The analyticity of response functions and scattering amplitudes implies powerful relations between low-energy observables and the underlying short-distance dynamics. These "IR/UV" relations are ...rooted in basic physical principles, such as causality and unitarity. In this paper, we seek similar connections in inflation, relating cosmological observations to the physics responsible for the accelerated expansion. We assume that the inflationary theory is Lorentz invariant at short distances, but allow for nonrelativistic interactions and a nontrivial speed of propagation at low energies. Focusing on forward scattering, we derive a "sum rule" which equates a combination of low-energy parameters to an integral which is sensitive to the high-energy behavior of the theory. While for relativistic amplitudes unitarity is sufficient to prove positivity of the sum rule, this is not guaranteed in the nonrelativistic case. We discuss the conditions under which positivity still applies, and show that they are satisfied by all known UV completions of single-field inflation. In that case, we obtain a consistency condition for primordial non-Gaussianity, which constrains the size and the sign of the equilateral four-point function in terms of the amplitude of the three-point function. The resulting bound rules out about half of the parameter space that is still allowed by current observations. Finding a violation of our consistency condition would point toward less conventional theories of inflation, or violations of basic physical principles.
•Define the hesitant fuzzy linguistic geometric consistency index (HFLGCI).•Build a feedback consensus algorithm for hesitant fuzzy linguistic decision making.•Analyze the critical values of HFLGCI ...with different orders of preference relations.•Provide an online decision-making portal based on the proposed algorithm.•Present a case concerning performance evaluation of venture capital guiding funds.
Hesitant fuzzy linguistic preference relation (HFLPR) is of interest because it provides an efficient way for opinion expression under uncertainty. For enhancing the theory of group decision making (GDM) with HFLPR, the paper introduces a method for addressing the GDM based on consistency and consensus measurements, which involves (1) defining a hesitant fuzzy linguistic geometric consistency index (HFLGCI) and proposing an algorithm for consistency checking and inconsistency improving for HFLPR; (2) proposing a worst consensus index based on the minimum similarity measure between each individual HFLPR and the overall perfect HFLPR in order to build a consensus reaching algorithm based on the acceptable HLFPRs. The convergence and monotonicity of the proposed two procedures is proved. Experiments are performed to investigate the critical values of the defined HFLGCI, and comparative analyses are conducted to show the effectiveness of the proposed method. A case concerning the performance evaluation of venture capital guiding funds is given to illustrate the applicability of the proposed method. As an application of our work, an online decision-making portal is finally provided for decision makers to utilize the proposed method to solve GDM with HFLPRs.
The semisupervised soft sensor has gradually become a more practical solution due to the difficulty in collecting labels for the industrial soft sensors. Currently, the commonly used manifold ...regularization assumes similar inputs result in similar outputs, but such similarity assumption fails when temporality exists. Moreover, the performance of the soft sensor is vulnerable to the anomaly, so anomaly is generally discarded before modeling. The latent information in anomaly has seldom been explored for modeling yet. To overcome the limitation of manifold regularization and explore the usability of anomaly, a novel semisupervised soft sensor, named consistent-contrastive network (CC-Net), is proposed to build a temporality-aware and robust-to-anomaly soft sensor. Specifically, CC-Net consists of two branches, namely label branch and feature branch, to map the input into label and feature, respectively. In the label branch, labeled samples are used in a supervised manner. For unlabeled samples, two regularization terms, consistency and temporal consistency, are designed to constrain the pseudo-labels consistent against noise and along time, respectively, which can adapt to temporality. In the feature branch, the idea of contrastive learning is applied to separate normal samples and anomalies. Such a design properly utilizes the distribution information in anomaly as a regularizer. With the designed regularization terms and the utilization of anomalies, CC-Net implements a temporality-aware and robust-to-anomaly soft sensor, which is demonstrated by real denitrification and desulfurization cases.
•A novel Semi-Supervised Learning method for label-efficient Surgical workflow recognition (SurgSSL), which progressively utilizes unlabeled data in two learning stages, from implicit excavation to ...explicit excavation.•A novel intra-sequence Visual and Temporal Dynamic Consistency (VTDC) scheme for implicit excavation from unlabeled data. By adding regularization from both visual and temporal perspectives, it encourages model to excavate motion cues from unlabeled videos.•Pre-knowledge pseudo label is designed to continue to optimize the model for explicit excavation from unlabeled data. With prior unlabeled data knowledge encoded for the Pre-knowledge pseudo label, it demonstrates more precise supervision capability compared with conventional pseudo labels.•Outstanding experimental results shown on two popular benchmark surgical phase recognition dataset demonstrate the effectiveness of our SurgSSL method.
Surgical workflow recognition is a fundamental task in computer-assisted surgery and a key component of various applications in operating rooms. Existing deep learning models have achieved promising results for surgical workflow recognition, heavily relying on a large amount of annotated videos. However, obtaining annotation is time-consuming and requires the domain knowledge of surgeons. In this paper, we propose a novel two-stage Semi-Supervised Learning method for label-efficient Surgical workflow recognition, named as SurgSSL. Our proposed SurgSSL progressively leverages the inherent knowledge held in the unlabeled data to a larger extent: from implicit unlabeled data excavation via motion knowledge excavation, to explicit unlabeled data excavation via pre-knowledge pseudo labeling. Specifically, we first propose a novel intra-sequence Visual and Temporal Dynamic Consistency (VTDC) scheme for implicit excavation. It enforces prediction consistency of the same data under perturbations in both spatial and temporal spaces, encouraging model to capture rich motion knowledge. We further perform explicit excavation by optimizing the model towards our pre-knowledge pseudo label. It is naturally generated by the VTDC regularized model with prior knowledge of unlabeled data encoded, and demonstrates superior reliability for model supervision compared with the label generated by existing methods. We extensively evaluate our method on two public surgical datasets of Cholec80 and M2CAI challenge dataset. Our method surpasses the state-of-the-art semi-supervised methods by a large margin, e.g., improving 10.5% Accuracy under the severest annotation regime of M2CAI dataset. Using only 50% labeled videos on Cholec80, our approach achieves competitive performance compared with full-data training method.
Hesitant fuzzy linguistic preference relations (HFLPRs) can be used to describe hesitant judgments of decision makers (DMs). To further research the utilization of HFLPRs, this paper develops a new ...group decision making (GDM) method with HFLPRs. First, a consensus checking method is proposed to measure the consensus level of individual HFLPRs. Then, a definition of acceptable consensus is introduced. Furthermore, to help experts whose consensus levels are below a predefined threshold to revise their preferences, a consensus reaching procedure is developed, where the acceptable multiplicative consistency of the revised individual HFLPRs is guaranteed. After that, a step-by-step algorithm is developed to help us solve GDM problems with HFLPRs. An application example is implemented to indicate the utilization of our method and to compare with previous research.
Gravitational waves searches for compact binary mergers with LIGO and Virgo are presently a two stage process. First, a gravitational wave signal is identified. Then, an exhaustive search over ...possible signal parameters is performed. It is critical that the identification stage is efficient in order to maximize the number of gravitational wave sources that are identified. Initial identification of gravitational wave signals with LIGO and Virgo happens in real-time which requires that less than one second of computational time must be used for each one second of gravitational wave data collected. In contrast, subsequent parameter estimation may require hundreds of hours of computational time to analyze the same one second of gravitational wave data. The real-time identification requirement necessitates efficient and often approximate methods for signal analysis. We describe one piece of real-time gravitational-wave identification: an efficient method for ascertaining a signal's consistency between multiple gravitational wave detectors suitable for real-time gravitational wave searches for compact binary mergers. This technique was used in analyses of Advanced LIGO's second observing run and Advanced Virgo's first observing run.
This paper presents a new method to coping with group decision making with incomplete fuzzy preference information. To do this, it first defines an additively consistent index of fuzzy preference ...relations, and then gives a method to calculating the priority vector for additively consistent fuzzy preference relations. When the individual fuzzy preference relation is incomplete, a goal programming model is constructed, by which the missing values can be obtained. Then, an iterative approach to obtain the acceptably additive consistency of fuzzy preference relations is introduced. After that, an induced hybrid weighted aggregation (IHWA) operator is presented to aggregate the collective fuzzy preference relation. The main features of this aggregation operator are that the group consistency is no smaller than the highest individual inconsistency, and the group consensus is no smaller than the smallest consensus between the individual fuzzy preference relations. As a series of development, an algorithm based on the acceptable consistency and the group consensus is developed. Finally, three examples are given to show the efficiency and feasibility of the developed procedure, and comparisons are also offered.
Best‐worst method (BWM) is extended to uncertain situations, hesitant fuzzy best‐worst method (HFBWM) is proposed by using hesitant fuzzy multiplicative preference relation for multiple‐criteria ...group decision‐making problems. The reference comparison of the best criterion and the worst criterion are described by the linguistic terms, which are expressed in hesitant fuzzy elements, of the decision makers. Weights of criteria are calculated by using score function. Using the concept of BWM, nonlinearly constrained optimization problems are formed to obtain hesitant fuzzy weights (HFWs) of different criteria and alternatives. To check the reliability of the HFBWM, consistency ratio is proposed. The advantage and suitability of the proposed HFBWM are determined by three case studies. The results indicate that the HFBWM, due to higher comparison consistency as compared to BWM, obtain plausible preference ranking for alternatives.
Previous research has shown that making complex judgments and decisions entails a mental reconstruction of the task in a way that increases the state of coherence between the emerging conclusion and ...its underlying attributes: The attributes that support the conclusion grow stronger, whereas the attributes that support the losing option weaken. This coherence effect is understood to occur bidirectionally, in that conclusions follow from the decision-maker's evaluation of the attributes, while the evaluations of the attributes shift to cohere with the emerging conclusion. The current studies were designed to extend the coherence effect to encompass cognitions that could be considered "hot," such as valence evaluations, motivation toward outcomes of events, liking and disliking of actors, and emotions toward actors. Study 1 found that evaluations of a complex social relationship were accompanied not only by supportive interpretations of the ambiguous facts, but also by concordant hot cognitions. Studies 2 through 4 included manipulations to demonstrate the spreading of coherence from cold to hot cognitions and in the opposite direction. We observed these effects following a manipulation of the facts (Study 2), a manipulation of participants' emotions toward the actor (Study 3), and a manipulation of participants' motivation toward the outcome of the case (Study 4). These results support the proposition that complex judgments and decisions are performed by coherence-based reasoning: a holistic, connectionist process that maximizes coherence among and between the myriad of factors involved in the tasks and the hot cognitive reactions to them.
In response to the frequent problem of inconsistent quality of billet castings and their rolled products from each strand by a five-strand tundish, the flow field in tundish is optimized by ...presenting new flow control devices and conducting isothermal physical modelling along with numerical simulation. The results show that the dead volume fraction of the optimized case A6 is reduced from 27.74% to 19%, the stagnation time is prolonged from 12 s to 35 s, and the flow dynamic consistency for each strand is improved as well. In the subsequent industry production tests, the temperature difference of molten steel at the outlet of each strand is reduced to 1~5 °C. The maximum difference of the as-cast equiaxed crystal rate among five strands is reduced from 5.67% to 2.7%, and the consistency of carbon segregation index is also improved with a basically identical appearance through the billet cross section. The maximum differences in oxygen and nitrogen contents for the rolled products of all strands are 2.7 ppm and 5.7 ppm respectively, which are lower than 5.0 ppm and 13.8 ppm before tundish optimization. The yield strength of rolled products is stabilized with much less divergence as compared to the products with the original tundish. Thus, it is believed that the reasonable flow field optimization to a multi-strand tundish not only will have a well-known positive impact on its tranditional metallurgical effect, but also may bring out an approaching identical steel quality from the same caster as we expected.
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