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During scenic opera rehearsals, the participants create performance bodies – fictive behaviours that portray the characters in the libretto. They use depictions – interactional practices comprised of short scenes staged for the other participants – to propose and negotiate performance bodies that suit the developing aesthetics of the production. In this paper, we focus on non-serious proposal depictions: depictions that become treated as laughable and not suitable for the performance. Non-serious depictions can accomplish joint fictionalizations, especially with teasing (Cantarutti, 2022), and are used in contrast with an ideal performance (Keevallik, 2010). Building on this work, we analyze how non-serious depictions are used to decide what the wished performance will be. We discuss two types of non-serious depictions in the workplace setting of the opera rehearsal process and show how negotiations over the seriousness of depictions achieve aesthetic intersubjectivity among the colleagues. The ambiguity between serious and non-serious proposals is exploited as a resource when navigating the unknown territories of a piece of art under development. The material consists of 20 h of video-recorded opera rehearsals in Swedish and English, with an Italian libretto. • Opera rehearsal participants use depictions in proposals to create a performance. • Depictions can be designed as either serious or non-serious. • This paper focuses on how depictions are designed and treated as non-serious. • Non-serious depictions can be treated as tangential to the interactional agenda. • They can also be a tool to manage aesthetic intersubjectivity and affiliation. [ABSTRACT FROM AUTHOR]

Since the full system deployment of BDS-3 in July 2020, it has provided stable and reliable positioning, navigation and timing (PNT) services for users around the world. It is an urgent task to explore the potential performance of precise point positioning (PPP) based on the full constellation of BDS-2/BDS-3. This research presented the global satellite visibility of BDS-2/BDS-3, analyzed the stability and accuracy of BDS-2 and BDS-3 rubidium clocks and hydrogen clocks, jointly estimated the BDS-2 and BDS-3 fractional cycle biases (FCB) and evaluated the PPP performance with ambiguity resolution (AR). Due to the fact that International GNSS Service (IGS) precise products have a great impact on narrow-lane (NL) FCB estimation, the FCB obtained by using the precise products from CODE (Center for Orbit Determination in Europe) and GBM (Deutsches GeoForschungsZentrum) in two different analysis centers and the FCB obtained by using GBM precise products with different calculation strategies are analyzed. In addition, the experimental data from Multi-GNSS Experiment (MGEX), EUREF (EPN) and Germany (GPN) observation networks are used to evaluate the influence of orbital errors on BDS FCB estimation. For wide-lane (WL) FCB, MGEX WL FCB has the highest accuracy, followed by EPN and GPN. On the contrary, for NL FCB, MGEX NL FCB has the worst accuracy, followed by EPN, and GPN is the best. Eventually, numerous experiment results showed that compared with the static float solution, the PPP fixed solutions in East (E), North (N) and Up (U) directions are improved by 32.9%, 27.7%, 13.8% for BDS-3 and 10.0%, 7.5%, 3.8% for BDS-2/BDS-3. Compared with the kinematic float solution, the PPP fixed solutions in E, N and U directions are improved by 27.9%, 23.4%, 16.6% for BDS-3 and 15.9%, 8.9%, 5.3% for BDS-2/BDS-3. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) network analysis benefits from precise point positioning with undifferenced (UD) or double differenced (DD) integer ambiguity resolution (IAR). Position time series with UD-IAR and a practical DD-IAR are compared for a mixture network consisting of 57 global and 82 regional stations with 4-year observations. The sparse global stations are more affected by different IAR methods than the regional stations of Crustal Movement Observation Network of China (CMONOC). The most significant differences between UD-IAR and the practical DD-IAR are observed in velocity estimations, with averages of 0.15 and 0.30 mm/yr for the horizontal and vertical components, respectively. UD-IAR can achieve smaller root mean square at 70–83 % of the daily solutions than the practical DD-IAR for global stations. Nevertheless, moderate differences of frame parameters and annual variations are introduced by different IAR methods. The IAR is also proved to be one of the potential origins for Global Positioning System (GPS) draconitic signals and white noise at high frequency. [ABSTRACT FROM AUTHOR]

With the capability for single-receiver AR (Ambiguity Resolution), real-time GNSS (Global Navigation Satellite System) satellite integer clock has been the research hotspot of GNSS community in the past decade. Robust ambiguity datum is one prerequisite for high-quality integer clock. In this paper, with undifferenced AR model derived, a robust ambiguity datum definition method is proposed to isolate the signal biases from the integer ambiguity. Firstly, based on the ambiguity graph, the ambiguity datum is selected with the minimum spanning tree algorithm. Secondly, the ambiguity datum is transferred to the subsequent epochs through signal bias connection. Moreover, additional datum ambiguities are introduced to accommodate the arising of receiver and satellite in real-time circumstance. With the combination of ambiguity graph and signal bias, the efficient definition and smooth transition of ambiguity datum are assured for the real-time processing. To validate the approach, simulated real-time integer clock estimation for GPS/Galileo/BDS-3 satellites are conducted with one-month (January 2022) data from about 100 global IGS (International GNSS Service) network stations. Experiments show that the proposed robust ambiguity datum can produce accurate real-time integer clock. The average WL (Wide-Lane) and NL (Narrow-Lane) fixed rates exceed 99 % and 95 %, respectively, for most of the GPS/Galileo satellites. While comparable WL fixed rate is achieved for the BDS-3 satellites, the NL fixed rate is about 5 % lower as a result of poor orbit quality and imperfect measurement model. With eclipsing satellites excluded, the standard deviations between real-time integer clock and post-processed reference clock reach 0.018 ns and 0.016 ns for the GPS and Galileo constellations, respectively, which are 28 % and 24 % smaller than those of corresponding float clocks. In contrast, the standard deviations of float and integer clocks are similar for the BDS-3 constellation. Further improvements on the background models, including unconservative force modeling, satellite attitude modeling and antenna calibration are required to achieve ultimate accuracy, especially for the BDS-3 satellites. [ABSTRACT FROM AUTHOR]

Photometric calibration is essential to many computer vision applications. One of its key benefits is enhancing the performance of Visual SLAM, especially when it depends on a direct method for tracking, such as the standard KLT algorithm. Additionally, it proves valuable in extracting sensor irradiance values from measured intensities, serving as a pre-processing step for a number of vision algorithms such as shape-from-shading. Current photometric calibration systems rely on a joint optimization problem and encounter an ambiguity in the estimates, which can only be resolved using ground truth information. We propose a novel method that solves for photometric parameters using a sequential estimation approach. To enhance the decoupling of CRF and Vignette estimation, we strategically utilize keyframes with high exposure ratios and small displacements for the former, and keyframes with relatively large displacements for the latter. Our proposed method achieves high accuracy in estimating all parameters; furthermore, the formulations are linear and convex, which makes the solution fast and suitable for online applications. Experiments on a Visual Odometry system validate the proposed method and demonstrate its advantages. • Overcoming the exponential ambiguity found in previous approaches. • Eliminating the need for CRF ground truth data point. • Yielding a quick and computationally efficient estimation of the parameters. • Accuracies that are at par with (or better than) state-of-the-art in estimating photometric parameters. [ABSTRACT FROM AUTHOR]

With the rapid progress of Global Navigation Satellite Systems (GNSS), integer ambiguity resolution has gradually become one of the key factors that affecting Precise Point Positioning (PPP) to achieve high-precision positioning results and fast convergence. Fortunately, the proposal of observable-specific code/phase biases (OSB) has greatly alleviated this problem tremendously. Different from obtaining OSB by dual-frequency linear combinations, this research proposed BeiDou Navigation Satellite System (BDS) OSB estimation method from raw observations. This method allows for more flexible estimation in a global network, without the need to compose the complex linear combinations when involving multi-frequency measurements processing. The BDS-3 observation data collected from 112 stations over one month (October 2022), containing B1C/B1I/B2a/B2b/B3I frequencies, were used for five-frequency phase OSBs estimation. Subsequently, static and kinematic PPP were executed utilizing two different ambiguity resolution strategies to corroborate the efficacy of this method. The results revealed that the BDS-3 five-frequency phase OSBs possessed good monthly stability, with standard deviation less than 0.3 cycle. High position accuracy was achieved in the static mode by fixing the ambiguity directly, with root mean square (RMS) of 3.0, 4.8 and 9.9 mm. Meanwhile, the ambiguity fixed rate was maintained at a high level around 96 % in multi-frequency tests. Simultaneously, the kinematic mode also presented the centimeter-level dynamic positioning results and the positioning accuracy improved by 55, 31 and 17 % in the East, North and Up components, respectively. Compared to the float solutions, the adoption of multi-frequency OSBs significantly accelerated the convergence speed by 55 % on average. [ABSTRACT FROM AUTHOR]

Multi-frequency integration can accelerate the ambiguity resolution (AR) and shorten the convergence time of precise point positioning (PPP). BDS-3 satellites can broadcast legacy B1I/B3I and new B1C/B2a/B2b/B2a + b signals, which facilitates the multi-frequency PPP. An uncalibrated phase delay (UPD) estimation method and a fast AR method are extended for BDS-3 six-frequency uncombined PPP. The characteristics of UPD estimates are analyzed in terms of stability, ambiguity residuals, and usage rate. The data sets on seven consecutive days from 224 globally distributed stations are selected for UPD estimation, and those from 21 stations are employed for PPP AR. The results indicate that the static convergence time of BDS-3 six-frequency PPP ambiguity-fixed solutions (with a threshold of 10 cm) can reach 8.2, 4.0, and 13.4 min in the east, north, and up directions, respectively, and the three-dimensional (3D) convergence time is shortened by 21.1 % and 26.5 % compared with the six-frequency ambiguity-float solutions and the dual-frequency ambiguity-fixed solutions, respectively. The corresponding kinematic convergence time can reach 13.0, 7.0, and 33.2 min in three directions, respectively, and the corresponding improvement rates are 21.0 % and 33.7 %, respectively. In addition, the convergence time of BDS-3 six-frequency PPP ambiguity-fixed solutions can be slightly shortened even compared with the five-frequency case. The static and kinematic positioning accuracy of BDS-3 six-frequency PPP ambiguity-fixed solutions can reach 4, 3, and 9 mm, and 18, 14, and 38 mm in three directions, respectively, which is slightly better than other cases. [ABSTRACT FROM AUTHOR]

• Households that buy more on discount waste less food. • We combined actual waste data, shopping behavior data and psychographic data. • Environmentally concerned consumers produce less food waste. • Larger households are observed to produce more actual food waste. • Perceived food waste correlates positively with actual food waste. In the debate about food waste, pricing mechanisms used in marketing are criticized to trigger consumers to over-purchase and consequently waste food. Despite the ambiguity of the scientific results on the matter, the criticism persists, prompting many retailers to abolish 'buy one get one free' price promotions. However, evidence whether this initiative is justified are lacking. In this paper we aim to empirically explore the relation between retail price promotions and household level food waste. We collected actual food waste data, gathered discount shopping behaviour data through shopping receipts, as well as attitudinal data through a questionnaire from a sample of 118 households in Denmark. Our results provide evidence to counter the criticism and show that households with the largest actual proportion of food bought on discount waste less food, as well as those who are environmentally conscious, whereas larger households and those who lack skills in food practices waste more food. [ABSTRACT FROM AUTHOR]

In this paper, we investigate ambiguity in language use that is intended by the speaker. Our overall argument is that, as a general feature of language resulting from the indeterminacy of meaning at all levels of language, ambiguity can be taken advantage of in specific contexts for a host of purposes. Given that an ambiguous utterance has (at least) two possible meanings – A and B – there are two logical possibilities for a speaker in a deliberate ambiguity: to mean both A and B or to mean only A (or B). However, a closer examination of the literature and available data reveal a far more complicated picture: there are more strategies under either of these possibilities, each of which closely interacts with the context and is motivated by the speaker's purpose of communication in that context. The treatment of deliberate ambiguity in the present study is thus more coherent theoretically and more comprehensive in coverage than what is found in the sizable literature on the subject. • Ambiguity can be deliberate and advantageous for both the speaker and hearer. • Deliberate ambiguity leads to seven strategies. • Five of the seven are speaker-oriented, two are hearer-oriented. • Deliberate ambiguity strategies are motivated by specific contextual factors. [ABSTRACT FROM AUTHOR]

This paper proposes a new, fully automatic and robust approach to generating triangular meshes directly from volumetric data (scanned images), particularly when these images contain multiple adjacent labels. Current meshing techniques produce a number of mesh elements directly related to the number of components (voxels) in the image. This number can be considerable if the image is large. The proposed methodology is significantly less dense in terms of the number of elements compared to marching cube methods. The proposed method presents no configuration ambiguity and is faithful to the original morphology of the images regardless of the thinness of the topologies or the presence of possible erratic morphological configurations that may lead to geometric interpretation indecisions. [Display omitted] • New fully automatic and robust approach to generate triangular meshes directly from volumetric data, in particular when these images contain adjoining multiple-label. • The proposed methodology is significantly less dense in terms of the number of elements compared to Marching Cube Methods. • The proposed method presents no configuration ambiguity and is faithful to the original morphology of the images regardless of the thinness of the topologies or the presence of possible erratic morphological configurations that may lead to geometric interpretation indecisions. [ABSTRACT FROM AUTHOR]

The success of a multi-kilometre drive by a solar-powered rover at the lunar south pole depends upon careful planning in space and time due to highly dynamic solar illumination conditions. An additional challenge is that the rover may be subject to random faults that can temporarily delay long-range traverses. The majority of existing global spatiotemporal planners assume a deterministic rover-environment model and do not account for random faults. In this paper, we consider a random fault profile with a known, average spatial fault rate. We introduce a methodology to compute recovery policies that maximize the probability of survival of a solar-powered rover from different start states. A recovery policy defines a set of recourse actions to reach a safe location with sufficient battery energy remaining, given the local solar illumination conditions. We solve a stochastic reach-avoid problem using dynamic programming to find an optimal recovery policy. Our focus, in part, is on the implications of state space discretization, which is required in practical implementations. We propose a modified dynamic programming algorithm that conservatively accounts for approximation errors. To demonstrate the benefits of our approach, we compare against existing methods in scenarios where a solar-powered rover seeks to safely exit from permanently shadowed regions in the Cabeus area at the lunar south pole. We also highlight the relevance of our methodology for mission formulation and trade safety analysis by comparing different rover mobility models in simulated recovery drives from the LCROSS impact region. • The safety of a solar-powered rover depends on its location, energy and time of day. • Maximally safe behaviours depend on when and where delays occur during a traverse. • The probability of survival of a rover varies non-monotonically with time. • Min–max dynamic programming can conservatively account for the temporal risk ambiguity. [ABSTRACT FROM AUTHOR]

Academic and industry experts build their careers on accurate and precise definitions and meanings of domain-specific terminology, whereas novice practitioners and lay-persons often conflate similar or similar-sounding terms to describe concepts in the same domain of interest. This is common in the space sector when individuals of different academic or practitioner backgrounds discuss "commercial" topics. In English, as in other languages, the word "space" is often followed by words such as "industry," "industry segment," "industrial base," "market," "ecosystem" and others. The practice of casually using words with ambiguous meanings may often be appropriate for informal discussion if it facilitates the communication and exchange of ideas and all parties generally understand the implications of the casual usage. This practice, however, is problematic for detailed and analytical applications requiring precision and order of thought. Grounded in contemporary organization theory and economic practice, this paper provides background discussions of different levels of analysis that characterize embedded hierarchical social systems of organizations as they pertain to space. This paper then recommends a hierarchical taxonomy for defining specific terms that describe organizations and activities within the space domain for use by practitioners, analysts and academics requiring more precise language to communicate their market analysis ideas. This taxonomy places specific terms within each level of analysis discussed and provides definitions of each, with the intention of stimulating critical discussion and potential modifications or improvements. • Using phrases like "space industry" can be vague and problematic for precise analytical tasks. • For accurate space terminology, understanding analysis levels in organizational systems is key. • This paper offers a taxonomy for clearer language in space market analysis communication. • Proper terminology aids in strategic decisions for everyone in the space sector. • Feedback on this proposed taxonomy is encouraged for potential enhancements. [ABSTRACT FROM AUTHOR]

• An improved SICB correction model is proposed based on elevation and azimuth angles. • The parameters are estimated for each BDS frequency with node-separation of 1°. • The threshold of elevation for the new model is determined based on the statistics. • Weight-average algorithm with SNR is used to obtain the optimal MP deviations. The satellite-induced code bias (SICB) resulting in code-phase divergences of nearly 1.0 m has been observed in the code measurements of BeiDou navigation satellite system (BDS). Since the SICB is proved to be elevation- and frequency-dependent, several SICB correction models have been proposed to remove the systematic bias, but most of them only consider elevation angles, overlooking the influence of azimuth angles, which would limit the accuracy of the correction models, especially for Medium Earth Orbit (MEO) satellites. To deal with this problem, we proposed an improved SICB piecewise correction model as a function of elevation and azimuth angles and estimated parameters for each BDS un-GEO (Geostationary Earth Orbit) satellite and each available frequency with node-separation of 1°. The epoch-difference method is employed to remove the effect of stable terms, such as phase-ambiguities, receiver- and satellite-dependent hardware delays. Moreover, to reduce the effect of code noise and multipath at low-elevation conditions, the threshold of elevation angle for the improved model is determined based on the statistics of Multipath (MP) combinations. Besides, weight-average algorithm is used to obtain the optimal MP deviations and improve the accuracy of proposed correction model in consideration of signal-to-noise (SNR). The experiment results with real GNSS data show that the SICB models are more suitable for BDS-2 MEO satellites than Inclined Geosynchronous Orbit (IGSO) satellites. The code measurements on B1 signal are more susceptible to the SICB than that on B3 signal. After applying the improved SICB correction models, the RMS errors of MP series are significantly reduced for BDS-2 un-GEO satellites. However, for BDS-3 satellites, only centimeter-level SICB deviations can be detected for all frequency-bands, and the RMS of MP variations have slight changes after applying the SICB corrections, indicating that the systematic bias in MP combinations is not obvious, especially for BDS-3 MEO satellites. [ABSTRACT FROM AUTHOR]

A major concern with Bayesian decision making under uncertainty is the use of a single probability measure to quantify all relevant uncertainty. This paper studies prior robustness as a form of continuity of the value of a decision problem. I show that this notion of robustness is characterized by a form of stable choice over a sequence of perturbed decision problems, in which the available acts are perturbed in a precise fashion. I then introduce a choice-based measure of prior robustness and apply it to models of climate mitigation and portfolio choice. • Belief Robustness and Continuity: Beliefs are "robust" if minor changes in beliefs lead to minor changes in choices. This notion can be tested by evaluating perturbations of feasible bets. • Measure of Robustness: A comparative measure of belief robustness can quantify the sensitivity of optimal choices to parametric assumptions. • Impact on Key Economic Models: The measure shows that heavy-tailed distributions yield more robust choices in portfolio and economy-climate models, highlighting its practical importance. [ABSTRACT FROM AUTHOR]

Complexity of the problem of choosing among uncertain acts is a salient feature of many of the environments in which departures from expected utility theory are observed. I study a class of Generalized Simple Bounds preferences in which acts that are complex from the perspective of the decision maker are bracketed by "simple" acts to which they are related by statewise dominance. I then study a refinement of the model in which the size of the partition with respect to which an act is measurable arises endogenously as a measure of subjective complexity. Finally, I consider choice behavior characterized by a "cautious completion" of Simple Bounds preferences, and discuss the relationship between this model and models of ambiguity aversion. I develop general comparative statics results, and explore applications to portfolio choice and insurance choice. • Problems of choice problems under uncertainty are complex. • From behavioral axioms, I characterize the set of acts that are well-understood. • Decision makers use acts they understand well to bracket those they do not. • The model is tractable, and admits rich comparative statics. • The model helps explain observed choice patterns in applications. [ABSTRACT FROM AUTHOR]

In this short note, we show that the generalized tree shift operation increases the distance spectral radius, distance signless Laplacian spectral radius, and the D α -spectral radius of complements of trees. As a consequence of these results, we correct some ambiguities in the proofs of some known results. [ABSTRACT FROM AUTHOR]

Nowadays, the BDS-3 satellites transmit new signals B1c and B2a that are compatible with GPS and Galileo, which is meaningful to fully take advantage of the BDS positioning capability. However, further research and analysis are needed to investigate the precise point positioning ambiguity resolution (PPP-AR) performance of the new BDS-3 signal combination. In this manuscript, the characteristics and quality of differential code bias (DCB), inter frequency clock bias (IFCB) and uncalibrated phase delay (UPD) of BDS-3 new signals are analyzed based on 30 days observation data from 70 multi GNSS experiment (MGEX) stations. The results show that the mean value of IFCB between B1I/B3I and B1C/B2a is 1.18 cm, which indicates that the IFCB can be ignored when performing the clock errors reference conversion of BDS-3 satellites. Besides, the DCB of the new BDS-3 signals varies within 0.2 ns over 30 days, and with a difference of 0.08 ns compared to the DCB product provided by chinese academy of science (CAS). The percentage of the estimated residuals less than 0.25 cycles for new signals WL UPD and less than 0.15 cycles for new signals NL UPD are 99.76% and 97.19%, respectively. The above results demonstrate the reliability of the DCB and UPD products, which can be used to estimate the carrier phase observable specific signal bias (OSB) product for the new signals. The average positioning errors of the BDS-3 new signals static PPP-AR in the N, E and U directions is 0.50 cm, 0.43 cm and 1.62 cm respectively, with an improvement of 18.3%, 28.3% and 11.5% compared to the ambiguity float static PPP. The average positioning errors of the kinematic PPP-AR are 2.09 cm, 2.67 cm and 4.79 cm in three directions, respectively, with an improvement of 17.1%, 23.9% and 15.7% compared to the ambiguity float PPP. The convergence time of the static and kinematic PPP-AR are 26.36 min and 29.12 min, respectively. [ABSTRACT FROM AUTHOR]

Ambiguity resolution (AR) is critical for enhancing the orbit accuracy in precise orbit determination (POD) for low earth orbit (LEO) satellites. While orbit estimation using single-difference (SD) AR has been widely researched, the investigation of orbit estimation using undifferenced (UD) AR for LEO satellites is limited due to time-varying hardware biases at the LEO receiver end. To address this deficiency, we propose an improved UD AR method for LEO satellite orbit determination. The method employs the optimal integer datum ambiguity for a 1-day observation arc, and the random-walk clock model is utilized to transfer the integer ambiguity datum to the other epochs, which enables the arc-wise ambiguities to regain the integer property within the time frame. To validate the effectiveness of our improved method, numerical experiments are conducted. Moreover, we assess the performance of the random-walk clock model for a spaceborne ultra-stable oscillator (USO). The high frequency stability of the USO establishes the satisfactory requirements for this study. Both in the kinematic and dynamic modes, the AR success rates of our improved method are ∼ 2% higher than that of the current SD AR method, and the orbit results indicate a slight improvement. The 3-Dimensional (3D) root-mean-squares (RMS) of the orbit differences between JPL precise science orbits (PSO) and our orbits are reduced by up to 4% and 5% for kinematic and dynamic orbits, respectively. The subtle benefit is also proven by K-band ranging (KBR) system validations. In the case of the Gravity Recovery and Climate Experiment Follow-on (GFO) satellites, the results indicate that the effect of our improved UD AR can be equivalent to that of SD AR. Our improved UD AR method can be a good alternative for its superior ability to generate hardware delay at the LEO receiver end. [ABSTRACT FROM AUTHOR]

Preventing postoperative organ dysfunction is integral to the practice of anaesthesia. Although intraoperative hypotension is associated with postoperative end organ dysfunction, there remains ambiguity with regards to its definition, targets, thresholds for initiating treatment, and ideal treatment modalities. [ABSTRACT FROM AUTHOR]

Wind turbines and solar photovoltaic (PV) systems are intermittent and uncertain energy sources that disturb grid planning operations. In this paper, we establish a multi-objective distributionally robust optimization model for optimal locations of wind turbines and solar photovoltaics (PV) that minimize the variance of renewable energy sources and maximize power production. Moreover, this paper evaluates the accuracy of the Autoregressive Moving Average (ARMA), Deep Learning Gated Recurrent unit (GRU), and Deep Learning Long Short-Term Memory (LSTM) as forecasting models for wind speed and solar irradiation and compares their root mean square errors (RMSE). Using the forecasting error information, we characterize the uncertain variables in the ambiguity set, incorporating the bounds, means, and covariance values. Furthermore, we propose a modified multi-objective non-dominated sorting genetic algorithm (NSGA-II) approach to achieve a tractable Pareto front solution. To verify the effectiveness of the model, we use the actual candidate sites for wind turbines and solar photovoltaic (PV) systems in Saudi Arabia. The results demonstrate that our proposed model is an attractive and less conservative solution than a multi-objective robust optimization model when considering forecasting uncertainties. [ABSTRACT FROM AUTHOR]

Clasts of feldspathic lithologies such as magnesian anorthosites, anorthositic troctolites, and granulites in lunar meteorites have greatly expanded knowledge of the lithologic diversity of the lunar crust. However, their origins and relationships to other major crustal lithologies such as the ferroan anorthosites, magnesian-suite, and alkali-suite are not fully understood. Here we present the results of phase equilibrium modeling using the MELTS and MAGFOX programs designed to investigate the origins of lunar crustal lithologies and petrologic connections between them. We show that the major and trace element compositions of the Mg- and alkali-suites are consistent with partial melting of hybridized sources and are inconsistent with decompression melting + assimilation models. Our results also show that the vertical trend in Mg# in mafic silicates vs. An# in plagioclase characteristic of feldspathic meteoritic lithologies and ferroan anorthosites can be produced by fractional crystallization of KREEP-free Mg-suite melts, but that these melts are not likely to contribute significantly to the composition of the global crust. Furthermore, we calculated potential parental melt compositions for these crustal lithologies using the abundances of REEs in plagioclase in FANs, the Mg- and alkali-suites, and clasts in feldspathic meteorites. Our results show that despite low bulk rock abundances of incompatible trace elements in many feldspathic lunar meteorites, parental melts with overall REE abundances similar to or in excess of KREEP are needed to reproduce the REE abundances in plagioclase in clasts from feldspathic lunar meteorites. However, the lack of a Na enrichment trend in their plagioclase compositions with decreasing Mg# requires very Na-depleted melts without a KREEP component. The available data regarding magnesian anorthosites, anorthositic troctolites, and granulites in lunar meteorites, and inferences made here regarding their parental melt compositions, lead to contradictory and ambiguous conclusions regarding their origins. [ABSTRACT FROM AUTHOR]

We study how to learn semantic segmentation of 3D point clouds from small training sets. The problem arises because annotating 3D point clouds is a lot more time-consuming and error-prone than annotating 2D images. On the one hand this means that one cannot afford to create a large enough training dataset for each new project. On the other hand it also means that there is not nearly as much public data available as there is for images, which one could use to pretrain a generic feature extractor that could then, with only little dedicated training data, be adapted ("fine-tuned") to the task at hand. To address this bottleneck we explore the possibility to transfer knowledge from the 2D image domain to 3D point clouds. That strategy is of particular interest for mobile mapping systems that capture both point clouds and images, in a fully calibrated setting that makes it easy to connect the two domains. We find that, as expected, naively segmenting in image space and mapping the resulting labels onto the point cloud is not sufficient, as visual ambiguities, residual calibration errors, etc. affect the result. Instead, we propose a system that learns to merge image evidence from a varying number viewpoint, and 3D geometry information, into a common representation that encodes point-wise 3D semantics. To validate our approach we make use of a new mobile mapping dataset with 88M annotated 3D points and 2205 oriented multi-view images. In a series of experiments, we show how much label noise is caused by simplistic label transfer, and how well existing semantic segmentation architectures can correct it. Finally, we demonstrate that adding our learned 2D-to-3D multi-view label transfer significantly improves the performance of different segmentation backbones. [ABSTRACT FROM AUTHOR]

• A novel group decision methods classification framework has been presented. • A review of existing multiple-source aggregation methods has been carried out. • Existing methods have been compared with current needs in the scientific field. • Future research pathways are provided to enhance upcoming techniques. • A multiple-source aggregation methods guide is provided. Interest in group decision-making (GDM) has been increasing prominently over the last decade. Access to global databases, sophisticated sensors which can obtain multiple inputs or complex problems requiring opinions from several experts have driven interest in data aggregation. Consequently, the field has been widely studied from several viewpoints and multiple approaches have been proposed. Nevertheless, there is a lack of general framework. Moreover, this problem is exacerbated in the case of experts' weighting methods, one of the most widely-used techniques to deal with multiple source aggregation. This lack of general classification scheme, or a guide to assist expert knowledge, leads to ambiguity or misreading for readers, who may be overwhelmed by the large amount of unclassified information currently available. To invert this situation, a general GDM framework is presented which divides and classifies all data aggregation techniques, focusing on and expanding the classification of experts' weighting methods in terms of analysis type by carrying out an in-depth literature review. Results are not only classified but analysed and discussed regarding multiple characteristics, such as MCDMs in which they are applied, type of data used, ideal solutions considered or when they are applied. Furthermore, general requirements supplement this analysis such as initial influence, or component division considerations. As a result, this paper provides not only a general classification scheme and a detailed analysis of experts' weighting methods but also a road map for researchers working on GDM topics or a guide for experts who use these methods. Furthermore, six significant contributions for future research pathways are provided in the conclusions. [ABSTRACT FROM AUTHOR]

In addition to the legacy B1I and B3I signals, the BeiDou global navigation satellite system (Beidou-3) are capable of transmitting B1C/B2a/B2b/B2 several new navigation signals. The multi-frequency signals of Beidou-3 are expected to benefit precise point positioning with ambiguity resolution (PPP AR). Currently, most international GNSS service (IGS) analysis center adopt the Beidou-3 B1I/B3I ionosphere-free (IF) combination to estimate the satellite orbit and clock products, the Beidou-3 B1C/B2a/B2b/B2 phase biases should be aligned with satellite B1I/B3I clocks. Therefore, in this paper, the multi-frequency phase observable-specific phase biases (OSB) estimation method is developed for Beidou-3 system, which is compatible with satellite B1I/B3I legacy clock. Experimenting on the Beidou-3 multi-frequency observations over 30 days from the globally distributed stations, the characteristics of the estimated Beidou-3 multi-frequency phase OSB and the positioning performance of the IF kinematic PPP AR with different frequency combination are assessed. After the OSB are used in the raw observations, the results demonstrate that the wide-lane ambiguities residuals within ± 0.25 cycle are approximately 97%, 94%, 97% and 93% for Beidou-3 B1I/B3I, B1C/B2b, B1C/B3I and B1C/B2a combinations, respectively. When the OSB and satellite B1I/B3I legacy clock are used in different dual-frequency combination PPP AR tests, over 94% of the corresponding narrow-lane ambiguities are within ± 0.25 cycle. In addition, owing to the low noise of the B1C/B2a combination, the positioning performance of B1C/B2a PPP AR outperforms that of the other frequency combinations. More specifically, in case of B1I/B3I PPP AR, almost 22.7 min is required to successfully achieve 10 cm for east, north and up components, while only 18.8 min is needed for B1C/B2a PPP AR. The results show that such phase OSB, along with Beidou-3 satellite B1I/B3I legacy clock, can fully recover the PPP ambiguities based on all available frequency choices and observable combinations. [ABSTRACT FROM AUTHOR]

We analyze the effect of strategic ambiguity and heterogeneous attitudes towards such ambiguity on optimal mitigation and adaptation. Pessimistic players tend to invest more in mitigation, while optimists favor adaptation. When adaptation is more expensive than mitigation, three types of equilibria can obtain depending on the level and distribution of ambiguity aversion: (i) a mitigation equilibrium, (i i) an adaptation equilibrium and (i i i) a mixed equilibrium with both adaptation and mitigation. The interaction between ambiguity attitudes and wealth distribution plays a crucial role for the aggregate environmental policy: a wealth transfer from pessimistic to optimistic agents increases total mitigation. A similar result applies to the choice of an optimal mitigation subsidy, which is shown to increase in optimism, but decrease following a transfer of income towards the more optimistic players. Finally, we show that under strategic ambiguity, the introduction of a non-binding standard can impact agents' beliefs about their opponents' behavior and as a result lower total equilibrium mitigation. Our results highlight the necessity to consider attitudes towards strategic ambiguity in the design of economic policies targeting climate change. They might also shed some light on the slow rate of convergence of environmental policies across countries. [ABSTRACT FROM AUTHOR]

• Previous empirical evidence on the impact of universal preschool on cognitive skills is ambiguous. • Preschool and primary school enrollment age may be correlated, which can lead to a negative impact of earlier preschool enrollment. • We estimate the impact of earlier preschool enrollment on 6th, 8th, and 10th grade mathematics and reading test scores, while controlling for any indirect impact through primary school enrollment age. • Preschool enrollment has a significant positive impact on test scores, especially among students with less advantaged backgrounds, as long as it does not lead to earlier school enrollment. • These results explain some of the variation seen in previous evidence, support the beneficial impact of preschool, and present an important policy consideration for planned preschool expansion. Previous empirical evidence is mixed regarding the impact of universal preschool on cognitive skills. We show that preschool enrollment can impact test scores positively if it does not lead to earlier school enrollment. We examine rich student data and use different enrollment cutoff dates in Hungary to separate the beneficial direct effect of earlier preschool enrollment from a negative indirect effect that may occur through earlier school enrollment. We find significant direct impacts: 6th-grade reading (math) test scores increase by 9.0 (6.3) percent of a standard deviation for children who enroll in preschool a year earlier. This impact persists through 10th grade and is larger among disadvantaged children. The findings support the importance of universal preschool for improving cognitive skills and equity. They highlight a key consideration for policy evaluation and design and help reconcile ambiguities in the previous empirical evidence. [ABSTRACT FROM AUTHOR]

The current GLONASS system suffers from the inter-frequency code bias (IFCB) and the inter-frequency phase bias (IFPB) due to its employment of the frequency division multiple access (FDMA) technology. The IFCB affects the code observation's accuracy and further degrades the positioning accuracy, while the IFPB can be absorbed into the float ambiguity term. With increasing demand on precise point positioning (PPP) in mass market applications using low-cost single-frequency (SF) receivers, the impact of IFCB on SF-PPP with low-cost receivers need to be investigated. In this paper, we propose the use of the University of Calgary (UofC) model to estimate the IFCB in SF-PPP. The feasibility of the proposed approach is first verified using GNSS observations from the IGS stations. Comparing to the traditional SF-PPP model, the UofC model enables more stable IFCB estimations with 80.2% of the maximum changes less than 1.0 ns within one week. Then, the stability of IFCB in the low-cost SF receiver is analyzed with static data collected with u-blox M8T, and the STandard Deviation (STD) of IFCB for each satellite within 5 days is less than 1.5 ns when applying real-time orbit, clock and ionospheric corrections. Further, it's verified that the power off won't affect the IFCB's value significantly in the low-cost receiver. Finally, six different strategies are designed to evaluate the impact of the GLONASS IFCB on SF-PPP. Comparing to those strategies ignoring IFCB, the strategies considering IFCB in SF-PPP can improve the positioning accuracy in both static and kinematic tests. [ABSTRACT FROM AUTHOR]

People differ in their assessment of whether they consider spoilers to be problematic or not. Recent studies have investigated the effects of spoilers. However, the definition of spoilers in those studies was ambiguous, so it is difficult to say that the impact of spoilers has been investigated. In this work, we propose a definition of comic spoilers and a dataset construction method to avoid ambiguity in the definition of spoilers. Furthermore, we construct a dataset and compare our dataset with the spoilers used in previous studies to clarify the characteristics of spoilers. Then, we found that the newly constructed dataset not only covered the previous dataset but also detected important spoiler scenes that were not included in the previous dataset, such as goal scenes and scenes that reveal the killer's motive and so on, as spoilers. [ABSTRACT FROM AUTHOR]

The issue of modeling holds significant importance for the applicability of control theory, particularly at higher control levels where the verbal description of control objects becomes increasingly crucial. However, a notable disadvantage of such verbal descriptions is the inherent ambiguity and vagueness of the obtained results. This article aims to address this challenge by proposing an approach that utilizes Petri nets (PN) to model and control a specific component within an intelligent home. Additionally, the Simulink modeling tool and Petri net analysis tools are introduced as aids in the modeling process. The article focuses on creating a model that accurately represents the temperature regulation system in the home, considering the constant rise in energy consumption and costs. Furthermore, the transfer of the model to the Simulink environment using IF THEN rules is demonstrated, allowing for an evaluation of its performance. Through the comprehensive application of this approach, the aim is to optimize energy usage and reduce costs within household environments. [ABSTRACT FROM AUTHOR]

How much does it cost a decisionmaker to base her payoff on interdependent, biased information sources? This question is relevant in economics, statistics, and politics. When there are many information sources, their dependence may be unknown or uncertain, which creates multivariate ambiguity. One approach to answer our leading question involves decoupling inequalities from probability theory. We present new inequalities which hold for any type of dependence. We apply our method to a simple formalization of risky asset investment, and to a voting model where citizens face dependent political signals. For a given set of marginal information, the decoupling bound is the sup over all possible joint distributions connecting the marginals. The bound may therefore be useful in other contexts, when a decisionmaker faces unawareness about the joint distribution of information. Our method highlights a frontier which bounds the maximum value of the decisionmaker's payoff from dependent multidimensional signals. Beneath the bound lies the set of possible payoffs one could obtain from the signals. In this setting, decoupling performs a somewhat similar function to that of the threshold of acceptance sets, in choice under uncertainty. We show that a conservative decisionmaker's maximal payoff is approximately 50% more than if the signals were independent. • We develop universal, simple, decoupling inequalities, and apply them to asset investment and voting. • We present decoupling results for sums, unions, and maxima of dependent multidimensional signals. • We provide and illustrate economic examples of unknown dependence. • We outline potential parallels of decoupling to acceptance sets in choice under uncertainty, and to unawareness of dependence across multidimensional signals. [ABSTRACT FROM AUTHOR]

Recent neuroscientific findings challenge the widely held assumption that similar neural mechanisms underlie different types of conscious awareness, such as seeing, feeling, knowing, and willing. These data show that, even within visual awareness, the brain regions involved, the latency of neural activity, and the information flow patterns underlying conscious perception vary depending on both external and internal factors. Different types of conscious awareness – such as perception and volition – likely have different neural processing architecture. Thus, a pluralistic approach to investigating the neurobiological underpinnings of consciousness may be more fruitful. A new framework, the JDT, is proposed which has the potential to accommodate different brain circuit-level mechanisms underlying different aspects of conscious awareness. Theories of consciousness are often based on the assumption that a single, unified neurobiological account will explain different types of conscious awareness. However, recent findings show that, even within a single modality such as conscious visual perception, the anatomical location, timing, and information flow of neural activity related to conscious awareness vary depending on both external and internal factors. This suggests that the search for generic neural correlates of consciousness may not be fruitful. I argue that consciousness science requires a more pluralistic approach and propose a new framework: joint determinant theory (JDT). This theory may be capable of accommodating different brain circuit mechanisms for conscious contents as varied as percepts, wills, memories, emotions, and thoughts, as well as their integrated experience. [ABSTRACT FROM AUTHOR]

Liquid storage tanks play a vital role in the modern chemical process industry (CPI). The strong ground motion caused by large-scale earthquakes may easily impose severe structural damage on liquid storage tanks, leading to a series of catastrophic cascaded events. The seismic damage estimation of liquid storage tanks is a challenging problem, as the fluid-structure interaction exhibits extremely complicated and non-stationary response behavior. This study develops a novel data-driven methodology to estimate the seismic damage state probability distribution of liquid storage tanks in the contexts of label ambiguity and data imbalance. With the support of the advanced deep learning framework, synthetic oversampling methods, and label enhancement techniques, a hybrid deep belief network-based label distribution learning system (HDBN-LDLS) is proposed for probability distribution learning. The proposed HDBN-LDLS is evaluated on the widely used ALA database. Simulation results indicate that HDBN-LDLS can achieve a balanced estimation for all damage states while maintaining sufficient robustness to cope with label ambiguity. The reliability of the obtained data-driven model is validated by a damaged tank in the 2006 Silakhor earthquake. For practical applications, a more natural way to estimate a seismic damaged tank is to assign a membership degree to each possible damage state. The proposed methodology can quickly obtain the seismic damage state probability curves of a specific liquid storage tank, which can be used to support quantitative risk assessment and seismic design. [ABSTRACT FROM AUTHOR]

Magnitude-based PDFF (Proton Density Fat Fraction) and R 2 ∗ mapping with resolved water-fat ambiguity is extended to calculate field inhomogeneity (field map) using the phase images. The estimation is formulated in matrix form, resolving the field map in a least-squares sense. PDFF and R 2 ∗ from magnitude fitting may be updated using the estimated field maps. The limits of quantification of our voxel-independent implementation were assessed. Bland-Altman was used to compare PDFF and field maps from our method against a reference complex-based method on 152 UK Biobank subjects (1.5 T Siemens). A separate acquisition (3 T Siemens) presenting field inhomogeneities was also used. The proposed field mapping was accurate beyond double the complex-based limit range. High agreement was obtained between the proposed method and the reference in UK. Robust field mapping was observed at 3 T, for inhomogeneities over 400 Hz including rapid variation across edges. Field mapping following unambiguous magnitude-based water-fat separation was demonstrated in-vivo and showed potential at 3 T. [ABSTRACT FROM AUTHOR]

Distance measurement based on the accumulated phase of intensity-modulated continuous-wave lasers is an established approach for pointwise geometry acquisition in uncontrolled conditions, enabling some of the most accurate Lidar/laser-scanning solutions. Inherently affected by cycle ambiguities due to the modulo 2 π phase observations, several modulation wavelengths are typically used in practical implementations to extend the measurement range. One of the main limitations of these systems arises from their inability to decouple mixed signals affected by different delays. This is especially relevant when the laser beam illuminates simultaneously two or more surfaces at significantly different distances, producing mixed-pixel measurements in which the estimated distance does not reliably correspond to any of the involved scatterers. Algorithms for detection and filtering of mixed pixels within point-cloud data have been proposed based on analyzing consistency across local neighborhoods. The resolution of multiple scatterers on individual measurements, however, has not been yet reported nor is provided by undisclosed commercial implementations. In this work, we analyze the problem of signal mixing in multi-wavelength phase-based distance estimation, and propose an algorithm for joint phase ambiguity resolution and mixed pixel detection based on mixed integer linear programming. We first analyze theoretically the performance limits of the proposed solution, demonstrating unbiasedness in the presence of noise and robustness in the presence of multiple scatterers. In addition, we investigate empirically the impact of noise, mixed measurements and choice of modulation wavelengths on the performance of the proposed algorithm. The results demonstrate the capacity of the presented approach, given an adequate wavelength selection, to provide unambiguous distance estimates under realistic noise conditions and to identify measurements affected by mixed pixels while approximating the position of the dominant scatterer. Aside from contributing to improving the reliability and resolution capability of Lidar and laser-scanning data, the proposed solution sets a promising step towards fully resolved multi-target distance measurement. [ABSTRACT FROM AUTHOR]

Tight integration can enhance the model strength and positioning performance by considering the characteristic of differential inter-system bias (DISB), especially in obstructed environments. However, limited work emphasizes the comprehensive analysis of five-frequency DISBs between BDS-3 and other systems considering the receiver type, receiver configuration, and antenna type. In addition, the overlapping DISBs between BDS-3 and BDS-2 are also in great demand for further investigation since they are often regarded as one system. In this study, one DISB-float model is introduced to estimate the DISBs, and one DISB-fixed model and one DISB-free model are formulated to enhance the model strength of tight integration. Four dedicated datasets were collected to estimate the DISBs, which are also comprehensively analyzed considering the receiver type, receiver configuration, and antenna type. The results show that the DISBs between BDS-3 and other systems are rather stable over a certain period and are related to the receiver type and receiver configuration, whereas are not related to the antenna type. More interestingly, the B1I code DISB between BDS-3 and BDS-2 exhibits significant magnitude with a mean value of −1.44 m for the baseline composed of two different receivers. In this case, the B1I code DISB must be considered and the tight integration between BDS-3 and BDS-2 considering its calibration can improve the positioning performance. Besides, the tight integration of the DISB-fixed model can significantly improve the positioning accuracy between multiple GNSS. Compared to the loose integration, the improvement of 60.6 %, 56.6 %, and 61.2 % can be obtained in the E, N, and U directions, when only two satellites are available for each system. In real obstructed environments, the tight integration of the DISB-free model can also improve the positioning performance in terms of positioning availability and accuracy, as well as the ambiguity resolution performance. [ABSTRACT FROM AUTHOR]

Real-world evidence (RWE) studies are increasingly being used to support healthcare decisions. Various frameworks, tools, and checklists exist for ensuring quality of real-world data, designing robust studies, and assessing potential for bias. In January 2021, Structured Template and Reporting Tool for RWE (STaRT-RWE) was released to further reduce ambiguity, assumptions, and misinterpretation while planning, implementing, and reporting RWE studies of the safety and effectiveness of treatments. The objective of this study was to identify gaps in the reporting quality of published RWE studies by using this template for critical appraisal. Two reviewers conducted a keyword search on PubMed for free-full-text research articles using real-world data, RWE design, and safety with or without effectiveness outcomes of a medicinal product or intervention in humans of any age or gender, published in English between January 13, 2021, and January 13, 2022. Assessment of risk of bias was done using Assessment of Real-World Observational Studies critical appraisal tool. Deficiencies in methods and findings as per STaRT-RWE template were reported as frequencies. A total of 54 of 2374 retrieved studies were included in the review. Based on the STaRT-RWE template, the studies inadequately reported empirically defined covariates, power and sample size calculation, attrition, sensitivity analyses, index date (day 0) defining criterion, predefined covariates, outcome, metadata about data source and software, objective, inclusion and exclusion criteria, analysis specifications, and follow-up. The use of STaRT-RWE template along with its tables, design diagram, and library of published studies has a potential of improving robustness of RWE studies. • Real-world evidence (RWE) studies are increasingly being used to generate supportive evidence regarding safety, effectiveness, and generalizability of medicinal products or interventions for healthcare decision makers, whereas methods and findings of these studies are full of ambiguity, assumptions, or misinterpretations. • This study did critical assessment of the quality of reporting of the published RWE studies using the Structured Template and Reporting Tool for RWE template and uncovered many deficiencies, which could have affected the robustness of the evidence. • Structured Template and Reporting Tool for RWE template has a potential of improving robustness of the RWE studies and should be used for planning, implementing, and reporting such studies. [ABSTRACT FROM AUTHOR]

• The ISB between the BDS-2 and BDS-3 is considered in UPD estimation. • The time-varying characteristics of ISB and the quality of BDS UPD are analyzed. • The satellite-induced bias of BDS-2 in UPD estimation is evaluated. • The impact of ISB is evaluated from the performance of BDS PPP and PPP-AR. Intra-system biases (ISBs) between BDS-2 and BDS-3 are of critical importance when combining observations from the BDS-2 and BDS-3 systems, which is meaningful to fully take advantage of the BDS positioning capability. Meanwhile, ISBs should also be considered in the estimation of BDS uncalibrated phase delays (UPDs). In this research, we present a BDS-2/BDS-3 joint-processing scheme, as well as a method for estimating BDS UPDs. The characteristics of ISBs and the quality of BDS UPDs are analyzed based on 30-day data from 130 multi-GNSS experimental (MGEX) stations. Our results indicate that the ISBs are related to the type and version of the receiver. The ISBs can be regarded as constant across the course of a given day, and the mean standard deviation (STD) values of ISBs over one month for different types of receivers are generally within 0.2 m. Moreover, to assess the quality of UPD products, the residuals of the estimated UPDs and the utilization rates of the observation data are computed. The results show that the quality of BDS UPDs can be improved by correcting the satellite-induced pseudo-range variations, and by estimating the wide-lane (WL) UPD difference between BDS-2 and BDS-3. The average RMS values of the estimated residuals of WL UPD and narrow-lane (NL) UPD are 0.07 and 0.09 cycles, respectively; moreover, the utilization rate of the observation data of WL UPD and NL UPD can reach above 90 %. The performance of BDS precise point positioning (PPP) and PPP ambiguity resolution (PPP-AR) is analyzed in terms of positioning accuracy and convergence performance in both the static and kinematic modes. Compared with PPP ambiguity-float solutions, the positioning accuracy of PPP-AR is significantly improved, especially in the east direction. The impact of ISBs on PPP and PPP-AR is also analyzed, and the results indicate that ISBs can improve the convergence speed of float PPP, but can be disregarded in PPP-AR. [ABSTRACT FROM AUTHOR]

Multi-View Stereo (MVS) algorithms rely on common photometric consistency measures and, therefore, in cases of low-textured surfaces tend to generate unreliable depth estimates or lack completeness due to matching ambiguities. Such textureless areas often imply dominant planar structures, typically occurring in man-made scenes. To support depth estimation in scenarios where challenging surfaces are present, we propose an extended PatchMatch pipeline using an adaptive accumulated matching cost calculation based on estimated prior plane hypotheses and the local textureness. Plane priors are detected in the object space and guided by quadtree structures in order to generate depth and normal hypothesis for every pixel, supporting, in this way, the propagation of more reliable depth estimates across the image. Experiments on the ETH3D high-resolution dataset and on custom real-world scenes demonstrate that our approach can favor the reconstruction of problematic regions by adding small complexity while preserving fine details in rich textured regions, achieving thus competitive results compared to state-of-the-art methods. The source code of the developed method is available at https://github.com/3DOM-FBK/openMVS. [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS)-based attitude determination has been widely adopted in a wide variety of terrestrial, sea, air, and space applications. Recently, the emergence of commercial multi-GNSS common-clock receivers has brought new opportunities for high-precision GNSS-based attitude determination with single-differenced (SD) model. However, the key requirement of using this approach is the accurate estimation of the troublesome line bias (LB) in real-time. In this contribution, we propose a particle filter-based real-time phase LB estimation approach that apply to SD model with single-system single-frequency observations from common-clock receiver. We first analyzed the relationship between the integer ambiguity ratio value and the phase LB. It is proved that the accuracy of a given phase LB value can be qualified by the related ambiguity resolution ratio value, and the normalized ratio value can therefore be used to represent the likelihood function of observations. Then, we presented the particle filter-based real-time phase LB estimation procedure, and assessed its performance using GPS L1/BDS B1I observations from two datasets collected with different types of common-clock receivers in terms of the accuracy and convergence time of phase LB estimation, the computation load, and the positioning and attitude determination accuracy with respect to the double-differenced (DD) model. Experimental results demonstrated that the phase LB could be accurately estimated with short convergence time (generally within 15 epochs). Moreover, compared with the classical DD approach, the particle filter-based SD approach delivers comparable positioning root-mean-square (RMS) errors in the North and East components but significantly smaller RMS errors in the Up component. Accordingly, the achievable yaw accuracy is comparable whereas the pitch accuracy is remarkably improved. The improvements of positioning accuracy in the Up component and pitch accuracy are approximately 35.7 % to 63.7 %, and 33.3 % to 63.1 %, respectively. Additionally, the single-epoch computation time with our particle filter-based SD approach is generally 0.08 s, which is obviously larger than the DD approach but could still meet the requirements of real-time applications below 10 Hz sampling. [ABSTRACT FROM AUTHOR]

• Attitude and inertial parameter are estimated without 3D model. • Ill-posed point cloud is detected and avoided. • Combination of MEKF and pose graph reduces error of estimate. Determining the attitude and inertial parameters of a noncooperative target is essential in an on-orbit servicing mission. Various methods based on machine vision have been proposed, but most of them require the 3D model of the target. This paper proposes a model-free method through sequentially registering point clouds captured by a depth camera. Our main contributions are the avoidance of the ambiguity in registration, and the combination of the multiplicative extended Kalman filter and the pose graph optimization to reduce the effect of measurement noise and drift error. A hardware experiment was performed to generate the sequence of point clouds of a three-axis free-floating target and validate our method. The result shows that the proposed method outperforms existing methods and effectively identifies the inertial parameters, including the normalized principal moments of inertia and the orientation of principal axes. [ABSTRACT FROM AUTHOR]

The degree of ambiguity (respectively, the path size) of a nondeterministic automaton, on a given input, measures the number of accepting computations (respectively, the number of all computations). It is known that deciding the finiteness of the degree of ambiguity of a nondeterministic pushdown automaton is undecidable. Also, it is undecidable for a given k ≥ 3 to decide whether the path size of a nondeterministic pushdown automaton is bounded by k. As the main result, we show that deciding the finiteness of the path size of a nondeterministic pushdown automaton can be done in polynomial time. Also, we show that the k -path problem for nondeterministic input-driven pushdown automata (respectively, for nondeterministic finite automata) is complete for exponential time (respectively, complete for polynomial space). • We show that finiteness of path size of nondeterministic pushdown automata can be solved in polynomial time. • We show that deciding k-path property is PSPACE complete for nondeterministic finite automata. • We show that deciding k-path property is exponential time complete for nondeterministic input-driven pushdown automata. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) precise positioning can be significantly affected by severe multipath effects and outliers in harsh environments, and highly relies on quality control strategies. Previous studies mainly focus on the posterior residuals to check and exclude the outliers in GNSS observations, limited work emphasizes the combined quality control method considering both the prior and posterior knowledge simultaneously. This paper proposed a real-time combined quality control method to process the multipath effects and outliers in harsh environments simultaneously. Specifically, in the prior stage, a modified multipath processing strategy is proposed for both phase and code observations, then a modified detection, identification, and adaptation (DIA) method considering the maximum times of data snooping is studied in the posterior stage. Two dedicated experiments in real harsh environments were carried out to evaluate the performance of the proposed combined quality control method. For the static experiment, the proposed method exhibits smaller positioning errors, the best positioning accuracy, and the highest availability in this study. Specifically, the proposed method exhibits an improved percentage of 55.4 %, 56.3 %, and 59.7 % for positioning accuracy compared to those without the quality control method in the E, N, and U directions, respectively. Besides, the proposed method can further improve the performance of ambiguity resolution with an improved percentage of 32.2 %. For the kinematic experiment, the three-dimensional positioning accuracy of the proposed method is 0.577 m, which exhibits a 40.0 % improvement compared to those without the quality control method. Also, the proposed method exhibits better performance under relatively strong multipath effects. In this sense, the proposed real-time combined quality control method is highly appreciated in terms of positioning availability, accuracy, and ambiguity resolution for GNSS precise positioning, especially in harsh environments. [ABSTRACT FROM AUTHOR]

Using multiple ion beam analysis measurements or techniques combined with self-consistent data processing generally allows for extracting more (or more accurate) information than processing data from single measurements separately. Solving ambiguities, improving the final depth resolution, defining constraints and extending applicability are the main strengths of the data-fusion approach, which essentially consists in formulating a multi-objective minimization problem that can be tackled by the adoption of the weighted-sum method. A simulation study is reported in order to evaluate the systematic error inserted in the analysis by the choice of a specific objective function, or even by the weights or normalization adopted in the weighted-sum method. We demonstrate that the bias of the analysed objective functions asymptotically converges to the true value for better statistics and that the measurement can be ranked by its information content, where some combinations of measurements better constrain the multi-objective optimization. [ABSTRACT FROM AUTHOR]

Previous research has reported hyperresponsivity in the amygdala and hyporesponsivity in ventral portions of the medial prefrontal cortex to threat-related stimuli in posttraumatic stress disorder (PTSD). Whether such findings generalize to more ambiguous stimuli and whether such brain activation abnormalities reflect familial vulnerabilities, trauma-exposure, or acquired characteristics of PTSD remain unclear. In this study, we measured brain responses to emotionally ambiguous stimuli (i.e., surprised facial expressions) in identical twin pairs discordant for trauma exposure to elucidate the origin of brain activation abnormalities. Participants with PTSD (n = 12) and their trauma-unexposed identical cotwins (n = 12), as well as trauma-exposed participants without PTSD (n = 15) and their trauma-unexposed identical cotwins (n = 15), passively viewed surprised and neutral facial expressions during functional magnetic resonance imaging (fMRI). Afterward, participants labeled and rated each facial expression on valence and arousal. Amygdala activation to Surprised and Neutral facial expressions (versus Fixation) was greater in the participants with PTSD and their trauma-unexposed identical cotwins without PTSD, compared to the control twin pairs. In contrast, medial frontal gyrus (MFG) activation to Surprised facial expressions (versus Fixation) was diminished in the PTSD group relative to the other three groups. Amygdala hyperresponsivity to emotionally ambiguous facial expressions may be a familial vulnerability factor that increases the likelihood of developing PTSD after experiencing a traumatic event. In contrast, MFG hyporesponsivity may be an acquired characteristic of the disorder. [ABSTRACT FROM AUTHOR]

This study's purposes were to examine how selected demographic variables affect frequency of use of precautionary measures when shopping for clothing in retail stores; and how uncertainty avoidance/ambiguity intolerance and fashion innovativeness affect (a) precautionary measures used when shopping in retail stores during a pandemic and (b) compensatory consumption. Participants (122 US men; 209 US women aged 20 to 64) completed an online questionnaire containing demographic items plus measures of uncertainty avoidance/ambiguity intolerance, compensatory consumption, precautionary measures, and fashion innovativeness. Data analysis included reliability, factor analysis, M/ANOVA and SNK. Older adults, adults with higher education, and married adults more frequently used precautionary measures when shopping in retail stores. Men and women reported similar frequency of use. Fashion innovators and consumers with less tolerance for uncertainty/ambiguity more frequently used precautionary measures. Fashion innovators and consumers higher in uncertainty avoidance/ambiguity intolerance engaged in more compensatory consumption. Generalization of the results is limited because the data are context-specific: country (US), time period (during a pandemic), and sample. Guidelines for the general public regarding precautionary measures came from within organizations, between organizations and experts but the general public was not consulted (public open innovation) perhaps hindering compliance with precautionary measures. [ABSTRACT FROM AUTHOR]

• We rethink and decompose the uncertainty in facial expression recognition. • We propose a novel method to mine the ambiguity and noise in facial expression recognition datasets. • The tri-regularization module employs different training strategies for the clean, ambiguous, and noisy label expressions. • Our proposed method achieves performance 90.25% on RAF-DB, 62.47% on AffectNet, and 89.86% on FERPlus. Due to the ambiguous expressions and the subjectiveness of annotators, annotation ambiguity is a serious obstacle for facial expression recognition (FER). Ambiguous annotation exists in similar and dissimilar classes, which we call ambiguity and noise. The previous state-of-the-art approaches use uncertainty to generalize the two categories, and adopt uncertainty learning to suppress uncertainty samples. However, ambiguous expressions are confused with noisy label expressions may bias the model toward easy samples and hurt the generalization capability. To solve this problem, we propose a novel approach to m ine a mbiguity and n oise (MAN) in FER datasets. Specifically, we design a co-division module, which divides the datasets into clean, ambiguous and noisy label expressions based on the consistency and inconsistency between the predictions of two networks and the given labels. To effectively learn the clean expressions, improve discriminative ability and avoid memorizing noisy labels, the tri-regularization module employs supervised learning, mutuality learning and unsupervised learning for the three subsets, respectively. Extensive experiments have shown that MAN can effectively mine the real ambiguity and noise, and achieve state-of-the-art performance in both synthetic noisy datasets and popular benchmarks. [ABSTRACT FROM AUTHOR]

Decision making in clinical practice is only possible if we are able to understand the limits of our own knowledge and manage the associated uncertainty. Tolerance of ambiguity is therefore an important attribute for trainee pharmacists and medical students to develop. This study aimed to explore the impact of an interprofessional simulation on the tolerance of ambiguity of trainee pharmacists and medical students. Trainee pharmacists and final year medical students participated in interprofessional simulation in two regions of Scotland. Participants completed pre- and post-session tolerance of ambiguity questionnaires. Analysis included differences between the pre-session scores of the two groups; the trainee pharmacists' pre- and post-session scores; and the medical students' pre- and post-session scores. A total of 15 trainee pharmacists and 15 medical students participated. Baseline tolerance of ambiguity was slightly higher in medical students than trainee pharmacists (56.9 vs. 52.6), but the study was insufficiently powered to detect whether this was a true difference (P =.21). Trainee pharmacists showed a statistically significant increase in self-reported tolerance of ambiguity (52.6 to 60.8, P =.004), but medical students did not (56.9 to 63.8, P =.04). Trainee pharmacists' tolerance of ambiguity was improved following participation in an interprofessional simulation. Further research could establish whether the improvements differ between professional groups, and explore the reasons why tolerance of ambiguity may be affected by interprofessional simulation. [ABSTRACT FROM AUTHOR]

Over the last couple of years, few-shot learning (FSL) has attracted significant attention towards minimizing the dependency on labeled training examples. An inherent difficulty in FSL is handling ambiguities resulting from having too few training samples per class. To tackle this fundamental challenge in FSL, we aim to train meta-learner models that can leverage prior semantic knowledge about novel classes to guide the classifier synthesis process. In particular, we propose semantically-conditioned feature attention and sample attention mechanisms that estimate the importance of representation dimensions and training instances. We also study the problem of sample noise in FSL, towards utilizing meta-learners in more realistic and imperfect settings. Our experimental results demonstrate the effectiveness of the proposed semantic FSL model with and without sample noise. [ABSTRACT FROM AUTHOR]

Donepezil hydrochloride (DH) is the most used anti-Alzheimer's disease drug, however, its classification according to the Biopharmaceutics Classification System (BCS) is not clear in the literature. BCS is one of the accepted criteria used to grant biowaiver (waiver of in vivo bioequivalence studies) of new drug products. So, the purpose of this work was to elucidate the BCS classification of DH and to raise the discussion about the possibility of biowaiver for new medicines containing it. The polymorphic form was previously identified as form III of DH. The drug showed high solubility in the entire pH range evaluated (1.2 to 6.8, at 37 °C) with a pH-dependent solubility profile. The effective permeability (P eff) values obtained with different DH concentrations, using in situ closed-loop perfusion model were statistically similar (p > 0.05), even when compared to high permeability control used (ketoprofen), demonstrating that DH has high permeability which, associated with its high solubility, allows to classify DH as BCS class 1. Relevant data to evaluate for granting a biowaiver for new medicines were also reviewed from the literature. Based on information reunited new immediate-release drug products containing DH should be eligible for BCS-based biowaiver. [Display omitted] [ABSTRACT FROM AUTHOR]