En este ensayo propongo una lectura de los contornos de la primera Meditación. Con ella intento resaltar una dimensión importante del pensamiento de Descartes en torno a la virtud de la precisión. Contrasto la preocupación por la verdad en la vida cotidiana y en la indagación cartesiana, exploro la manera en que Descartes enfrenta algunos riesgos epistémicos en el curso de su meditación, señalo algunos aspectos de su concepción de la agencia epistémica y concluyo con una breve disquisición alrededor de (...) la apuesta de Pascal. (shrink)

Although continued investments in nanotechnology are made, atomically precise manufacturing (APM) to date is still regarded as speculative technology. APM, also known as molecular manufacturing, is a token example of a converging technology, has great potential to impact and be affected by other emerging technologies, such as artificial intelligence, biotechnology, and ICT. The development of APM thus can have drastic global impacts depending on how it is designed and used. This paper argues that the ethical issues that arise from APM (...) - as both a standalone technology or as a converging one - affects the roles of stakeholders in such a way as to warrant an alternate means furthering responsible innovation in APM research. This paper introduces a value-based design methodology called Value Sensitive Design (VSD) that may serve as a suitable framework to adequately cater to the values of stakeholders. Ultimately, it is concluded that VSD is a strong candidate framework for addressing the moral concerns of stakeholders during the preliminary stages of technological development. (shrink)

The success of precision medicine depends on obtaining large amounts of information about at-risk populations. However, getting consent is often difficult. Why? In this commentary I point to the differentials in social status involved. These differentials are inevitable once personal information is surrendered, but are particularly intense when the studied populations are socioeconomically or socioculturally disadvantaged and/or ethnically stigmatized groups. I suggest how the deep distrust of the latter groups can be partially justified as a lack of confidence that (...) their core values or interests will sufficiently be taken into account. Hence, the ethical challenge here lies not in avoiding status differentials, but in dealing with them appropriately. Scientists should not assume trust from others but adopt a norm of “demonstrating trustworthiness”. (shrink)

As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological, medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues (...) relating to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in theEthical Framework for Big Data in Health and Research to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner. (shrink)

Autism Spectrum Condition presents a challenge to social and relational accounts of the self, precisely because it is broadly seen as a disorder impacting social relationships. Many influential theories argue that social deficits and impairments of the self are the core problems in ASC. Predictive processing approaches address these based on general purpose neurocognitive mechanisms that are expressed atypically. Here we use the High, Inflexible Precision of Prediction Errors in Autism approach in the context of cultural niche construction to (...) explain atypicalities of the relational self, specifically its minimal, extended, and intersubjective aspects. We contend that the social self in ASC should not be seen as impaired, but rather as an outcome of atypical niche construction. We unpack the scientific, ethical, and practical consequences of this view, and discuss implications for how the challenges that autistic persons face should be approached. (shrink)

Twenty-two experienced action video-game players (AVGPs) and 18 non-VGPs were tested on a pen-and-paper line bisection task that was untimed. Typically, right-handers bisect lines 2 % to the left of true centre, a bias thought to reflect the dominance of the right-hemisphere for visuospatial attention. Expertise may affect this bias, with expert musicians showing no bias in line bisection performance. Our results show that experienced-AVGPs also bisect lines with no bias with their right hand and a significantly reduced bias with (...) their left hand compared to non-AVGPs. Bisections by experienced-AVGPs were also more precise than those of non-AVGPs. These findings show the cognitive proficiencies of experienced-AVGPs can generalize beyond computer based tasks, which resemble their training environment. (shrink)

Atomically precise manufacturing (APM) is the assembly of materials with atomic precision. APM does not currently exist, and may not be feasible, but if it is feasible, then the societal impacts could be dramatic. This paper assesses the net societal impacts of APM across the full range of important APM sectors: general material wealth, environmental issues, military affairs, surveillance, artificial intelligence, and space travel. Positive effects were found for material wealth, the environment, military affairs (specifically nuclear disarmament), and space (...) travel. Negative effects were found for military affairs (specifically rogue actor violence and AI. The net effect for surveillance was ambiguous. The effects for the environment, military affairs, and AI appear to be the largest, with the environment perhaps being the largest of these, suggesting that APM would be net beneficial to society. However, these factors are not well quantified and no definitive conclusion can be made. One conclusion that can be reached is that if APM R&D is pursued, it should go hand-in-hand with effective governance strategies to increase the benefits and reduce the harms. (shrink)

Following from my criticisms of Calcott’s analysis on the permissive/instructive distinction, I rebut his claims that 1) he clarifies my measure one-to-one specificity; 2) for all intents and purposes of his analysis his notion of precision is different from my measure of one- to-one specificity; 3) Waddington box is a better and different model than the extension of Woodward’s radio I propose.

Visual experiences often feel crisper, sharper or more vivid when one pays attention to the seen object. According to some representationalist theories of perception, these felt effects occur because attentive experiences represent more determinate or precise properties than their inattentive counterparts: a color experience represents vermillion rather than red if the color is perceived with attention rather than without it. Recently, this idea has been expressed in terms of ranges of feature values represented, so that attentive experiences shall represent narrower (...) ranges than inattentive experiences. However, the latter claim seems to run counter to evidence, recently discussed by Block (2015), that neither spatial attention nor attention as deployed in the attentional blink paradigm alters the ranges of properties represented in visual perception. In this chapter, I clarify what these findings in fact tell us about the representational contents of visual perception and argue that they do not refute the representational account of the attentional effects. (shrink)

The full Bayesian signi/cance test (FBST) for precise hypotheses is presented, with some illustrative applications. In the FBST we compute the evidence against the precise hypothesis. We discuss some of the theoretical properties of the FBST, and provide an invariant formulation for coordinate transformations, provided a reference density has been established. This evidence is the probability of the highest relative surprise set, “tangential” to the sub-manifold (of the parameter space) that defines the null hypothesis.

This project, titled Bird Species Identification Using Deep Learning, aims to develop a robust system that can identify bird species from images with high precision. The core of this project involves training a CNN model on a diverse dataset of bird images. This dataset includes species from various geographical locations and environments, capturing a wide range of appearances, postures, and behaviors. By preprocessing and augmenting the dataset, the model is designed to handle challenges such as variations in lighting, background (...) noise, and partial occlusions. The implementation of this project has numerous practical applications. For instance, it can be integrated into mobile or web-based platforms to provide real-time bird identification tools for researchers, birdwatchers, and environmental agencies. It can also be utilized in wildlife monitoring programs to track species distribution and migration patterns. Furthermore, the system can support educational initiatives, allowing individuals to learn about bird species and their significance in the ecosystem. (shrink)

Precision medicine functions by grouping patients along genetic, molecular, and related ‘-omics’ factors. This stratification relies on large, growing databases of patient-volunteered information. Both private companies and government bodies incentivize patients to volunteer this genetic information appealing to the creation of collaborative patient partnerships and the concept of empowerment. This paper aims to address two related questions: (1) what is the actual nature of patient participation in precision medicine research? And (2) is this participation in precision medicine (...) research really that empowering for the average patient? I contend that firstly, the nature of this participation is best conceived of as merely contributory. Contributory participation, as it will be shown, falls short of collaboration. The partnership created by participation in precision medicine research is not one of sharing values, equal say in decisions, or shared benefit. Secondly, I contend that there are important caveats to claims that patient participation in precision medicine is empowering. Empowerment is hindered by the type of participation, the practical use or actionability of genetic data, genetic literacy, the cost of precision drugs for patients that qualify for them, and bioethical considerations of informed consent. (shrink)

Reflective equilibrium (RE) is often regarded as a powerful method in ethics, logic, and even philosophy in general. Despite this popularity, characterizations of the method have been fairly vague and unspecific so far. It thus may be doubted whether RE is more than a jumble of appealing but ultimately sketchy ideas that cannot be spelled out consistently. In this paper, we dispel such doubts by devising a formal model of RE. The model contains as components the agent’s commitments and a (...) theory that tries to systematize the commitments. It yields a precise picture of how the commitments and the theory are adjusted to each other. The model differentiates between equilibrium as a target state and the dynamic equilibration process. First solutions to the model, obtained by computer simulation, show that the method allows for consistent specification and that the model’s implications are plausible in view of expectations on RE. In particular, the mutual adjustment of commitments and theory can improve one’s commitments, as proponents of RE have suggested. We argue that our model is fruitful not only because it points to issues that need to be dealt with for a better understanding of RE, but also because it provides the means to address these issues. (shrink)

This chapter presents a re-understanding of the contents of our analog magnitude representations (e.g., approximate duration, distance, number). The approximate number system (ANS) is considered, which supports numerical representations that are widely described as fuzzy, noisy, and limited in their representational power. The contention is made that these characterizations are largely based on misunderstandings—that what has been called “noise” and “fuzziness” is actually an important epistemic signal of confidence in one’s estimate of the value. Rather than the ANS having noisy (...) or fuzzy numerical content, it is suggested that the ANS has exquisitely precise numerical content that is subject to epistemic limitations. Similar considerations will arise for other analog representations. The chapter discusses how this new understanding of ANS representations recasts the learnability problem for number and the conceptual changes that children must accomplish in the number domain. (shrink)

Clark acknowledges but resists the indirect mind–world relation inherent in prediction error minimization (PEM). But directness should also be resisted. This creates a puzzle, which calls for reconceptualization of the relation. We suggest that a causal conception captures both aspects. With this conception, aspects of situated cognition, social interaction and culture can be understood as emerging through precision optimization.

Abstract : A measurement result is never absolutely accurate: it is affected by an unknown “measurement error” which characterizes the discrepancy between the obtained value and the “true value” of the quantity intended to be measured. As a consequence, to be acceptable a measurement result cannot take the form of a unique numerical value, but has to be accompanied by an indication of its “measurement uncertainty”, which enunciates a state of doubt. What, though, is the value of measurement uncertainty? What (...) is its numerical value: how does one calculate it? What is its epistemic value: how one should interpret a measurement result? Firstly, we describe the statistical models that scientists make use of in contemporary metrology to perform an uncertainty analysis, and we show that the issue of the interpretation of probabilities is vigorously debated. This debate brings out epistemological issues about the nature and function of physical measurements, metrologists insisting in particular on the subjective aspect of measurement. Secondly, we examine the philosophical elaboration of metrologists in their technical works, where they criticize the use of the notion of “true value” of a physical quantity. We then challenge this elaboration and defend such a notion. The third part turns to a specific use of measurement uncertainty in order to address our thematic from the perspective of precision physics, considering the activity of the adjustments of physical constants. In the course of this activity, physicists have developed a dynamic conception of the accuracy of their measurement results, oriented towards a future progress of knowledge, and underlining the epistemic virtues of a never-ending process of identification and correction of measurement errors. (shrink)

This paper focuses on Precision Public Health (PPH), described in the scientific literature as an effort to broaden the scope of precision medicine by extrap- olating it towards public health. By means of the “All of Us” (AoU) research pro- gram, launched by the National Institutes of Health in the U.S., PPH is being devel- oped based on health data shared through a broad range of digital tools. PPH is an emerging idea to harness the data collected for (...) precision medicine to be able to tai- lor preventive interventions for at-risk groups. The character of these data concern genetic identity, lifestyle and overall health and therefore affect the ‘intimacy’ of personhood. Through the concept of biological citizenship, we elucidate how AoU and its recruitment tactics, by resonating ‘diversity’, at the same time appeal to and constitute identity, defining individuals as ‘data sharing subjects’. Although PPH is called for; the type of bio-citizenship that is enacted here, has a particular definition, where participant recruitment focuses on ‘citizenship’ in terms of empowerment (front), it is the ‘bio’ prefix that has become the main focus in terms of research. i.e. biosubjectivities vs biocapital. This raises the question whether the societal chal- lenges that often underlie public health issues can be sufficiently dealt with based on the way ‘diversity’ is accounted for in the program. We suggest that the AoU still risks of harming underrepresented groups based on the preconditions and the design of the program. (shrink)

Policies that require male-female sex comparisons in all areas of biomedical research conflict with the goal of improving health outcomes through context-sensitive individualization of medical care. Sex, like race, requires a rigorous, contextual approach in precision medicine. A “sex contextualist” approach to gender-inclusive medicine better aligns with this aim.

Cancer has garnered increasing interest among philosophers. This chapter focuses on cancer medicine and precision oncology, an influential approach to cancer which seeks to individualize treatment on the basis of genetic or molecular features of disease. It reviews a range of ontological, epistemic, and ethical questions raised by precision oncology, relating developments in cancer medicine to broader issues in the philosophy of science and medicine.

Biologists and philosophers of biology have argued that learning rules that do not lead organisms to play evolutionarily stable strategies (ESSes) in games will not be stable and thus not evolutionarily successful. This claim, however, stands at odds with the fact that learning generalization---a behavior that cannot lead to ESSes when modeled in games---is observed throughout the animal kingdom. In this paper, I use learning generalization to illustrate how previous analyses of the evolution of learning have gone wrong. It has (...) been widely argued that the function of learning generalization is to allow for swift learning about novel stimuli. I show that in evolutionary game theoretic models learning generalization, despite leading to suboptimal behavior, can indeed speed learning. I further observe that previous analyses of the evolution of learning ignored the short term success of learning rules. If one drops this assumption, I argue, it can be shown that learning generalization will be expected to evolve in these models. I also use this analysis to show how ESS methodology can be misleading, and to reject previous justifications about ESS play derived from analyses of learning. (shrink)

Many philosophers and scientists claim that there is a ‘hard problem of consciousness’, that qualia, phenomenology, or subjective experience cannot be fully understood with reductive methods of neuroscience and psychology, and that there is a fact of the matter as to ‘what it is like’ to be conscious and which entities are conscious (Chalmers, 1995). Eliminativism and related views such as illusionism argue against this; they claim that consciousness does not exist in the ways implied by everyday or scholarly language. (...) However, this debate has largely consisted of each side jousting analogies and intuitions against the other. Both sides remain unconvinced. To break through this impasse, I present _consciousness semanticism_, a novel eliminativist theory that sidesteps analogy and intuition. Instead, it is based on a direct, formal argument drawing from the tension between the vague semantics in definitions of consciousness such as ‘what it is like’ to be an entity (Nagel, 1975) and the precise meaning implied by questions such as, ‘Is this entity conscious?’ I argue that semanticism naturally extends to erode realist notions of other philosophical concepts, such as morality and free will. Formal argumentation from precise semantics exposes these as pseudo-problems and removes their apparent mysteriousness and intractability. (shrink)

:The emergence of personalized healthcare, or precision medicine, represents a paradigm shift in the medical field, focusing on tailoring medical treatments and interventions based on individual patient data. Cloud computing, when combined with Big Data analytics and Artificial Intelligence (AI), has the potential to revolutionize personalized healthcare by enabling the storage, processing, and analysis of vast amounts of patient data from multiple sources. This paper explores the role of cloud computing in advancing personalized healthcare, with a focus on its (...) ability to support Big Data and AI technologies for precision medicine. By facilitating real-time access to patient data, enhancing data interoperability, and enabling predictive analytics, cloud-based platforms provide healthcare providers with the tools necessary to deliver more accurate, effective, and individualized care. Additionally, this paper discusses how cloud computing enables collaboration among healthcare professionals, researchers, and patients, further enhancing the capabilities of personalized medicine. Through case studies and examples, we demonstrate how cloud infrastructure has already started transforming healthcare practices and delivering more personalized, data-driven solutions. (shrink)

Meta-learning, or learning to learn, has emerged as a powerful paradigm for creating adaptive models that can quickly adapt to new tasks with minimal data. In the context of personalized healthcare, meta-learning holds the potential to revolutionize precision medicine by enabling models that can personalize treatments based on individual characteristics. These models can leverage prior knowledge across multiple patients or conditions to provide rapid and accurate predictions for new patients, improving the efficiency and effectiveness of healthcare delivery. This paper (...) explores how meta-learning techniques can be applied to personalized healthcare, addressing the challenges of patient variability, data scarcity, and the need for individualized predictions. We also discuss several meta-learning strategies, such as Model-Agnostic Meta-Learning (MAML) and Prototypical Networks, and their integration into healthcare systems. Furthermore, we present case studies in areas like chronic disease management and treatment recommendation, highlighting the promise of meta-learning in precision medicine. (shrink)

The predictive coding (PC) theory of attention identifies attention with the optimization of the precision weighting of prediction error. Here we provide some challenges for this identification. On the one hand, the precision weighting of prediction error is too broad a phenomenon to be identified with attention because such weighting plays a central role in multimodal integration. Cases of crossmodal illusions such as the rubber hand illusion and the McGurk effect involve the differential precision weighting of prediction (...) error, yet attention does not shift as one would predict. On the other hand, the precision weighting of prediction error is too narrow a phenomenon to be identified with attention, because it cannot accommodate the full range of attentional phenomena. We review criticisms that PC cannot account for volitional attention and affect-biased attention, and we propose that it may not be able to account for feature-based and intellectual attention. (shrink)

This paper presents a new model for valuing hybrid defaultable financial instruments, such as, convertible bonds. In contrast to previous studies, the model relies on the probability distribution of a default jump rather than the default jump itself, as the default jump is usually inaccessible. As such, the model can back out the market prices of convertible bonds. A prevailing belief in the market is that convertible arbitrage is mainly due to convertible underpricing. Empirically, however, we do not find evidence (...) supporting the underpricing hypothesis. Instead, we find that convertibles have relatively large positive gammas. As a typical convertible arbitrage strategy employs delta-neutral hedging, a large positive gamma can make the portfolio highly profitable, especially for a large movement in the underlying stock price. (shrink)

Time passes, and the inexorability of its passing has deep emotional significance. One of the main themes of this thesis involves an investigation into the metaphysical nature of the passage of time. What sort of metaphysical account of passage should be given? And do our emotional responses to temporal passage have metaphysical implications? The other main theme of the thesis is the issue of the metaphysics of persistence. When a thing is present at more than one time, what is the (...) metaphysical underpinning of its persistence? Some subsidiary issues, which are nevertheless important in their own right, are also discussed. There are interesting connections between the issues of persistence and these further issues, which include the nature of vagueness, and the distinction between intrinsic and extrinsic properties. (shrink)

My paper provides reasons in support of the view that vague identity claims originate from a conflict between rigidity and precision in designation. To put this stricly, let x be the referent of the referential terms P and Q. Then, that the proposition “that any x being both a P and a Q” is vague involves that the semantic intuitions at work in P and Q reveal a conflict between P and Q being simultaneously rigid and precise designators. After (...) having shortly commented on an example of vague identity claim, I make the case for my proposal, by discussing how reference by baptism conflicts with descriptive attitudes towards understanding conceptual contents. (shrink)

Effective altruism (EA) requires that when we donate to charity, we maximize the beneficial impact of our donations. While we are in broad sympathy with EA, we raise a practical problem for EA, which is that there is a crucial empirical presupposition implicit in its charity assessment methods which is false in many contexts. This is the presupposition that the magnitude of the benefits (or harms) generated by some charity vary continuously in the scale of the intervention performed. We characterize (...) a wide class of cases where this assumption fails, and then draw out the normative implications of this fact. (shrink)

A Bayesian measure of evidence for precise hypotheses is presented. The intention is to give a Bayesian alternative to significance tests or, equivalently, to p-values. In fact, a set is defined in the parameter space and the posterior probability, its credibility, is evaluated. This set is the “Highest Posterior Density Region” that is “tangent” to the set that defines the null hypothesis. Our measure of evidence is the complement of the credibility of the “tangent” region.

Effective water management is vital for sustainable agriculture. This project introduces an IoT-based, AI-driven irrigation system that optimizes valve control using real-time soil moisture data. Soil moisture sensors continuously assess root zone moisture and relay this data to an AI framework, determining precise irrigation needs. The system autonomously adjusts valves according to environmental conditions, minimizing water waste and enhancing crop hydration. Machine learning algorithms, notably the random forest algorithm, analyze soil moisture and weather variables to inform irrigation schedules.

This chapter makes the argument for both the practicality and impracticality of philosophy as it relates to liberal education. An exploration of the history of science in the seventeenth and eighteenth centuries reveals that a study of philosophy cultivates a skill set of logic and critical thinking that are crucial for those who study science and mathematics. It also situates philosophy as a unifying discipline for liberal education and STEM studies (Science, Technology, Engineering, and Mathematics). The study of philosophy also (...) is impractical, as is liberal education, in that it does not prepare students for any specific profession. But it is this impracticality that makes philosophy, and liberal education, central to its identity and value: it creates an individual who is more empathic, open-minded, and self-aware that would not be possible if philosophy and liberal education were subordinated to some practical goal. (shrink)

There are common concepts between euthanasia and suicide because euthanasia is historically connected with the discourse on suicide. In widespread literature on euthanasia there is confusion over the concepts and definitions. These definitions are analyzed in this paper and along with other conclusions and distinctions the researcher has substantially defended his definition of euthanasia. There are two different usages of the term euthanasia: a narrow construal of euthanasia and broad construal of euthanasia. Contrary to other researches, the researcher agrees only (...) with the narrow construal of euthanasia, i.e. active euthanasia. The researcher’s definition of euthanasia is: intentionally causing a terminally ill person’s death through an action performed by a physician. As a result, passive euthanasia is expunged from the definition of euthanasia. In addition to that, the definition excludes suicide, assisted suicide, and physician-assisted suicide. (shrink)

Abstract: Artificial Intelligence (AI) is revolutionizing the agricultural sector by enhancing productivity and sustainability. This paper explores the transformative impact of AI technologies on agriculture, focusing on their applications in precision farming, predictive analytics, and automation. AI-driven tools enable more efficient management of crops and resources, leading to improved yields and reduced environmental impact. The paper examines key AI technologies, including machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. (...) Additionally, it addresses challenges such as data privacy, technology adoption barriers, and the ethical implications of AI in farming. By integrating AI into agricultural practices, the industry can achieve greater efficiency and sustainability, paving the way for future advancements. (shrink)

Philosophy strives to give us a firmer hold on our concepts. But what about their hold on us? Why place ourselves under the sway of a concept and grant it the authority to shape our thought and conduct? Another conceptualization would carry different implications. What makes one way of thinking better than another? This book develops a framework for concept appraisal. Its guiding idea is that to question the authority of concepts is to ask for reasons of a special kind: (...) reasons for concept use, which tell us which concepts to adopt, adhere to, or abandon, thereby shoring up—or undercutting—the reasons for action and belief that guide our deliberations. Traditionally, reasons for concept use have been sought either in timeless rational foundations or in concepts’ inherent virtues, such as precision and consistency. Against this, the book advances two main claims: that we find reasons for concept use in the conceptual needs we discover when we critically distance ourselves from a concept by viewing it from the autoethnographic stance; and that sometimes, concepts that conflict, or exhibit other vices such as vagueness or superficiality, are just what we need. By considering not what concepts are absolutely best, but what concepts we now need, we can reconcile ourselves to the contingency of our concepts, determine the proper place of efforts to tidy up thought, and adjudicate between competing conceptions of things—even things as contested as liberty or free will. A needs-based approach separates helpful clarification from hobbling tidy-mindedness, and authoritative definition from conceptual gerrymandering. (shrink)

Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. (...) Additionally, it discusses challenges like data privacy, barriers to technology adoption, and the ethical implications of AI in farming. Integrating AI into agricultural practices holds the promise of greater efficiency and sustainability, paving the way for future innovations. (shrink)

All humans share a universal, evolutionarily ancient approximate number system (ANS) that estimates and combines the numbers of objects in sets with ratio-limited precision. Interindividual variability in the acuity of the ANS correlates with mathematical achievement, but the causes of this correlation have never been established. We acquired psychophysical measures of ANS acuity in child and adult members of an indigene group in the Amazon, the Mundurucú, who have a very restricted numerical lexicon and highly variable access to mathematics (...) education. By comparing Mundurucú subjects with and without access to schooling, we found that education significantly enhances the acuity with which sets of concrete objects are estimated. These results indicate that culture and education have an important effect on basic number perception. We hypothesize that symbolic and nonsymbolic numerical thinking mutually enhance one another over the course of mathematics instruction. (shrink)

Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and (...) Policy-relevant Ethics in Singapore Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. (shrink)

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies that (...) provide personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. (shrink)

To explore the question of physical thinking – using the body as an instrument of cognition – we collected extensive video and interview data on the creative process of a noted choreographer and his company as they made a new dance. A striking case of physical thinking is found in the phenomenon of marking. Marking refers to dancing a phrase in a less than complete manner. Dancers mark to save energy. But they also mark to explore the tempo of a (...) phrase, or its movement sequence, or the intention behind it. Because of its representational nature, marking can serve as a vehicle for thought. Importantly, this vehicle is less complex than the version of the same phrase danced ‘full-out’. After providing evidence for distinguishing different types of marking, three ways of understanding marking as a form of thought are considered: marking as a gestural language for encoding aspects of a target movement, marking as a method of priming neural systems involved in the target movement, and marking as a method for improving the precision of mentally projecting aspects of the target. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

Traditional Bayesianism requires that an agent’s degrees of belief be represented by a real-valued, probabilistic credence function. However, in many cases it seems that our evidence is not rich enough to warrant such precision. In light of this, some have proposed that we instead represent an agent’s degrees of belief as a set of credence functions. This way, we can respect the evidence by requiring that the set, often called the agent’s credal state, includes all credence functions that are (...) in some sense compatible with the evidence. One known problem for this evidentially motivated imprecise view is that in certain cases, our imprecise credence in a particular proposition will remain the same no matter how much evidence we receive. In this article I argue that the problem is much more general than has been appreciated so far, and that it’s difficult to avoid it without compromising the initial evidentialist motivation. _1_ Introduction _2_ Precision and Its Problems _3_ Imprecise Bayesianism and Respecting Ambiguous Evidence _4_ Local Belief Inertia _5_ From Local to Global Belief Inertia _6_ Responding to Global Belief Inertia _7_ Conclusion. (shrink)

According to what we will call subjectivity theories of consciousness, there is a constitutive connection between phenomenal consciousness and subjectivity: there is something it is like for a subject to have mental state M only if M is characterized by a certain mine-ness or for-me-ness. Such theories appear to face certain psychopathological counterexamples: patients appear to report conscious experiences that lack this subjective element. A subsidiary goal of this chapter is to articulate with greater precision both subjectivity theories and (...) the psychopathological challenge they face. The chapter’s central goal is to present two new approaches to defending subjectivity theories in the face of this challenge. What distinguishes these two approaches is that they go to great lengths to interpret patients’ reports at face value – greater length, at any rate, than more widespread approaches in the extant literature. (shrink)

Abstract: A corn crop typically refers to a large-scale cultivation of corn (also known as maize) for commercial purposes such as food production, animal feed, and industrial uses. Corn is one of the most widely grown crops in the world, and it is a major staple food for many cultures. Corn crops are grown in various regions of the world with different climates, soil types, and farming practices. In the United States, for example, the Midwest is known as the "Corn (...) Belt" due to its extensive corn production. Corn crops can be grown using a variety of methods, including conventional tillage, no-till farming, and the use of genetically modified crops. Due to its importance, there is a need for discovering the corn diseases and treating them. The aim of this study is to propose a deep learning model for the classification of corn leaf diseases based on Convolutional Neural Network. A model for classifying maize diseases was developed using a dataset for classifying Corn diseases that contains 4 classes of disease. The dataset was collected from Keggel website. The proposed model was trained, validated, and tested. The F1-score (99.83%), Recall (99.83%), precision (99.83%), Accuracy (99.83%). (shrink)

In this article, we discuss epistemological limitations relating to the use of ethnoracial categories in biomedical research as devised by the Office of Management and Budget’s institutional guidelines. We argue that the obligation to use ethnoracial categories in genomics research should be abandoned. First, we outline how conceptual imprecision in the definition of ethnoracial categories can generate epistemic uncertainty in medical research and practice. Second, we focus on the use of ethnoracial categories in medical genetics, particularly genomics-based precision medicine, (...) where ethnoracial identity is understood as a proxy for medically relevant differences among individuals. Notably, extensive criticisms have been made already against the genetic interpretation of races, but, nonetheless, the concept of race remains a key element of contemporary genomics. This motivates us to explore possible reasons why such criticisms may have been ineffective in redirecting attention to other (non-race-based) ways of controlling for human variability. We contend that popular arguments against the idea that human races have a genetic basis, though convincing in many respects, are not sufficient to exclude the pragmatic use of race and ethnicity as proxies for genetic variability related to complex phenotypes. Finally, we provide two further arguments to support the idea that ethnoracial categories are unlikely to provide meaningful insights into medical genetics, which implies that even the interpretation of race as a useful tool to stratify disease risk is unwarranted. (shrink)

The ontology behind quantum mechanics has been the subject of endless debate since the theory was formulated some 100 years ago. It has been suggested, at one time or another, that the objects described by the theory may be individual particles, waves, fields, ensembles of particles, observers, and minds, among many other possibilities. I maintain that these disagreements are due in part to a lack of precision in the use of the theory’s various semantic designators. In particular, there is (...) some confusion about the role of representation, reference, and denotation in the theory. In this article, I first analyze the role of the semantic apparatus in physical theories in general and then discuss the corresponding ontological implications for quantum mechanics. Subsequently, I consider the extension of the theory to quantum fields and then analyze the semantic changes of the designators with their ontological consequences. In addition to the classical arguments to rule out a particle ontology in the case of non-relativistic quantum field theory, I show how the existence of black holes makes the proposal of a particle ontology in general spacetimes unfeasible. I conclude by proposing a provisional pluralistic ontology of fields and spacetime and discussing some prospects for possible future ontological economies. (shrink)

Recent research has begun to investigate sensory processing in relation to nonclinical variation in traits associated with the autism spectrum disorders (ASD). We propose that existing accounts of autistic perception can be augmented by considering a role for individual differences in top-down expectations for the precision of sensory input, related to the processing of state-dependent levels of uncertainty. We therefore examined ASD-like traits in relation to the rubber-hand illusion: an experimental paradigm that typically elicits crossmodal integration of visual, tactile, (...) and proprioceptive information in an unusual illusory context. Individuals with higher ASD-like traits showed reduced effects of the rubber-hand illusion on perceived arm position and reach-to-grasp movements, compared to individuals with lower ASD-like traits. These differences occurred despite both groups reporting the typical subjective experience of the illusion concerning visuotactile integration and ownership for the rubber hand. Together these results suggest that the integration of proprioceptive information with cues for arm position derived from the illusory context differs between individuals partly in relation to traits associated with ASD. We suggest that the observed differences in sensory integration can be best explained in terms of differing expectations regarding the precision of sensory estimates in contexts that suggest uncertainty. (shrink)

Abstract: Fish are important for both nutritional and economic reasons. They are a good source of protein, vitamins, and minerals and play a significant role in human diets, especially in coastal and island communities. In addition, fishing and fish farming are major industries that provide employment and income for millions of people worldwide. Moreover, fish play a critical role in marine ecosystems, serving as prey for larger predators and helping to maintain the balance of aquatic food chains. Overall, fish play (...) a vital role in supporting human well-being and the health of our planet. . Fishes have a lots of types such that: Red Mullet, Sea Bass, Striped Red Mullet and Shrimp. Each of them has its own shape and characteristics that differ from other types. We proposed a system that recognize nine types of fishes using deep learning. We trained the model with a dataset that contain 9000 images that were slit into 6300 images for training, 1350 for validation and 1350 for testing. The proposed model achieved accuracy (99.68%), precision (99.69%), recall (99.68%), and f1-score (99.68%). This indicates that our proposed model can effectively predicate and classify different types of fish with very high accuracy. (shrink)