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In this contribution we introduce the dual mixed-integer least-squares problem and study it in relation to its primal counterpart. The dual differs from the primal formulation in the order in which the integer ambiguity vector a ∈ Z n and baseline vector b ∈ R p are estimated. As not the ambiguities, but rather the entries of b are usually the parameters of interest, the attractiveness of the dual formulation stems from its direct computation of b. It is shown that this potential advantage relies on the ease with which an implicit integer least-squares problem of the dual can be solved. For the convoluted cases, we introduce two methods of simplifying approximations. To be able to describe their quality, we provide a complete distributional analysis of their estimators, thus allowing users to judge whether or not the approximations are acceptable for their application. It is shown that this approach implicitly introduces a new class of admissible integer estimators of which we also determine the pull-in regions. As the dual function is shown to lack convexity, special care is required to be able to compute its global minimizer b ˇ . Our proposed method, which has finite termination with a guaranteed ϵ -tolerance, is constructed from combining the branch-and-bound principle, with a special convex-relaxation of the dual, to which the projected-gradient-descent method is applied to obtain the required bounds. Each of the method's three constituents are described, whereby special emphasis is given to the construction of the required continuously differentiable, convex lower bounding function of the dual. [ABSTRACT FROM AUTHOR]

In this paper we investigate the dynamics of a duopoly game with ambiguity aversion regarding uncertainty in demand and constant expectations concerning competitor production. The focus is on an asymmetric Cournot game where players engage in robust optimization and have different beliefs about the possible realizations of the random parameters of the price function. The players' ambiguity aversion introduces multiple equilibria and instability that otherwise would not be present. The investigation of the global dynamics of the game reveals the emergence, through border-collision bifurcations, of periodic and chaotic dynamics. [ABSTRACT FROM AUTHOR]

The Pama Croaker, Otolithoides pama, is an economically important fish species in Bangladesh. Intra-family similarities in morphology and typical barcode sequences of cox1 create ambiguities in its identification. Therefore, morphology and the complete mitochondrial genome of O. pama, and comparative mitogenomics within the family Sciaenidae have been studied. Extracted genomic DNA was subjected to Illumina-based short read sequencing for De-Novo mitogenome assembly. The complete mitogenome of O. pama (Accession: OQ784575.1) was 16,513 bp, with strong AC biasness and strand asymmetry. Relative synonymous codon usage (RSCU) among 13 protein-coding genes (PCGs) of O. pama was also analyzed. The studied mitogenomes including O. pama exhibited consistent sizes and gene orders, except for the genus Johnius which possessed notably longer mitogenomes with unique gene rearrangements. Different genetic distance metrics across 30 species of Sciaenidae family demonstrated 12S rRNA and the control region (CR) as the most conserved and variable regions, respectively, while most of the PCGs undergone a purifying selection. Different phylogenetic trees were congruent with one another, where O. pama was distinctly placed. This study would contribute to distinguishing closely related fish species of Sciaenidae family and can be instrumental in conserving the genetic diversity of O. pama. [ABSTRACT FROM AUTHOR]

Traditional methods for evaluating decision-making provide valuable insights yet may fall short in capturing the complexity of this cognitive capacity, often providing insufficient for the multifaceted nature of decisions. The Kalliste Decision Task (KDT) is introduced as a comprehensive, ecologically valid tool aimed at bridging this gap, offering a holistic perspective on decision-making. In our study, 81 participants completed KDT alongside established tasks and questionnaires, including the Mixed Gamble Task (MGT), Iowa Gambling Task (IGT), and Stimulating & Instrumental Risk Questionnaire (S&IRQ). They also completed the User Satisfaction Evaluation Questionnaire (USEQ). The results showed excellent usability, with high USEQ scores, highlighting the user-friendliness of KDT. Importantly, KDT outcomes showed significant correlations with classical decision-making variables, shedding light on participants' risk attitudes (S&IRQ), rule-based decision-making (MGT), and performance in ambiguous contexts (IGT). Moreover, hierarchical clustering analysis of KDT scores categorized participants into three distinct profiles, revealing significant differences between them on classical measures. The findings highlight KDT as a valuable tool for assessing decision-making, addressing limitations of traditional methods, and offering a comprehensive, ecologically valid approach that aligns with the complexity and heterogeneity of real-world decision-making, advancing research and providing insights for understanding and assessing decision-making across multiple domains. [ABSTRACT FROM AUTHOR]

Objectives: Test the effect of perceived likelihood of arrest on criminal behavior under a relaxed set of measurement assumptions. Specifically, responses that are commonly associated with inaccurate reporting practices–particularly, the 0%, 50%, and 100% categories–can be treated as partially identified. By doing so, scholars are able to bound the effect of perceived arrest risk on criminality, which provides more credible, although less precise, estimates of β 1 . Scholars can use this approach to not only produce more defensible findings, on the whole, but also gain insight into the possible threat posed by measurement misspecification. Methods: Point estimates of a perceived certainty effect were elicited via Quasi-Poisson regression using data derived from the Pathways to Desistance study. These estimates were subsequently bounded under progressively weaker measurement assumptions by a series of hill-climb algorithms. Results: In nine out of seventeen total algorithms, the worst-case bound remained in the expected direction and was statistically significant. For as long as a relatively minor level of response inaccuracy is assumed, supportive conclusions can be drawn. Conclusions: Support for a certainty effect can be found under relaxed measurement assumptions, up to a point. This not only provides further support for the deterrence hypothesis, but also implies the effect might be somewhat resilient to measurement error. [ABSTRACT FROM AUTHOR]

In the alternative uses task (AUT), a well-established creativity assessment, participants propose alternative uses for common items (e.g., a brick) within a 2–3 min timeframe. While idea evaluation is likely involved, the emphasis is strongly on idea generation. Here, we test the value of presenting a word overlapping an image compared to a word only prompt, and we introduce a cyclic adaptation of the AUT explicitly calling on participants to choose their best idea. In Experiment 1, as compared to word only, word + image prompts increased idea fluency but reduced idea originality and variability within a group of native Polish speakers. Thus, word + image prompts improve AUT baselining. In Experiment 2, different participants produced as many ideas as possible within two minutes (List) or their single best idea at the end of each of three 30 s ideation cycles (Cycle). Although originality did not differ between List and Cycle overall, the first three ideas in List were rated as less creative than the ideas in Cycle. Overall, we conclude that using disambiguating images reduces spurious interindividual variability in the AUT while introducing idea evaluation in the task allows us to assess creativity beyond idea generation. [ABSTRACT FROM AUTHOR]

In this study, we have presented a novel probabilistic model called the neutrosophic Burr-III distribution, designed for applications in neutrosophic surface analysis. Neutrosophic analysis allows for the incorporation of vague and imprecise information, reflecting the reality that many real-world problems involve ambiguous data. This ability to handle vagueness can lead to more robust and realistic models especially in situation where classical models fall short. We have also explored the neutrosophic Burr-III distribution in order to deal with the ambiguity and vagueness in the data where the classical Burr-III distribution falls short. This distribution offers valuable insights into various reliability properties, moment expressions, order statistics, and entropy measures, making it a versatile tool for analyzing complex data. To assess the practical relevance of our proposed distribution, we applied it to real-world data sets and compared its performance against the classical Burr-III distribution. The findings revealed that the neutrosophic Burr-III distribution outperformed than the classical Burr-III distribution in capturing the underlying data characteristics, highlighting its potential as a superior modeling toolin various fields. [ABSTRACT FROM AUTHOR]

Discontinuous named entity recognition (NER) is a more challenging task compared to continuous NER. It aims to extract discontinuous entities composed of multiple no-adjacent spans, which requires representing and combining all the spans of each discontinuous entity. However, discontinuous NER may suffer from decoding ambiguity due to the large space of span combinations and the lack of association information between spans. To address this problem, we propose a simple yet effective span-level tagging scheme for discontinuous NER. The tagging scheme defines simple span-level tags to represent and associate all the spans of each discontinuous entity simultaneously, effectively solving the decoding ambiguity problem. Moreover, the proposed model employs a co-predictor consisting of a span-level graph-based predictor and a position-aware biaffine predictor to predict span-level tags. The span-level graph-based predictor enhances span representations by employing graph convolutional network on span-level graphs to capture the dependence between spans of each discontinuous entity. The position-aware biaffine predictor incorporates relative position information into biaffine to enrich the structural information of span representations. To verify the effectiveness of our method, we conduct experiments on three benchmark datasets (i.e., CADEC, ShARe 13 and ShARe 14). The results show our method significantly outperforms previous state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The conflicting views that agents and voters have about redistributive taxation have been broadly studied. The literature has focused on situations where the counterfactual outcomes that would have occurred had other actions been chosen are observable or point identified. I analyze this problem from an econometric standpoint, in a context of ambiguity. The extent to which individuals are responsible for their own fate is partially identified. Agents have partial knowledge of the relative importance of effort in the generation of income inequality and, therefore, the magnitude of the incentive costs. I present a simple model of redistribution and show that multiple equilibria might arise even in the presence of ambiguity: One where the rate of redistribution is high, agents are pessimistic, and exert low effort (Pessimism/Welfare State), and another where the redistribution tax rate is low, agents are optimistic, and exert high effort (Optimism/Laissez Faire). [ABSTRACT FROM AUTHOR]

In this paper, a class of reinsurance contracting problems is examined under a continuous-time principal–agent framework with mean-variance criteria, where a reinsurer and an insurer are assigned the roles of the principal and the agent, respectively. Both parties can manage their insurance risk by investing in a financial portfolio comprising a risk-free asset and a risky asset. It has been assumed that both the insurer and the reinsurer are concerned about model uncertainty and that they aim to find a robust reinsurance contract and robust investment strategies by maximizing their respective mean-variance cost functionals taking sets of probability scenarios into account. To articulate the time-inconsistency issue attributed to the mean-variance optimization criteria, the optimization procedure of each decision-maker has been formulated as a non-cooperative game and discussed by using an extended HJB equation, which is consistent with the extant work on time-consistent control. Moreover, explicit expressions for the robust reinsurance contract, the robust investment strategies and the value functions of the insurer and reinsurer have been obtained and presented. The numerical results and their economic interpretations are then discussed. [ABSTRACT FROM AUTHOR]

Some of the extreme space weather events like the geomagnetic storm of 17–18 March 2015 have produced dramatic effects on low-latitude ionosphere as reported in various studies. Similarly, some of the extreme storms are studied to emphasize the anomalous deviations in ionospheric delay; however, the effects of excess ionospheric delay on GPS-based positioning are rarely reported from India. This paper presents a robust analysis including estimation of GPS receiver position during 6 major geomagnetic storms using dual frequency GPS signals in L1 and L2 bands. Epoch-wise solution estimates are computed using precise orbit SP3 files for satellite orbit and clock corrections and receiver specific differential code biases are obtained from IGS-CODE center. A range domain Kalman filter has also been developed to smooth the 30 s sampled code pseudorange using carrier phase data and resolve the initial ambiguity. Then, the precise positioning residuals are estimated in two different cases of ionospheric corrected and ionospheric uncorrected error models. We then compare the results with ionospheric corrected position estimates to finally obtain the effect of excess daytime ionospheric variations during main phase of some extreme geomagnetic storms. Results from 5 GPS receivers located within 77°–80° E longitude sector are obtained for two 2-hour windows covering local noon during 6 storms including the St. Patrick's Day storm. The effect of severity of the storms and their impact on static precise positioning are brought out by comparing the results with performance on a quiet day. It is found that the magnitude of error in estimated altitude exhibits maximum deviations due to ionospheric variations during storms, and the dip latitude of station is important in terms of magnitude of ionospheric error in positioning over the equatorial ionization anomaly region. [ABSTRACT FROM AUTHOR]

The objective of this research is to enhance the precision and efficiency of design concept assessments during the initial stages of new product creation. Design concept evaluation, which occurs at the end of the conceptual design phase, is a critical step in product development. The outcome of this evaluation significantly impacts the product's eventual success, as flawed design concepts are difficult to remedy in later stages. However, the evaluation of new product concepts is a procedure that encompasses elements of subjectivity and ambiguity. In order to deal with the problem, a novel decision-making method for choosing more logical new product concepts is introduced. Basically, the evaluation process is outlined in three main phases: the construction of evaluation index system for design concept alternatives, the calculation of weights for evaluation criteria and decision-makers, the selection of the best design concept alternatives. These stages are composed of a hybrid method based on kano model, multiplicative analytic hierarchy process (AHP) method, the entropy of IVPFS and improved grey relational projection (GRP) under interval-valued picture fuzzy set (IVPFS). The novel approach integrates the strength of interval-valued picture fuzzy number in handling vagueness, the advantage of multiplicative AHP and the merit of improved GRP method in modelling multi-criteria decision-making. In final, the effectiveness of the proposed model is validated through comparisons with other models. The potential applications of this study include but are not limited to product development, industrial design, and innovation management, providing decision-makers with a more accurate and comprehensive design concept evaluation tool. [ABSTRACT FROM AUTHOR]

Individual traits and reactions to ambiguity differ and are conceptualized in terms of an individual's attitudes toward ambiguity or ambiguity tolerance. The development of natural language processing technology has made it possible to measure mental states and reactions through open-ended questions, rather than predefined numerical rating scales, which have traditionally been the dominant method in psychological research. This study presented three ambiguity-related situations and responses collected online from 591 participants in an open-ended format. After the analysis with bidirectional encoder representations from transformers, correlations were calculated using scores from the numerical evaluation by conventional questionnaire, and a significant moderate positive correlation was found. Therefore, this study found that attitudes toward ambiguity can be measured using an open-ended response method of reporting everyday life states. It is a novel methodology that can be expanded to other scales in psychology and can potentially be used in educational and clinical situations where participants can be asked to respond with minimal burden. [ABSTRACT FROM AUTHOR]

English speakers use probabilistic phrases such as likely to communicate information about the probability or likelihood of events. Communication is successful to the extent that the listener grasps what the speaker means to convey and, if communication is successful, individuals can potentially coordinate their actions based on shared knowledge about uncertainty. We first assessed human ability to estimate the probability and the ambiguity (imprecision) of twenty-three probabilistic phrases in a coordination game in two different contexts, investment advice and medical advice. We then had GPT-4 (OpenAI), a Large Language Model, complete the same tasks as the human participants. We found that GPT-4’s estimates of probability both in the Investment and Medical Contexts were as close or closer to that of the human participants as the human participants’ estimates were to one another. However, further analyses of residuals disclosed small but significant differences between human and GPT-4 performance. Human probability estimates were compressed relative to those of GPT-4. Estimates of probability for both the human participants and GPT-4 were little affected by context. We propose that evaluation methods based on coordination games provide a systematic way to assess what GPT-4 and similar programs can and cannot do. [ABSTRACT FROM AUTHOR]

Purpose: Social inclusion of people living with serious mental illness is widely promoted. However, only limited consideration has been given to the meanings of social inclusion within different settings and the ways in which it is envisioned, negotiated, and practised. In this paper, we explore meanings and practises of social inclusion from the perspectives of people living with serious mental illness and their families in Ghana and Palestine and how this is shaped by differing political and socio-cultural contexts. Methods: This paper draws on comparative ethnographic research including participant observation and interviews with people living with mental illness and family members in Ghana and the occupied Palestinian territory. Data were triangulated and analysed using thematic analysis. Results: Participants described experiences of social inclusion and participation within communities, home and family life, friendships and social life, and work and livelihoods. This revealed how such experiences were variously shaped by differing political contexts and socio-cultural norms and expectations within the two settings. These in turn intersected with aspects such as gender roles, age, and socio-economic status. Aspirations for inclusion included greater awareness and understanding within society, accompanied by opportunities and support for meaningful inclusion at the political as well as community level. Conclusion: Findings point to the value of a contextual understanding of social inclusion, taking account of the impact of the wider socio-cultural, political, and economic environment. They also point to the need for an intersectoral approach, beyond communities and mental health services, to provide meaningful opportunities and support for social inclusion. [ABSTRACT FROM AUTHOR]

Topological entanglement entropy (TEE), the sub-leading term in the entanglement entropy of topological order, is the direct evidence of the long-range entanglement. While effective in characterizing topological orders on closed manifolds, TEE is model-dependent when entanglement cuts intersect with physical gapped boundaries. In this paper, we study the origin of this model-dependence by introducing a model-independent picture of partitioning the topological orders with gapped boundaries. In our picture, the entanglement boundaries (EBs), i.e. the virtual boundaries of each subsystem induced by the entanglement cuts, are assumed to be gapped boundaries with boundary defects. At this model-independent stage, there are two choices one has to make manually in defining the bi-partition: the boundary condition on the EBs, and the coherence between certain boundary states. We show that TEE appears because of a constraint on the defect configurations on the EBs, which is choice-dependent in the cases where the EBs touch gapped boundaries. This choice-dependence is known as the ambiguity in entanglement entropy. Different models intrinsically employ different choices, rendering TEE model-dependent. For D(ℤ2) topological order, the ambiguity can be fully characterized by two parameters that respectively quantifies the EB condition and the coherence. In particular, calculations compatible with the folding trick naturally choose EB conditions that respect electric-magnetic duality and set specific parameter values. [ABSTRACT FROM AUTHOR]

Formal pattern languages are used in industry to communicate and analyse requirements, as they are said to be both machine-readable and intuitively understandable for humans. The questions arise to what extent this intuitive understanding of a pattern language is in agreement with its formal semantics and whether this understanding can be increased systematically. We present two consecutive empirical experiments to address these questions. The formal semantics serves as an objective judge on the intuitive understanding. Our experiments confirm the practical usefulness of HanforPL insofar the intuition matches the formal semantics in most practically relevant cases. They also reveal a number of edge cases where even a prior exposure to formal logic is not a guarantee for correct understanding. We present and validate systematic adjustments to the patterns, leading to several large increases in understandability but come at the cost of new, but less impactful ambiguities. We demonstrate how an inquiry on the alignment of the intuitive and formal semantics of a pattern language can help to understand and improve the language. While results regarding the understandability of HanforPL are favourable in commonly used cases, there is potential for improvement. The systematic adaption of patterns shows that small modifications may have large effects on the alignment of formal and intuitive semantics, and that modification must be considered with caution in the context of the respective pattern to avoid unintentionally adding new ambiguities. This article is an extension of our published REFSQ paper. [ABSTRACT FROM AUTHOR]

Word Sense Disambiguation (WSD) is a key step for many natural language processing tasks such as information search, automatic translation, and sentiment analysis. WSD is the process that identifies appropriate senses of ambiguous words in the text. With the increasing number of words to be disambiguated in large amount of text data, WSD becomes very challenging and that is why an exhaustive search for the best set of senses may be unpractical. Recently, several metaheuristic approaches have been proposed for different complex optimization problems and have achieved good results. Therefore, in order to improve the WSD process, in this paper, the WSD problem is modeled as a combinatorial optimization problem, and the Discrete Student Psychology-Based Optimization (DSPBO) metaheuristic is proposed and used to selecting appropriate senses. A DSPBO-based WSD is proposed to disambiguate more ambiguous words together in function to their contexts in the target text, and a Lesk-based fitness function is used to guide the DSPBO metaheuristic to optimize the general semantic similarity of selected senses. The proposed approach is evaluated and compared to several recent WSD approaches on the well-known corpuses SensEval-2, SensEval-3, SemEval-2007, SemEval-13, and SemEval-15. The comparison is made in terms of F-Measure, precision, and recall. Experiments show a significant improvement both over existing knowledge lexicon-based approaches and metaheuristic-based approaches, with a higher F-measure of 84.21%, 83.33%, 87.5%, 77.58%, and 81.08% on SensEval-2, SensEval-3, SemEval-2007, SemEval-13, and SemEval-15, respectively. [ABSTRACT FROM AUTHOR]

The 14 April 1895 (Mw 6.1, in the area of Ljubljana, Slovenia) earthquake is still not fully understood. The aim of this work is to derive information about its source from the inversion of an updated dataset of intensities (evaluated with EMS-98). This was done via automatic non-linear geophysical inversion KF-NGA, which was performed using a Niching Genetic Algorithm and has been presented in other articles. The distribution of damage caused by this earthquake is not homogeneous and often shows significant intensity differences between neighbouring sites. Statistical analysis of the intensities, epicentral distances and geologic nature of the sites suggests some site effects. Nevertheless, the resulting solution is consistent with regional seismotectonics, i.e. an almost pure dip-slip mechanism: strike 282° ± 5°, dip 38° ± 7°, rake 86° ± 9° (± 180° because of the intrinsic ambiguity of the KF-NGA-inversion). Since the rake angle is close to 90°, there is an almost perfect ambiguity between the two planes of the focal mechanism. Therefore, our solution has a Dinaric direction and could be associated either with a fault plane that dips NE or with one that dips SW. [ABSTRACT FROM AUTHOR]

An experiment investigated the "good-enough" processing account regarding how people parse sentences with late-closure ambiguity, such as While Anna dressed the baby who was cute and cuddly spit up on the bed. One possible result of an initial misparse of the sentence (thinking that Anna dressed the baby) is that the correct parse then cannot be created. The alternative is that, although the misparse may linger in the comprehender's mind, the correct parse is eventually established and coexists with the misparse. This study approached this issue through an analogy to quantum physics. When photons are directed toward two small slits, it appears as if each photon passes through both slits ("bothness"). If particles not subject to quantum effects are directed at the slits, they pass through one or the other ("oneness"). Participants read sentences containing the late-closure ambiguity and afterward answered two questions about each sentence. These could query the potential misparse (Did Anna dress the baby?), correct-parse (Did Anna dress herself?), or the main clause (Did the baby spit up on the bed?), and the order of the question types was varied to test for quantum measurement context effects. The results supported "oneness," that is, the correct-parse and misparse of the subordinate clause do not coexist. Participants rarely said "yes" to both the misparse and correct-parse questions, and the "yes" response proportions for these two questions invariably added up to around 1.0. Furthermore, no quantum-like measurement-order effect between misparse and correct-parse questions was found. [ABSTRACT FROM AUTHOR]

The octopus of the genus Tremoctopus from the Arabian Sea has not been investigated in the past. This study reports on the rare pelagic octopus Tremoctopus gracilis from the south-eastern Arabian Sea. Twenty females with dorsal mantle lengths (DML) ranging between 62 and 160 mm and weighing between 31 and 719 g were examined. These specimens were collected from commercial fishing grounds, and amongst them was a single mature female with 12,618 oocytes in the ovary (size varied from 0.61 to 1.67 mm (mean = 1.13 mm). The detailed morphometric and morphological characteristics were recorded, and the specimens were identified up to the genus level. Further molecular species identification was carried out through mitochondrial markers (COI & 16S rRNA). Phylogenetic analysis, based on partial sequences of the above markers, generated a single clade that included sequences from the Indian and Pacific Oceans, including those misidentified as Tremoctopus violaceus from the Pacific. Evolutionary divergence estimation revealed a high inter-specific genetic divergence from T. violaceus sequences in the Mediterranean Sea and Atlantic Ocean, thus confirming the Tremoctopus species in our study as T. gracilis and authenticated that GenBank sequences of T. violaceus from the Pacific Ocean are indeed T. gracilis. [ABSTRACT FROM AUTHOR]

Human resource management is the process of making a company's human resources decisions. In general, these decisions include hiring, firing, training, and developing people according to their positions and the needs of the organization. It includes a variety of policies and strategies designed to recognize the contribution that people make to an organization. The core goal of this article is to depict a novel fuzzy multi-criteria decision-making methodology for selecting employees. The purpose behind selecting employees is to identify and hire individuals who possess the required skills, qualifications, and attributes that align with the organization's goals and job requirements. To reflect an inadequate assessment, ambiguity, and anxiety in making choices, the intuitionistic fuzzy hypersoft expert set is an extension of the intuitionistic fuzzy soft expert and hypersoft sets. It is a novel approach to decisions and intelligent computing in the face of uncertainty. The intuitionistic fuzzy hypersoft expert set has a better ability to handle ambiguous and unclear data. In the research that follows, the ideas and characteristics of the correlation coefficient and the weighted correlation coefficient of the intuitionistic fuzzy hypersoft expert sets are proposed. Under the aegis of intuitionistic fuzzy hypersoft expert sets, a TOPSIS based on correlation coefficients and weighted correlation coefficients is introduced. Aside from that, we also covered aggregation operators, including intuitionistic fuzzy hypersoft weighted geometric operators. The decision-making process is suggested in an intuitionistic fuzzy hypersoft expert environment to resolve uncertain and ambiguous information, relying on the well-established TOPSIS approach and aggregation operators. An illustration of decision-making challenges shows how the suggested algorithm can be used. The efficacy of this strategy is lastly demonstrated by comparing its benefits, effectiveness, flexibility, and numerous current studies. [ABSTRACT FROM AUTHOR]

The symmetric triangular approximation under a general condition was first proposed by Ban and Coroianu (Ban and Coroianu Soft Comput 20:1249–1261, 2016). Ban and Coroianu completed the calculation and provided a computational formula. However, symmetric trapezoidal approximation cannot be deduced from their conclusions. The aim of this paper is to continue and foster the development of this topic. The calculation for the symmetric trapezoidal approximations of a fuzzy number under a general condition will be completed in this paper. Some applications related with value, expected value and ambiguity, which are important parameters, are given too. Numerical examples are given and the quantitative improvement in approximation is also addressed. [ABSTRACT FROM AUTHOR]

Chinese toponym recognition is crucial in named entity recognition and has significant implications for improving geographic information systems. Based on the real-time nature of social media and rich geographical data contained in social media, it is important to identify Chinese toponyms, including compound toponyms, informal toponyms, and other forms of social media content, for automatic geospatial information extraction. However, the strong word-building ability, diverse features, and ambiguity of Chinese toponyms combined with the linguistic irregularities of social media pose significant challenges for accurately locating toponym boundaries and resolving ambiguities. Furthermore, existing Chinese toponym recognition methods often ignore the fusion of local and global features during feature extraction, resulting in semantic information loss. Therefore, we used the Chinese-roberta-wwm-ext pre-trained language model to encode input text and obtain character-level information. An improved SoftLexicon-based statistical method was employed to acquire word-level semantic information, which was then integrated with character-level semantic information. A two-channel neural network layer comprising a bi-directional long short-term memory and an inception-dilated convolutional neural network was utilized to extract global and local features from text. Additionally, a conditional random field was applied to establish label constraints. The proposed deep neural network model, called CHTopoNER, is designed to identify various forms of Chinese toponyms in irregular Chinese social media content. Its effectiveness was validated on four publicly available annotated toponym datasets and a custom social media dataset. CHTopoNER surpasses state-of-the-art Chinese toponym recognition models and achieves promising results for extracting various types of toponyms and spatial location terms. [ABSTRACT FROM AUTHOR]

Definitions are an integral aspect of mathematics. In particular, they form the backbone of deductive reasoning and facilitate precision in mathematical communication. However, when multiple non-equivalent definitions for the same term exist, their ability to serve these purposes can be called into question. While ambiguity can be productive, the lack of an agreed upon definition can make the truth value of certain mathematical statements unclear. In this study, we presented pre-service and in-service mathematics teachers with three mathematical claims in which definitional ambiguity was a consideration; then, we asked them to determine the correctness of each claim, justify their positions, and anticipate what reasoning might lead to a possible counterargument. Based on their responses, we identified several factors that influenced the participants' approaches in each situation as well as cross-cutting themes that describe their considerations. Finally, we consider the pedagogical implications of employing such a task in teacher preparation programs and in-service teacher education. [ABSTRACT FROM AUTHOR]

Gender categorization and stereotyping can lead to discrimination. Researchers have mostly studied cisgender, gender-conforming individuals as the targets when examining these processes. In two factorial survey experiments, we investigated gender categorization and stereotyping of gender-ambiguous targets based on facial features and behavioral information. We manipulated femininity/masculinity/ambiguity of face, expression, and occupation. Participants completed a gender categorization task, and stereotype and attitude measures. The findings indicated that face was most influential for categorization: When face was unambiguously masculine or feminine, participants mostly categorized targets as male or female, respectively. In these cases, expression and occupation had little influence on categorization. When face was ambiguous, this additional information significantly influenced categorization. Nonbinary categorization was more likely for ambiguous faces, and most likely for ambiguous faces combined with ambiguous expression and ambiguous or feminine occupation. Our findings suggest that categorizing gender-ambiguous targets is more complex compared to clearly gendered targets. Primarily relying on face when it appears clearly gendered likely causes categorization errors when encountering TGNC individuals. [ABSTRACT FROM AUTHOR]

A long-standing question in sentence processing research concerns the online parsing process in clause-boundary garden-path sentences, such as After Mary dressed John bathed. In this sentence, "John" must be parsed as the matrix subject DP but can be locally analysed as the object of the embedded verb. There is considerable evidence that the parser misanalyses these garden-path sentences. However, the controversy lies in whether the parser revises them during the online parsing process. The present study investigated this revision process through two self-paced reading experiments utilising grammatical constraints on reflexives and subject or object relative clauses embedded within the locally ambiguous DP. The results provided evidence of revision when a subject relative clause was embedded but not when an object relative clause was embedded. These findings suggest that the parser assigns grammatical structures that correspond to input strings during the revision of clause-boundary ambiguities but that object relative clauses affect the online revision process. [ABSTRACT FROM AUTHOR]

A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS). However, an NLI that is trained in the general domain is hard to apply in the spatial domain due to the idiosyncrasy and expressiveness of the spatial semantics. Moreover, there are a wide range of database servers available, and a unilingual NLI model limits its practical usage. In this article, we propose to not only address the spatial domain generalization challenge, but also support multilingual back-end, i.e., supporting different query languages, such as SQL and Prolog. For the challenge of spatial semantics, we propose a spatial comprehension model that is able to recognize the meaning of spatial entities based on the semantics of context and effectively resolve the ambiguity given the spatial semantics. The spatial semantics learned from the spatial comprehension model is then injected to the natural language question to ease the burden of capturing the spatial-specific semantics. We also propose to add a prefix symbol to support the multilingual back-end (e.g., query languages). With our spatial comprehension model and symbol injections, our NLI for the spatial domain, named SpatialNLI, is able to capture the semantic structure of the question and translate it to the corresponding syntax of an executable query accurately. We also experimentally ascertain that SpatialNLI outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Using restriction levels and interval type-2 fuzzy sets into linguistic summarization evaluation is a novel strategy presented in this paper. The uncertainty and ambiguity in the linguistic summaries are handled by the suggested method by using interval type 2 fuzzy sets. While it complies with the requirements that evaluation methods should meet, the suggested method additionally makes use of restriction levels to offer a more reliable evaluation framework. On the basis of real-world data, the proposed method's efficacy is shown. The findings demonstrate that the suggested approach can offer a thorough and precise evaluation of the linguistic summary techniques. The suggested method can assist practitioners and researchers in evaluating linguistic summaries results in a simple yet consistent fashion. [ABSTRACT FROM AUTHOR]

This essay offers a framework for scholars and educators to use when considering graphic novel adaptations that is exemplified by a close-reading of P. Craig Russell's 2019 graphic novel adaptation of Lois Lowry's The Giver. I apply comic book and adaptation theories to argue that Russell's adaptation refocuses the narrative from the novel's philosophical societal questions toward a heartfelt emotional core: what can two people who care about each other do to help a community that has been lost? Russell shifts focus from the cerebral to the emotional by leveraging a limited color palette and a color braid throughout the text to deepen the relationship between Jonas and The Giver. The limited color palette allows Russell to comment editorially throughout the narrative. His graphic novel adaptation shifts focus to emphasize reflection, relationships, and emotions. While students love graphic novels, teachers are often at a loss as to how to utilize them in a classroom. Drawing on picture book and comic book code and adaptation theory, my work provides a framework for teaching graphic narrative in the classroom to support teachers in the inclusion of graphic narrative into the curriculum. [ABSTRACT FROM AUTHOR]

When examining memory effects of semantic attributes, it is common practice to manipulate normed mean (M) ratings of the attributes (i.e., attribute intensity) in learning materials. Meanwhile, the standard deviations (SDs) of attribute ratings (i.e., attribute ambiguity) are usually treated as indexes of measurement error. However, some recent research found that recall accuracy varied as a function of both the intensity and ambiguity of semantic attributes such as valence, categorization, concreteness, and meaningfulness. These findings challenged the traditional interpretation of attribute rating SDs as noise indexes. In the current study, we examined the recognition effects of ambiguity, intensity, and Ambiguity × Intensity interactions for 21 attributes using mega study data for over 5,000 words. Our results showed that attribute ambiguity had reliable recognition effects beyond those of attribute intensity, and that it sometimes explained more unique variance in recognition than attribute intensity. Thus, we concluded that attribute ambiguity is a distinct psychological dimension of semantic attributes, which is processed separately from attribute intensity during encoding. Two theoretical hypotheses had been proposed for the memory effects of attribute ambiguity. We discuss the implications of our findings for the two theoretical hypotheses about how attribute ambiguity influences episodic memory. [ABSTRACT FROM AUTHOR]

Medical social work is gradually developing in China, and children's hospitals greatly need medical social workers. However, different groups have different perceptions of medical social workers' roles. The role of nascent Chinese medical social workers in children's hospitals is unclear, which hinders the growth of this profession. Therefore, this study aimed to resolve role ambiguity by comprehensively exploring the specific roles of Chinese medical social workers in a children's hospital. A descriptive qualitative research design was adopted. Twenty-six participants, including medical social workers (n = 8), primary caregivers of children (n = 6), and medical staff (n = 12, including 6 doctors and 6 nurses), were recruited by purposive sampling at a tertiary-level children's hospital in China between October and November 2022. Face-to-face semi-structured interviews were conducted to collect data. Three major themes with eleven roles were extracted through Colaizzi's 7-step method of phenomenological data analysis: (1) the role identified by all three groups: psychological supporter, resource linker, and consultant; (2) the role identified by healthcare professionals: coordinator, planner, assessor, advocate, organizer, promoter, and educator; and (3) the role identified by medical social workers themselves: manager. This qualitative study was the first to comprehensively describe the eleven roles assumed by Chinese pediatric medical social workers in the hospital and role ambiguity among different groups. The lack of medical background and practitioners, as well as lower social status, led to medical social workers' role ambiguity problems. Recommendations to strengthen policy support, cultivate a new medical social work culture, and increase the social understanding and recognition of medical social work to resolve role ambiguity are provided. [ABSTRACT FROM AUTHOR]

Although uncertainty and ambiguity have been used interchangeably within the anxiety literature, no studies have directly compared the psychometric properties and predictive utility of two commonly used scales to assess these constructs, the Intolerance of Uncertainty Scale (IUS) and the Tolerance of Ambiguity Scale (TOA). Furthermore, previous findings of the individual scales have been inconsistent regarding their factor structure and potential clinical utility. Therefore, the current study aims to independently evaluate the factor structure of the IUS and TOA utilizing exploratory and confirmatory factor analysis. Additionally, predictive utility of the scales was evaluated with correlation and regression analyses. Data were collected across 3 studies. Results suggest two-factor solutions provide the best-fitting models for both scales; however, these findings differ from previous studies. The IUS and its subscales provide greater predictive utility for anxiety and related areas of psychopathology compared to the TOA, suggesting that the IUS and uncertainty may be more closely related to anxiety and related symptoms. Future studies should continue to examine the interconnected role of uncertainty and ambiguity within anxiety and other forms of psychopathology. [ABSTRACT FROM AUTHOR]

Grid theory is rather commonly-used through out the research of integer ambiguity. In order to promote the efficiency of computation, it is of great necessity to reduce the correlations of the grid basis through the reduction. The classical reduction algorithm is known as the LLL (Lenstra–Lenstra–Lovász) algorithm. So as to further enhance the reduction effect, the deep-insertion LLL algorithm can be utilized as an alternative to the basis vector exchange algorithm. In practice, the deep-insertion LLL algorithm can achieve a better reduction effect, but it requires more time for reduction. The PotLLL algorithm replaces the basis vector exchange condition of deep-insertion LLL with an improving in the basis quality, and it can run in polynomial time, but with certain limitations. Therefore, this article proposes a global deep-insertion PLLL algorithm (GS-PLLL) to address the issue of integer ambiguity. GS-PLLL adopts a global strategy for deep-insertion processing, and introduces a rotation sorting method for preconditioning the grid basis. Comparative evaluations were conducted using simulation experiments and real-world measurements on the LLL, DeepLLL, PotLLL, and GS-PLLL algorithms. The experimental results indicate that the GS-PLLL algorithm achieves a better reduction effect than the PotLLL algorithm while improving the efficiency of reduction. [ABSTRACT FROM AUTHOR]

With the increase in the market share of data trading, the risks such as identity authentication and authority management are increasingly intensified. Aiming at the problems of centralization of identity authentication, dynamic changes of identities, and ambiguity of trading authority in data trading, a two-factor dynamic identity authentication scheme for data trading based on alliance chain (BTDA) is proposed. Firstly, the use of identity certificates is simplified to solve the problems of large calculation and difficult storage. Secondly, a two-factor dynamic authentication strategy is designed, which uses distributed ledger to achieve dynamic identity authentication throughout the data trading. Finally, a simulation experiment is carried out on the proposed scheme. The theoretical comparison and analysis with similar schemes show that the proposed scheme has lower cost, higher authentication efficiency and security, easier authority management, and can be widely used in various fields of data trading scenarios. [ABSTRACT FROM AUTHOR]

Web image datasets curated online inherently contain ambiguous in-distribution instances and out-of-distribution instances, which we collectively call non-conforming (NC) instances. In many recent approaches for mitigating the negative effects of NC instances, the core implicit assumption is that the NC instances can be found via entropy maximization. For "entropy" to be well-defined, we are interpreting the output prediction vector of an instance as the parameter vector of a multinomial random variable, with respect to some trained model with a softmax output layer. Hence, entropy maximization is based on the idealized assumption that NC instances have predictions that are "almost" uniformly distributed. However, in real-world web image datasets, there are numerous NC instances whose predictions are far from being uniformly distributed. To tackle the limitation of entropy maximization, we propose (α , β) -generalized KL divergence, D KL α , β (p ‖ q) , which can be used to identify significantly more NC instances. Theoretical properties of D KL α , β (p ‖ q) are proven, and we also show empirically that a simple use of D KL α , β (p ‖ q) outperforms all baselines on the NC instance identification task. Building upon (α , β) -generalized KL divergence, we also introduce a new iterative training framework, GenKL, that identifies and relabels NC instances. When evaluated on three web image datasets, Clothing1M, Food101/Food101N, and mini WebVision 1.0, we achieved new state-of-the-art classification accuracies: 81.34 % , 85.73 % and 78.99 % / 92.54 % (top-1/top-5), respectively. [ABSTRACT FROM AUTHOR]

Translation equivalents are widely used in bilingual research concerning word processing (e.g., Eddington & Tokowicz, 2013; Jouravlev & Jared, 2020) and second-language vocabulary learning (e.g., Bracken et al., 2017; Degani et al., 2014). Although translation norms exist in several languages, to date there are no Malay-English translation norms. This study presents the first Malay-English translation norms collected with highly proficient Malay-English bilinguals. Furthermore, the study investigates the impact of lexical characteristics on translation ambiguity. The forward translation (FT) task (N = 30) collected English translations for 1004 Malay words selected from the Malay Lexicon Project (Yap et al., 2010), and subsequently the backward translation (BT) task (N = 30) gathered Malay translations for 845 English words obtained from the FT phase. The data revealed a high prevalence of translation ambiguity in both translation directions. Specifically, verbs, adjectives, and class-ambiguous words were more translation-ambiguous than nouns. Furthermore, within-language semantic variability and word length were positively correlated with translation ambiguity, whereas word frequency only correlated with translation ambiguity in FT. Word length and word frequency of the source words and their translations were positively correlated. Intriguingly, only in FT were bilinguals with higher Malay proficiency more likely to provide accurate and dominant translations for the Malay words. These findings contrast with those reported in translation norming studies involving other language pairs. The translation norms provide a useful resource for bilingual language studies involving Malay-English bilinguals. [ABSTRACT FROM AUTHOR]

Key message: We demonstrate how an algorithm that uses cheap genetic marker data can ensure the taxonomic assignments of genebank samples are complete, intuitive, and consistent—which enhances their value. To maximise the benefit of genebank resources, accurate and complete taxonomic assignments are imperative. The rise of genebank genomics allows genetic methods to be used to ensure this, but these need to be largely automated since the number of samples dealt with is too great for efficient manual recategorisation, however no clearly optimal method has yet arisen. A recent landmark genebank genomic study sequenced over 10,000 genebank accessions of peppers (Capsicum spp.), a species of great commercial, cultural, and scientific importance, which suffers from much taxonomic ambiguity. Similar datasets will, in coming decades, be produced for hundreds of plant taxa, affording a perfect opportunity to develop automated taxonomic correction methods in advance of the incipient genebank genomics explosion, alongside providing insights into pepper taxonomy in general. We present a marker-based taxonomic assignment approach that combines ideas from several standard classification algorithms, resulting in a highly flexible and customisable classifier suitable to impose intuitive assignments, even in highly reticulated species groups with complex population structures and evolutionary histories. Our classifier performs favourably compared with key alternative methods. Possible sensible alterations to pepper taxonomy based on the results are proposed for discussion by the relevant communities. [ABSTRACT FROM AUTHOR]

Although pay transparency is a widely accepted remedy for the gender pay gap, research has devoted little attention to the specific types of salary information that are available to job seekers and whether the framing of this information moderates gender differences in negotiation outcomes. We first conducted an exploratory study to investigate whether men and women differ in how much they rely on and perceive the usefulness of various sources of salary information. A survey of experienced MBA students revealed that, relative to women, men tend to place greater value on sources that involve direct social comparisons (e.g., professional networks) as opposed to more aggregate, general sources (e.g., websites). We then conducted a controlled experiment using a hypothetical job offer negotiation to determine whether framing salary information in terms of social comparison moderates gender gaps in salary requests. Our results supported a moderated mediation model, revealing that men requested higher salaries than women when presented with upward social comparison information (i.e., what a more qualified employee earns), but not downward (i.e., what a less qualified employee earns) or lateral comparison information (i.e., what a similarly qualified employee earns). These effects were driven by heightened competitiveness among men. We also discovered that both men and women experienced the greatest boost in competitiveness and salary requests when presented with downward social comparison information. Overall, salary information is effective in reducing gender gaps, but the effects are nuanced, as they depend on the social comparison inherent to the information. [ABSTRACT FROM AUTHOR]

Past research suggests positive affective states promote state authenticity. However, in those studies, positive affective states are confounded with approach motivation, leaving some ambiguity in what is driving such effects. To address this limitation, we studied the effect of anger—a negative affective state related to approach motivation—on state authenticity. In two experiments (total N = 824), we experimentally induced different affective states (via movie-clips in Study 1 and autobiographical recall in Study 2) and had participants report state authenticity thereafter. We compared the anger condition to an amusement condition (Study 1), a fear condition (Studies 1 and 2) and a disgust condition (Study 2). We also measured affective valence and approach states in Study 2 to test for mediation. The results revealed that anger reduced authenticity relative to amusement but did not differ from fear or disgust. Moreover, an indirect effect of affective valence (but not approach states, Study 2) emerged: anger made people feel less pleasant, which explained their lower state authenticity. These findings suggest that affective valence is more important to state authenticity than approach/avoidance motivation. [ABSTRACT FROM AUTHOR]

Rating norms for semantic attributes (e.g., concreteness, familiarity, valence) are widely used to study the content that people process as they encode meaningful material. Intensity ratings of individual attributes have been manipulated in numerous experiments with a range of memory paradigms, but those manipulations are contaminated by substantial correlations with the intensity ratings of other attributes. A method of controlling such contamination is needed, which requires a determination of how many distinct attributes there are among the large collection of attributes for which published norms are available. Identification of overlapping words in multiple rating projects yielded a data base containing normed values for each word's perceived intensity (M rating) and ambiguity (rating SD) on 20 different attributes. Principal component analyses then revealed that the intensity space was spanned by just three latent semantic attributes, and the ambiguity space was spanned by five. Psychologically, the big three intensity factors (emotional valence, size, age) were highly interpretable, as were the big five ambiguity factors (discrete emotion, emotional valence, age, meaningfulness, and verbatim memory). We provide a data base of intensity and ambiguity factor scores that can be used to conduct uncontaminated studies of the memory effects of the intensity and ambiguity of latent semantic attributes. [ABSTRACT FROM AUTHOR]

This article addresses some crucial assumptions that are rarely acknowledged when organisms and machines are compared. We begin by presenting a short historical reconstruction of the concept of "machine." We show that there has never been a unique and widely accepted definition of "machine" and that the extant definitions are based on specific technologies. Then we argue that, despite the concept's ambiguity, we can still defend a more robust, specific, and useful notion of machine analogy that accounts for successful strategies in connecting specific devices (or mechanisms) with particular living phenomena. For that purpose, we distinguish between what we call "generic identity" and proper "machine analogy." We suggest that "generic identity"—which, roughly stated, presumes that some sort of vague similarity might exist between organisms and machines—is a source of the confusion haunting many persistent disagreements and that, accordingly, it should be dismissed. Instead, we endorse a particular form of "machine analogy" where the relation between organic phenomena and mechanical devices is not generic but specific and grounded on the identification of shared "invariants." We propose that the machine analogy is a kind of analogy as proportion and we elucidate how this is used or might be used in scientific practices. We finally argue that while organisms are not machines in a generic sense, they might share many robust "invariants," which justify the scientists' use of machine analogies for grasping living phenomena. [ABSTRACT FROM AUTHOR]

We seek to provide practicable approximations of the two-stage robust stochastic optimization model when its ambiguity set is constructed with an f-divergence radius. These models are known to be numerically challenging to various degrees, depending on the choice of the f-divergence function. The numerical challenges are even more pronounced under mixed-integer first-stage decisions. In this paper, we propose novel divergence functions that produce practicable robust counterparts, while maintaining versatility in modeling diverse ambiguity aversions. Our functions yield robust counterparts that have comparable numerical difficulties to their nominal problems. We also propose ways to use our divergences to mimic existing f-divergences without affecting the practicability. We implement our models in a realistic location-allocation model for humanitarian operations in Brazil. Our humanitarian model optimizes an effectiveness-equity trade-off, defined with a new utility function and a Gini mean difference coefficient. With the case study, we showcase (1) the significant improvement in practicability of the robust stochastic optimization counterparts with our proposed divergence functions compared to existing f-divergences, (2) the greater equity of humanitarian response that the objective function enforces and (3) the greater robustness to variations in probability estimations of the resulting plans when ambiguity is considered. [ABSTRACT FROM AUTHOR]

In order to deploy robots that could be adapted by non-expert users, interactive imitation learning (IIL) methods must be flexible regarding the interaction preferences of the teacher and avoid assumptions of perfect teachers (oracles), while considering they make mistakes influenced by diverse human factors. In this work, we propose an IIL method that improves the human–robot interaction for non-expert and imperfect teachers in two directions. First, uncertainty estimation is included to endow the agents with a lack of knowledge awareness (epistemic uncertainty) and demonstration ambiguity awareness (aleatoric uncertainty), such that the robot can request human input when it is deemed more necessary. Second, the proposed method enables the teachers to train with the flexibility of using corrective demonstrations, evaluative reinforcements, and implicit positive feedback. The experimental results show an improvement in learning convergence with respect to other learning methods when the agent learns from highly ambiguous teachers. Additionally, in a user study, it was found that the components of the proposed method improve the teaching experience and the data efficiency of the learning process. [ABSTRACT FROM AUTHOR]

The uncertainty and ambiguity in information is the major problem to aggregate. Some fuzzy frameworks and aggregation operators (AOs) have been formalized to cope with the uncertainty in information. A framework known as a spherical fuzzy set (SFS) covers the maximum possible information from real-life scenarios. Hence, the use of the SFS in multi-attribute decision-making (DM) (MADM) is very significant. This article consists of the development of some new AOs for the framework of the SFS. A family of the AOs spherical fuzzy Schweizer-Sklar (SS) McLaurin symmetric mean (MSM) (SFSSMSM), spherical fuzzy SS weighted MSM (SFSSWMSM), spherical fuzzy SS dual MSM (SFSSDMSM), and spherical fuzzy SS dual weighted MSM (PFSSDWMSM) is introduced, and their basic properties are investigated. The developed AOs are applied to the MADM problem where some pilot health projects (PHP) are assessed. [ABSTRACT FROM AUTHOR]

Uncertainty is prevalent in various health contexts. It is imperative to understand how health-related uncertainty can impact individuals' healthcare experiences and health decision making. The purpose of the present paper is to provide five overarching recommendations from an interdisciplinary team of experts to address gaps in the literature on health-related uncertainty. We present a case study of health-related uncertainty within the specific context of alcohol use to demonstrate these gaps and provide context for the recommendations. The five recommendations concerning health-related uncertainty include: (1) use common, consistent terminology to discuss uncertainty, (2) clarify measures of individual differences in response to uncertainty, (3) increase research on uncertainty and affect, (4) investigate the impact of the channel through which uncertainty is communicated, and (5) develop theory-driven interventions to improve uncertainty management. We conclude by reviewing health contexts in which health-related uncertainty exists and note how our recommendations complement existing reviews and data. [ABSTRACT FROM AUTHOR]

Magnetic fields are responsible for a multitude of solar phenomena, including potentially destructive events such as solar flares and coronal mass ejections, with the number of such events rising as we approach the peak of the 11-year solar cycle in approximately 2025. High-precision spectropolarimetric observations are necessary to understand the variability of the Sun. The field of quantitative inference of magnetic field vectors and related solar atmospheric parameters from such observations has been investigated for a long time. In recent years, very sophisticated codes for spectropolarimetric observations have been developed. Over the past two decades, neural networks have been shown to be a fast and accurate alternative to classic inversion methods. However, most of these codes can be used to obtain point estimates of the parameters, so ambiguities, degeneracies, and uncertainties of each parameter remain uncovered. In this paper, we provide end-to-end inversion codes based on the simple Milne-Eddington model of the stellar atmosphere and deep neural networks to both parameter estimation and their uncertainty intervals. The proposed framework is designed in such a way that it can be expanded and adapted to other atmospheric models or combinations of them. Additional information can also be incorporated directly into the model. It is demonstrated that the proposed architecture provides high accuracy results, including a reliable uncertainty estimation, even in the multidimensional case. The models are tested using simulations and real data samples. [ABSTRACT FROM AUTHOR]

Human reliability analysis (HRA) is a systematic technique for determining the impact that humans have on high-risk systems. Dependence assessment is a crucial aspect of HRA, as it quantifies the impact of an operator's inability to accomplish one task on the failure probabilities of subsequent tasks. Many traditional methods for dependence assessment usually need analysts to make a definitive judgment all by themselves. It strongly relies on the knowledge and experience of domain specialists. To address this issue, a model based on the correlation coefficient in the Dempster–Shafer evidence theory and the analytic hierarchy process method is proposed in this paper. We use Dempster–Shafer evidence theory (D–S evidence theory) to model uncertainty and subjectivity in the assessment process. And the correlation coefficient is used to modify the basic probability assignment function to make the fusion formula get more accurate results. At the same time, the analytic hierarchy process is used to model the hierarchical relationship of multifactor decision-making problems. Benefiting from these functions and a united framework structure, the method has an extraordinary ability to reduce subjectivity and ambiguity in language evaluation. In the case study, the proposed method was used to analyze the probability of dependence between two human operations on actual nuclear power plant data. The proposed method is particularly suitable for dependency assessments that are intrinsically linguistic in nature. [ABSTRACT FROM AUTHOR]

Synthesizing novel views from a single view image is a highly ill-posed problem. We discover an effective solution to reduce the learning ambiguity by expanding the single-view view synthesis problem to a multi-view setting. Specifically, we leverage the reliable and explicit stereo prior to generate a pseudo-stereo viewpoint, which serves as an auxiliary input to construct the 3D space. In this way, the challenging novel view synthesis process is decoupled into two simpler problems of stereo synthesis and 3D reconstruction. In order to synthesize a structurally correct and detail-preserved stereo image, we propose a self-rectified stereo synthesis to amend erroneous regions in an identify-rectify manner. Hard-to-train and incorrect warping samples are first discovered by two strategies, (1) pruning the network to reveal low-confident predictions; and (2) bidirectionally matching between stereo images to allow the discovery of improper mapping. These regions are then inpainted to form the final pseudo-stereo. With the aid of this extra input, a preferable 3D reconstruction can be easily obtained, and our method can work with arbitrary 3D representations. Extensive experiments show that our method outperforms state-of-the-art single-view view synthesis methods and stereo synthesis methods. [ABSTRACT FROM AUTHOR]

Hamilton–Jacobi–Bellman equation (HJBE) and backward stochastic differential equation (BSDE) are the two faces of stochastic control. We explore their equivalence focusing on a system of self-exciting and affine stochastic differential equations (SDEs) arising in streamflow dynamics. Our SDE is a finite-dimensional Markovian embedding of an infinite-dimensional jump-driven process called the superposition of continuous-state branching processes (a supCBI process). We formulate new ergodic control problems to evaluate the worst-case streamflow discharge in the long run and derive their HJBEs and ergodic BSDEs. The constant ambiguity aversion classically used in assessing model ambiguity must be modified in our case so that the optimality equations become well-posed. With a suitable modification of the ambiguity-aversion coefficient depending on the distributed reversion speed, we demonstrate that the solutions to the optimality equations are equivalent to each other in the sense that they lead to the same result. Finally, we apply the proposed framework to the computation of realistic cases with an existing record of discharge through a numerical Markovian embedding. [ABSTRACT FROM AUTHOR]