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Objectives: Ambiguity in communication of key study parameters limits the utility of real-world evidence (RWE) studies in healthcare decision-making. Clear communication about data provenance, design, analysis, and implementation is needed. This would facilitate reproducibility, replication in independent data, and assessment of potential sources of bias.Methods: The International Society for Pharmacoepidemiology (ISPE) and ISPOR-The Professional Society for Health Economics and Outcomes Research (ISPOR) convened a joint task force, including representation from key international stakeholders, to create a harmonized protocol template for RWE studies that evaluate a treatment effect and are intended to inform decision-making. The template builds on existing efforts to improve transparency and incorporates recent insights regarding the level of detail needed to enable RWE study reproducibility. The over-arching principle was to reach for sufficient clarity regarding data, design, analysis, and implementation to achieve 3 main goals. One, to help investigators thoroughly consider, then document their choices and rationale for key study parameters that define the causal question (e.g., target estimand), two, to facilitate decision-making by enabling reviewers to readily assess potential for biases related to these choices, and three, to facilitate reproducibility.Strategies To Disseminate and Facilitate Use: Recognizing that the impact of this harmonized template relies on uptake, we have outlined a plan to introduce and pilot the template with key international stakeholders over the next 2 years.Conclusion: The HARmonized Protocol Template to Enhance Reproducibility (HARPER) helps to create a shared understanding of intended scientific decisions through a common text, tabular and visual structure. The template provides a set of core recommendations for clear and reproducible RWE study protocols and is intended to be used as a backbone throughout the research process from developing a valid study protocol, to registration, through implementation and reporting on those implementation decisions. [ABSTRACT FROM AUTHOR]

We prove a central limit theorem for a sequence of random variables whose means are ambiguous and vary in an unstructured way. Their joint distribution is described by a set of (suitably equivalent) probability measures. The limit is defined by a backward stochastic differential equation that can be interpreted as modeling an ambiguous continuous-time random walk. [ABSTRACT FROM AUTHOR]

The special geographical locations and socioeconomic functions of seaports and dry ports in the global trading system render them vulnerable to diversified risks. Targeted preventive and adaptive investments can reduce the probability and potential damage of corresponding risks. This study proposes a two-period game to explore the scale and timing of prevention and adaptation by seaport and dry port authorities regarding regular and disruption risks in a competitive environment under ambiguity and asymmetric risk behaviors. The results show that risk aversion increases prevention and adaptation while risk probability ambiguity aversion decreases prevention and adaptation under the effect of information accumulation. Under risk aversion, early prevention and late adaptation are best for port authorities, whereas waiting until the next period to invest is a better option in regard to ambiguity aversion as long as the ambiguity of risk probability can be reduced through information accumulation. Moreover, we extend the risk sensitivity and probability ambiguity models to seaports and dry ports that cooperate and exhibit a leader-follower relationship. Our analysis shows that the cooperative relationship leads to the free-rider effect among port authorities, while the leader-follower relationship enables the seaport and dry port authorities to achieve a Nash equilibrium when choosing early prevention or late adaptation. The findings offer significant economic and managerial insights for the decision-making and policy-making of ports and government authorities. • Simultaneously consider both targeted preventive and adaptive investments in diversity risks. • Incorporate the asymmetric risk behaviors of port authorities and risk probability ambiguity into the model. • Clarify the investment timing under the combined effect of information accumulation and risk behaviors. [ABSTRACT FROM AUTHOR]

Earlier work has shown that in a "costly attention" setup – where group members incur costs to pay attention to informative signals - smaller group sizes generally result in more accurate decisions, contrary to the Condorcet jury theorem. This result obtains whether group members deliberate or whether they vote secretly without deliberations. Moreover, when group sizes are small, barring deliberations leads to more accurate decision-making, while allowing deliberations is optimal in larger groups. In the current paper, I analyze a costly attention setup where some group members are ambiguity averse (ambiguity exists about the other group members' likelihood of paying attention) and find that groups larger than a threshold make the best decisions. Thus, this supports Condorcet's conclusion about the benefits of a larger group, but under very different conditions. Implications for whether to allow or bar deliberations in these large committees are more nuanced and depend on whether attentive members may be sure of making the correct judgment. The results yield policy implications regarding optimal committee size and on whether committees should allow or bar deliberations. [ABSTRACT FROM AUTHOR]

Label distribution learning (LDL) is a new machine learning paradigm to solve label ambiguity and has drawn increasing attention in recent years. The importance of all labels needs to be considered under the LDL settings. A series of approaches have been proposed to deal with the LDL problem by considering the correlation of labels or instances. However, none of them focuses on finding interpretable bases to reduce the dimensions of the feature space. Inspired by the semi-nonnegative matrix factorization (semi-NMF) method, we propose a new LDL learning framework to deal with the problem through learning nonnegative components. The key insight is to explore the bases, each of which represents a class, through the label distribution and to transform the input matrix into a coefficient matrix of the space constructed by the bases. Consequently, a maximum entropy model can be adopted to learn the label distribution from the coefficient matrix. Experimental results on real-world datasets comparing our method with several state-of-the-art methods validate the performance of our approach. [ABSTRACT FROM AUTHOR]

Applying a deep learning algorithm to real financial trading records can effectively establish a profitable trading strategy, but leaking of private information during the process is a concern. To solve this problem, previous studies have proposed a privatized data "release" mechanism, but this mechanism is unable to truly protect privacy. To preserve privacy while still being able to learn useful information, we proposed a privacy-preserving generative adversarial imitation network (PPGAIN), a novel deep learning algorithm that imitates behaviors of general financial investors while preserving individual privacy. The core concept of PPGAIN is to provide class-ambiguous outputs to preserve privacy while generating sequences of trading behavior that are similar to a real investor's behavior. We used an auxiliary classifier as an adversary to ensure that the generated results are class ambiguous. We also proposed weakness replay to enforce the training of a discriminator. The PPGAIN model was trained in two stages, in the first stage, the model was trained to be realistic. In the second stage, it was trained against the auxiliary classifier to ensure that the outputs were highly realistic but could not be easily classified as an imitation from any specific individual to preserve privacy. Using real and synthetic trading records in TAIEX, we proved that our proposed method (PPGAIN) can achieve a generation quality similar to that obtained using conditional variational autoencoder models and demonstrated that PPGAIN provides better class ambiguity to preserve individual privacy than privacy-preserving adversarial network. [ABSTRACT FROM AUTHOR]

Multi-GNSS multi-frequency observations improve the accuracy, availability and reliability of the single-site RTK positioning but also simultaneously raise the communication bandwidth needed for the transmission of differential corrections. A RTK positioning algorithm is proposed that transmit the phase-only differential corrections to achieve the ambiguity-fixed RTK positioning, significantly reducing the communication bandwidth requirements. The essence of the presented method is that the code differential positioning solution implied in the conventional RTK positioning procedure can be replaced by the single point positioning solution of the rover receiver to realize RTK positioning. For typical application scenarios, the transmitted bits in a single epoch can be reduced by more than 25% when the corrections are sent with the RTCM protocol. The experiments by the GPS dual-frequency data over the 10 km and 180 km baselines shown that the performance of the presented RTK algorithm are comparable to that of the conventional RTK positioning, which verifies its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

Deep image-based modeling has received a lot of attention in recent years. Sketch-based modeling in particular has gained popularity given the ubiquitous nature of touchscreen devices. In this paper, we (i) study and compare diverse single-image reconstruction methods on sketch input, comparing the different 3D shape representations: multi-view, voxel- and point-cloud-based, mesh-based and implicit ones; and (ii) analyze the main challenges and requirements of sketch-based modeling systems. We introduce the regression loss and provide two variants of its formulation for the two most promising 3D shape representations: point clouds and signed distance functions. We show that this loss can increase general reconstruction accuracy, and the view- and style-robustness of the reconstruction methods. Moreover, we demonstrate that this loss can benefit the disentanglement of latent space to view-invariant and view-specific information, resulting in further improved performance. To address the figure-ground ambiguity typical for sparse freehand sketches, we propose a two-branch architecture that exploits sparse user labeling. We hope that our work will inform future research on sketch-based modeling. [Display omitted] • Sketch-based modeling robust to view and style change. • A new formulation of the regression loss fitted to work with SDFs. • Latent space disentanglement with regression loss increases reconstruction accuracy. • Challenges inherent to sketch input in the context of deep-reconstruction methods. • Auxiliary network learns to predict foreground mask supportting user sparse labels. [ABSTRACT FROM AUTHOR]

Background: The manifestations of Polycystic Ovary Syndrome (PCOS), including acne, hirsutism, obesity, uncertain fertility, etc., can make women anxious, worried, or even depressed with their appearance and body. However, little relevant research has been conducted in the Chinese context. This mixed-method study aimed to understand how women with PCOS in China perceive their bodies and to examine the association between body image and depression.Methods: First, 101 PCOS patients participated in a survey using the Body Surveillance subscale of the Objectified Body Consciousness Scale, the Short-form Mishel Uncertainty in Illness Scale, the Appearance Anxiety Scale, and the Beck Depression Inventory-II, which measured participants' self-objectification, illness ambiguity, appearance anxiety, and depression, respectively. Second, fifteen women joined face-to-face semi-structured in-depth interviews, investigating their illness ambiguity, objectified experience, and behaviors to pursue beauty.Results: Results indicated a high level of self-objectification, illness ambiguity, appearance anxiety, and depression among women with PCOS in China and supported the significant associations among the outcomes. Qualitative findings presented a body image of the precarious body, indiscernible identity, and distraught mind.Limitations: A convenient sampling method was used. The generalization of the study results needs further validation. Future longitudinal studies are necessary to clarify the causal relationships among outcomes.Conclusions: This study presented women's body image with PCOS and found the negative impact of body image on their depression levels. This study was of both theoretical and practical significance. Appropriate mind-body therapies were suggested for them. [ABSTRACT FROM AUTHOR]

• We model subsidy policies on transport facility adaptation under incomplete information and ambiguity. • Incomplete information and ambiguity make the facility output, adaptation investment and social welfare downwardly distorted. • Two countervailing effects, the "pooling effect" and the "agency cost effect," jointly determine the downward distortions. • When the ambiguity is sufficiently high (or low, respectively), a complete pooling (or partial pooling, respectively) strategy is adopted. • Under ambiguity, a more optimistic government leads to a larger distortion on the regulation outcomes. This paper models subsidy policies on transport facility's adaptation to climate change-related disasters under the government's incompleteness and ambiguity of disaster information. We consider a transport facility (an airport or a seaport) that is in a vertical structure with an upstream facility operator and a few downstream carriers. The exact probability of disaster damage level is privately known by the facility operator, while the government has incomplete information and has to form a probability distribution. If the government cannot accurately perceive such probability (misjudgment), ambiguity emerges. This is the first modeling work to integrate the incomplete information and ambiguity in one framework to analyze the transport investment under uncertainty. Modeling results suggest that such incomplete information and ambiguity in many cases allow the facility operator to secure information rent, and make the facility output, adaptation investment and social welfare downwardly distorted (i.e., less than the levels under complete information). The ambiguity can bring two countervailing effects under incomplete information, namely the "pooling effect" and the "agency cost effect," which jointly determine the downward distortions on market outcomes and the welfare-maximizing subsidy policy. When its degree is low, the ambiguity causes a more dominant "pooling effect" that can help alleviate the distortion, improving social welfare. In presence of ambiguity, the increasing optimism of the government would deteriorate the distortion, likely to damage social welfare. Our analytical results provide support to subsidy on adaptation and policies to promote adaptation effectiveness and carrier competition in downstream market. To alleviate possible distortion, the government also needs to be cautious to reduce ambiguity through more information acquisition, and not be too optimistic on the disaster damage. [ABSTRACT FROM AUTHOR]

The selection of a reliable and competent transportation company is a typical multi-criteria group decision-making (MCGDM) challenge in supply chain management. MCGDM has been widely used for decision support under ambiguity and uncertainty. This paper considers this problem in the setting of interval-valued q-rung orthopair fuzzy soft sets (IVq-ROFSS), a novel extension of fuzzy sets that presents an integrated approach to interpreting imperfect and ambiguous data. This study explores the novel Einstein aggregation operators (AOs) for this model, specifically the interval-valued q-rung orthopair fuzzy soft Einstein weighted average (IVq-ROFSEWA) and interval-valued q-rung orthopair fuzzy soft Einstein weighted geometric (IVq-ROFSEWG). These operators can consider large amounts of data that include all connections among parameters. Their fundamental properties (such as idempotency, boundedness, homogeneity, monotonicity, and shift invariance) are presented and proven. With the assistance of the new Einstein AOs, we design a novel MCGDM approach. A case study is presented to choose the most reliable transportation company that endorses the rationality and credibility of the proposed decision-making technique in supply chain management. Hence, this research helps with an innovative decision-support structure for assessing transport corporations. [ABSTRACT FROM AUTHOR]

Multi-criteria group decision-making (MCGDM) problems are fundamentally characterized by uncertainty and ambiguity. The quantification of such data is vital for determining the most optimal course of action. In this paper, a double-layer possibility degree-based interval-valued q-rung orthopair fuzzy sets (IV q -ROFSs) Vlsekriterijumska Optimizacija i Kompromisno Resenje (VIKOR) approach is presented. First, the possibility degrees of IV q -ROFSs are calculated and processed to establish a new neutralized decision matrix. Second, the components of the new decision matrix are taken into account for obtaining a new pair of positive and negative ideal solutions. The utility and regret measures in the VIKOR approach are ultimately determined by combining criteria weights and cumulative possibility degrees to rank the alternatives. The results of the study show that the proposed approach contributes to acquiring accurate alternative rankings in situations where traditional approaches have had difficulty generating ranks. Also, by eliminating the early normalization stage, which has a direct impact on the final ranking, the proposed approach concludes a unique ranking for each decision-making problem. Several comparative and sensitivity analysis are also conducted to exhibit robustness and applicability of the proposed approach in real world. • A novel VIKOR approach is proposed based on possibility degrees of IV q -ROFSs. • A neutralized decision matrix is constructed based on cumulative possibility degrees. • A double-layer VIKOR is proposed to consider more imprecision of linguistic data. • Normalization step of linguistic data is eliminated for the neutralized decision matrix. • A construction material selection case is explored by using the proposed approach. [ABSTRACT FROM AUTHOR]

Over the past few decades, short text classification has emerged as a critical downstream task in natural language processing (NLP). One crucial classification research issue is how to advance semantic understanding considering the short length, feature sparsity, and high ambiguity in short texts. Recently, prompt-tuning has been proposed to insert a template into the input and convert text classification tasks into equivalent cloze-style tasks. However, among most of the previous approaches, either the crafted template methods are time-consuming and labor-intensive, or automatic prompt generation methods cannot achieve satisfied performance. In this paper, we introduce a novel approach called Soft Knowledgeable Prompt-tuning for short text classification. Our method considers both the template generation and classification performance to construct prompts for label prediction. We employ five different strategies to expand the label words space for modifying soft prompts, and the integration of these strategies is used as the final verbalizer. Despite being automatic, experimental results show that our method achieved more desirable performance even than the crafted template methods, outperforming the state-of-the-art by more than 14 Accuracy points on four well-known benchmarks. • Both the automatic prompt generation and text classification performance are achieved. • 5 different strategies are used to construct verbalizer effectively and efficiently. • The comprehensive experiments evaluate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

The selection of a reliable and competent transportation company is a typical multi-criteria group decision-making (MCGDM) challenge in supply chain management. MCGDM has been widely used for decision support under ambiguity and uncertainty. This paper considers this problem in the setting of interval-valued q-rung orthopair fuzzy soft sets (IVq-ROFSS), a novel extension of fuzzy sets that presents an integrated approach to interpreting imperfect and ambiguous data. This study explores the novel Einstein aggregation operators (AOs) for this model, specifically the interval-valued q-rung orthopair fuzzy soft Einstein weighted average (IVq-ROFSEWA) and interval-valued q-rung orthopair fuzzy soft Einstein weighted geometric (IVq-ROFSEWG). These operators can consider large amounts of data that include all connections among parameters. Their fundamental properties (such as idempotency, boundedness, homogeneity, monotonicity, and shift invariance) are presented and proven. With the assistance of the new Einstein AOs, we design a novel MCGDM approach. A case study is presented to choose the most reliable transportation company that endorses the rationality and credibility of the proposed decision-making technique in supply chain management. Hence, this research helps with an innovative decision-support structure for assessing transport corporations. [ABSTRACT FROM AUTHOR]

Program induction is a crucial paradigm for complex question answering over knowledge bases. In the existing learning framework, the predicted program is required to strictly align (word-by-word) with a gold program, which could cause over penalization for minor deviations. Meanwhile, due to the existence of synonyms, program induction question answering often fails to retrieve answer from the knowledge base because individual function argument cannot perfectly replicate the target argument. To address above misalignment problems, we propose a triple alignment-enhanced complex question answering (TACQA) method by incorporating global token alignment, function alignment, and argument alignment. First, apart from the classical global token alignment, the predicted functions are extracted and aligned separately with the gold functions, enabling efficient learning of implicit structural information related to the query framework of program from input questions. Second, an argument alignment is introduced to correct the ambiguous function arguments, which enhances the disambiguation processing efficiency of multi-argument by optimizing candidate pool construction and similarity calculation. The experiments on KQA Pro show that our method consistently outperforms the SOTA methods, demonstrating the effectiveness of triple alignment processing mechanism for simultaneously addressing function misalignment and argument ambiguity in program induction and further improving the model performance. [ABSTRACT FROM AUTHOR]

In space magnetic exploration missions, the measurements obtained from the magnetic sensors mounted on a spacecraft are often influenced by the spacecraft's own magnetic interference. Therefore, to ensure precise magnetic exploration, it is imperative to conduct a thorough magnetic characterization of the spacecraft. Conducting the magnetic characterization of spacecraft within a magnetic shielded room (MSR) can mitigate the influence of environmental noise on the characterization process, thereby minimizing uncertainty and enhancing modeling accuracy. However, the utilization of high-permeability materials (such as permalloy) in the MSR introduces a disparity in magnetic characteristics between the spacecraft inside and outside the MSR. According to the method of images, the magnetic field generated by the spacecraft inside the MSR is the superposition of the magnetic fields produced by the spacecraft and that produced by several images of itself. Therefore, this may lead to an ambiguity problem, where spacecrafts with different magnetic characteristics and their corresponding images can generate identical magnetic field distribution in the MSR. To address the aforementioned issues, this paper proposes a novel method for magnetic characterization that integrates both the magnetic field vector data within the MSR and the magnetic field gradient data outside the MSR generated by the spacecraft, allowing for the elimination of the ambiguity problem without introducing environmental magnetic noise interference. As a result, it obtains a unique model for precisely representing the spacecraft's magnetic characteristics in terms of the spherical harmonic coefficients. [ABSTRACT FROM AUTHOR]

Conventional chance-constrained programming methods suffer from the inexactness of the estimated probability distribution of the underlying uncertainty from data. To this end, a distributionally robust approach to the problem allows for a level of ambiguity considered around a reference distribution. In this work, we propose a novel formulation for the distributionally robust chance-constrained programming problem using an ambiguity set constructed from a variant of optimal transport distance that was developed for Gaussian Mixture Models. We show that for multimodal process uncertainty, our proposed method provides an effective way to incorporate statistical moment information into the ambiguity set construction step, thus leading to improved optimal solutions. We illustrate the performance of our method on a numerical example as well as a chemical process case study. We show that our proposed methodology leverages the multimodal characteristics from the uncertainty data to give superior performance over the traditional Wasserstein distance-based method. • Ambiguity set constructed from optimal transport between Gaussian Mixture Models. • Hedging against the right family of candidate distributions to avoid unnecessary conservatism. • A tractable distributionally robust chance constrained optimization formulation. • Applicability to more generate type of uncertain constraints. • Better objective-constraint satisfaction trade-off performance than classical Wasserstein DRCCP model. [ABSTRACT FROM AUTHOR]

This paper investigates the multi-sensor distributed fusion with memory configuration for multi-target automatic tracking (MTAT) in clutter. Aimed at fully addressing the problems of both track origin ambiguity and the strong-correlation among system track and its paired local tracks in the framework, this paper derives an Exact Unified Multisensory Tracks Fusion with Memory (EU-MTFwM) method that fuses the system track with enumerated combinatorial tracks to achieve fusing all sensors' local tracks at once. It deploys track-to-track probabilistic association (T2TPA) and target-existence based track automatic management (TAM) to mitigate track origin ambiguity caused by clutter disturbance and the presence of multiple targets. Additionally, it implements the exact distributed kinematic state estimation (exact DKSE) to remove strong correlation stemming from common historical measurements and process noise. However, as the number of sensors to be fused increases, the proposed EU-MTFwM suffers from the combinatorial explosion. An Implementable Unified Multisensory Tracks Fusion with Memory (IU-MTFwM) method is then derived to sequentially fuse the system track with origin-ambiguous local tracks from multiple sensors one-by-one, providing a unified but computationally efficient alternative to the EU-MTFwM. Furthermore, a novel system track initialization technology is proposed to trigger the multisensory tracks fusion recursion. Numerical results show the proposed two unified methods achieve comparable tracking performance to the centralized fusion benchmark but with significantly reduced communication consumption, and demonstrate intensive improvement over the enhanced state-of-the-art that incorporates our proposed system track initialization into its fusion recursion. [ABSTRACT FROM AUTHOR]

Multilayer perceptron (MLP) fails to discriminate the ambiguous inputs belonging to the overlapping regions of multiple classes, resulting in misclassification. To classify the input samples accurately according to their classes, removing the ambiguity that occurred due to the sharing of common input space is important. In this article, a novel neural network framework, called Discriminative Regularized Input Manifold MLP (DRIM-MLP) is proposed to reduce this ambiguity and improve the classification accuracy. The proposed framework consists of two different feed forward networks that are trained simultaneously: (i) DRIM and (ii) MLP. The proposed DRIM learns the input distribution during its training and the learnt information is incorporated during the training of MLP to reduce the ambiguity. Simultaneously, the class information learnt by MLP is incorporated into the DRIM to learn discriminative information of the input distribution. The hidden layer output of DRIM estimates (i) the input of DRIM using the weights of hidden and output layers of DRIM and (ii) the class output of MLP using the weights of hidden and output layers of MLP. Here, the estimated class output, learnt by the DRIM that contains information of the input distribution, is used as a regularizer for the MLP to minimize the difference between the estimated class output of DRIM and the estimated class output of MLP itself. The hidden layer of MLP also estimates: (i) the class output of MLP using the weights between hidden and output layers of MLP and (ii) the input of DRIM using the weights between hidden and output layers of DRIM. Here, the estimated input learnt by MLP that contains class information is used as a regularizer for DRIM to minimize the difference between the estimated input of DRIM itself and the estimated input of MLP. These two regularizers are respectively called the Input manifold Regularizer (ImR) and the Discriminative Regularizer (DiscR). The experimental results based on ten standard data sets strongly support the effectiveness of the proposed DRIM-MLP compared to conventional MLP, auto encoder based MLP (AE-MLP), denoising auto encoder based MLP (DAE-MLP), AE-MLP with KLD, DAE-MLP with KLD along with two recent works of state-of-the-art. • The proposed DRIM regularizes the MLP to increase its feature discrimination capability. • The proposed DRIM-MLP learns discriminative information using regularization. • The discrimination capability of DRIM-MLP is mathematically proved. • The effectiveness of DRIM-MLP is compared with seven related methods. [ABSTRACT FROM AUTHOR]

Anomalous object detection (AOD) in medical images aims to recognize the anomalous lesions, and is crucial for early clinical diagnosis of various cancers. However, it is a difficult task because of two reasons: (1) the diversity of the anomalous lesions and (2) the ambiguity of the boundary between anomalous lesions and their normal surroundings. Unlike existing single-modality AOD models based on deterministic mapping, we constructed a probabilistic and deterministic AOD model. Specifically, we designed an uncertainty-aware prototype learning framework, which considers the diversity and ambiguity of anomalous lesions. A prototypical learning transformer (Pformer) is established to extract and store the prototype features of different anomalous lesions. Moreover, Bayesian neural uncertainty quantizer, a probabilistic model, is designed to model the distributions over the outputs of the model to measure the uncertainty of the model's detection results for each pixel. Essentially, the uncertainty of the model's anomaly detection result for a pixel can reflect the anomalous ambiguity of this pixel. Furthermore, an uncertainty-guided reasoning transformer (Uformer) is devised to employ the anomalous ambiguity, encouraging the proposed model to focus on pixels with high uncertainty. Notably, prototypical representations stored in Pformer are also utilized in anomaly reasoning that enables the model to perceive diversities of the anomalous objects. Extensive experiments on five benchmark datasets demonstrate the superiority of our proposed method. The source code will be available in github.com/umchaohuang/UPformer. [ABSTRACT FROM AUTHOR]

Multivariate curve resolution (MCR) methods are sometimes faced with missing or erroneous data, e.g., due to sensor saturation. In some cases, an estimation of the missing data is possible, but often MCR works with the largest submatrix without missing entries. This ignores all rows and columns of the data matrix that contain missing values. A successful approach to deal with incomplete data multisets has been proposed by Alier and Tauler (2013), but it does not include a factor ambiguity analysis. Here, the missing data problem is addressed in combination with a factor ambiguity analysis. An approach is presented that minimizes the factor ambiguity by extracting a maximum of spectral information even from incomplete rows and columns of the spectral data matrix. The method requires a high signal-to-noise ratio. Applications are presented for UV/Vis and HSI data. • Incomplete data sets raise problems for MCR analyses. • Bilinearity enables a partial reconstruction of the data set. • An algorithm is suggested to compute the feasible bands for incomplete data. [ABSTRACT FROM AUTHOR]

High-precision segmentation of skin lesions is essential for early diagnosis of skin cancer and improved patient survival. However, this task becomes challenging due to the irregularity of skin lesion regions, significant differences in color shades, ambiguity of boundaries, and other complex interfering factors. In this study, a transformer-based multi-attention hybrid network, TMAHU-Net, is proposed to cope with the complexity of skin lesion regions. The model introduces an innovative hybrid module that fully combines the advantages of CNNs and transformers to capture both global and local feature information. We also employ a deeply separable convolutional attention module to dynamically assign attention weights, which enhances the channel and spatial dimensions of feature information. To improve contextual relevance, we introduce an extended gated external attention module. Finally, a multi-scale aggregation module is used to fuse feature information at different scales to compensate for the differences in processing images with different resolutions. The model performs well in dealing with irregular, color shades, fuzzy boundaries, and complex interference lesion regions, which provides strong support for doctors to accurately determine complex lesion regions. Critically evaluated on three publicly available datasets, ISIC2016, ISIC2017, and PH2, TMAHU-Net achieved significant improvements over state-of-the-art methods, with F1 scores and IoU scores increasing by 1.01% and 1.01%, 1.22% and 1.40%, and 0.92% and 1.50%, respectively. • Transformer-based multi-attention hybrid network for skin lesion segmentation. • Focus on segmentation of complex lesion areas. • The LCT module integrates CNN and Transformer strengths. • Multiple attention mechanisms enable the model to focus on the target region. [ABSTRACT FROM AUTHOR]

Focused crawler traverses the Web to only collect pages that are relevant to a particular topic, and is increasingly considered as a way to get around the scalability issues with current general-purpose search engines. But the data diversity in the Web forces these crawlers to face three significant problems: (i) inconsistency, (ii) ubiquity, and (iii) ambiguity, which causes misguidance in crawling. To handle these issues, this paper proposes a weakly supervised Gated Recurrent Unit (GRU) mechanism for an adaptive focused web crawler framework that matches semantically relevant t o p i c s and w e b p a g e c o n t e n t. This weakly supervised Gated Recurrent Unit model accepts the vector form of the t o p i c and the fetched w e b p a g e as input to produce meaningful s e m a n t i c vectors and incorporates the Manhattan distance rule to compute the topical relevance of the w e b p a g e. The proposed mechanism guides the focused crawler in downloading more relevant web pages by finding the relevant hyperlinks and omitting the irrelevant hyperlinks concerning the topic. The proposed method helps the focused crawler to semantically find, arrange, and index the web pages in a relatively narrow segment of the web to solve the inconsistency, ubiquity, and ambiguity problems of the focused crawlers. The experimental results indicate that the proposed technique outperforms the s t a t e − o f − t h e − a r t approaches in terms of h a r v e s t r a t e , p r e c i s i o n , r e c a l l , h a r m o n i c m e a n , and i r r e l e v a n c e r a t i o. In summary, the strategy described here works well and is important for focused crawlers. [ABSTRACT FROM AUTHOR]

In real-world vision, people prioritise the most informative scene regions via eye-movements. According to the cognitive guidance theory of visual attention, viewers allocate visual attention to those parts of the scene that are expected to be the most informative. The expected information of a scene region is coded in the semantic distribution of that scene. Meaning maps have been proposed to capture the spatial distribution of local scene semantics in order to test cognitive guidance theories of attention. Notwithstanding the success of meaning maps, the reason for their success has been contested. This has led to at least two possible explanations for the success of meaning maps in predicting visual attention. On the one hand, meaning maps might measure scene semantics. On the other hand, meaning maps might measure scene features, overlapping with, but distinct from, scene semantics. This study aims to disentangle these two sources of information by considering both conceptual information and non-semantic scene entropy simultaneously. We found that both semantic and non-semantic information is captured by meaning maps, but scene entropy accounted for more unique variance in the success of meaning maps than conceptual information. Additionally, some explained variance was unaccounted for by either source of information. Thus, although meaning maps may index some aspect of semantic information, their success seems to be better explained by non-semantic information. We conclude that meaning maps may not yet be a good tool to test cognitive guidance theories of attention in general, since they capture non-semantic aspects of local semantic density and only a small portion of conceptual information. Rather, we suggest that researchers should better define the exact aspect of cognitive guidance theories they wish to test and then use the tool that best captures that desired semantic information. As it stands, the semantic information contained in meaning maps seems too ambiguous to draw strong conclusions about how and when semantic information guides visual attention. [ABSTRACT FROM AUTHOR]

Charges of hypocrisy are usually thought to be to be damning. Yet when a hypocrisy charge is made, there often remains disagreement about whether or not its target really is a hypocrite. Why? Three pre-registered experiments (N = 2599) conceptualize and test the role of perceived comparability in evaluating hypocrisy. Calling someone a hypocrite typically entails invoking a comparison—one meant to highlight internal contradiction and cast moral character into question. Yet there is ambiguity about which sorts of comparisons are valid in the first place. We argue that disagreements about moral hypocrisy often boil down to disagreements about comparability. Although the comparability of two situations should not depend on whose behavior is being scrutinized, observers shift comparability judgments in line with social motives to criticize or defend. In short, we identify a cognitive factor that can help to explain why, for similar patterns of behavior, people see hypocrisy in their enemies but consistency in themselves and their allies. [ABSTRACT FROM AUTHOR]

The Wigner–Ville distribution (WD) and ambiguity function (AF) are serviceable tools for the global analysis of non-stationary signals. However, when dealing with time-varying signals that exhibit changing characteristics and require real-time signal processing, they have high limitations due to the existence of cross-items. In this paper, we propose the short-time Wigner–Ville distribution (STWD) as a novel approach that effectively analyzes time-varying signals with changing characteristics. First, the definition of STWD and short-time ambiguity function (STAF) and their properties are advanced. Next, three uncertainty principles that define the lower bound for the product of signal spread and its bandwidth are derived. Then, the discrete STWD (DSTWD) and the process of computerization are also proposed which can be used more widely in practice. Finally, the optimization of window functions based on Rényi entropy is discussed. Simulation experiments show that the STWD can be effectively applied for the analysis of local change characteristics and real-time processing of signals with valid and accurate results. • The short-time Wigner-Ville distribution (STWD) is proposed. • Three uncertainty principles of the STWD are derived. • Discrete STWD and window function optimization based on Rényi entropy are discussed. [ABSTRACT FROM AUTHOR]

To enable organizations to achieve carbon neutrality through agile capabilities, this paper establishes an integrative framework of carbon-zero agility consisting of three dimensions: search scope agility, search locus agility, and search pace agility. However, applying common agile methodologies like Scrum, Kanban, and Lean to cultivate these capabilities inevitably introduces feedback ambiguity, which can paralyze decision-making and increase errors due to inherent human cognitive limitations. To address this, tailored carbon-zero feedback algorithms are proposed to complement human judgment in agile workflows. Specifically, prescriptive analytics, federated learning, and probabilistic programming are injected into Scrum, Kanban, and Lean respectively to restore clarity amidst ambiguity. The framework is grounded in cases from the textile industry to demonstrate applicability in practical settings. By targeting the roots of distortions with human-algorithm collaborations, it provides an actionable roadmap to implement carbon-zero agility. • Establishes carbon-zero agility framework: search scope, locus, and pace agility. • Identifies behavioral challenges, and feedback ambiguity in agile for carbon-zero. • Proposes tailored carbon-zero feedback algorithms to enhance agility. • Connects agile processes with broader organizational sustainability capabilities. [ABSTRACT FROM AUTHOR]

[Display omitted] • ZTD retrieval performance of GPS/BDS/Galileo frequency combinations are analyzed. • Optimal frequency combination of GPS/BDS/Galileo ZTD retrieval is L1/L2, B2/B6, and E1/E7. • GNSS-ZTD retrieval performance is related with similarity of the used frequency. • Effect analysis of ambiguity resolution and GNSS system combination on ZTD retrieval. • GNSS-ZTD retrieval performance is affected by La Niña event. The Global Navigation Satellite System (GNSS) is promising technology for zenith tropospheric delay (ZTD) retrieval. With the emergence of new multi-GNSS frequencies and maturity of precise point positioning ambiguity resolution (PPP-AR) technology, opportunities and challenges have arisen in high-precision GNSS-ZTD retrieval. However, certain strategies have usually been adopted, such as the traditional dual-frequency combination of American global positioning system (GPS) technology, for ZTD retrieval, but the influence of AR on ZTD retrieval has been ignored. To address this issue, in this study, the GNSS-ZTD retrieval performance of all existing dual-frequency combinations was first comprehensively evaluated by raw observations. Then, the influences of AR and system combinations were analysed with popular dual-frequency combinations. Finally, La Niña occurred during the GNSS data period considered in this article, and the effect on GNSS-ZTD retrieval was examined. The results of this study could not only provide a reference for GNSS-ZTD retrieval but also provide insight into the relationship between La Niña occurrence and GNSS-ZTD retrieval performance. [ABSTRACT FROM AUTHOR]

Underwater acoustic (UWA) channels exhibit a sparse multipath structure, with each path having its corresponding direction of arrival (DOA). Conventional MIMO receivers process received signals hydrophone by hydrophone, disregarding the angle domain information. This paper presents a MIMO beamforming receiver for UWA communications. The proposed method estimates DOAs of multipath signals firstly, then forms directional signals with high signal-to-noise ratio by beamforming. Subsequently, it demodulates MIMO signals for these directional signals, leading to performance improvement. The wideband and high-frequency characteristics of UWA communication signals result in the receiving array element spacing being significantly larger than the signal wavelength. The proposed method adopts frequency-difference (FD) DOA estimation, solving the issue of bearing ambiguity arising from the large element spacing. Moreover, non-overlapping pilots of MIMO-OFDM signals are employed by the proposed method to estimate multipath DOA, resulting in increased resolution for multipath DOA from different transducers. Simulation results demonstrate the outstanding performance of the proposed beamforming receiver under low signal-to-noise ratio (SNR) and multi-interference conditions. The stability of the proposed beamforming receiver is additionally validated in lake trials employing UWA communication signals in the frequency bands of 25 − 31 kHz and 20.5 − 53.5 kHz. The receiver utilizes an 8-element receiving array with a 0.25 m element spacing. • Underwater acoustic channels exhibit a sparse structure in angle domain. • The receiving array element spacing is significantly larger than the underwater acoustic communication signal wavelength. • Utilizing the angle domain characteristics of multipath signals to improve communication system performance. • Frequency-difference multipath DOA estimation solves the issue of bearing ambiguity arising from the large element spacing. • Non-overlapping pilots based multipath DOA estimation increases resolution for multipath DOA from different transducers. [ABSTRACT FROM AUTHOR]

• The boundary conditions of Fu's equations in Budyko Framework are highlighted. • Equilibrium evaporation coefficient was adjusted by Fu's equations' interrelation. • We improved the complementary principle function for better evaporation estimate. • Our blended function substantially enhances annual E estimation performance. • The improved method shows the highest improvement of daily E in extreme climates. The complementary principle function presents a framework for estimating evaporation but faces long-term challenges in calibration with a free parameter-equilibrium evaporation coefficient. The blending the evaporation precipitation ratio with complementary principle function is proposed to provide a physical basis for estimation of the equilibrium evaporation coefficient within the Budyko framework; however, it overlooks the limited possible maximum evaporation and precipitation condition from Fu's equations. In this study, we expanded the numerical ambiguity boundary conditions of Fu's equations and introduced a new parameter γ c into the blended function to fully capture the environmental conditions, aiming to mitigate errors related to extreme climates. The result indicated our improved blended function performed better than the original function. Compared to the original function, the estimated annual average evaporation across stations improved from 0.12 to 0.57 in coefficient of determination (R2) and from 322.83 mm to 172.47 mm in Root Mean Square Error. For daily evaporation estimation, the Nash-Sutcliffe Efficiency coefficient (NSE) increased from 0.32 to 0.47 and R2 increased from 0.38 to 0.48 in all validation sites. The modified blended method showed a better performance in most of the AmeriFLUX stations, especially with the highest improvement in extremely dry and wet sites. Our new approach represents a promising evaporation method in extreme climate conditions and provides support for future research to identify the rigorous equation constraints of Budyko equations. [ABSTRACT FROM AUTHOR]

Sb(III) and As(III) share similar chemical features and coexist in the environment. However, their oxidase enzymes have completely different sequences and structures. This raises an intriguing question: Could Sb(III)-oxidizing prokaryotes (SOPs) also oxidize As(III), and vice versa? Regarding this issue, previous investigations have yielded unclear, incorrect and even conflicting data. This work aims to address this matter. First, we prepared an enriched population of SOPs that comprises 55 different AnoA genes, lacking AioAB and ArxAB genes. We found that these SOPs can oxidize both Sb(III) and As(III) with comparable capabilities. To further confirm this finding, we isolated three cultivable SOP strains that have AnoA gene, but lack AioAB and ArxAB genes. We observed that they also oxidize both Sb(III) and As(III) under both anaerobic and aerobic conditions. Secondly, we obtained an enriched population of As(III)-oxidizing prokaryotes (AOPs) from As-contaminated soils, which comprises 69 different AioA genes, lacking AnoA gene. We observed that the AOP population has significant As(III)-oxidizing activities, but lack detectable Sb(III)-oxidizing activities under both aerobic and anaerobic conditions. Therefore, we convincingly show that SOPs can oxidize As(III), but AOPs cannot oxidize Sb(III). These findings clarify the previous ambiguities, confusion, errors or contradictions regarding how SOPs and AOPs oxidize each other's substrate. [Display omitted] • Sb(III)-oxidizing and As(III)-oxidizing prokaryotes, SOPs and AOPs, were prepared. • SOPs, with Sb(III) oxidase but no As(III) oxidase, oxidize both Sb(III) and As(III). • Cultivable SOP, with Sb(III) oxidase but no As(III) oxidase, oxidize both Sb(III) and As(III). • AOPs, with As(III) oxidase but no Sb(III) oxidase, oxidize As(III), not Sb(III). • Cultivable AOP, with As(III) oxidase but no Sb(III) oxidase, oxidize As(III), not Sb(III). [ABSTRACT FROM AUTHOR]

3D human pose estimation from a single image is still a challenging problem despite the large amount of work that has been performed in this field. Generally, most methods directly use neural networks and ignore certain constraints (e. g. , reprojection constraints, joint angle, and bone length constraints). While a few methods consider these constraints but train the network separately, they cannot effectively solve the depth ambiguity problem. In this paper, we propose a GAN-based model for 3D human pose estimation, in which a reprojection network is employed to learn the mapping of the distribution from 3D poses to 2D poses, and a discriminator is employed for 2D–3D consistency discrimination. We adopt a novel strategy to synchronously train the generator, the reprojection network and the discriminator. Furthermore, inspired by the typical kinematic chain space (KCS) matrix, we introduce a weighted KCS matrix and take it as one of the discriminator's inputs to impose joint angle and bone length constraints. The experimental results on Human3.6M show that our method significantly outperforms state-of-the-art methods in most cases. • We propose a synchronized adversarial training framework for 3D pose estimation. • A reprojection network learns the 3D-2D distribution instead of 3D-2D correspondence. • Synchronized training provides more reliable weakly supervised information. • A wKCS matrix is designed to impose constraints on bone lengths and joint angles. • The proposed model shows significant improvement over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The aim of this study is to better understand the prevalence, scope, and unique challenges of undergraduate and first year librarians over the last decade, through the use of quantitative and qualitative data from job listings, online surveys, and phone interviews. The findings indicate that there is very little consensus about what the undergraduate librarian does, how the work is structured on a daily basis, and how its goals are negotiated and assessed. Further, undergraduate librarians face a number of issues, including navigating vaguely defined responsibilities, establishing their professional credibility, and communicating their role within the library and to the university. Survey participants reported experiencing tension between the traditional (reference, instruction, collection development) and undergraduate-specific aspects of their duties, indicating that it is difficult for them to prioritize engagement and outreach. While the literature on blended librarianship anticipates some of these issues, undergraduate librarians are unique because they provide an intersection between broader higher education priorities and the academic library. The author stipulates that the lack of definition, strain, and the perception of undergraduate librarianship as an entry-level position is incongruent with the importance colleges and universities place on undergraduate student success. [ABSTRACT FROM AUTHOR]

This paper studies the fundamental vessel deployment problem in the liner shipping industry, which decides the numbers of mixed-type ships and their sailing frequencies on fixed routes to provide sufficient vessel capacity for fulfilling stochastic shipping demands with high probability. In reality, it is usually difficult (if not impossible) to acquire a precise joint distribution of shipping demands, as they may fluctuate heavily due to the fast-changing economic environment or unpredictable events. To address this challenge, we leverage recent advances in distributionally robust optimization and propose distribution-free robust joint chance constrained models. In the first model, we only assume support, mean as well as lower-order dispersion information of the shipping demands and provide high-quality solutions via a sequential convex optimization algorithm. Comparing with existing literature that chiefly studies individual chance constraints based on concentration inequalities and the union bound, our approach yields solutions that are less conservative and less vulnerable to the magnitude of demand dispersion. We also extend to a data-driven model based on the Wasserstein distance, which suits well in situations where limited historical demand samples are available. Our distributionally robust chance constrained models could serve as a baseline model for vessel deployment, into which we believe additional practical constraints could be incorporated seamlessly. • Distributionally robust joint chance constrained models for the vessel deployment problem. • Examples on the meaning and applications of the mean and dispersion ambiguity set in maritime industry. • Extensive experiments in data-driven setting. • More robust but less conservative deployment plans. [ABSTRACT FROM AUTHOR]

With the development of commercial services in the European Union, the advanced designs of Galileo E6 signals can be freely offered to Global Navigation Satellite System (GNSS) users. However, only a few studies have analyzed the benefit of Galileo E6 signals, and their application in the real-time instantaneous precise point positioning (PPP) with ambiguity resolution (AR) has not been investigated. In this study, based on the triple-frequency ionosphere-free combination, the positioning performance of instantaneous decimeter-level PPP AR was assessed with real-time orbit, clock and phase biases provided by the Centre National d'Études Spatiales Navigation Team (CNES/NAV). Experimenting on the GPS/Galileo observations from the 42 globally distributed stations, the wide-lane (WL) ambiguity residuals within ± 0.25 cycle for GPS L1/L2/L5, Galileo E1/E5a/E5b and E1/E5a/E6 frequency combinations were 92.54%, 90.72% and 96.48% with single-epoch data, respectively. When the WL ambiguities are fixed instantaneously, owing to the low combination noise of Galileo E1/E5a/E6, the positioning performance of instantaneous PPP AR with GPS/Galileo (E1/E5a/E6 used) outperforms that of GPS/Galileo (E1/E5a/E5b used). If the position dilution of precision is <1.5, the Root Mean Square (RMS) of GPS/Galileo (E1/E5a/E6 used) for east, north and up components are 0.13 m, 0.13 m and 0.57 m, respectively, while those of GPS/Galileo (E1/E5a/E5b used) are 0.17 m, 0.19 m and 0.62 m, respectively. Moreover, the WL ambiguity fixing rate of GPS/Galileo (E1/E5a/E6 used) is higher than that of GPS/Galileo (E1/E5a/E5b used), which shows an improvement of approximately 9% in the relatively low satellite elevation angles (20°< elevation angle < 40°). The results indicate that the Galileo E6 signal can bring a significant improvement in real-time instantaneous decimeter-level PPP AR. [ABSTRACT FROM AUTHOR]

Quantisation is a highly successful heuristic tool for constructing a quantum theory out of a classical theory --- even though it is typically accepted that the classical is derivative on the quantum. With this paper, I aim to show that there are overlooked philosophical issues concerning theory generation, and theory change, that appear when considering the quantisation of classical theories. More concretely, I will reveal three major challenges to quantisation qua method of generation and discuss how they can be met. To begin with, given that quantisation goes the wrong way (at least on any orthodox account on the relationship between the classical and the quantum), it must involve ambiguities. A central task in understanding quantisation better as a method of generation is thus to investigate the scope of these ambiguities as well as possible remedies to them. This will be the topic of the first half of the paper. Ambiguities are, however, just one aspect --- albeit a central one --- of quantisation qua method of generation. How to think of quantisation as method of generation overall? In the second part of the paper, I offer a conceptualisation of quantisation as a form of prescription for translating between two theoretical frameworks --- namely a classical and a quantum one. Making use of the characterisation of quantisation as prescriptional theory change, we make a first stab at assessing quantisation with respect to two other central issues of a generative strategy (over and above the ambiguity issue): that is how deep the structure it generates is (deep structure issue), and how it is justified (justification issue). Independently of my hope to offer the reader various specific insights on quantisation as a method of generation, this paper has already achieved its goal if it manages to reveal and map out the rich but unappreciated philosophical problems associated with quantisation qua method of generation. [ABSTRACT FROM AUTHOR]

Minimum resolution set and associated metric dimension provide the basis for unique and systematic labeling of nodes of a graph (G) using distances to a set of landmarks. Such a distance vector set, however, may not uniquely identify the graph or allow for its exact construction. The concept of construction set is presented, which overcomes these limitations. Link dimension (ξ (G)) is the minimum cardinality of a construction set. We study ambiguity in the given distance vector set and conditions for resolving them. Bounds over link dimension, proofs for link dimension for various graph families and relationship between graph dimensions and graph spectra are presented. The inverse problem of identifying whether a given set of non-negative distance vectors can be associated with a graph is also addressed, introducing the concept of graphability of distance vectors. A graph of N nodes can now be compressed to a representation with an N × ξ (G) matrix which allows for its exact reconstruction. • A graph dimension that goes beyond unique labeling to capturing complete topology. • Overcomes ambiguity in reconstructing a graph from its resolution set. • A unique way to represent a graph with a compact matrix. • Conditions for a given distance vector set to be graphable. • Conditions for a given distance vector set to correspond to a resolution set. [ABSTRACT FROM AUTHOR]

• Uncertainty about thresholds associated with tipping points often leads to considerable challenges in CPR management. • Data from an economic laboratory experiment is used to investigate the effects of threshold uncertainty and attitudes about uncertainty on CPR use. • Results suggest that threshold uncertainty increases CPR use, while attitudes about uncertainty are not significantly correlated with CPR use behavior. • Communication improves coordination, reduces the likelihood of resource collapse, and increases efficiency. Tipping points can occur in many complex environmental systems and often produce abrupt and irreversible change. Thresholds associated with tipping points are difficult to predict in many cases, leading to considerable challenges associated with the management of natural resources. We use a common pool resource (CPR) framework to investigate the impact of different types of threshold uncertainty on CPR use and the efficacy of coordination among group members. Participants in an economic laboratory experiment made decisions on the use of a CPR under three types of threshold treatments – a known threshold (certainty), an uncertain threshold with a known probability distribution of possible thresholds (risk), and an uncertain threshold with an unknown probability distribution (ambiguity). We also tested the effect of communication on coordination among participants in each treatment. We find that while threshold uncertainty (both risk and ambiguity) tends to increase CPR use, communication reduces the use of shared resources and increases social efficiency. Communication reduces the incidence of coordination failure in the presence of both types of threshold uncertainty. A takeaway from our results is that encouraging communication is likely to improve CPR management, while improving information about the probability of specific thresholds may only be useful if thresholds can be identified with certainty. [ABSTRACT FROM AUTHOR]

Point cloud semantic segmentation is an ingredient in understanding real-world scenes. Most existing approaches perform poorly on scene boundaries and struggle with recognizing objects of different scales. In this paper, we propose a novel framework that incorporates Transformer into the U-Net architecture for inferring pointwise semantics. Specifically, the Transformer-based cross-feature fusion module is designed first to employ geometric and semantic information to learn feature offsets to overcome the border ambiguity of segmentation results, and then it utilizes the Transformer to learn cross-feature enhanced and fused encoder features. Additionally, to facilitate the overall network's structure-to-detail perception capabilities, the adaptive perception module is designed, which employs cross-attention to adaptively allocate weights to encoder features at varying resolutions, establishing long-range contextual dependencies. Ablation studies validate the individual contributions of our module design choices. Compared with the existing competitive methods, our approach achieves state-of-the-art performance and exhibits superior results on benchmarks. Code is available at https://github.com/xiluo-cug/TCFAP-Net. • A novel framework that incorporates Transformer into the U-Net architecture. • An encoder leverages Transformer to facilitate cross-feature fusion. • An adaptive perception module captures multiscale and contextual information. • Achieving outstanding performance on diverse large-scale point cloud datasets. [ABSTRACT FROM AUTHOR]

Monocular estimation of 3D human poses is challenging due to ambiguity in depths and partial occlusion. Most recent works define this as a 2D-to-3D lifting task, taking 2D key point sequences and using spatial and temporal relationships. However, prior works focus on capturing spatio-temporal correlations but ignore the motion of joints that is needed for continuous estimation. To extend the potential of 2D-to-3D pose estimation, we propose TSwinPose , which learns multi-scale spatio-temporal representations from 2D key point locations and patterns of motion. The input 2D key point sequences are enhanced by JointFlow, which encodes the motion of each human joint. Based on Swin-Transformer, we designed a temporal domain Swin-Unet structure to model multi-scale spatio-temporal relationships of human joints across different temporal windows. The final 3D pose generated by multi-stage representations is consistent temporally and has a higher accuracy. Experiments conducted on three benchmark datasets, Human3.6M, MPI-INF-3DHP, and HumanEva-I, demonstrate that TSwinPose achieves performance that is on par with state-of-the-art methods. Moreover, the introduction of JointFlow as a plug-in extension enhances performance significantly, particularly benefiting long-term 2D-to-3D lifting human pose estimation methods. • TSwinPose, a novel multi-scale spatio-temporal monocular 3D pose estimation method. • JointFlow, a plug-in extension which boosts 2D-to-3D lifting human pose estimation. • Thorough analysis shows SOTA results and confirms the performance boost of JointFlow. [ABSTRACT FROM AUTHOR]

Adequate representation of individual opinions, as well as effective multi-criteria group decision-making (MCGDM) models, are critical to the evaluation of livable cities. Problems often encountered in expressing individual opinions include how to characterize ambiguity, credibility, and uncertainty simultaneously. In this study, an interval-value Z-probabilistic double hierarchy linguistic MCGDM model based on the ratio system, the reference point, and the full multiplicative form (IZPDHL-RRF) is proposed, which offers a comprehensive information representation and addresses ambiguity, credibility and uncertainty in livable city assessments. Firstly, the Interval-Value Z-probabilistic double hierarchy linguistic term set (IZPDHLTS) is introduced, which can represent individual opinions more adequately. Then, a score function based on prospect theory is proposed to effectively compare linguistic variables, and the correlation and distance measures of IZPDHLVs are studied. Finally, the IZPDHL-RRF method for evaluating habitable city selection has been established. Comparative and sensitivity analysis are conducted to validate the efficacy and superiority of this method. [ABSTRACT FROM AUTHOR]

Semantic segmentation models comprise an encoder to extract features and a classifier for prediction. However, the learning of the classifier suffers from the ambiguity which is caused by two factors: (1) the weights of a classifier for similar categories may have positive similarities lowing the performance for similar categories, named correlation ambiguity , and (2) the classifier is prone to predict the category with a larger ℓ 2 norm and vice versa, termed prior ambiguity. To comedy the issues, we propose Category-Basis Prototype (CBP), frozen and mutually orthogonalized prototypes with equal ℓ 2 norm. Orthogonalization prevents the prototypes from being similar to each other and the equality decouples the prediction from the ℓ 2 norm. To better shape the feature space, we propose Online Centroid Contrastive Loss (OCCL) equipped with centroid and category-level losses. Experiments show that our method yields compelling results over two widely applied benchmarks indicating the effectiveness of our methods. • Frozen prototypes outperform the learning-based classifier in semantic segmentation. • We propose a new loss function to further shape the feature space for frozen prototypes. • Discriminatively initialized prototypes contain much potential for future tasks. [ABSTRACT FROM AUTHOR]

It is widely acknowledged that opaque orthographies place additional demands on learning, often requiring many years to fully acquire. It is less widely recognized, however, that such opacity may offer certain benefits in the context of reading. For example, heterographic homophones such as ⟨knight⟩ and ⟨night⟩ (words that sound the same but which are spelled differently) impose additional costs in learning but reduce ambiguity in reading. Here, we consider the possibility that—left to evolve freely—writing systems will sometimes choose to forego some simplicity for the sake of informativeness when there is functional pressure to do so. We investigate this hypothesis by simulating the evolution of orthography as it is transmitted from one generation to the next, both with and without a communicative pressure for ambiguity avoidance. In addition, we consider two mechanisms by which informative heterography might be selected for: differentiation, in which new spellings are created to differentiate meaning (e.g., ⟨lite⟩ vs. ⟨light⟩), and conservation, in which heterography arises as a byproduct of sound change (e.g., ⟨meat⟩ vs. ⟨meet⟩). Under pressure from learning alone, orthographic systems become transparent, but when combined with communicative pressure, they tend to favor some additional informativeness. Nevertheless, our findings also suggest that, in the long term, simpler, transparent spellings may be preferred in the absence of top-down explicit teaching. [ABSTRACT FROM AUTHOR]

• A method of surface reconstruction of deep holes is proposed, which overcomes the convex-concave ambiguity in the reconstruction methods based on image brightness, and realizes the measurement of defect contour and depth on the inner surface of deep holes. • The inBRDF statistical model of the inner surface of the hole is established to accurately describe the reflection characteristics of the hole, and the mapping relationship between the brightness of the endoscope image and the normal deviation angle of the inner surface is obtained. • A distortion correction method for deep hole endoscope images is proposed, which improves the correction accuracy of the edge area of the endoscope image and realizes the accurate measurement of the defect contour. A reconstruction method is proposed based on the reflection characteristics to measure the defect profile and depth of the inner surface of the hole. In addition to capturing geometric information of the scene, this method decomposes the distortion in the endoscope image into circumferential and axial distortions, thereby improving correction accuracy and enabling accurate measurement of the defect contour. The bidirectional reflection distribution function (BRDF) is established based on reflection characteristic theory, allowing for mapping between image brightness and normal declination. By accumulating tangent values of normal deflection angles, the height of axial contour lines inside the hole can be reconstructed to achieve defect shape reconstruction and depth measurement. This approach overcomes convex-concave ambiguity in shape from shading (SFS), as demonstrated by experimental results. The proposed detection method offers equipment versatility and high measurement efficiency, making it suitable for machine vision-based detection of internal surface defects in holes. [ABSTRACT FROM AUTHOR]