Embodied Meaning in Comprehending Abstract Chinese Counterfactuals

Embodied cognition theories propose that language comprehension triggers a sensorimotor system in the brain.However,most previous research has paid much attention to concrete and factual sentences,and little emphasis has been put on the research of abstract and counterfactual sentences.The primary challenges for embodied theories lie in elucidating the meanings of abstract and counterfactual sentences.The most prevalent explanation is that abstract and counterfactual sentences are grounded in the activation of a sensorimotor system,in exactly the same way as concrete and factual ones.The present research employed a dual-task experimental paradigm to investigate whether the embodied meaning is activated in comprehending action-related abstract Chinese counterfactual sentences through the presence or absence of action-sentence compatibility effect(ACE).Participants were instructed to read and listen to the action-related abstract Chinese factual or counterfactual sentences describing an abstract transfer word towards or away from them,and then move their fingers towards or away from them to press the buttons in the same direction as the motion cue of the transfer verb.The action-sentence compatibility effect was observed in both abstract factual and counterfactual sentences,in line with the embodied cognition theories,which indicated that the embodied meanings were activated in both action-related abstract factuals and counterfactuals.

What-If XAI Framework (WiXAI): From Counterfactuals towards Causal Understanding

People learn causal relations since childhood using counterfactual reasoning. Counterfactual reasoning uses counterfactual examples which take the form of “what if this has happened differently”. Counterfactual examples are also the basis of counterfactual explanation in explainable artificial intelligence (XAI). However, a framework that relies solely on optimization algorithms to find and present counterfactual samples cannot help users gain a deeper understanding of the system. Without a way to verify their understanding, the users can even be misled by such explanations. Such limitations can be overcome through an interactive and iterative framework that allows the users to explore their desired “what-if” scenarios. The purpose of our research is to develop such a framework. In this paper, we present our “what-if” XAI framework (WiXAI), which visualizes the artificial intelligence (AI) classification model from the perspective of the user’s sample and guides their “what-if” exploration. We also formulated how to use the WiXAI framework to generate counterfactuals and understand the feature-feature and feature-output relations in-depth for a local sample. These relations help move the users toward causal understanding.

Chinese Native Speakers' Counterfactuals Revisited

This paper examines whether or not Chinese native speakers (CNSs) have difficulties in understanding English counterfactuals, whether CNSs have counterfactual reasoning problems in their own language, what the causes of these difficulties may be, and the problems in teaching English subjunctives. It also proposes on how to improve CNSs’ English counterfactual comprehension.

Logical Difficulty from Combining Counterfactuals in the GHZ-Bell Theorems

In eliminating the fair sampling assumption, the Greenberger, Horne, Zeilinger (GHZ) theorem is believed to confirm Bell’s historic conclusion that local hidden variables are inconsistent with the results of quantum mechanics. The GHZ theorem depends on predicting the results of sets of measurements of which only one may be performed. In the present paper, the noncommutative aspects of these unperformed measurements are critically examined. Classical examples and the logic of the GHZ construction are analyzed to demonstrate that combined counterfactual results of noncommuting operations are in general logically inconsistent with performed measurement sequences whose results depend on noncommutation. The Bell theorem is also revisited in the light of this result. It is concluded that negative conclusions regarding local hidden variables do not follow from the GHZ and Bell theorems as historically reasoned.

Counterfactuals and Context-Sensitivity

It is commonly agreed that when evaluating the validity of an argument involving context-sensitive expressions, the context should be held fixed. In their 2008 essay 'Counterfactuals and Context,' Brogaard and Salerno argue further that context should be held fixed when evaluating an argument involving counterfactuals for validity, since, as many will agree, counterfactuals are context-sensitive. In the present paper, it will however be argued that Brogaard and Salerno fail to distinguish between two different roles that context plays in determining the meaning of a given counterfactual. If they were fully aware of the distinction between these two roles played by context, they might propose a contextualist approach to counterfactuals, as has been developed by Ichikawa in his 2011 paper 'Quantifiers, Knowledge, and Counterfactuals.'

Influence of ecological function protection zone on the water conservation in Gansu-Qinghai Contiguous Region of the upper Yellow River

The implementation of Ecological Function Protection Zone(EFPZ)policy is significant for the ecological restoration and conservation of soil and water in the territory space.This manuscript analyzed and quantified the impact of EFPZ on the regional water conservation function,based on land use data from 2005,2008,2010,2015 and 2020,by conducting a counterfactual simulation along with the GeoSOS-FLUS model and the InVEST model.The results demonstrate that the delineation of EFPZ can significantly influence the water conservation.(1)From 2010 to 2020,as the EFPZ was implemented,the water conservation in the study area was increasing year by year,with a growth rate of 0.03×10^(8) m^(3)∙a^(-1).On the other hand,the simulated water conservation capacity without the implementation of EFPZ decreased year by year,with a decrease rate of 0.01×10^(8) m^(3)∙a^(-1).(2)The EFPZ accounts for only 23%of the total area,but the contribution rate of water conservation reaches 80%.The actual values of water conservation and average water yield per unit pixel in the EFPZ show an increasing trend both internally and externally,while the counterfactual simulation values exhibit a decreasing trend.(3)The water conservation is much higher within the EFPZ than without EFPZ.The implementation of EFPZ has a significant effect on the improvement of the water conservation capacity in Maqu EFPZ and Yellow River Source EFPZ.The protection effectiveness should be enhanced in Qilian Mountain EFPZ and afforestation activities need to be carefully considered in Loess Plateau EFPZ.

Unveiling the impact and mechanism of digital technology on agricultural economic resilience

Enhancing the economic resilience of agriculture is essential for promoting sustainable and high-quality agricultural development.The emergence of digital technology has created new opportunities in this field.However,existing research predominantly focuses on traditional agricultural factors and technologies.Therefore,the impact of digital technology on agricultural economic resilience within the broader context of the“production-operation-industry”system in agriculture has not been comprehensively explored.To bridge this gap,this study analyzes panel data from 30 Chinese provinces from 2011 to 2020.It employs the static Van Dorn’s law and a dynamic spatial panel model to examine how digital technology empowers agricultural resilience.The findings indicate a continuous strengthening of digital technology development in China,albeit with significant polarization and spatial imbalances.Moreover,the resilience of the agricultural economy undergoes notable fluctuations,initially narrowing and subsequently displaying an upward trend.Digital technology clearly plays a pivotal role in empowering resilience through agricultural scale operation,industrial transformation,and technological progress.Its impact,particularly on the promotion of resilience in the eastern region and non-grain-producing areas and on high-level agricultural economies,also shows regional and technological variations.

Graph CA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing

With the popularity of online learning in educational settings, knowledge tracing(KT) plays an increasingly significant role. The task of KT is to help students learn more effectively by predicting their next mastery of knowledge based on their historical exercise sequences. Nowadays, many related works have emerged in this field, such as Bayesian knowledge tracing and deep knowledge tracing methods. Despite the progress that has been made in KT, existing techniques still have the following limitations: 1) Previous studies address KT by only exploring the observational sparsity data distribution, and the counterfactual data distribution has been largely ignored. 2) Current works designed for KT only consider either the entity relationships between questions and concepts, or the relations between two concepts, and none of them investigates the relations among students, questions, and concepts, simultaneously, leading to inaccurate student modeling. To address the above limitations,we propose a graph counterfactual augmentation method for knowledge tracing. Concretely, to consider the multiple relationships among different entities, we first uniform students, questions, and concepts in graphs, and then leverage a heterogeneous graph convolutional network to conduct representation learning.To model the counterfactual world, we conduct counterfactual transformations on students’ learning graphs by changing the corresponding treatments and then exploit the counterfactual outcomes in a contrastive learning framework. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed Graph CA method compared with several state-of-the-art baselines.

Cooperative and Competitive Multi-Agent Systems:From Optimization to Games

Multi-agent systems can solve scientific issues related to complex systems that are difficult or impossible for a single agent to solve through mutual collaboration and cooperation optimization.In a multi-agent system,agents with a certain degree of autonomy generate complex interactions due to the correlation and coordination,which is manifested as cooperative/competitive behavior.This survey focuses on multi-agent cooperative optimization and cooperative/non-cooperative games.Starting from cooperative optimization,the studies on distributed optimization and federated optimization are summarized.The survey mainly focuses on distributed online optimization and its application in privacy protection,and overviews federated optimization from the perspective of privacy protection me-chanisms.Then,cooperative games and non-cooperative games are introduced to expand the cooperative optimization problems from two aspects of minimizing global costs and minimizing individual costs,respectively.Multi-agent cooperative and non-cooperative behaviors are modeled by games from both static and dynamic aspects,according to whether each player can make decisions based on the information of other players.Finally,future directions for cooperative optimization,cooperative/non-cooperative games,and their applications are discussed.

Causal Inference

Causal inference is a powerful modeling tool for explanatory analysis,which might enable current machine learning to become explainable.How to marry causal inference with machine learning to develop explainable artificial intelligence(XAI)algorithms is one of key steps toward to the artificial intelligence 2.0.With the aim of bringing knowledge of causal inference to scholars of machine learning and artificial intelligence,we invited researchers working on causal inference to write this survey from different aspects of causal inference.This survey includes the following sections:“Estimating average treatment effect:A brief review and beyond”from Dr.Kun Kuang,“Attribution problems in counterfactual inference”from Prof.Lian Li,“The Yule–Simpson paradox and the surrogate paradox”from Prof.Zhi Geng,“Causal potential theory”from Prof.Lei Xu,“Discovering causal information from observational data”from Prof.Kun Zhang,“Formal argumentation in causal reasoning and explanation”from Profs.Beishui Liao and Huaxin Huang,“Causal inference with complex experiments”from Prof.Peng Ding,“Instrumental variables and negative controls for observational studies”from Prof.Wang Miao,and“Causal inference with interference”from Dr.Zhichao Jiang.

Security proof of counterfactual quantum cryptography against general intercept-resend attacks and its vulnerability

Counterfactual quantum cryptography, recently proposed by Noh, is featured with no transmission of signal parti- cles. This exhibits evident security advantages, such as its immunity to the well-known photon-number-splitting attack. In this paper, the theoretical security of counterfactual quantum cryptography protocol against the general intercept- resend attacks is proved by bounding the information of an eavesdropper Eve more tightly than in Yin＇s proposal [Phys. Rev. A 82 042335 （2010）]. It is also shown that practical counterfactual quantum cryptography implementations may be vulnerable when equipped with imperfect apparatuses, by proving that a negative key rate can be achieved when Eve launches a time-shift attack based on imperfect detector efficiency.

Assessing the spillover effects of U.S.monetary policy normalization on Nigeria sovereign bond yield

This study examines the spillover effects of U.S.monetary policy normalization on Nigeria 10-Year Treasury bond yield between 2011 and 2017,using the vector error correction model approach.Our results reveal that domestic factors,such as exchange rate and inflation,rather than the U.S.10-Year sovereign bond yield,are the key drivers of Nigeria 10-Year bond yield.Additionally,the spillover effect from the U.S.monetary policy was amplified by oil price shocks and changes in Nigeria’s monetary policy rates.Our counterfactual analysis confirms the findings.

Determination of Causal Effect in Observational Studies： Analysis of Correlated Data with Binary End-Points

Identifying the causal impact of＇ some intervention challenging when one is faced with correlated binary end-points in observational studies is a challenging task, and it is even more The statistical literature on analyzing such data is well documented. Dependence between observations from the same study subject in correlated data renders invalid the usual chi-square tests of independence and inflates the variance ofparameter estimates. Disaggregated approaches such as hierarchical linear models which are able to adjust for individual level covariate：s are favoured in the analysis of such data, thereby gaining power over aggregated and individual-level analyses. In this article the authors, therefore, address the issue of analyzing correlated data with dichotomous end-points by using hierarchical logistic regression, a generalization of the standard logistic regression model for independent outcomes.

The Effect of Counterfactual Thinking on the Sunk Cost Effect

Counterfactual thinking is helpful to comprehend the mistakes made previously and to move toward by proposing future actions to facilitate the success of self-regulation.The sunk cost effect rationalizes a strategy,which is the aim of studies on decision-making.However,few of them have discussed the influence of counterfactual thinking to the sunk cost effect.This study assumes that downward counterfactual thinking can regulate the unhappy mood at the moment for relief,which may reduce the sunk cost fallacy;upward counterfactual thinking,on the contrary,emphasizes the improvement of future behaviors,which may increase the sunk cost fallacy.

Confounding of Three Binary-Variable Counterfactual Model with DAG

Confounding of three binary-variable counterfactual model with directed acyclic graph (DAG) is discussed in this paper. According to the effect between the control variable and the covariate variable, we investigate three causal counterfactual models: the control variable is independent of the covariate variable, the control variable has the effect on the covariate variable and the covariate variable affects the control variable. Using the ancillary information based on conditional independence hypotheses and ignorability, the sufficient conditions to determine whether the covariate variable is an irrelevant factor or whether there is no confounding in each counterfactual model are obtained.

Agricultural market policy reforms and growth in Nigerian export crops

The paper investigated the prospect of selected export crops in meeting the demand of emerging commodity and futures market in Nigeria within the context of a counterfactual model, The data covered about three decades （1973-2000）. The model was estimated to capture two economic policies in the period namely stabilization （1973-1985） before introduction of Structural Adjustment Programme （SAP） and liberalization policy （1986-2005） that followed SAP The results indicated positive divergence between factual and counterfactual trends for palm kernel, cocoa, coffee and rubber but neuative for groundnut after SAP

The Ontology of Counter Factual Causality and Conditional

The author is interested here in the examination of the ontological justification for counterfactual causality and conditional. Ontology metaphysically has to do with what exists and what does not exist. about objects in their associated and individual existence. It is an attempt to further philosophy of causality scientific law, and theory of confirmation and establish a link counter factual. In terms of realism, it is the search for adequate among if-then ＂〉＂ and

Modelling HIV/AIDS Cases in Zambia: A Comparative Study of the Impact of Mandatory HIV Testing

In this study, a time series modeling approach is used to determine an ARIMA model and advance counterfactual forecasting at a point of policy intervention. We consider monthly data of HIV/AIDS cases from the Ministry of Health (Copperbelt province) of Zambia, for the period 2010 to 2019 and have a total of 120 observations. Results indicate that ARIMA (1, 0, 0) is an adequate model which best fits the HIV/AIDS time series data and is, therefore, suitable for forecasting cases. The model predicts a reduction from an average of 3500 to 3177 representing 14.29% in HIV/AIDS cases from 2017 (year of policy activation) to 2019, but the actual recorded cases dropped from 3500 to 1514 accounting for 57.4% in the same time frame.

A Study on High Incomes in China's Monopoly Industries

High incomes in state monopoly industries have drawn extensive public concerns in China. The management of state-owned enterprises(SOEs) is motivated and empowered to set payment schemes in their own favor, resulting in the greater extent to which their senior executives are overpaid as compared with average workers. Hence, the level of unjustified high incomes is heterogeneous among employees of different income levels in state monopoly industries. Using Machado-Mata counterfactual decomposition method based on the regression of multiple quantiles, this paper measures the proportions of reasonable and unreasonable parts of pay gap between state monopoly industries and competitive industries. Our study found that the workforce of state monopoly industries are overpaid across various wage levels, while the extent to which they are overpaid increases with the rise of wage level. The implication is that compared with average workers, executives in state monopoly industries are overpaid to an even greater extent. This requires that the government focus on curbing the high executive income in addition to putting a lid on the gross payroll of SOEs in state monopoly industries. The fundamental strategy to resolving the excessive high income in state monopoly industries is to bring different types of SOEs under different corporate management models and income systems.

Practical Stabilization of Counterfactual Quantum Cryptography

A novel counterfactual quantum key distribution scheme was proposed by T.-G. Noh and a strict security analysis has been given by Z.-Q.Yin, in which two legitimate geographical separated couples may share secret keys even when the key carriers are not traveled in the quantum channel. However, there are still plenty of practical details in this protocol that haven’t been discussed yet, which are of significant importance in physical implementation. In this paper, we will give a practical analysis on such kind of counterfactual quantum cryptography in the aspects of quantum bit error rate (QBER) and stabilization. Furthermore, modified schemes are proposed, which can obtain lower QBER and will be much more robust on stabilization in physical implementation.