ABSTRACT This paper examines the role of values in the emergence of curiosity, focusing on its so-called “chicken-egg problem”: do we become curious because we already care about something, or do we start caring because we are curious about it? Drawing on an embodied and ecological perspective on cognition, as well as insights from Ignorance Studies, I analyze the affective and epistemic conditions under which curiosity arises. I defend two main claims: (1) curiosity emerges as an embodied response to the affordances of unfamiliar objects, which agents engage with because they already value them in some way; and (2) once curiosity arises, it reshapes how agents care for that content. To capture this dynamic, I propose a compound definition of curiosity as a bodily, emotional, and cognitive state, where caring guides curiosity and curiosity reveals caring.

How life starts from small molecules to RNAs and further to modern life is an unanswered question. Cyanosulfitic chemistry established the synthesis of building blocks, including 12 proteinogenic amino acids, 4 ribo- and deoxyribo-nucleosides, and phospholipids, from hydrogen cyanide and hydrogen sulfite under prebiotically plausible conditions. Later on, the non-enzymatic monomer extension of nucleotides provided a plausible pathway from mononucleotides to RNAs giving rise to the RNA world. RNA is one of the key components for the origin of life, firstly, the sequence information can be heritage by template copying reaction. Secondly, RNA is able to fold into a secondary structure which has the capability to catalyze chemical reactions. The RNA world scenario has perfectly overcome the chicken-egg problem, but it still cannot explain why peptides are involved in modern life. Most recently, with the establishment of the reaction between RNA and peptides, the trajectory to the RNA-peptide world theory has opened up a new era of the origin of life research. Here I will discuss the current results relevant to the RNA world to RNA-peptide world theory.

Since the electrical vehicles (EVs) are infrastructure-dependent technology, their penetration faces the problem of lacking recharging infrastructure. Thus, there is a dilemma of chicken-egg in the penetration of EVs and their charging infrastructure for the decision-makers. The article examines the e-mobility scenario of 3 Indian cities to understand issues and challenges in implementing EVs. The study suggests a co-diffusion strategy for the EVs and charging infrastructure. Firstly, the priority EV segment has been decided based on the transport mode preference. Then, suitability of charging facilities according to the segment of the EVs has been presented by analyzing the turnover rate and time spent at several places. The study recommends policies on the upfront cost of EVs, charging infrastructure, awareness generation and others, while leveraging the existing government schemes like Atmanirbhar Bharat and FAME-II.

Corporate social (ir)responsibility towards employees and financial performance: using time to solve the chicken-egg problem

This study aims to compare two approaches using only emission PET data and a convolution neural network (CNN) to correct the attenuation (μ) of the annihilation photons in PET. One of the approaches uses a CNN to generate μ-maps from the non-attenuation-corrected (NAC) PET images (μ-CNNNAC). In the other method, CNN is used to improve the accuracy of μ-maps generated using maximum likelihood estimation of activity and attenuation (MLAA) reconstruction (μ-CNNMLAA). We investigated the improvement in the CNN performance by combining the two methods (μ-CNNMLAA+NAC) and the suitability of μ-CNNNAC for providing the scatter distribution required for MLAA reconstruction. Image data from 18F-FDG (n = 100) or 68Ga-DOTATOC (n = 50) PET/CT scans were used for neural network training and testing. The error of the attenuation correction factors estimated using μ-CT and μ-CNNNAC was over 7%, but that of scatter estimates was only 2.5%, indicating the validity of the scatter estimation from μ-CNNNAC. However, CNNNAC provided less accurate bone structures in the μ-maps, while the best results in recovering the fine bone structures were obtained by applying CNNMLAA+NAC. Additionally, the μ-values in the lungs were overestimated by CNNNAC. Activity images (λ) corrected for attenuation using μ-CNNMLAA and μ-CNNMLAA+NAC were superior to those corrected using μ-CNNNAC, in terms of their similarity to λ-CT. However, the improvement in the similarity with λ-CT by combining the CNNNAC and CNNMLAA approaches was insignificant (percent error for lung cancer lesions, λ-CNNNAC = 5.45% ± 7.88%; λ-CNNMLAA = 1.21% ± 5.74%; λ-CNNMLAA+NAC = 1.91% ± 4.78%; percent error for bone cancer lesions, λ-CNNNAC = 1.37% ± 5.16%; λ-CNNMLAA = 0.23% ± 3.81%; λ-CNNMLAA+NAC = 0.05% ± 3.49%). The use of CNNNAC was feasible for scatter estimation to address the chicken-egg dilemma in MLAA reconstruction, but CNNMLAA outperformed CNNNAC.

The structure of the metabolic network is highly conserved, but we know little about its evolutionary origins. Key for explaining the early evolution of metabolism is solving a chicken–egg dilemma, which describes that enzymes are made from the very same molecules they produce. The recent discovery of several nonenzymatic reaction sequences that topologically resemble central metabolism has provided experimental support for a “metabolism first” theory, in which at least part of the extant metabolic network emerged on the basis of nonenzymatic reactions. But how could evolution kick-start on the basis of a metal catalyzed reaction sequence, and how could the structure of nonenzymatic reaction sequences be imprinted on the metabolic network to remain conserved for billions of years? We performed an in vitro screening where we add the simplest components of metabolic enzymes, proteinogenic amino acids, to a nonenzymatic, iron-driven reaction network that resembles glycolysis and the pentose phosphate pathway (PPP). We observe that the presence of the amino acids enhanced several of the nonenzymatic reactions. Particular attention was triggered by a reaction that resembles a rate-limiting step in the oxidative PPP. A prebiotically available, proteinogenic amino acid cysteine accelerated the formation of RNA nucleoside precursor ribose-5-phosphate from 6-phosphogluconate. We report that iron and cysteine interact and have additive effects on the reaction rate so that ribose-5-phosphate forms at high specificity under mild, metabolism typical temperature and environmental conditions. We speculate that accelerating effects of amino acids on rate-limiting nonenzymatic reactions could have facilitated a stepwise enzymatization of nonenzymatic reaction sequences, imprinting their structure on the evolving metabolic network.

The Chicken or the Egg: Causality Between Trade and Innovation

Crowdsourcing systems provide an easy means of acquiring labeled training data for supervised learning. However, the labels provided by non-expert crowd workers (labelers) often appear low quality. In order to solve this problem, in practice each sample always obtains a multiple noisy label set from multiple different labelers, then ground truth inference algorithms are employed to obtain integrated labels of samples. So ground truth inference methods directly determine the label quality of samples. In this paper, we propose a novel label integration method based on game theory. We assume that there is an adversary in crowdsourcing system who intentionally provides incorrect integrated labels. We model the interaction between the data miner and the adversary as a Stackelberg game in which one player (the data miner) controls the predictive model whereas another (the adversary) tries to choose the integrated labels which would be most harmful for the current classifier. On this basis, we transform the label integration problem into a repeated Stackelberg model. We call our method Stackelberg label inference (SLI). SLI does not need to estimate the quality of labelers, and avoids the chicken-egg problem that can lead to poor result. Moreover, because SLI has little involvement of multiple noisy label sets on the noise data set, it is not very sensitive to the number of labelers. SLI shows better performance when the number of labelers is relatively small. In term of both label quality and model quality, the experimental results show that SLI is superior to the other state-of-the-art ground truth inference methods used to compare.

Chicken or the egg?: question

A solution to renewable hydrogen economy for fuel cell buses – A case study for Zhangjiakou in North China

A comprehensive charging network planning scheme for promoting EV charging infrastructure considering the Chicken-Eggs dilemma

The chicken-or-egg dilemma with nocturia: Which matters most, the water or the salt?

Optical flows and video frame interpolation are considered as a chicken-egg problem such that one problem affects the other and vice versa. This paper presents a stack of deep networks to estimate intermediate optical flows from the very first intermediate synthesized frame and later generate the very end interpolated frame by combining the very first one and two learned intermediate optical flows based warped frames. The primary benefit is that it glues two problems into a single comprehensive framework that learns altogether by using both an analysis-by-synthesis technique for optical flow estimation and Convolutional Neural Networks (CNN) kernels-based frame synthesis. The proposed network is the first attempt to merge two previous branches of previous approaches, optical flow-based synthesis and CNN kernels-based synthesis into a comprehensive network. Experiments are carried out with various challenging datasets, all showing that the proposed network outperforms the state-of-the-art methods with significant margins for video frame interpolation and the estimated optical flows are more accurate for challenging movements. Furthermore, the proposed Motion Estimation Motion Compensation (MEMC) network shows its outstanding enhancement of the quality of compressed videos.

The Study of Chicken Egg Problem in Subspecies of Software Product

Ongoing debates focus on the role of forest-sourced bioenergy within climate mitigation efforts, due to the long rotation lengths of forest biomass. Valuing sequestration is debated due to its reversibility; however, dynamic modelling of biogenic carbon (Cbio) flows captures both negative and positive emissions. The objective of this work is to respond to the key issue of timing sequestration associated with two opposed modelling choices (historic vs. future) in the context of dynamic life cycle assessment (LCA). The outputs of a partial-equilibrium model are used to inform prospective evaluations of the use of forest wood residues in response to an energy transition policy. Dynamic forest carbon modelling represents the carbon cycle between the atmosphere and technosphere: Cbio fixation and release through combustion and/or decay. Time-dependent characterization is used to assess the time-sensitive climate change effects. The two Cbio sequestration perspectives for bioenergy (forest biomass use) and reference (no use) scenarios are contrasted to assess (i) their temporal profiles, (ii) their climatic consequences concerning C-complete (fossil + biogenic C) vs. C-neutral (fossil C) approaches, and (iii) the implications of comparing the two approaches with dynamic LCA. Full lifetime carbon accounting confirms that Cbio entering the bioenergy system equals the Cbio leaving it in the net balance, but not within annual dynamic balances, which alter the atmospheric greenhouse gas composition. The impacts of the historic approach differed considerably from those of the future. Moreover, the “no use” scenario yielded higher forcing effects than the “bioenergy” due to the higher methane proportions. The chicken-egg dilemma arises in attributional LCA: as the forcing depends on the timing of the Cbio sequestration and its allocation to a harvest activity. A decision tree supported by case study applications provides general rules for selecting the adequate time-related modelling approach based the criteria of provision of wood and regrowth from managed and unmanaged forests, determined by the origin of biotic resources and related spheres. Excluding dynamic Cbio introduces under- (future) or over- (historic) estimation of the results, misleading mitigation decisions. Further research is needed to close the gap between forest stand and landscape level, addressing issues beyond the chicken-egg dilemma and developing complete dynamic LCA studies.

The cliches, “leaders are born, not made and leaders are made, not born” have been on the social sphere of leadership for quite a while. Is this a chicken-egg dilemma? Leadership generally has certain qualities that humans can point at, acting as conspicuous factors to be emulated. These qualities can either be innate or acquired. This paper looks at the role of traits in the leadership process. It focuses mainly on the trait theory, distinguishing the desired and the not so admired ones in as far as leadership is a concern. We also employ the great man theory to historically give flesh to where and how leadership has evolved to the 21st century. Transformational/charismatic leadership comes forth as the lottery winning mode to a sure way to a successful and productive form that leaders should have to deliver in this technologically advancing planet. We touched on Narcissist and Machiavellian types of leadership but only as contrary examples to the recommended. The paper is, therefore, a handy requirement for today’s leaders. A snapshot for those wishing to guide others in the visionary path of leadership. Commitment, passion, empathy, honesty, integrity communication skills and decision-making capabilities can be learned and used to better the leader-follower relationship process. The business world and other forms of social governance are out of the stagnation of the great man theory of leadership. Flexibility in behavior, actions and thought make the best of a leader.

The Chicken-and-Egg Quandry [President's Corner

Preprocessing of GIS data for electric vehicle charging stations analysis and evaluation of the predictors significance

Quantum chicken-or-egg experiment blurs the distinction between before and after

A hydrogen refuelling stations infrastructure deployment for cities supported on fuel cell taxi roll-out