Nevertheless, the relative novelty of diceCT as a neuroanatomical visualization tool also demands ground‐truthing by determining its practical reliably for distinguishing precise boundaries between brain structures. For example, some adjacent, tissue‐level neuroanatomical features are poorly differentiated due to staining similarity or inherently obscure boundaries (such as the thalamus, which houses many small white‐matter tracts). To address this, we compare 3D orientation‐matched image stacks of diceCT juvenile Sprague Dawley rat brains to the Paxinos and Watson atlas, a gold‐standard rat‐brain reference. Visual identification of brain structures was attempted for all brain regions. Cardinal boundaries for each structure's dorsal, ventral, medial, and lateral edges were scored as distinguishable or non‐distinguishable. Right/left staining asymmetries were also scored. We used this outline to compare and contrast the neuroanatomical structures identified by diceCT with those described by Paxinos and Watson. Preliminary results indicate that 42% of all scored structures were visible via most (e.g., 3–4) of their cardinal boundaries, whereas 25% of atlas structures showed no distinguishable boundaries using diceCT. Overall, diceCT rat‐brain sections align well with standard histological sections, and visibility of neuroanatomical features is similar to tissue‐based, 3D magnetic resonance imaging atlases of comparable resolution.

Generalized Vector-Valued Hardy Functions

We consider analytic functions in tubes Rn+iB⊂Cn with values in Banach space or Hilbert space. The base of the tube B will be a proper open connected subset of Rn, an open connected cone in Rn, an open convex cone in Rn, and a regular cone in Rn, with this latter cone being an open convex cone which does not contain any entire straight lines. The analytic functions satisfy several different growth conditions in Lp norm, and all of the resulting spaces of analytic functions generalize the vector valued Hardy space Hp in Cn. The analytic functions are represented as the Fourier–Laplace transform of certain vector valued Lp functions which are characterized in the analysis. We give a characterization of the spaces of analytic functions in which the spaces are in fact subsets of the Hardy functions Hp. We obtain boundary value results on the distinguished boundary Rn+i{0¯} and on the topological boundary Rn+i∂B of the tube for the analytic functions in the Lp and vector valued tempered distribution topologies. Suggestions for associated future research are given.

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Identifying the influential and spreader nodes in complex networks solves many types of complex scientific problems. In social networks, identifying the influential individuals can be useful for structuring techniques that accelerate or hinder information propagation. Each node in the network has unique characteristics that reflect its importance. These characteristics are used by researchers to design many different centrality algorithms. Unfortunately, current survey papers categorize these algorithms into broad classes and do not draw distinguishable boundaries among the specific techniques adopted by them. This can result in misclassifying unrelated algorithms into the same analysis category. To overcome this, we introduce a methodology-based taxonomy for classifying the algorithms that identify top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> influential spreaders into hierarchically nested, specific, and fine-grained categories. We survey 184 papers and discuss their algorithms, which fall under 26 specific techniques. Our methodological taxonomy classifies the algorithms hierarchically into the following manner: Analysis type → analysis scope → analysis approach → analysis category → analysis sub-category → analysis specific technique. We introduce in this paper a comprehensive survey, review, and experimental evaluation of the recent and state-of-the-art algorithms that identify the top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> and influential spreader nodes in social networks.

Double charge polarity switching in Sb-doped SnSe for enhanced thermo-electric power generation

The pristine and antimony (Sb) doped tin selenide (SnSe) has been prepared by vacuum melting followed by ball milling process. The crystallographic pattern of both doped and undoped samples shows the formation of orthorhombic SnSe without any impurities. The influence of Sb on SnSe were confirmed from Projected Density of States (PDOS) and electron density analysis. The Elemental Probe Micro Analyzer (EPMA) technic used to confirm the presence of Sn, Se and Sb elements along with homogenous distribution. HR-TEM micrographs reveal highly crystalline nature of the samples as well as the formation of defects and distinguishable grains and grain boundaries, which helps to reduce the thermal conductivity of 10 wt% Sb doped SnSe samples to 0.55 W/mK at 600 K. Seebeck coefficient analysis has been discloses the double charge polarity switching in Sb substituted samples. Thus, 10 wt% Sb doped SnSe samples achieved a comparable negative Seebeck coefficient with respect to pristine SnSe, which can be a breakthrough in thermoelectric devices.

Clark measures and de Branges–Rovnyak spaces in several variables

Let denote the unit ball of , , and let denote a finite product of , . Given a non-constant holomorphic function , we study the corresponding family , , of Clark measures on the distinguished boundary . We construct a natural unitary operator from the de Branges–Rovnyak space onto the Hardy space . As an application, for and an inner function , we show that the property is directly related to the membership of an appropriate explicit function in .