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This paper presents a new direct adaptive control (DAC) technique using Caputo's definition of the fractional-order derivative. This is the first time a fractional-order adaptive law is introduced to work together with an integer-order stable manifold for approximating the uncertainty of a class of nonlinear systems. The DAC approach uses universal function approximators such as multi-layer perceptrons with one hidden layer or fuzzy systems to approximate the controller. This paper presents a new lemma, which elucidates and clarifies the link between the Caputo and the Riemann–Liouville definitions. The introduced lemma is useful in developing a Lyapunov candidate to prove the stability of using the proposed fractional-order adaptive law. This is further explained by a numerical example, which is provided to elucidate the practicality of using the fractional-order derivative for updating the approximator parameters. The main novelty of the results in this paper is a rigorous stability proof of the fractional DAC approach for a class of nonlinear systems that is subjected to unstructured uncertainty and deals with the adaptation mechanism using a traditional integer-order stable manifold. This makes the control scheme easier to implement in practice. The fractional-order adaptation law provides greater degrees of freedom and a potentially larger functional control structure than the conventional adaptive control. Finally, the paper demonstrates that traditional integer-order DAC is a special case of the more general fractional-order DAC scheme introduced here. [ABSTRACT FROM AUTHOR]

The diverse shapes and scales, complicated backgrounds, blurred boundaries, and similar appearances challenge the current organ segmentation methods in medical scene images. It is difficult to acquire satisfactory performance to directly extend the object segmentation methods in the natural scene images to the medical scene images. In this paper, we propose a discriminant feature pyramid (DFPNet) network for organ segmentation in the original medical images, which consists of two sub-networks: the feature steered network and the border network. To be specific, the feature steered network takes a top-down step-wise manner to extract abundant context information, which is conducive to suppressing the cluttered background and perceiving the scale variation of objects. The border network utilizes a bottom-up step-wise manner to optimize the boundary feature map, which aims at distinguishing adjacent edge features with similar appearances but diverse labels. A series of experiments were conducted on three publicly available medical datasets (i.e., LUNA 16, RIM-ONE-R1, and VNC datasets) to evaluate the validity and generalization of the proposed DFPNet. Experimental results indicate that our network achieves superior performance in terms of the receiver operating characteristic (ROC) curve, F-Score, Jaccard index, and Hausdorff distance. The code will be available at: https://github.com/Xie-Xiwang/DFPNet. [ABSTRACT FROM AUTHOR]

Preferential Attachment (PA) models the scientific citation process. In the PA model, a new paper attaches itself to the citation network based only on the popularity of the currently existing papers. This invariably leads to a network whose degree distribution satisfies the Power Law. Yet, empirical results show that paper age should also play a role in the citation process. In other words, when references are chosen for a new paper, the age of an existing paper may also affect the choice for citing. In this paper, we derive a generalized PA model that includes the effect of aging, with analytical solution. Such a model can be used to analyze the competing influence of preferential attachment and aging effect quantitatively in citation process and explain differences in various research domains by the extent of aging. It may also serve as a general model of network formation. [ABSTRACT FROM AUTHOR]

Deepfake videos present a significant challenge in the current media landscape. While current deepfake detection methods demonstrate satisfactory performance, there is still room for improvement in their ability to generalize and detect unseen scenarios, particularly those involving imperceptible cues. This paper introduces a novel multi-modal deepfake detection model named SpectraVisionFusion Transformer (SVFT), which incorporates spatial and frequency domain statistical artifacts to improve generalization performance. The SVFT framework uses two different backbone encoder models to take advantage of both spatial and frequency domain cues in video sequences, along with a decoder and classifier, for common cross-attention and classification, respectively. The spatial domain branch uses a convolutional transformer-based encoder to analyze facial visual features. In contrast, the frequency domain branch employs a language transformer encoder. Additionally, we introduce a weighted feature embedding fusion mechanism that integrates spectral-based statistical feature embeddings and visual cues to achieve a more comprehensive and balanced spatial-frequency feature representation. By coordinately analyzing these modalities, our model exhibits improved detection and generalization capabilities in unseen scenarios. Our proposed SVFT model achieved 92.57% and 80.63% accuracy in extensive cross-manipulation and dataset evaluation, respectively, while surpassing the performance of traditional and single-domain-based approaches. [ABSTRACT FROM AUTHOR]

The concept of the Constituency Development Fund (CDF) has been appreciated as presenting an alternative vehicle for driving community development in countries in the developing world. As a relatively recent development, existing evaluation studies on the effectiveness of the vehicle are still emerging and are insufficient for conclusive generalisations to be made. This paper attempts to analyse some of the experiences of implementing the CDF idea in one of Zimbabwe's 210 constituencies, the Mufakose Constituency. Data were gathered using documentary reviews and in-depth interviews with key informants and beneficiaries of development projects in the Mufakose communities. Findings of the study show that the CDF initiative in Mufakose has so far produced rather mixed results, where good project ideas have been executed over time but have often been let down by resource limitations and limited citizen cooperation stemming from a negative political perception of the whole initiative. The paper concludes that success with the CDF idea depends so much on the trade-offs between the roles of key stakeholders and local contextual variables, which can either facilitate or inhibit development efforts. To produce good results and worthwhile outcomes, the central government should increase the financial allocation to constituencies. Additionally, there is a need for an improvement in the citizen participation culture to increase the chances of harnessing the merits of bottom-up developmental approaches. [ABSTRACT FROM AUTHOR]

The formalism of Simple Temporal Networks (STNs) provides methods for evaluating the feasibility of temporal plans. The basic formalism deals with the consistency of quantitative temporal requirements on scheduled events. This implicitly assumes a single agent has full control over the timing of events. The extension of Simple Temporal Networks with Uncertainty (STNU) introduces uncertainty into the timing of some events. Two main approaches to the feasibility of STNUs involve (1) where a single schedule works irrespective of the duration outcomes, called Strong Controllability, and (2) whether a strategy exists to schedule future events based on the outcomes of past events, called Dynamic Controllability. Case (1) essentially assumes the timing of uncertain events cannot be observed by the agent while case (2) assumes full observability. The formalism of Partially Observable Simple Temporal Networks with Uncertainty (POSTNU) provides an intermediate stance between these two extremes, where a known subset of the uncertain events can be observed when they occur. A sound and complete polynomial algorithm to determining the Dynamic Controllability of POSTNUs has not previously been known; we present one in this paper. This answers an open problem that has been posed in the literature. The approach we take factors the problem into Strong Controllability micro-problems in an overall Dynamic Controllability macro-problem framework. It generalizes the notion of labeled distance graph from STNUs. The generalized labels are expressed as max/min expressions involving the observables. The paper introduces sound generalized reduction rules that act on the generalized labels. These incorporate tightenings based on observability that preserve dynamic viable strategies. It is shown that if the generalized reduction rules reach quiescence without exposing an inconsistency, then the POSTNU is Dynamically Controllable (DC). The paper also presents algorithms that apply the reduction rules in an organized way and reach quiescence in a polynomial number of steps if the POSTNU is Dynamically Controllable. Remarkably, the generalized perspective leads to a simpler and more uniform framework that applies also to the STNU special case. It helps illuminate the previous methods inasmuch as the max/min label representation is more semantically clear than the ad-hoc upper/lower case labels previously used. [ABSTRACT FROM AUTHOR]

This paper presents new methods for analyzing and evaluating generalized plans that can solve broad classes of related planning problems. Although synthesis and learning of generalized plans has been a longstanding goal in AI, it remains challenging due to fundamental gaps in methods for analyzing the scope and utility of a given generalized plan. This paper addresses these gaps by developing a new conceptual framework along with proof techniques and algorithmic processes for assessing termination and goal-reachability related properties of generalized plans. We build upon classic results from graph theory to decompose generalized plans into smaller components that are then used to derive hierarchical termination arguments. These methods can be used to determine the utility of a given generalized plan, as well as to guide the synthesis and learning processes for generalized plans. We present theoretical as well as empirical results illustrating the scope of this new approach. Our analysis shows that this approach significantly extends the class of generalized plans that can be assessed automatically, thereby reducing barriers in the synthesis and learning of reliable generalized plans. [ABSTRACT FROM AUTHOR]

Semantic correspondence and intention can be used as distinctive criteria for the four kinds of semantic information: intentional information, unintentional information, disinformation, and misinformation (4I). In order to seriously tackle standard problems included in the discussion on the kinds of semantic information, e.g., the truthfulness or the misleadingness of information, further elaboration of its possible applications is needed. The paper identifies three basic phases of the simple communication model in which semantic correspondence and intention find such an application. Based on these findings, the paper focuses on the generalization of the suggested 4I model into a form that is more suitable for its utilization in the standard contexts of the philosophy of information. [ABSTRACT FROM AUTHOR]

The paper discusses the statistical inference problem of the compound Poisson vector process (CPVP) in the domain of attraction of normal law but with infinite covariance matrix. The empirical likelihood (EL) method to construct confidence regions for the mean vector has been proposed. It is a generalization from the finite second-order moments to the infinite second-order moments in the domain of attraction of normal law. The log-empirical likelihood ratio statistic for the average number of the CPVP converges to F distribution in distribution when the population is in the domain of attraction of normal law but has infinite covariance matrix. Some simulation results are proposed to illustrate the method of the paper. [ABSTRACT FROM AUTHOR]

Black fungus is a rare and dangerous mycology that usually affects the brain and lungs and could be life-threatening in diabetic cases. Recently, some COVID-19 survivors, especially those with co-morbid diseases, have been susceptible to black fungus. Therefore, recovered COVID-19 patients should seek medical support when they notice mucormycosis symptoms. This paper proposes a novel ensemble deep-learning model that includes three pre-trained models: reset (50), VGG (19), and Inception. Our approach is medically intuitive and efficient compared to the traditional deep learning models. An image dataset was aggregated from various resources and divided into two classes: a black fungus class and a skin infection class. To the best of our knowledge, our study is the first that is concerned with building black fungus detection models based on deep learning algorithms. The proposed approach can significantly improve the performance of the classification task and increase the generalization ability of such a binary classification task. According to the reported results, it has empirically achieved a sensitivity value of 0.9907, a specificity value of 0.9938, a precision value of 0.9938, and a negative predictive value of 0.9907. [ABSTRACT FROM AUTHOR]

Vector Fitting (VF) is a successful methodology for both time and frequency domain estimation of dynamic systems. These algorithms have gained popularity as a system identification tool due to their ability to provide a practical procedure for accurately approximating the measured frequency response behavior of complex and highly resonant dynamic systems using rational models. Instrumental Variable (IV) techniques can also be added to the VF algorithm to enhance the optimality of the solutions in the presence of measurement noise. In this context, this paper presents a multiple-input multiple-output (MIMO) generalization of instrumental variable (IV-)VF. The proposed algorithm can be applied to accurately estimating models formed by either continuous- or discrete-time rational basis functions (RBFs). We show that such a generalization preserves the main implementation features observed in Standard VF, but with improved optimality property for its solutions. Additionally, in order to alleviate convergence problems that may arise from the fixed high-frequency normalization, we also present in this paper how to incorporate in the proposed IV-VF method the relaxed formulation found in standard VF for both the continuous and discrete-time cases. The resulting MIMO IV-VF algorithm can be easily understood by VF users, and its applicability is discussed by using an estimation case study with two power system pieces of equipment (an 88 kV Transformer and a 550 kV Instrument Transformer) for which actual frequency domain measurements were collected in the field. It is shown that the proposed algorithm significantly improves the modeling accuracy when compared to standard VF algorithms. • Advances in optimal rational approximation of systems using frequency response data. • A MIMO generalization of the Instrumental Variables Vector Fitting method is proposed. • Such a generalization can be used for further refining parameter estimates. • Convergence problems are mitigated by relaxing the proposed method. • Practical validation is performed by fitting actual admittance data of HV transformers. [ABSTRACT FROM AUTHOR]

The paper investigates possible forms of explanatory monism for the cases of non-causal explanations (primarily Reutlinger 2018; Woodward 2018). In the conceptual analysis, the advantages and weaknesses of the counterfactual view of explanation are examined. Although this conception of explanation provides a common explanatory framework, it cannot sufficiently take into account the specificity of individual nomic generalizations and, in the non-causal case, it is difficult to construct a non-interventionist form of counterfactual. Therefore, the paper offers a return to the unificacionist view of explanation (primarily Kitcher 1981), which is a type of explanatory monism, does not suffer from the mentioned problems, and also offers a solution to the problem of asymmetry of noncausal explanations. [ABSTRACT FROM AUTHOR]

This paper discusses a generalization of logical expressivism. It is shown that, in the wide sense defined here, the expressivist approach is neutral with respect to different theories of inference and offers a natural framework for understanding logical forms and their function. An expressivist strategy for explaining the development of logical forms is then applied to the analysis of Frege’s Begriffsschrift, Gentzen’s sequent calculus and Belnap’s display logic. [ABSTRACT FROM AUTHOR]

The core principle of Variational Inference (VI) is to convert the statistical inference problem of computing complex posterior probability densities into a tractable optimization problem. This property enables VI to be faster than several sampling-based techniques. However, the traditional VI algorithm is not scalable to large data sets and is unable to readily infer out-of-bounds data points without re-running the optimization process. Recent developments in the field, like stochastic-, black box-, and amortized-VI, have helped address these issues. Generative modeling tasks nowadays widely make use of amortized VI for its efficiency and scalability, as it utilizes a parameterized function to learn the approximate posterior density parameters. In this paper, we review the mathematical foundations of various VI techniques to form the basis for understanding amortized VI. Additionally, we provide an overview of the recent trends that address several issues of amortized VI, such as the amortization gap, generalization issues, inconsistent representation learning, and posterior collapse. Finally, we analyze alternate divergence measures that improve VI optimization. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Despite the considerable advances in deep learning for object recognition, there are still several factors that hinder the performance of deep learning models. One of these factors is domain shift, which occurs due to variations in the distribution of the testing and training data. This paper addresses the issue of compact feature clustering in domain generalization, with the aim of optimizing the embedding space from multi-domain data. Specifically, we propose a domain-aware triplet loss for domain generalization, which not only facilitates clustering of similar semantic features but also disperses features that arise from the domain. Unlike previous methods that focus on aligning distributions, our algorithm disperses domain information in the embedding space. Our approach is based on the assumption that embedding features can be clustered based on domain information, which is supported mathematically and empirically in this paper. Furthermore, in our investigation of feature clustering in domain generalization, we observe that the factors that influence the convergence of metric learning loss in domain generalization are more significant than the pre-defined domains. To address this issue, we utilize two methods to normalize the embedding space and reduce the internal covariate shift of the embedding features. Our ablation study illustrates the effectiveness of our algorithm. Additionally, our experiments on benchmark datasets, including PACS, VLCS, and Office-Home, demonstrate that our method outperforms related approaches that focus on domain discrepancy. Notably, our results on RegnetY-16GF are substantially better than state-of-the-art methods on the benchmark datasets. Our code is available at https://github.com/workerbcd/DCT. • We find that the feature embeddings may be clustered due to the domain gaps. • We visualize this domain clustering phenomenon and support it theoretically. • We propose a domain-aware triplet (D-Triplet) loss to generalize the model • D-Triplet loss can efficiently mitigate the domain-cluster issue on feature level. • We utilize various normalization techniques to prompt the convergence of D-Triplet. • The experimental results validate the effectiveness of our method. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper analyses the process of generalisation of an innovative government-led public practice in the healthcare sector. The scaling and embedding involved in this generalisation process are assumed to be dependent on the multiple implementation processes (consecutive or simultaneous) that lead to a routine use of the innovation in different adopters. This paper, therefore, proposes the use of a configurational theory approach to conceptualise each implementation of the innovation during the generalisation process and shed light on the generalisation's scaling and embedding efforts. It suggests a set of recommendations and practices for generalisation managers, most notably: i) they should regard generalisations as organic processes where their main role is to create space for experimentation, learning and negotiation, and ii) they should adopt different modes of governance to identify adequate mechanisms and strategies and guide their actions. This configurational perspective allows them to monitor and manage the evolution of implementations, informs the valuable learning processes that take place in a generalisation and has been found to be a useful tool to support the crucial collaboration among the actors involved in a generalisation. • Configurational model for innovation implementations in generalisations • Model useful for managing scaling and embedding of government-led public practices • Configurational perspective informs valuable learning processes in generalisations. • Tool useful to support managerial actions to promote generalisation • Framing adequate to leverage the use of flexibility within socio-technical systems [ABSTRACT FROM AUTHOR]

The present paper offers the rule of existential generalisation (EG) that is uniformly applicable within extensional, intensional and hyperintensional contexts. In contradistinction to Quine and his followers, quantification into various modal contexts and some belief attitudes is possible without obstacles. The hyperintensional logic deployed in this paper incorporates explicit substitution and so the rule (EG) is fully specified inside the logic. The logic is equipped with a natural deduction system within which (EG) is derived from its rules for the existential quantifier, substitution and functional application. This shows that (EG) is not primitive, as often assumed even in advanced writings on natural deduction. Arguments involving existential generalisation are shown to be valid if the sequents containing their premises and conclusions are derivable using the rule (EG). The invalidity of arguments seemingly employing (EG) is explained with recourse to the definition of substitution. [ABSTRACT FROM AUTHOR]

Copyright of Paradigma is the property of Universidad Pedagogica Experimental Libertador and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fuzzy sets have undergone several expansions and generalisations in the literature, including Atanasov's intuitionistic fuzzy sets, type 2 fuzzy sets, and fuzzy multisets, to name a few. They can be regarded as fuzzy multisets from a formal standpoint; nevertheless, their interpretation differs from the two other approaches to fuzzy multisets that are currently available. Hesitating fuzzy sets (HFS) are very useful if consultants have hesitation in dealing with group decision-making problems between several possible memberships. However, these possible memberships can be not only crisp values in [0,1], but also interval values during a practical evaluation process. Hesitant bipolar valued fuzzy set (HBVFS) is a generalization of HFS. This paper aims to introduce a general framework of multi-attribute group decision-making using social network. We propose two types of decision-making processes: Type-1 decision-making process and Type-2 decision-making process. In the Type-1 decision-making process, the experts' original opinion is proces for the final ranking of alternatives. In Type-2 decision making processs, there are two major aspects we consider. First, consistency tests and checking of consensus models are given for detecting that the judgments are logically rational. Otherwise, the framework demands (partial) decision-makers to review their assessments. Second, the coherence and consensus of several HBVFSs are established for final ranking of alternatives. The proposed framework is clarified by an example of software packages selection of a university. [ABSTRACT FROM AUTHOR]

In this paper, we introduce new classes of nonsmooth second-order cone-convex functions and respective generalizations in terms of first and second-order directional derivative. These classes encapsulate several already existing classes of cone-convex functions and their weaker variants. Second-order KKT type sufficient optimality conditions and duality results for a nonsmooth vector optimization problem are proved using these functions. The results have been supported by examples. [ABSTRACT FROM AUTHOR]

This paper aims to explore the generalization of address terms in online discourse, a largely unheeded pragmatic phenomenon. Taking the generalized Chinese kinship term "son" (érzi) as an example, it analyzes its referents and functions. The analysis was based on a sizable data set collected from WeChat, and interviews with some WeChat users. It demonstrates that the address term "son" (érzi) conveys its faithful meaning when referring to the male child of (a) parent(s) but virtual meaning when referring to the addresser's friends, classmates or pets. It is also argued that the generalized use of the address term "son" (érzi) can function to enhance relationships, make jocular abuse, and express emotions. These functions suggest the users' identity avoidance and relating needs in a virtual community. This study attempts to contribute to a better understanding of the virtualization of address terms and rapport management in online discourse. [ABSTRACT FROM AUTHOR]

This paper aims to explore the generalization of address terms in online discourse, a largely unheeded pragmatic phenomenon. Taking the generalized Chinese kinship term "son" (érzi) as an example, it analyzes its referents and functions. The analysis was based on a sizable data set collected from WeChat, and interviews with some WeChat users. It demonstrates that the address term "son" (érzi) conveys its faithful meaning when referring to the male child of (a) parent(s) but virtual meaning when referring to the addresser's friends, classmates or pets. It is also argued that the generalized use of the address term "son" (érzi) can function to enhance relationships, make jocular abuse, and express emotions. These functions suggest the users' identity avoidance and relating needs in a virtual community. This study attempts to contribute to a better understanding of the virtualization of address terms and rapport management in online discourse. [ABSTRACT FROM AUTHOR]

Anonymization technique has been extensively studied and widely applied for privacy-preserving data publishing. In most previous approaches, a microdata table consists of three categories of attributes, namely explicit-identifier, quasi-identifier (QI), and sensitive attribute. In general, individuals may have different views on the sensitivity of different attributes. Therefore, there is another type of attribute that contains both QI values and sensitive values, termed semi-sensitive attribute. In this paper, we propose a new anonymization technique, called Local Generalization and Bucketization, to prevent identity disclosure and protect the sensitive values on each semi-sensitive attribute and sensitive attribute. The rationale is to use local generalization and local bucketization to divide the tuples into local equivalence groups and partition the sensitive values into local buckets, respectively. The protections of local generalization and local bucketization are independent, so that they can be implemented by appropriate algorithms without weakening other protection. Besides, the protection of local bucketization for each semi-sensitive attribute and sensitive attribute is also independent. Consequently, local bucketization can comply with various principles in different attributes according to the actual requirements of anonymization. We conducted extensive experiments to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Copyright of Revista FAMECOS - Mídia, Cultura e Tecnologia is the property of EDIPUCRS - Editora Universitaria da PUCRS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A centralizing monoidM is a set of unary operations which commute with some set F of operations. Here, F is called a witness of M. On a 3-element set, a centralizing monoid is maximal if and only if it has a constant operation or a majority minimal operation as its witness. In this paper, we take one such majority operation, which corresponds to a maximal centralizing monoid, on a 3-element set and obtain its generalization, called mb, on a k-element set for any k = 3. We explicitly describe the centralizing monoid M(mb) with mb as its witness and then prove that it is not maximal if k > 3, contrary to the case for k = 3. [ABSTRACT FROM AUTHOR]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Web-based applications are prone to many web security attacks because they are openly-accessible and convenient. Most techniques used to prevent web attacks have some limitations; they cannot detect zero-day attacks and cannot analyze complex attacks, and should be maintained and updated regularly by security experts. Recently, there have been more research work on using deep learning for detecting web intrusions. Moreover, since most high risk web attacks are injected into HTTP web requests, detecting most web attacks needs classifying HTTP web requests into normal and anomalous. In this paper, we propose an approach based on Word2vec embedding and a stacked generalization ensemble model for LSTMs to detect malicious HTTP web requests. We evaluate our classification model performance using the HTTP CSIC 2010 dataset. We show that the combination of different word-level embeddings in a stacked generalization ensemble model for LSTMs has good performance both in terms of classification metrics and training time. [ABSTRACT FROM AUTHOR]

Retrieval of mathematical information from scientific documents is one of the crucial tasks. Numerous Mathematical Information Retrieval (MIR) systems have been developed, which mainly focus on the improvement over the indexing and the searching mechanism, the poor results obtained for evaluation measures depict major limitations of such systems. These enhance the scope of improvement and new innovations through the inclusion of functionalities, which can resolve the challenges of MIR system. Further, to improve the performance of the MIR systems, this paper proposed a formula embedding and generalization approach with the context, in addition to innovative relevance measurement technique. In this approach, documents are preprocessed by the document preprocessor module and extracted the formulas in Presentation MathML format with their context. The formula embedding and generalization modules of the proposed approach formed the binary vectors where '1' represents the presence, and '0' represents the absence of a particular entity in a formula, and subsequently, the vectors of formulas with context are indexed by the indexer. The innovative relevance measurement technique of the proposed approach ranked those documents first, which are retrieved by both formula embedding and generalization modules as compared to the individual one. The proposed approach has been tested on the MathTagArticles of Wikipedia of NTCIR-12, and the obtained results verify the significance of the context of the formula and the dissimilarity factor in the retrieval of mathematical information. [ABSTRACT FROM AUTHOR]

In this paper, at a qualitative level, we mean the concept as an abstraction based on the characteristics of the perceived reality. In analytic activity, a concept is considered to be some label imposed on a phenomenon, which forces us to connect individual observations and perform generalizations. For convenience, it is identified with the name given to observations and events. Each concept is associated with some kind of its properties. A variable is often understood as a measured concept as a property that is associated with the concept and changes when measured. If the property does not change, then it is considered a constant. We proceed from the fact that concepts should have a mechanism for change. Under these assumptions, a two-parameter commutative diagram is constructed that connects possible worlds and property transformations. The resulting semantic map sets the framework for their variation. Thus, the regulation of the variability of the associated structure of variable concepts is achieved, which determines the possible relationships of the displaced concepts. [ABSTRACT FROM AUTHOR]

Image classification, especially unbalanced image classification, holds considerable promise for practical applications. Existing research focuses mainly on enhancing the effectiveness of classifiers through approaches such as data resampling and loss function adjustments. To date, most of the approaches address the class unbalanced issue by transforming unbalanced data distributions into balanced [id=SecondEdit]ones. Consequently, a critical challenge is to directly develop high-performance classifiers that are adaptive to diverse unbalanced data distributions. In this paper, for unbalanced image classification tasks, we propose a Reinforcement Learning Fine-tuning approach to Unbalanced image Classification (RLF-UC). Specifically, we train classification pretraining models on [d=FirstEdit]fivethree unbalanced datasetsid=SecondEdit], and train [d=FirstEdit]the correspondinga reward function model designed to optimize fine-tuning policy. Then, we train a reinforcement learning fine-tuning classification model and optimize its policy to maximize the cumulative expected reward. Finally, we guide the model to prioritize minority category knowledge and incorporate distribution distance constraints, which are derived from the disparities between the [d=SecondEdit]pre-trainedpretrained model and the fine-tuning model to dynamically adjust the fine-tuning classifier model. Experimental results on [d=FirstEdit]fivethree reprocessed unbalanced image datasets demonstrate that our RLF-UC method provides comparable or better classification and generalization capabilities than other baselines. • Implemented the unbalanced image classification task using reinforcement learning fine-tuning method. • This method relies on the reward function model to maximize cumulative rewards. • The fine-tuning process should control the distribution distance between the pre-trained model and the fine-tuning model. [ABSTRACT FROM AUTHOR]

Single image dehazing is a fundamental but challenging task in image processing. Various deep learning-based methods have achieved great dehazing performance. However, there are still hazy residues, even color distortion and texture loss when removing haze from complex outdoor images in dense hazy scenes. A densely integrated multi-branch attentive net for image dehazing is proposed in the paper to address the above problems. The network includes a multi-scale feature extraction module and a dense-feature fusion module. The multi-scale feature extraction module adopts a multi-branch structure composed of residual channel attention blocks, which can expand the receptive field and filter the extracted features of diverse scales by weighting for fusion. It raises network learning accuracy. The dense-feature fusion module contains a multi-level feature fusion module for front and back layers, a color information renovation module, and a feature enhancement module. It achieves dynamically adjusting the channel weights of features at diverse scales, learning rich context information and suppressing redundant information, and compensating for the lack of color and texture information. The dense-feature fusion module bolsters the generalization capability of the network. The proposed method achieves superior objective and subjective evaluation results via quantitative and qualitative experiments on synthetic hazy images and natural hazy images, with better generalization ability and dehazing effect than the current SOTA dehazing algorithms, and effectively ameliorates the color distortion and incomplete dehazing. [ABSTRACT FROM AUTHOR]

Generalizing to out-of-distribution (OOD) data is a challenging task for existing deep learning approaches. This problem largely comes from the common but often incorrect assumption of statistical learning algorithms that the source and target data come from the same i.i.d. distribution. To tackle the limited variability of domains available during training, as well as domain shifts at test time, numerous approaches for domain generalization have focused on generating samples from new domains. Recent studies on this topic suggest that feature statistics from instances of different domains can be mixed to simulate synthesized images from a novel domain. While this simple idea achieves state-of-art results on various domain generalization benchmarks, it ignores structural information which is key to transferring knowledge across different domains. In this paper, we leverage the ability of humans to recognize objects using solely their structural information (prominent region contours) to design a Structural-Aware Feature Stylization method for domain generalization. Our method improves feature stylization based on mixing instance statistics by enforcing structural consistency across the different style-augmented samples. This is achieved via a multi-task learning model which classifies original and augmented images while also reconstructing their edges in a secondary task. The edge reconstruction task helps the network preserve image structure during feature stylization, while also acting as a regularizer for the classification task. Through quantitative comparisons, we verify the effectiveness of our method upon existing state-of-the-art methods on PACS, VLCS, OfficeHome, DomainNet and Digits-DG. The implementation is available at this repository. • Feature stylization alone led to losing structural domain-invariant information. • Image contours contain important domain-agnostic structural information. • Enforcing structural consistency while stylization boost model's generalization [ABSTRACT FROM AUTHOR]

Numerous studies focus on diagnosing heart diseases using deep learning methods applied to 12-lead electrocardiographic (ECG) records. However, these studies often face limitations due to reliance on specific datasets, which vary in size and parameters such as patient metadata, the number of doctors annotating ECGs, types of devices used for ECG recording, data preprocessing techniques, etc. It is well-known that high-quality deep neural networks trained on one ECG dataset may not necessarily perform well on other datasets or in different clinical settings. In this paper, we propose a methodology designed to enhance the quality of heart disease prediction regardless of the dataset. We achieve this by first training neural networks on a variety of labeled datasets, then fine-tuning for specific datasets, significantly improving prediction accuracy. We demonstrate its applicability by training various neural networks on a large private dataset TIS, which contains a wide range of ECG records from multiple hospitals, and on a relatively smaller public dataset, PTB-XL. Our results show that networks trained on a large dataset improves classification performance. Furthermore, these networks fine-tuned on PTB-XL outperform those trained exclusively on smaller datasets. Additionally, we made the weights of our pre-trained models publicly available, enabling researchers and clinicians to adapt these models to their specific datasets. • Models trained on distinct ECG records may notably differ in quality across datasets. • Pre-training models on various ECG datasets improves the classification performance. • Fine-tuning networks on smaller ECG datasets significantly improves performance. • Researchers can use pre-trained model weights to adapt them for their tasks and data. [ABSTRACT FROM AUTHOR]

Knowledge distillation is one of the most persuasive approaches to model compression that transfers the representational expertise from large deep-learning teacher models to a small student network. Although numerous techniques have been proposed to improve teacher representations at the logits level, no study has examined the weaknesses in the representations of the teacher at the feature level during distillation. On the other hand, in a trained deep-learning model, all the kernels are not uniformly activated to make specific predictions. Transferring this knowledge may result in a student learning a suboptimal intrinsic distribution and restrict the existing distillation methods from exploiting their highest potential. Motivated by these issues, this study analyses the generalization capability of teachers with or without a uniformly activated channel distribution. Preliminary investigations and theoretical analyses show that partly uniforming or smoothing feature maps offer improved representation that enriches the generalization capability. Based on these observations, it is hypothesized that distillation-based explicit supervision using smoothed feature maps and cross-entropy loss plays a significant role in improving generalization. Hence, this paper proposes a novel technique called P artly U nified R ecalibrated F eature (PURF) map distillation. The proposed method recalibrates the feature maps by intercommunicating the representational cues among nearest-neighbor channels. PURF increases the performance of state-of-the-art knowledge distillation (KD) methods across architectures by improving generalization, model compression, few-shot training, transferability, and robustness transfer on standard benchmark datasets. PURF achieves 1.51% average accuracy improvements on seven diverse architectures in image classification tasks. PURF increases the performance of the state-of-the-art knowledge distillation methods by an average accuracy of 1.91% across architectures. Moreover, PURF achieves an average of 2.02%, and 0.96% higher accuracy in transferability and robustness tasks, respectively, on standard benchmark datasets. • A novel technique, PURF, helps SOTA-KD methods maximize their potential. • Generalization technique without decreasing training performance. • Improves the performance of knowledge distillation tasks. • A generalization method by imposing explicit feature-level smoothness constraints. • An extensive analysis of feature-level smoothness as a regularization technique. • Can be employed as the top-head unit for the existing distillation techniques. [ABSTRACT FROM AUTHOR]

Making decisions in complex environments is a key challenge in artificial intelligence (AI). Situations involving multiple decision makers are particularly complex, leading to computational intractability of principled solution methods. A body of work in AI has tried to mitigate this problem by trying to distill interaction to its essence: how does the policy of one agent influence another agent? If we can find more compact representations of such influence, this can help us deal with the complexity, for instance by searching the space of influences rather than the space of policies. However, so far these notions of influence have been restricted in their applicability to special cases of interaction. In this paper we formalize influence-based abstraction (IBA), which facilitates the elimination of latent state factors without any loss in value, for a very general class of problems described as factored partially observable stochastic games (fPOSGs). On the one hand, this generalizes existing descriptions of influence, and thus can serve as the foundation for improvements in scalability and other insights in decision making in complex multiagent settings. On the other hand, since the presence of other agents can be seen as a generalization of single agent settings, our formulation of IBA also provides a sufficient statistic for decision making under abstraction for a single agent. We also give a detailed discussion of the relations to such previous works, identifying new insights and interpretations of these approaches. In these ways, this paper deepens our understanding of abstraction in a wide range of sequential decision making settings, providing the basis for new approaches and algorithms for a large class of problems. [ABSTRACT FROM AUTHOR]

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Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the present paper we study 3-dimensional C12-manifolds admitting Ricci solitons and generalized Ricci solitons and then we introduce a new generalization of η-Ricci soliton. We give a class of examples. [ABSTRACT FROM AUTHOR]

In this paper, the Peukert's equation was studied experimentally and theoretically at various discharge currents for lithium-ion cells. The classical Peukert's equation is not applicable at small discharge currents as according to this equation, at the discharge current decrease, the capacity released by the cells tends to infinity. The generalized Peukert's equation C = Cm /(1+(i/i0)n) corresponds well to the experimental data obtained at small and middle discharge currents. However at high discharge currents, the capacity released by the lithium-ion cells drops much faster than it is predicted by the known Peukert's equation generalizations. In this paper both experimentally and theoretically, it is proved that the reason of the sharp decrease of the cells capacity at high discharge currents is the voltage drop at cells internal resistance. Based on the obtained results, the following equation was proposed: C = Cm (1−i/i1)/((1−i/i1)+in/i0n). This equation corresponds well to the experimental data at any discharge currents, as it takes into consideration the voltage drop, caused by the cell internal resistance. [ABSTRACT FROM AUTHOR]

It is often said that surveillance has massively transformed our social lives (Lyon, Haggerty, and Ball 2012: 1), but this claim is weakened by the admission that its "effects are difficult to isolate or observe, as they are embedded within many normal aspects of daily life." Picking up this analytic challenge, this paper investigates the everyday interactional practice and experience of being surveilled. To do so, it draws on Goffman's account of the interaction order, dwelling closely on unfocused interaction in which people maintain a side-of-the-eye, half-an-ear awareness of the people, objects, and events in the space around them. After introducing key concepts from Goffman, the paper discusses three scenes of surveillance: a woman walking down a city street, two men putting up street stickers (a civil offence), and passengers being scanned at an airport (Pütz 2012). It shows how different senses of potential threat and illegality enter the experience of surveillance, and it builds a rudimentary model. This paper considers only a tiny fraction of contemporary surveillance, but it shows Goffman's value as an analytic resource that can hold large-scale generalisations about the surveillance society to account, allowing us to see agentive responses to surveillance that are too subtle to be captured by notions like subversion and resistance. Indeed, Goffman corroborates Green and Zurawski's (2015) suggestion that surveillance is a basic mode of the social, elaborated in different ways in different environments. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a new way to think about groupoid actions through the intermediate of a generalization. We reformulate and extend various notion of groupoid morphisms using this notion of generalized action. [ABSTRACT FROM AUTHOR]

In this paper, we tackle the problem of detailed hand-object 3D reconstruction from monocular video with unknown objects , for applications where the required accuracy and level of detail is important, e.g. object hand-over in human–robot collaboration, or manipulation and contact point analysis. While the recent literature on this topic is promising, the accuracy and generalization abilities of existing methods are still lacking. This is due to several limitations, such as the assumption of known object class or model for a small number of instances, or over-reliance on off-the-shelf keypoint and structure-from-motion methods for object-relative viewpoint estimation, prone to complete failure with previously unobserved, poorly textured objects or hand-object occlusions. To address previous method shortcomings, we present a 2-stage pipeline superseding state-of-the-art (SotA) performance on several metrics. First, we robustly retrieve viewpoints relying on a learned pairwise camera pose estimator trainable with a low data regime, followed by a globalized Shonan pose averaging. Second, we simultaneously estimate detailed 3D hand-object shapes and refine camera poses using a differential renderer-based optimizer. To better assess the out-of-distribution abilities of existing methods, and to showcase our methodological contributions, we introduce the new SHOWMe benchmark dataset with 96 sequences annotated with poses, millimetric textured 3D shape scans, and parametric hand models, introducing new object and hand diversity. Remarkably, we show that our method is able to reconstruct 100% of these sequences as opposed to SotA Structure-from-Motion (SfM) or hand-keypoint-based pipelines, and obtains reconstructions of equivalent or better precision when existing methods do succeed in providing a result. We hope these contributions lead to further research under harder input assumptions. The dataset can be downloaded at https://download.europe.naverlabs.com/showme. • Object-agnostic hand-object 3D reconstruction from monocular hand-object motion video • Robust rigid-transformation estimation network that leverages large pre-trained model • Two-stage pipeline for 3D hand-object reconsruction • New hand-object dataset to benchmark hand-object 3D reconstruction • Rigid-transformation estimation performance analysis v/s object sizes and textures [ABSTRACT FROM AUTHOR]

Discovering trending social events (e.g., major meetings, political scandals, natural disasters, etc.) from social messages is vital because it emphasizes important events and can help people comprehend the world. However, the heterogeneous semantics enrichment, severe long-tail problems, and sparse text contents of social messages pose great challenges to event detection, often leading to limited generalization ability and accuracy. In this paper, we propose a novel Multi-Relational Meta-Enhanced Network (MRME-Net) architecture to learn social events. First, we model social messages into a multi-relational message graph, incorporating abundant meta-semantics along with various meta-relations. Second, we present a multi-relational graph attention network based on Sophia by using a dual-step message aggregation mechanisms to capture the local features of neighboring messages and global semantics of mutiple relations and ultimately learn social message embeddings. We use Sophia optimizer to reduce the massive time and cost of training. Third, in order to address the long-tail problem, we introduce a locally-adapted meta-learning framework in social event detection for the first time and propose a novel META-TAILENH embedding enhancement strategy to refine tail node embeddings in multi-relational graph. Eventually, we conduct the detection of social events according to the hierarchical clustering algorithm. Extensive experiments have been carried out to evaluate MRME-Net on the MAVEN and Twitter dataset, revealing a notable improvement of 3 %–13 %, 4 %–20 % and 6 %–30 % increases on NMI, AMI and ARI in the social event detection task. [ABSTRACT FROM AUTHOR]

Processes of innovation and scientific breakthroughs are not linear, and their outcomes are notoriously difficult to predefine. This paper contributes with an inductive longitudinal case study of how industrial R&D and innovation activities in a multinational telecom firm contributed to tracking the spread of Covid-19, a societal challenge unrelated to the starting point of the firm's relevant R&D. After twenty years, insights and methods from network analysis using call detail records of mobile phone users eventually ending up in the pandemic response toolbox. We find that the process was dependent upon prolonged activities of key individuals in the firm, serendipitous meetings between people with common interests, and space and support in the organisation for activities with an uncertain effect for the company. The shift from commercial to public health goals was a process of exaptation, and later scaling up was tied to the emergence and perception of Covid-19 as a major societal challenge, which also helped deal with complex issues like privacy to generalise the use of mobile phone data for handling pandemics. This played out over three distinct phases of experimentation, exaptation and scaling up. • Innovation process theories are used to analyse how call detail records from a multinational firm became used in Covid-19 models. • A longitudinal case design is used to set up a timeline with three phases over 20 years. • Exaptation is a key process through which new and unforeseen uses for the algorithms entered the activities of the firm. • The work was vulnerable due to the reliance on individuals and their personal networks. • Scaling up and diffusing was greatly facilitated by the emergence and perception of Covid-19 as a grand societal challenge. [ABSTRACT FROM AUTHOR]

Kleene algebra with tests (KAT) was introduced as an algebraic structure to model and reason about classic imperative programs, i.e. sequences of discrete transitions guarded by Boolean tests. This paper introduces two generalisations of this structure able to express programs as weighted transitions and tests with outcomes in non necessarily bivalent truth spaces: graded Kleene algebra with tests (GKAT) and a variant where tests are also idempotent (I-GKAT). In this context, and in analogy to Kozen's encoding of Propositional Hoare Logic (PHL) in KAT we discuss the encoding of a graded PHL in I-GKAT and of its while-free fragment in GKAT. Moreover, to establish semantics for these structures four new algebras are defined: FSET (T ), FREL(K; T ) and FLANG(K; T ) over complete residuated lattices K and T, and M(n;A) over a GKAT or I-GKAT A. As a final exercise, the paper discusses some program equivalence proofs in a graded context. [ABSTRACT FROM AUTHOR]

This paper extends Fitting's epistemic interpretation of some Kleene logics to also account for Paraconsistent Weak Kleene logic. To achieve this goal, a dualization of Fitting's "cut-down" operator is discussed, leading to the definition of a "track-down" operator later used to represent the idea that no consistent opinion can arise from a set including an inconsistent opinion. It is shown that, if some reasonable assumptions are made, the truth-functions of Paraconsistent Weak Kleene coincide with certain operations defined in this track-down fashion. Finally, further reflections on conjunction and disjunction in the weak Kleene logics accompany this paper, particularly concerning their relation with containment logics. These considerations motivate a special approach to defining sound and complete Gentzen-style sequent calculi for some of their four-valued generalizations. [ABSTRACT FROM AUTHOR]