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Possible generalizations of models and techniques of signature‐based preventive Maintenance have been, in particular, advocated in the extended survey recently published on ASMBI. In view of future work in this direction by the reliability community a few conceptual aspects, related with concepts of system signature, will be recalled here. [ABSTRACT FROM AUTHOR]

The article reports on the review of the fifty papers published in "Acta Anaesthesiologica Scandinavia," which focuses on the researcher bias and generalization of results in bias and limits of agreement analyses. It suggests ways on how to improve the generalization of the analyses results and how to limit the researcher bias, using the Altman-Bland approach.

Recently, deep learning‐based stereo matching has achieved great success. However, models trained on the source domain dataset encounter substantial performance degradation when directly tested on an unseen target domain dataset because of neglecting the generalization to out‐of‐distribution (OOD) stereo samples. This paper proposes a stereo sample generation‐based domain generalization network (SGDG‐Net) for stereo matching. Specifically, to expand the distribution span of training samples, OOD stereo samples are generated to assist training. To effectively generate OOD left samples, a style transfer‐based generation mechanism is proposed to transmit perturbations to the source left samples. In addition, to generate the OOD right samples, a disparity‐assisted generation strategy is proposed by using disparity map labels as auxiliary information. Experimental results demonstrate that the proposed SGDG‐Net produces remarkable results on four benchmark datasets. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to advance the concept of region reaching control for a single robot to the study of region reaching consistency for uncertain fully‐actuated multi‐robot systems formulated by Lagrange dynamics. By introducing a directed network communication topology and utilizing the pinning‐like control methods, a distributed region reaching consensus control strategy is proposed. The desired region shape can be specified by selecting an appropriate potential objective function. A feature of the developed region reaching consensus algorithm is fully distributed, which is also an important generalization of pinning‐like set point tracking scheme based on the typical proportion‐integration‐differentiation control scheme. More importantly, the scope of consistency in this paper can be explicitly defined, which is different from traditional consistency control algorithms. Subsequently, numerical examples are presented to illustrate the performance of the designed controllers. [ABSTRACT FROM AUTHOR]

For n⩾2$n\geqslant 2$, we determine the Dirichlet spectrum in Rn$\mathbb {R}^n$ with respect to a linear form and the maximum norm as the entire interval [0, 1]. This natural result improves on the recent work of Beresnevich et al. and complements a subsequent paper by the authors where the analogous result was proved for simultaneous approximation. Various generalizations that can be obtained by similar methods as in the latter paper are indicated. We believe that our results are an important step toward resolving the very open analogous problem for a general system of linear forms. [ABSTRACT FROM AUTHOR]

The lack of labelled data presents a common challenge in many fault diagnosis and machine learning tasks. It requires the model to be able to efficiently capture useful fault features from a smaller amount of labelled data. In this paper, a method to train multiple convolutional auto‐encoders by self‐learning method and integrate them using ensemble learning, called ensemble self‐taught learning convolutional auto‐encoders (STL‐CAEs), is proposed, which can effectively extract features of bearing vibration signals. First, an ensemble learning strategy is proposed to obtain two auto‐encoders that satisfy the strategy by optimizing the model parameters and structure. Then, a self‐taught learning training method is proposed to solve the problem of little label data. Finally, ensemble learning and fault diagnosis is achieved by the SoftMax classifier. Applying the proposed method to the bearing data from Case Western Reserve University, the STL‐CAEs have higher accuracy and generalization than common fault diagnosis methods such as CAE, CNN, SAE and EMD, and also have significant advantages in terms of diagnostic time and training time. [ABSTRACT FROM AUTHOR]

Let G be a simple graph and χ′(G) be the chromatic index of G. We call G a Δ‐critical graph if χ′(G − e ) = χ′( ) G − 1 = Δ for every edge e of G, where Δ is maximum degree of G. Let e x = y be an edge of Δ‐critical graph G and φ be an (proper) edge Δ‐coloring of G − e . An e‐fan is a sequence F x, e, y, e1, z1, ..., ep, zp of alternating vertices and distinct edges such that edge ei is incident with x or y, zi is another endvertex of ei and φ(ei ) is missing at a vertex before zi for each i with 1 ≤ i ≤ p. In this paper, we prove that if min, { ( dx) d(y)} ≤ Δ − 1, where d (x) and d ( y ) denote the degrees of vertices x and y, respectively, then colors missing at different vertices of V ( Fe ) are distinct. Clearly, a Vizing fan is an e‐fan with the restricting that all edges ei being incident with one fixed end vertex of edge e. This result gives a common generalization of several recently developed new results on multifan, double fan, Kierstead path of four vertices, and broom. By treating some colors of edges incident with vertices of low degrees as missing colors, Kostochka and Stiebitz introduced C‐fan. In this paper, we also generalize the C‐fan from centered at one vertex to one edge. [ABSTRACT FROM AUTHOR]

In this paper, a large‐scale dataset called the Iran Autonomous Driving Dataset (IADD) is presented, aiming to improve the generalization capability of the deep networks outside of their training domains. The IADD focuses on 2D object detection and contains more than 97,000 annotated images, covering six common object classes in the field of autonomous vehicles. To improve the generalization of the models, a wide variety of driving conditions and domains, including the city and suburban road settings, adverse weather conditions, and various traffic flows, are presented in the IADD images. The results of exhaustive evaluations conducted on several state‐of‐the‐art convolutional neural networks reveal that not only the trained architectures have performed successfully on test data of the IADD, but also they have upheld high precision in the assessments of generalization capability. In order to challenge the models, broad range of simulations have been performed in the CARLA software environment; which due to the synthetic nature of the simulated images, severe domain shifts have been observed between the CARLA and the IADD. Also, the cross‐domain evaluation results have confirmed the efficacy of the IADD in enhancing the generalization ability of the deep learning models. The dataset is available in: https://github.com/ahv1373/IADD. [ABSTRACT FROM AUTHOR]

The crux of this paper is to provide a concentrated critical evaluation of Galen Strawson's innovative approach to the self. To that end, I will first attempt to concisely introduce his general thesis, which seems appropriate to be broken up into two major pieces: the phenomenology (experience) of the self, what the self would have to be; and the metaphysics of the self (i.e., a query refers to its metaphysics [its existence and nature]: whether there is any). Explaining and discussing Strawson's twofold account of the self is my first target in this paper. And it is with these two parts that I take issues. Accordingly, I shall determinedly try to develop a counterargument according to which Strawson's establishment of his entire enterprise of the self is based merely on unjustified intuitive generalisation. Next, I will put more effort into making some more argumentative points, mainly to show how his metaphysics does not give much thought to some vital matters of the self in comparison with the systems of metaphysics of his forebears of Western philosophers. What all this means is that Strawsonian metaphysical analysis of the self so conceived and so described appears philosophically to drive itself to justly be placed in an ahistorical context. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient method to determine the material of spherical objects and the location of the receiving antenna relative to the object in bi‐static measurements using supervised learning techniques. From a single observation, we compare classification performances resulting from the application of several classifiers on different data types: the Ultra‐Wide Band scattered field in time and frequency domains and pre‐processed data from the singularity expansion method (SEM) which has seldom been used in classification because it is considered to be noise sensitive. We selected a robust SEM technique which is vector fitting to decompose the frequency response into complex natural resonances (CNRs) and residues. Indeed, CNRs are aspect independent and therefore, can be used to discriminate the objects. However, the residues associated to each pole depend upon the aspect angle, and hence, they were never exploited. In this paper, we propose a novel use of those residues. Additionally, we construct an original data set using SEM data in order to further improve the robustness to noise and the generalization capacity of the learning algorithms. The advantages of using SEM data for object classification are highlighted by comparing it with raw scattered field data in time and frequency domains where the classification algorithms are optimized in each case. The results are very promising, especially in terms of generalization, robustness to noise, and computation time, which are all reasons to take an interest in SEM for these purposes. Key Points: Classification of spherical objects is achieved by using preprocessed data from the singularity expansion method (SEM)The residues allow to determine the position of the antenna relative to an objectSEM data provide good noise robustness, high classification accuracy, and excellent generalization to any object size [ABSTRACT FROM AUTHOR]

ABSTRACT Unlike the simplification of individual lines, the generalization of contour clusters oriented to geographical features is a structured generalization behavior that extracts knowledge of geographical features form the perspective of spatial cognition. In decision level, terrain generalization is essentially a similarity transformation between the geomorphic structures corresponding to multi‐scale contour cluster. However, the multi‐scale structural similarity relations are not directly connected with the application of contour generalization. Therefore, this paper presents an automated method for terrain contour structured generalization considering multi‐scale structural similarity. Firstly, a drainage tree structure is constructed from contour lines to establish associations between valley branches and contour bends. Then, the quantitative relationships between multi‐scale structural similarity and map scale changes are explored using an indirect quantitative expression method. Finally, the contour structural generalization is fully automated through iterative optimization principle based on the multi‐scale structural similarity relations. The experiment results demonstrate the rationality and feasibility of fully automating the contour generalization process based on multi‐scale geomorphic structural similarity relations. And the proposed method not only overcomes the challenge of determining “how much to select” in map generalization, but also is valuable for enriching the content of spatial similarity relations and map generalization, thereby providing a theoretical method system and support for the construction of national basic vector databases. [ABSTRACT FROM AUTHOR]

The Gross–Pitaevskii (GP) equation is a model for the description of the dynamics of Bose–Einstein condensates. Here, we consider the GP equation in a two‐dimensional setting with an external periodic potential in the x$x$‐direction and a harmonic oscillator potential in the y$y$‐direction in the so‐called tight‐binding limit. We prove error estimates which show that in this limit the original system can be approximated by a discrete nonlinear Schrödinger equation. The paper is a first attempt to generalize the results from [19] obtained in the one‐dimensional setting to higher space dimensions and more general interaction potentials. Such a generalization is a non‐trivial task due to the oscillations in the external periodic potential which become singular in the tight‐binding limit and cause some irregularity of the solutions which are harder to handle in higher space dimensions. To overcome these difficulties, we work in anisotropic Sobolev spaces. Moreover, additional non‐resonance conditions have to be satisfied in the two‐dimensional case. [ABSTRACT FROM AUTHOR]

In this paper we continue the study of a natural generalization of Turán's forbidden subgraph problem and the Ruzsa–Szemerédi problem. Let ex F(n , G ) ${\text{ex}}_{F}(n,G)$ denote the maximum number of edge‐disjoint copies of a fixed simple graph F $F$ that can be placed on an n $n$‐vertex ground set without forming a subgraph G $G$ whose edges are from different F $F$‐copies. The case when both F $F$ and G $G$ are triangles essentially gives back the theorem of Ruzsa and Szemerédi. We extend their results to the case when F $F$ and G $G$ are arbitrary cliques by applying a number theoretic result due to Erdős, Frankl, and Rödl. This extension in turn decides the order of magnitude for a large family of graph pairs, which will be subquadratic, but almost quadratic. Since the linear r $r$‐uniform hypergraph Turán problems to determine ex rl i n(n , G ) ${\text{ex}}_{r}^{lin}(n,G)$ form a class of the multicolor Turán problem, following the identity ex rl i n(n , G ) = ex K r(n , G ) ${\text{ex}}_{r}^{lin}(n,G)={\text{ex}}_{{K}_{r}}(n,G)$, our results determine the linear hypergraph Turán numbers of every graph of girth 3 and for every r $r$ up to a subpolynomial factor. Furthermore, when G $G$ is a triangle, we settle the case F = C 5 $F={C}_{5}$ and give bounds for the cases F = C2 k + 1 $F={C}_{2k+1}$, k ≥ 3 $k\ge 3$ as well. [ABSTRACT FROM AUTHOR]

In a previous work, the hyperbolic conformality for bicomplex functions was introduced, and it was proved that, with the adequate hypothesis, a bicomplex holomorphic function is hyperbolic conformal. The aim of this paper is to extend this idea to 픻n, with 픻 the set of hyperbolic numbers. Thus, the fundaments of the analysis in 픻n are presented here, as well as the generalization of some geometric hyperbolic objects that were defined in the context of bicomplex analysis. [ABSTRACT FROM AUTHOR]

Micro‐LED is a new display technology that offers advantages such as high brightness, high contrast, and low power consumption. However, the performance of Micro‐LED is greatly affected by temperature, leading to color shift easily. To address this issue, this paper proposes a machine learning‐based approach for achieving global compensation, which exhibits advantages including high compensation accuracy, good smoothness, and strong generalization performance. Experimental validation using both measured and simulated data demonstrates that the proposed method achieves a single‐pixel single‐color(R) brightness prediction accuracy of 1% and the brightness prediction of multi‐pixel single‐color(R) also exhibits high accuracy. Consequently, the proposed method enables global brightness compensation and color shift compensation in Micro‐LED. [ABSTRACT FROM AUTHOR]

In this paper, we introduce deep learning (DL)‐based backlight extraction methods for local dimming display. The main objective is to better handle the trade‐off between the displayed image quality and power consumption. To this end, we propose an enhanced power regularization. Moreover, we design a smaller model with reduced computational complexity and propose an efficient post‐processing for better generalization. Experimental results show that the proposed methods reduce power consumption while better maintaining the image quality than the selected benchmarks. [ABSTRACT FROM AUTHOR]

Detecting tea shoots is the first and most crucial step in achieving intelligent tea harvesting. However, when faced with thousands of tea varieties, establishing a high‐quality and comprehensive database comes with significant costs. Therefore, it has become an urgent challenge to improve the model's generalization ability and train it with minimal samples to develop a model capable of achieving optimal detection performance in various environments and tea varieties. This paper introduces a model named You Only See Tea (YOST) which utilizes depth maps to enhance model's generalization ability. It is applied to detect tea shoots in complex environments and to perform cross‐variety tea shoots detection. Our approach differs from common data augmentation strategies aimed at enhancing model generalization by diversifying the data set. Instead, we enhance the model's learning capability by strategically amplifying its attention towards core target features while simultaneously reducing attention towards noncore features. The proposed module YOST is developed upon the You Only Look Once version 7 (YOLOv7) model, utilizing two shared‐weight backbone networks to process both RGB and depth images. Then further integrate two modalities with feature layers at the same scale into our designed Ultra‐attention Fusion and Activation Module. By utilizing this approach, the model can proficiently detect targets by capturing core features, even when encountering complex environments or unfamiliar tea leaf varieties. The experimental results indicate that YOST displayed faster and more consistent convergence compared with YOLOv7 in training. Additionally, YOST demonstrated a 6.58% enhancement in AP50 for detecting tea shoots in complex environments. Moreover, when faced with a cross‐variety tea shoots detection task involving multiple unfamiliar varieties, YOST showcased impressive generalization abilities, achieving a significant maximum AP50 improvement of 33.31% compared with YOLOv7. These findings establish its superior performance. Our research departs from the heavy reliance on high‐generalization models on a large number of training samples, making it easier to train small‐scale, high‐generalization models. This approach significantly alleviates the pressure associated with data collection and model training. [ABSTRACT FROM AUTHOR]

Hybrid modeling, with an appropriate blend of the mechanistic and data‐driven framework, is increasingly being adopted in bioprocess modeling, model‐based experimental design (digital‐twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data‐driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an "optimal" model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data‐driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid‐model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system. [ABSTRACT FROM AUTHOR]

In this paper, we introduce and analyze the approximation properties of bivariate generalization for the family of Kantorovich type exponential sampling series. We derive the basic convergence result and Voronovskaya type theorem for the proposed sampling series. Using logarithmic modulus of smoothness, we establish the quantitative estimate of order of convergence for the Kantorovich type exponential sampling series. Furthermore, we study the convergence results for the generalized Boolean sum (GBS) operator associated with bivariate Kantorovich exponential sampling series. At the end, we provide a few examples of kernels to which the presented theory can be applied along with the graphical representation and error estimates. [ABSTRACT FROM AUTHOR]

Background: Generalisation inferences let readers identify a conceptually superordinate statement to represent multiple subordinate concepts. This study measures text genres' influence on the scope and timing of generalisation inferences. Methods: To measure the scope, or breadth, of generalisation inferences, undergraduates (N = 266) read expository and literary texts containing target sentences that were consistent, inconsistent or off‐topic in relation to the generalisation implied in each paragraph. To test when the generalisations were inferred, target sentences were placed either early or late in each paragraph. Results: Readers drew broad generalisations early in the text and changed to specific generalisations later for exposition. The generalisations inferred from fairy tales were specific regardless of whether tested early or later in the text. Conclusions: Readers construct generalisation inferences while reading both expository and literary texts, although the timing and scope of the inferences vary slightly by genre. Implications for theories of reading comprehension and applications for reading interventions are discussed. Highlights: What is already known about the topic Readers infer superordinate concepts, or generalisations, as they read.Inferring generalisations is important for reading comprehension.Text genre affects reading behaviours, including inference construction. What this paper adds This study investigated how genre affects the scope, or breadth, of generalisation inferences and the timing of those inferences.Readers' inferences for expository texts began as broad but became specific as the text continued but were always specific for literary texts.Readers began drawing the inferences while reading, as opposed to after reading. Implications for theory, policy or practice Although readers often struggle to identify the main point of a text, these results suggest readers begin to find connections between ideas within paragraphs very early in the text.Readers reacted to inconsistent information differently in literary versus expository texts, suggesting continued instruction of genre‐specific reading strategies is appropriate. [ABSTRACT FROM AUTHOR]

In this paper, we consider the best polynomial approximation operator defined on an Orlicz–Lorentz space Λw,ϕ$\Lambda _{w,\phi }$, and its extension to Λw,ϕ′$\Lambda _{w,\phi ^{\prime }}$, where w is a non‐negative continuous weight function and ϕ′$\phi ^{\prime }$ is the derivative of ϕ, which is not required to be an Orlicz function. Our work generalizes a recent result in this field on an Orlicz–Lorentz space generated by an Orlicz function. In addition, we establish some properties and estimates for any extended best polynomial approximation. [ABSTRACT FROM AUTHOR]

This paper revisits the orientation systems in Situ Rgyalrong (Burmo‐Qiangic, Sino‐Tibetan). These systems reflect heliocentric (e.g., east/west) or topographic (e.g., upriver/downriver) orientation, typically within a frame of absolute reference. While scholars agree that the orientation systems of different Situ dialects are generalisable to a tri‐axial model, the precise semantic values reflected by the three axes have always been controversial. In explaining synchronic semantic divergences among Situ orientation systems from a diachronic perspective, the present work first reveals historical layers within the three axes. Through internal reconstruction, it then identifies the original meanings of the two sets constituting the innovative Axis II. Finally, using typological parallelisms and local topographic features, this paper analyses the Proto‐Situ orientation system as a tri‐axial system of orthogonality, and infers the original meanings of the system's three axes. Importantly, this study shows how the comparative method can be applied to solving semantic problems in languages rich in morphology. [ABSTRACT FROM AUTHOR]

In localization, some specific nodes (beacon set) are selected to locate all nodes of a network, and if an arbitrary node stops working and still selected nodes remain in the beacon set, then the chosen nodes are called fault‐tolerant beacon set. Due to the variety of metric dimension applications in different areas of sciences, many generalizations were proposed, fault‐tolerant metric dimension is one of them. A resolving (beacon) set Bf$$ {B}_f $$ is fault tolerant, if Bf\ν$$ {B}_f\backslash \nu $$ for each ν∈Bf$$ \nu \in {B}_f $$ is also a resolving set; it is also known as a fault‐tolerant beacon set; the minimum cardinality of such a beacon set is known as the fault‐tolerant metric dimension of a graph G$$ G $$. In this paper, we find the fault‐tolerant beacon set of hexagonal Möbius ladder network H(α,β)$$ H\left(\alpha, \beta \right) $$ and proved that all the different variations of α$$ \alpha $$ and β$$ \beta $$ in H(α,β)$$ H\left(\alpha, \beta \right) $$ has constant fault‐tolerant metric dimension. [ABSTRACT FROM AUTHOR]

The present paper examines a type of abstract domain‐general knowledge required for the process of constructing useable domain‐specific causal knowledge, the evident goal of causal learning. It tests the hypothesis that analytic knowledge of causal‐invariance decomposition functions is essential for this process. Such knowledge specifies the decomposition of an observed outcome into contributions from constituent causes under the default assumption that the empirical knowledge acquired is invariant across contextual/background causes. The paper reports two psychological experiments (and replication studies) with pre‐school‐age children on generalization across contexts involving binary cause and effect variables. The critical role of causal invariance for constructing useable causal knowledge predicts that even young children should (tacitly) use the causal‐invariance decomposition function for such variables rather than a non‐causal‐invariance decomposition function common in statistical practice in research involving binary outcomes. The findings support the rational shaping of empirical causal knowledge by the causal‐invariance constraint, ruling out alternative explanations in terms of non‐causal‐invariance decomposition functions, heuristics, and biases. For the same causal structure involving candidate causes and outcomes that are binary variables with a "present" value and an "absent" value, the paper argues against the possibility of multiple rational characterizations of the "sameness of causal influence" that justifies generalization across contexts. [ABSTRACT FROM AUTHOR]

In this paper, we investigate novel solutions of fractional‐order option pricing models and their fundamental mathematical analyses. The main novelties of the paper are the analysis of the existence and uniqueness of European‐type option pricing models providing to give fundamental solutions to them and a discussion of the related analyses by considering both the classical and generalized Mittag‐Leffler kernels. In recent years, the generalizations of classical fractional operators have been attracting researchers' interest globally and they also have been needed to describe the dynamics of complex phenomena. In order to carry out the mentioned analyses, we take the Laplace transforms of either classical or generalized fractional operators into account. Moreover, we evaluate the option prices by giving the models' fractional versions and presenting their series solutions. Additionally, we make the error analysis to determine the efficiency and accuracy of the suggested method. As per the results obtained in the paper, it can be seen that the suggested generalized operators and the method constructed with these operators have a high impact on obtaining the numerical solutions to the option pricing problems of fractional order. This paper also points out a good initiative and tool for those who want to take these types of options into account either individually or institutionally. [ABSTRACT FROM AUTHOR]

In an argumentation approach, legal evidential reasoning is modeled as the construction and attack of "trees of inference" from evidence to conclusions by applying generalizations to evidence or intermediate conclusions. In this paper, an argumentation‐based analysis of the Simonshaven case is given in terms of a logical formalism for argumentation. The formalism combines abstract argumentation frameworks with accounts of the structure of arguments, of the ways they can be attacked and of ways to evaluate conflicting arguments. The purpose of this paper is not to demonstrate or argue that the argumentation approach to modeling legal evidential reasoning is feasible or even preferable but to have a fully worked‐out example that can be used in the comparison with alternative Bayesian or scenario‐based analyses. Prakken gives an argumentation‐based analysis of the manslaughter case using logical tools developed in AI. Prakken regards evidential argumentation as the construction and attack of 'trees of inference' from evidence to conclusions by applying generalizations. He argues that this approach clearly shows how evidence and hypotheses relate and what are the points of disagreement, but that it cannot give a clear overview over a case and lacks a systematic account of degrees of uncertainty. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to further study the structure, parameters and constructions of the recently introduced minimal codes in the sum‐rank metric. These objects form a bridge between the classical minimal codes in the Hamming metric, the subject of intense research over the past three decades partly because of their cryptographic properties, and the more recent rank‐metric minimal codes. We prove some bounds on their parameters, existence results, and, via a tool that we name geometric dual, we manage to construct minimal codes with few weights. A generalization of the celebrated Ashikhmin–Barg condition is proved and used to ensure the minimality of certain constructions. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a deep generalisation for income inequality indices. A generalised income inequality index that depends on two parameters and that involves a large set of income inequality indices in the same framework is proposed. The two parameters control the sensitivity of the generalised index to different levels of the income distribution. A thorough investigation of the generalised index paves the way for understanding the influence of the low, middle and high incomes on various income inequality indices and thereby facilitates the choice of multiple indices simultaneously for a better analysis of inequality as advocated by several recent studies. Moreover, two methods for estimating the generalised index in the case of finite populations are shown. A new method for estimating the inequality indices is proposed. [ABSTRACT FROM AUTHOR]

A new generalization of the constrained modified KP hierarchy is presented in this paper, that is, (Lk)≤0=q∂−1r∂+cL−1$$ {\left({L}^k\right)}_{\le 0}=q{\partial}^{-1}r\partial +c{L}^{-1} $$. Then two equivalent formulations of this new generalization are given. One is the bilinear equations in terms of the wave functions and tau functions, where the corresponding Hirota bilinear forms are discussed. Another is expressed by the constraints on the tau functions. [ABSTRACT FROM AUTHOR]

Coherent configurations are a generalization of association schemes. Motivated by the recent study of Q‐polynomial coherent configurations, in this paper, we study the spherical embedding of a Q‐polynomial coherent configuration into some eigenspace by a primitive idempotent. We present a necessary and sufficient condition when the embedding becomes a Euclidean t $t$‐design (on two concentric spheres) in terms of the Krein numbers for t≤4 $t\le 4$. In addition, we obtain some Euclidean 2‐ or 3‐designs from spherical embedding of coherent configurations including tight relative 4‐ or 5‐designs in binary Hamming schemes and the union of derived designs of a tight 4‐design in Hamming schemes. [ABSTRACT FROM AUTHOR]

Summary: Today, thanks to the major breakthrough of sequences to sequences model in the field of natural language, most of the dialogue generation tasks are focused on generating more effective responses. However, the responses proposed by the chat‐bot are only a passive answer or assentation, which does not arouse the desire of people to continue communicating. How to transform the chat robot from a passive reply to an active questioner has become an urgent problem. In this paper, a question generalization method with four types of question proposing schemes are designed, implemented, and tested to automate question generation process. The proposed system is controlled by a probability‐triggered multiple conversion mechanism to actively propose different types of questions. We embed our methods in the mainstream dialogue generation model and demonstrate its effectiveness in dialogue response generalization on a standard dataset. In addition, it achieves good performance in subjective conversational assessment. [ABSTRACT FROM AUTHOR]

Semantic segmentation is a classical problem in computer vision, which is important in the field of autonomous driving. Although significant progress has been achieved in semantic segmentation, its generalization ability to unknown domains is still challenging. To effectively solve this problem, a semantic segmentation method ImDeeplabV3plus with instance selective whitening loss is proposed in this paper. DeeplabV3plus is selected as the baseline. In order to enhance the representation of the region of interest, the coordinate attention (CA) mechanism is added. To better integrate multiple low‐level features, the adaptively spatial feature fusion (ASFF) is employed to adaptively learn the importance of features at different levels for each location. For preferably coping with the domain changes, an instance selective whitening (ISW) loss is introduced in the early stage of the backbone. The model is trained with the Cityscapes dataset and then applied to the unknown domain RobotCar dataset. Compared with DeeplabV3plus, the authors' ImDeeplabV3plus model shows 1.29% mIoU improvement. When ISW loss is added, 2.08% improvement in mIoU is achieved compared with ImDeeplabV3plus. Experimental results show that the proposed method is simple and improves the domain generalization ability. [ABSTRACT FROM AUTHOR]

Scene perception and trajectory forecasting are two fundamental challenges that are crucial to a safe and reliable autonomous driving (AD) system. However, most proposed methods aim at addressing one of the two challenges mentioned above with a single model. To tackle this dilemma, this paper proposes spatio‐temporal semantics and interaction graph aggregation for multi‐agent perception and trajectory forecasting (ST‐SIGMA), an efficient end‐to‐end method to jointly and accurately perceive the AD environment and forecast the trajectories of the surrounding traffic agents within a unified framework. ST‐SIGMA adopts a trident encoder–decoder architecture to learn scene semantics and agent interaction information on bird's‐eye view (BEV) maps simultaneously. Specifically, an iterative aggregation network is first employed as the scene semantic encoder (SSE) to learn diverse scene information. To preserve dynamic interactions of traffic agents, ST‐SIGMA further exploits a spatio‐temporal graph network as the graph interaction encoder. Meanwhile, a simple yet efficient feature fusion method to fuse semantic and interaction features into a unified feature space as the input to a novel hierarchical aggregation decoder for downstream prediction tasks is designed. Extensive experiments on the nuScenes data set have demonstrated that the proposed ST‐SIGMA achieves significant improvements compared to the state‐of‐the‐art (SOTA) methods in terms of scene perception and trajectory forecasting, respectively. Therefore, the proposed approach outperforms SOTA in terms of model generalisation and robustness and is therefore more feasible for deployment in real‐world AD scenarios. [ABSTRACT FROM AUTHOR]

Skeleton‐based human action recognition has emerged recently thanks to its compactness and robustness to appearance variations. Although impressive results have been obtained in recent years, the performance of skeleton‐based action recognition methods has to be improved to be deployed in real‐time applications. Recently, a lightweight network structure named Double‐feature Double‐motion Network (DD‐Net) has been proposed for the skeleton‐based human action recognition. With high speed, the DD‐Net achieves state‐of‐the‐art performance on hand and body actions. The DD‐Net could not distinguish actions if they have a weak connection with the global trajectories. However, the DD‐Net is suitable for human action recognition where actions strongly correlate to the global trajectories. In this paper, the authors propose TD‐Net, an improved version of the DD‐Net in which a new branch is added. The new branch takes the normalised coordinates of joints (NCJ) to enrich the spatial information. On five datasets for skeleton‐based human activity recognition that are MSR‐Action3D, CMDFall, JHMDB, FPHAB, and NTU RGB + D, the TD‐Net consistently obtains superior performance compared with the baseline model DD‐Net. The proposed method outperforms different state‐of‐the‐art methods, including both hand‐designed and deep learning‐based methods on four datasets (MSR‐Action3D, CMDFall, JHMDB, and FPHAB). Furthermore, the generalisation of the proposed method is confirmed through cross‐dataset evaluation. To illustrate the potential use of the model for real‐time human action recognition, the authors have deployed an application on an edge device. The experimental result shows that the application can process up to 40 fps for pose estimation using MediaPipe. It takes only 0.04 ms to recognise an action from skeleton sequences. [ABSTRACT FROM AUTHOR]

Exponentially weighted moving average (EWMA) charts and cumulative sum (CUSUM) control charts based on fitting a generalized linear model (GLM) to estimate the time‐varying mean of the process have been used for health surveillance due to its efficiency to detect soon small shifts in count data as morbidity or mortality rates. However, in these proposals, the serial correlation is usually omitted implying that the charts may fail. In this paper, generalized autoregressive moving average (GARMA) models that include lagged terms to model the autocorrelation are proposed to analyze the performance of regression EWMA control charts based on fitting of GLM models with negative binomial distribution for monitoring time series. The main contributions of the current paper are two new statistics based on the likelihood function to be monitored and three procedures to build one‐sided EWMA charts and to measure the impact on the performance of these EWMA charts when the serial correlation is neglected in the regression model. For the simulated scenarios, the statistics based on the likelihood and the winsorized EWMA presented the best performance. Also, a real data analysis detected outbreaks in the hospitalization time series due to respiratory diseases of elderly people in São Paulo city. [ABSTRACT FROM AUTHOR]

Orthogonal systems in L2(ℝ), once implemented in spectral methods, enjoy a number of important advantages if their differentiation matrix is skew‐symmetric and highly structured. Such systems, where the differentiation matrix is skew‐symmetric, tridiagonal, and irreducible, have been recently fully characterised. In this paper we go a step further, imposing the extra requirement of fast computation: specifically, that the first N coefficients of the expansion can be computed to high accuracy in ONlog2N operations. We consider two settings, one approximating a function f directly in (−∞, ∞) and the other approximating [f(x) + f(−x)]/2 and [f(x) − f(−x)]/2 separately in [0, ∞). In each setting we prove that there is a single family, parametrised by α, β > − 1, of orthogonal systems with a skew‐symmetric, tridiagonal, irreducible differentiation matrix and whose coefficients can be computed as Jacobi polynomial coefficients of a modified function. The four special cases where α, β = ± 1/2 are of particular interest, since coefficients can be computed using fast sine and cosine transforms. Banded, Toeplitz‐plus‐Hankel multiplication operators are also possible for representing variable coefficients in a spectral method. In Fourier space these orthogonal systems are related to an apparently new generalisation of the Carlitz polynomials. © 2020 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The article is an overview of thirteen volumes of "Journal of Management Studies" concerning management studies and organizational behavior articles. It states that any generalizations would be superficial at best and at worse misleading, and that quantitative analysis, business strategy, and financial analysis papers in the magazine often focus on perennial issues concerning management control and quantitative control systems. It mentioned other issues focused on include resource allocation, decision making, and marketing strategy.

This paper presents some new Lie groups preserving fixed subspaces of geometric algebras (or Clifford algebras) under the twisted adjoint representation. We consider the cases of subspaces of fixed grades and subspaces determined by the grade involution and the reversion. Some of the considered Lie groups can be interpreted as generalizations of Lipschitz groups and spin groups. The Lipschitz groups and the spin groups are subgroups of these Lie groups and coincide with them in the cases of small dimensions. We study the corresponding Lie algebras. [ABSTRACT FROM AUTHOR]

An embedded curve in a symplectic surface Σ⊂X$\Sigma \subset X$ defines a smooth deformation space B$\mathcal {B}$ of nearby embedded curves. A key idea of Kontsevich and Soibelman is to equip the symplectic surface X$X$ with a foliation in order to study the deformation space B$\mathcal {B}$. The foliation, together with a vector space VΣ$V_\Sigma$ of meromorphic differentials on Σ$\Sigma$, endows an embedded curve Σ$\Sigma$ with the structure of the initial data of topological recursion, which defines a collection of symmetric tensors on VΣ$V_\Sigma$. Kontsevich and Soibelman define an Airy structure on VΣ$V_\Sigma$ to be a formal quadratic Lagrangian L⊂T∗(VΣ∗)$\mathcal {L}\subset T^*(V_\Sigma ^*)$ which leads to an alternative construction of the tensors of topological recursion. In this paper, we produce a formal series θ$\theta$ on B$\mathcal {B}$ which takes it values in L$\mathcal {L}$, and use this to produce the Donagi–Markman cubic from a natural cubic tensor on VΣ$V_\Sigma$, giving a generalisation of a result of Baraglia and Huang. [ABSTRACT FROM AUTHOR]

This paper introduces a generalization of the well‐known Riccati recursion for solving the discrete‐time equality‐constrained linear quadratic optimal control problem. The recursion can be used to compute problem solutions as well as optimal feedback control policies. Unlike other tailored approaches for this problem class, the proposed method does not require restrictive regularity conditions on the problem. This allows its use in nonlinear optimal control problem solvers that use exact Lagrangian Hessian information. We demonstrate that our approach can be implemented in a highly efficient algorithm that scales linearly with the horizon length. Numerical tests show a significant speed‐up of about one order of magnitude with respect to state‐of‐the‐art general‐purpose sparse linear solvers. Based on the proposed approach, faster nonlinear optimal control problem solvers can be developed that are suitable for more complex applications or for implementations on low‐cost or low‐power computational platforms. The implementation of the proposed algorithm is made available as open‐source software. [ABSTRACT FROM AUTHOR]

In this paper, we introduce new notions of mean values based on ideas of multicriteria optimization. The distances between the current point to all points in the sample are regarded as elements of a vector estimate. Such vector estimates are usually scalarized, for example, by taking the sum of all components. In contrast, we introduce preference relations on the set of all such vectors, based on the information about the preferences of the decision maker who could be a statistician, analyst or researcher. Such preference relations reflect the distances between points, including the case in which all distances are equally important. We define the mean values as the points whose corresponding vector estimates are nondominated with respect to the defined preference relation, and investigate their properties. Such mean values turn out to be multi‐valued. We further explore the relationship between the new notions of mean values with their conventional definitions and suggest computational approaches to the calculation of the suggested new means. We also outline generalisations of the suggested approach to the case of multidimensional data. [ABSTRACT FROM AUTHOR]

Let G $G$ be a simple graph and λ(G) $\lambda (G)$ the spectral radius of G $G$. For k≥2 $k\ge 2$, a k $k$‐edge decomposition (H1,...,Hk) $({H}_{1},{\rm{\ldots }},{H}_{k})$ is k $k$ spanning subgraphs such that their edge sets form a k $k$‐partition of the edge set of G $G$. In this paper, we obtain some sharp lower and upper bounds for λ(H1)+⋯+λ(Hk) $\lambda ({H}_{1})+\,\cdots \,+\lambda ({H}_{k})$ in terms of the clique number of Hi ${H}_{i}$ and the size of G $G$, and discuss what k $k$‐edge decomposition (H1,...,Hk) $({H}_{1},{\rm{\ldots }},{H}_{k})$ can maximize λ(H1)+⋯+λ(Hk) $\lambda ({H}_{1})+\cdots \,+\lambda ({H}_{k})$ when G $G$ is a complete graph. These generalize some Nordhaus–Gaddum‐type results on spectral radius for k=2 $k=2$, due to Nosal, Hong and Shu, and Nikiforov. [ABSTRACT FROM AUTHOR]

We enter the details of two recent articles concerning as many chemotaxis models, one nonlinear and the other linear, and both with produced chemoattractant and saturated chemorepellent. More precisely, we are referring respectively to the papers "Boundedness in a nonlinear attraction‐repulsion Keller–Segel system with production and consumption," by S. Frassu, C. van der Mee and G. Viglialoro [J. Math. Anal. Appl.504(2):125428, 2021] and "Boundedness in a chemotaxis system with consumed chemoattractant and produced chemorepellent," by S. Frassu and G. Viglialoro [Nonlinear Anal.213:112505, 2021]. These works, when properly analyzed, leave open room for some improvement of their results. We generalize the outcomes of the mentioned articles, establish other statements, and put all the claims together; in particular, we select the sharpest ones and schematize them. Moreover, we complement our research also when logistic sources are considered in the overall study. [ABSTRACT FROM AUTHOR]

This study proposes an unsupervised, learning‐based, reconstructed scheme and a residual analysis‐based defect detection model for colour‐patterned fabric defect detection problems in the clothing process industry. It solves the challenging problems of existing supervised fabric defect detection methods, such as high costs in manually labelling samples and designing features, unstable generalisation ability and scarcity of defective samples. First, for a specific texture, the training set was constructed by collecting easily accessible defect‐free colour‐patterned fabric images. Second, a multi‐scale U‐shaped denoising convolutional autoencoder was modelled using defect‐free samples, which can reconstruct the newly tested colour‐patterned fabric images automatically. Subsequently, a residual map between the original image and corresponding reconstructed image was calculated. Finally, the defective areas were detected and accurately localised by further opening operations. The experimental results indicated that the proposed method is valid and robust for detecting defects in various colour‐patterned fabrics. Moreover, with the YDFID‐1 dataset, compared with other models, the intersection over union index of the model proposed in the current paper was improved by at least 3.95%. [ABSTRACT FROM AUTHOR]

For an algebraic number α$\alpha$ and γ∈R$\gamma \in \mathbb {R}$, let be the house, h(α)$h(\alpha)$ be the (logarithmic) Weil height, and hγ(α)=(degα)γh(α)$h_\gamma (\alpha)=(\deg \alpha)^\gamma h(\alpha)$ be the γ$\gamma$‐weighted (logarithmic) Weil height of α$\alpha$. Let f:Q¯→[0,∞)$f:\overline{\mathbb {Q}}\rightarrow [0,\infty)$ be a function on the algebraic numbers Q¯$\overline{\mathbb {Q}}$, and let S⊂Q¯$S\subset \overline{\mathbb {Q}}$. The Northcott number Nf(S)$\mathcal {N}_f(S)$ of S$S$, with respect to f$f$, is the infimum of all X⩾0$X\geqslant 0$ such that {α∈S;f(α)

Recently, many streaming algorithms have utilized generalizations of the fact that the expected maximum distance of any 4‐wise independent random walk on a line over n steps is O(n)$$ O\left(\sqrt{n}\right) $$. In this paper, we show that 4‐wise independence is required for all of these algorithms, by constructing a 3‐wise independent random walk with expected maximum distance Ω(nlgn)$$ \Omega \left(\sqrt{n}\lg n\right) $$ from the origin. We prove that this bound is tight for the first and second moment, and also extract a surprising matrix inequality from these results. Next, we consider a generalization where the steps Xi$$ {X}_i $$ are k‐wise independent random variables with bounded pth moments. We highlight the case k=4,p=2$$ k=4,p=2 $$: here, we prove that the second moment of the furthest distance traveled is O∑Xi2$$ O\left(\sum {X}_i^2\right) $$. This implies an asymptotically stronger statement than Kolmogorov's maximal inequality that requires only 4‐wise independent random variables, and generalizes a recent result of Błasiok. [ABSTRACT FROM AUTHOR]

In a recent paper O. Gannot and M. Wrochna considered the Klein–Gordon equation on an asymptotically anti‐de Sitter spacetime subject to Robin boundary conditions, proving in particular a propagation of singularities theorem. In this work we generalize their result considering a more general class of boundary conditions implemented on the conformal boundary via pseudodifferential operators of suitable order. Using techniques proper of b‐calculus and of twisted Sobolev spaces, we prove also for the case in hand a propagation of singularity theorem along generalized broken bicharacteristics, highlighting the potential presence of a contribution due to the pseudodifferential operator encoding the boundary condition. [ABSTRACT FROM AUTHOR]

Two proofs are given which show that if some set of truths fall under finitely many concepts (so‐called Collectivity), then they all fall under at least one of them even if we do not know which one. Examples are given in which the result seems paradoxical. The first proof crucially involves Moorean propositions while the second is a reconstruction and generalization of a proof due to Humberstone free from any reference to such propositions. We survey a few solution routes including Tennant‐style restriction strategies. It is concluded that accepting Collectivity for some set of truths while also denying that any of the involved concepts in isolation capture all of them requires that one of these concepts cannot be closed under conjunction elimination. This is surprising since the paper surveys several applications in which Collectivity and the latter closure condition seemed jointly satisfiable for concepts of actual philosophical interest. [ABSTRACT FROM AUTHOR]

In this paper, we are interested in parallels to the classical notions of special subsets in R defined in the generalized Cantor and Baire spaces (2κ and κκ). We consider generalizations of the well‐known classes of special subsets, like Lusin sets, strongly null sets, concentrated sets, perfectly meagre sets, σ‐sets, γ‐sets, sets with the Menger, the Rothberger, or the Hurewicz property, but also of some less‐know classes like X‐small sets, meagre additive sets, Ramsey null sets, Marczewski, Silver, Miller, and Laver‐null sets. We notice that many classical theorems regarding these classes can be relatively easy generalized to higher cardinals although sometimes with some additional assumptions. This paper serves as a catalogue of such results along with some other generalizations and open problems. [ABSTRACT FROM AUTHOR]

The paper by Joy et al. (2018) contains several generalizations and appears to have followed a flawed analytical technique. We seek clarifications from the authors on specific points to ensure the credibility of the conclusions drawn by them. [ABSTRACT FROM AUTHOR]