Truth Values Research Articles

Customs Duty Evasion and Enforcement in the Arthaśāstra

The first part of Chapter 2.21 of the Arthaśāstra deals with the activities of the Customs Superintendent, containing recommendations regarding the collection of customs duty. Scholars have encountered several difficulties in understanding the meaning and purposes of the activities described. An auction-like procedure described in the text has been analyzed by some as the normal operation of a market, with the payments to the treasury mentioned there taken as a hitherto-unknown market tax—the price of trading in the market. Other paragraphs in the text describe seemingly self-contradictory trader actions. Interpretations of these actions have involved the imbuing of traders with complex psychological beliefs regarding the actions of other traders. This article proposes an economic model of the underlying market structure, which leads to a different and integrated reading of the text. In this reading, the author of the text, recognizing the incentive for importers to undervalue their goods, suggests the auction procedure and accom- panying penalty payments to elicit truthful revelation of importers’ valuations and to maximize customs levy collections. By comparing the procedure in the Arthaśāstra to modern-day regulations intended to combat tax evasion, the article provides early evidence of the use of sophisticated mechanisms to elicit truthful value revelation.

Is there truth in fiction? Lessons from readers’ responses to dementia fiction

This paper addresses the question ‘is there truth in fiction?’, by synthesising a range of disciplinary approaches to the issue, as well as drawing on empirical research carried out with...

Turkish teachers’ values with rational and non-rational truth and teacher emotions in teaching

The aim of this study was to clarify the relationships between personal values, truth-related values and emotions among Turkish teachers. The Schwartz’s Value Theory and Frenzel’s teacher emotion model were used as the theoretical framework. This study used a cross-sectional correlational research design. The data were collected from 279 teachers with the Portrait Value Questionnaire (PVQ) where rational truth and non-rational truth values were added, and with the Teacher Emotions Scale (TES). The circular structure of the Schwartz Value Theory was tested by multidimensional scaling. The data analysis aimed to uncover relationships between personal values, truth-related values, and emotions. Rational truth emerged near self-direction and self-transcendence, yet items measuring non-rational truth were scattered among values. Females had higher regard for self-direction and hedonism than males. Non-rational truth was negatively correlated with enjoyment, while it was positively correlated with anxiety. The implications for gender roles in Turkish society are discussed.

Improved gradient echo magnitude- and phase-based mapping of using multiple RF spoiling increments at 3T and 7T.

The transverse relaxation time T holds significant relevance in clinical applications and research studies. Conventional T mapping approaches rely on spin-echo sequences, which require lengthy acquisition times and involve high radiofrequency (RF) power deposition. An alternative gradient echo (GRE) phase-based T mapping method, utilizing steady-state acquisitions at one small RF spoil phase increment, was recently demonstrated. Here, a modified magnitude- and phase-based T mapping approach is proposed, which improves estimations by simultaneous fitting of and signal amplitude ( ) at three or more RF spoiling phase increments, instead of assuming a fixed value. The feasibility of the magnitude-phase-based method was assessed by simulations, in phantom and in vivo experiments using skipped-CAIPI three-dimensional-echo-planar imaging (3D-EPI) for rapid GRE imaging. , and PD estimations obtained by our method were compared to of the phase-based method and and PD of spoiled GRE-based multi-parameter mapping using a multi-echo version of the same sequence. The agreement of the proposed with ground truth and reference values was higher than that of phase-based in simulations and in phantom data. While phase-based overestimation increases with actual and , the proposed method is accurate over a large range of physiologically meaningful and values. At the same time, precision is improved. In vivo results were in line with these observations. Accurate magnitude-phase-based T mapping is feasible in less than 5 min scan time for 1 mm nominal isotropic whole-head coverage at 3T and 7T.

Indeterminate and vague causation

Is it sometimes indeterminate whether two events or variables are causally related? Can causal statements be vague? The analysis of three potential types of cases of causal indeterminacy and causal vagueness yields an affirmative answer. The claim that some cases of omission, and some cases of prevention, give rise to indeterminate causal relations depends on the premise that omissions and preventions can be causal. A second type of indeterminate causation corresponds to causal statements whose truth value is indeterminate because they contain vague predicates. Such vagueness can have two sources. Only one yields indeterminate causation. Finally, there is indeterministic probabilistic causation. If “indeterministic overdetermination” is interpreted as metaphysical rather than epistemic, it is inappropriate to speak of indeterminacy. Rather, there is no fact of the matter about which of the two sources of probability raising really is the cause. It is more appropriate to say that both are contributing causes. “Indeterministic trumping” gives rise to a form of indeterminacy that belongs to the category of the open future. Once the effect has occurred, no indeterminacy remains.

Presuppositions and the Content Implication

In the 1950s Peter Strawson analyzed the works of Bertrand Russell regarding fundamental definitions of meaning, sentences and truth value. The debate between them uncovered many issues that Fregean, truth-functional logics have when defining concepts from natural language. To reconcile the Fregean paradigm with the reality of language use, Strawson proposed the concept of presuppositions  necessary preconditions for the truth of other sentences. We believe that his proposition stemmed primarily from the problem caused by the fact that Fregean, truth-functional logics are not sensitive to the contents of sentences and reduce them to their logical values. This is bound to produce a mismatch between the way logic models reasoning and the way language users reason since real-life reasoning is performed on the contents of sentences and not their logical values. Inspired by the ideas of Strawson and Roman Suszko, who initiated the paradigm of non-Fregean logics, we propose a new solution to the debate between Strawson and Russell. In our solution, the content implication connective is used to express content relations between sentences. We move away from truth and falsehood as the sole two semantic correlates of sentences and instead work in a system where the contents of sentences are their semantic correlates.

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. We applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

Secchi Depth Retrieval in Oligotrophic to Eutrophic Chilean Lakes Using Open Access Satellite-Derived Products

The application of the Multispectral Instrument (MSI) aboard Sentinel-2A/B constellation for assessing water quality in Chilean lakes represents an emerging area of research, particularly for the environmental monitoring of optically complex water bodies. Similarly, atmospheric correction processors applied to aquatic environments, such as the Case 2 Networks (C2RCC-Nets), are notably underrepresented. This study evaluates the capability of C2RCC-Nets using different neural networks—Case-2 Regional/Coast Color (C2RCC), C2X-Extreme (C2X), and C2X-Complex (C2XC)—to estimate Secchi depth in Lake Lanalhue (eutrophic), Lake Villarrica (oligo-mesotrophic), and Lake Panguipulli (oligotrophic). The evaluation used different statistical methods such as Spearman’s correlation and normalized error metrics (nRMSE, nMAE, and nbias) to assess the agreement between satellite-derived data and in situ measurements. C2XC demonstrated the best fit for Lake Lanalhue, with an nRMSE = 33.13%, nMAE = 23.51%, and nbias = 8.57%, in relation to the median ground truth values. In Lake Villarrica, the C2XC neural network displayed a moderate correlation (rs = 0.618) and error metrics, with an nRMSE of 24.67% and nMAE of 20.67%, with an nbias of 4.21%. In the oligotrophic Lake Panguipulli, no relationship was observed between estimated and measured values, which could be related to the fact that the selected neural networks were developed for very case 2 waters. These findings highlight the need for methodological advancements in processing satellite-derived water quality products for Chile’s optical water types, particularly for very clear waters. Nonetheless, this study underscores the need for model-specific calibration of C2RCC-Nets, as lakes with different optical water types and trophic states may require tailored training ranges for inherent optical properties.

Encoding complex speech acts via sentence final particles

Abstract This paper examines the use of the sentence final particle tai (呔) in the Changsha dialect (Xiang Chinese). It is demonstrated that tai encodes complex speech acts: on the one hand, it expresses the speaker’s high degree of certainty about the truth value of the associated proposition p; on the other, it seeks the addressee’s confirmation, via an implicitly entailed biased question, of the truth value of p. In view of the discursive interplay between the speaker and the addressee expressed by tai, the present study adopts a syntax-pragmatics interface approach to the syntax of tai, decomposing the complex speech acts it conveys into three functional projections at the structural level. Two are related to the speaker’s and the addressee’s knowledge/belief in the utterance (i.e., GroundSpkrP and GroundAdrP) and one is responsible for the speaker’s call on the addressee to confirm (i.e., ResponseP). This study contributes to cross-linguistic investigations on the grammatical realization of complex speech acts, an area which remains in its infancy. Tai represents a new type of grammatical strategy for expressing complex speech acts, that is, to encode the speaker’s commitment and the addressee’s engagement via a single lexical element without any particular intonation pattern.

Peningkatan Interaktif Belajar melalui Program Rabu Gembira Bagi Siswa Kelas 1-6 SD di Kampung Rama

The Rabu Gembira program at GKSI Kampung Rama is an initiative designed to support the character development and spirituality of children through a series of creative and educational activities. With a comprehensive approach, this program not only teaches Christian values but also equips children with life skills and a high level of social awareness. Activities including crafts, spiritual discussions, and social interactions aim to shape individuals who are character-driven and capable of making a positive impact in their communities. Through this experience, children learn to embody values of truth, enhance their self-confidence, and develop social and emotional skills. The outcomes of this program are expected to prepare the younger generation to face the challenges of the times with confidence and strong personal qualities. This discussion provides an in-depth understanding of the goals, benefits, and positive impacts of the Rabu Gembira program in supporting the holistic development of children.

Investigation of coupling DSSAT with SCOPE-RTMo via sensitivity analysis and use of this coupled crop-radiative transfer model for sensitivity-based data assimilation

The increasing availability of remote sensing (RS) data and the advancement of computation abilities, combined with the demands for enhancing crop production, encourages the creation of a framework in which crop growth simulation can be updated sequentially to serve as a yield predictor and be part of a decision support system. However, crop model outputs and RS data must be linked via a radiative transfer model (RTM), which simulates the interaction between the crop and the intercepted radiation. In this study, a comprehensive coupling scheme between a crop model (DSSAT-CROPGRO-tomato) and an RTM (SCOPE-RTMo) was formulated and investigated through global sensitivity analysis (SA) and by testing the coupled model in a synthetic data assimilation (DA) experiment. The DA experiment utilized a sensitivity-based particle filter (PF) in which the SA results were used to enhance the PF convergence rate and accuracy. The SA results provide the sensitivity of simulated reflectance at different wavelengths to DSSAT-CROPGRO parameters throughout the season. This information can help guide future data assimilation experiments by choosing imaging instruments with appropriate spectral bands and timing the measurements to enhance model calibration. The results of the synthetic DA experiment showed a good convergence of the particle filter towards the ground truth. The results also demonstrated the strong relation between LAI and reflectance, as several model runs with different initial values of DSSAT-CROPGRO parameters all converged and predicted the synthetic LAI observations very well. The convergence of DSSAT-CROPGRO parameters to their ground truth values was only partial, and phenology-related parameters tended to converge better than growth-related parameters.

3D scanner measuring preterm infants’ head circumference and cranial volume: validation in a simulated care setting

IntroductionWeekly head circumference (HC) measurements using a measuring tape is the current standard for longitudinal brain growth monitoring of preterm infants. The MONITOR3D (M3D) 3D scanner has been developed to measure both HC and cranial volume (CrV) of preterm infants within incubators. The M3D’s usability, accuracy and precision were validated in a simulated setting in a neonatal intensive care unit (NICU).Materials and methodsDuring a simulated routine care moment, NICU nurses conducted M3D scans of a preterm doll simulating an extreme low birthweight preterm (ELBW; BW < 1,000 g) infant, followed by manual HC measurements using a measuring tape. Usability was quantified by percentage of successful HC and CrV measurements from scans. HC and CrV were calculated by marking anatomical landmarks on the 3D image. Measurements were compared to the real, ground truth (GT) values of the doll’s head, defined by an accurate medical scanner. Measurement accuracy was assessed using mean or median absolute measurement error (ME), and precision by the spread of ME, represented by the 95% interval of the ME range. ME intervals were compared with preterm weekly growth increases to assess clinical usability.ResultsRegarding usability, 56 M3D scan sessions resulted in 25 successful (44.6%) HC and CrV measurements, with incomplete 3D data being the primary cause of unsuccessful scans. Accuracy of the measuring tape for HC was 0.2 cm (proportional 0.9% of GT), and precision was 1.6 cm (6.3%). M3D’s accuracy of HC was 0.4 cm (1.5%), and precision was 0.7 cm (2.9%). For CrV, M3D’s accuracy was 8.0 mL (3.8%) and precision 22.6 mL (10.8%).ConclusionThe M3D scanner is suitable for measuring HC and CrV in ELBW infants. However, current scan success rate is too low for practical usability. The M3D’s accuracy and precision are clinically sufficient, while the precision of the current measuring tape method is inadequate for preterm infants. This makes the M3D a promising alternative for HC, offering less disturbance to the infant. In the future, the M3D technique could facilitate the creation of CrV growth reference charts for ELBW infants, enhancing the accuracy of clinical growth monitoring for preterm infants.

On the relative asymptotic expressivity of inference frameworks

We consider logics with truth values in the unit interval $[0,1]$. Such logics are used to define queries and to define probability distributions. In this context the notion of almost sure equivalence of formulas is generalized to the notion of asymptotic equivalence. We prove two new results about the asymptotic equivalence of formulas where each result has a convergence law as a corollary. These results as well as several older results can be formulated as results about the relative asymptotic expressivity of inference frameworks. An inference framework $\mathbf{F}$ is a class of pairs $(\mathbb{P}, L)$, where $\mathbb{P} = (\mathbb{P}_n : n = 1, 2, 3, \ldots)$, $\mathbb{P}_n$ are probability distributions on the set $\mathbf{W}_n$ of all $\sigma$-structures with domain $\{1, \ldots, n\}$ (where $\sigma$ is a first-order signature) and $L$ is a logic with truth values in the unit interval $[0, 1]$. An inference framework $\mathbf{F}'$ is asymptotically at least as expressive as an inference framework $\mathbf{F}$ if for every $(\mathbb{P}, L) \in \mathbf{F}$ there is $(\mathbb{P}', L') \in \mathbf{F}'$ such that $\mathbb{P}$ is asymptotically total variation equivalent to $\mathbb{P}'$ and for every $\varphi(\bar{x}) \in L$ there is $\varphi'(\bar{x}) \in L'$ such that $\varphi'(\bar{x})$ is asymptotically equivalent to $\varphi(\bar{x})$ with respect to $\mathbb{P}$. This relation is a preorder. If, in addition, $\mathbf{F}$ is at least as expressive as $\mathbf{F}'$ then we say that $\mathbf{F}$ and $\mathbf{F}'$ are asymptotically equally expressive. Our third contribution is to systematize the new results of this paper and several previous results in order to get a preorder on a number of inference systems that are of relevance in the context of machine learning and artificial intelligence.

Abstract 4139198: A Systematic Approach to Prompting Large Language Models for Automated Feature Extraction from Cardiovascular Imaging Reports

Introduction: Cardiovascular radiology reports contain valuable diagnostic information linked to images, but the unstructured text format makes feature extraction difficult on a large scale. Large language models (LLMs) allow for feature extraction where string parsing alone is insufficient, but require careful prompting for accurate results. Hypothesis: We hypothesize that a systematic prompting approach using LLMs can expedite the extraction of features from unstructured text in transesophageal echocardiography (TEE) reports. Methods: The data consisted of 7106 intraoperative TEE reports, 600 of which were manually reviewed to obtain pre- and post-intervention ground truth values for left ventricular ejection fraction (LVEF), right ventricular systolic function (RVSF), and tricuspid regurgitation (TR). Reports are paired with an imaging study consisting of 50-200 clips. For each feature considered, 100 of the 600 labeled reports were used to engineer a prompt in Llama-2 that maximized feature extraction accuracy. Results: We found that using multiple, shorter prompts yielded higher accuracy than did fewer, longer prompts. Additionally, when imposing semantic information onto a numerical scale, prompt engineering in combination with string parsing (Figure 1) gave the best results. When evaluated on the 500 labeled reports withheld for testing, the finalized prompts had accuracies of 94.1%, 94.8%, and 91.3% for LVEF, RVSF, and TR, respectively. Using this strategy, 5000 intraoperative TEE reports were analyzed and used to train and evaluate a regression model for LVEF estimation from TEE clips (Figure 2). Conclusion: We have shown that performing prompt engineering on Llama-2 can be used to extract features from unstructured TEE reports in an accurate manner. As an extension of these methods, automated feature prediction from echocardiograms can be used to create rapid, low-cost, and accessible cardiac assessments.

Accuracy Assessment of an Image-Based Cloud-Based Indoor Mapping Platform as an Alternative for Desktop Applications

Abstract. The generation of 3D spatial datasets, especially in indoor space, has continually been a challenge in developing spatial applications that are aimed for the realistic representation of the real world. Despite their potential, the creation of 3D spatial data remains a challenge, particularly in terms of cost and complexity associated with traditional methods. While this method proves to be a reliable and accurate method of creating such data, this approach entails challenges in economic, computational, and human resource aspects. Alternatively, omnidirectional images have emerged as cost-effective alternatives for representing 3D spaces, offering a comprehensive field of view and facilitating the generation of structure and appearance representations through mesh-based 3D maps. This study evaluates the geometric accuracy of a cloud-based image-based mapping platform for generating 3D representations of indoor spaces, aiming to assess its suitability as an alternative to desktop-based platforms. We obtained 30 sample lines and compare respective measurements from each of the generated meshes to a ground truth value. Results show that while there are no significant differences in the mean errors, the mesh generated from a cloud-based platform produced minimal errors. This demonstrates the potential of this platform for generating 3D representations of indoor space, acceptable in both geometric accuracy and visualization capabilities.

Nietzsche on Socrates, Jesus, and the slave revolt in morality

ABSTRACT This article shows how the triumph of Socratic optimism in Nietzsche’s first book, The Birth of Tragedy, is due to a slave revolt in morality that promulgates a religious faith in the value of scientific truth. This early argument parallels his critique of the ascetic ideal that infects science in The Genealogy of Morality. I draw out the resemblance between Socrates’s martyrdom in The Birth and Jesus’s crucifixion in the Genealogy in order to illuminate how ritual human sacrifice mythically immortalizes both figures, together inaugurating the ascetic ideals that Christianity will eventually unite. I discuss Nietzsche’s understanding of religious martyrdom and his explanation for the success of Christian Platonism, whose ascetic ideals ground the European value system that he is known for attacking.

PPSZ for General k-SAT and CSP—Making Hertli’s Analysis Simpler and 3-SAT Faster

The currently fastest known algorithm for k-SAT is PPSZ, named afterits inventors (Paturi et al. in J ACM 52(3):337-364, 2005. http://dx.doi.org/10.1145/1066100.1066101). Analyzing its running timeis much easier for input formulas with a unique satisfying assignment.In this paper, we achieve three goals. First, we simplify the analysisof Hertli (in 2011 IEEE 52nd Annual Symposium on Foundations ofComputer Science-FOCS 2011, Los Alamitos, 2011) for input formulaswith multiple satisfying assignments. Second, we show a “liftingresult”: if you improve PPSZ for k-CNF formulas with a unique satisfyingassignment, you will immediately get a (weaker) improvement forgeneral k-CNF formulas. In combination this with results by Hansen etal. (in Charikar and Cohen (ed) Proceedings of the 51st Annual ACMSIGACT Symposium on Theory of Computing, 2019) and Scheder (in62nd IEEE Annual Symposium on Foundations of Computer Science,2021), who all prove improved time bounds for Unique-k-SAT, this givesimproved bounds for general k-SAT. We also generalize our results tothe domain of Constraint Satisfaction Problems, i.e., satisfiability withmore than two truth values.

Refining Cellular Data for Accurate Trip Chain Identification: A Novel Approach for Urban Travel Analysis

Cellular data play a crucial role in supporting travel demand assessment and urban traffic planning because of their cost-effectiveness and extensive coverage. However, inherent inaccuracies, such as imprecise positioning, data duplication, and abnormal communication frequencies, hinder their ability to depict travelers’ trips accurately. In this paper, we present a novel approach to mitigate these issues by employing base station mapping to refine positioning data, eliminating duplicates and abnormal frequency records. This refinement enables more effective utilization of cellular data for trip chain identification. We address the ping-pong handover effect by employing a finite automaton machine and an approximate nearest neighbor searching method with carefully selected seeds to identify activities. Our method’s accuracy is validated through ground truth value analysis, focusing on permanent resident population estimation and metro passenger flow estimation. Furthermore, we demonstrate the practical effectiveness and value of our proposed method through a series of representative applications in a real-world case study in the city of Nanning, Guangxi Province, China.

Authority and Scientific Paradigms

The state of European science at the end of the sixteenth century resembles a kind of epistemological chaos in which there are no clear landmarks and boundaries of the world. Not surprisingly, as some order took shape within it, the revival of science began. It should not be forgotten, moreover, that the word "order" itself is etymologized as cosmos, and the discipline that studies it is cosmology. That is why the first fundamental works on astronomy – Copernicus's “On the Revolutions of the Celestial Spheres” and Galileo's “Dialogue Concerning the Two Chief World Systems: Ptolemaic and Copernican” – bear such pretentious titles, because they aim to put the whole "world system" in order. The concepts of scientific determination and the objects of cognition must be present in the problem field of the cognitive process, and the problems of knowledge translation and communicability between the subjects of cognition, as well as the extremely complicated interdependencies between the agency of the scientist and his cultural-historical context, should directly and unconcernedly raise the question of defining the range of epistemic values in which the value of truth a priori could not be put in contradiction to the value of one’s outer for the phenomenon heuristic potential or to the consistency of the relevant scientific noosphere.

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.