The notion of the “deep state” or a “state within a state” is creepy, to say the least. It indicates the existence of a shadowy group of unelected bureaucrats deeply embedded in the military-intelligence establishment secretly manipulating government policy. International relations scholars and public administration experts associate deep states with authoritarian regimes, such as Egypt, Turkey, Pakistan and pre-civil-war Syria. However, as we’re finding out, the U.S. has its own deep state. While some (...) media outlets portray deep state talk as tantamount to conspiracy theory, the deep state is quite real. (shrink)

Deep legal change occurs when, without legal justification, one legal rule is replaced by another. While often ignored in legal theory, these rule-breaking normative transformations are common and significant enough to warrant careful attention. In this thesis, I analyse the structure of deep legal change and discuss how a philosophically rigorous jurisprudence should approach a legal phenomenon that appears to be legally inexplicable. In particular, I focus on the implications of rule-breaking rule-changes for our conception of courts and (...) legal reasoning. To understand why deep legal change occurs, we need to provide a normative explanation that clarifies why courts, as courts, have reasons to break the law. My solution takes me to G. W. F. Hegel’s Philosophy of Right. Hegel invites us to think of the judicial process as being grounded in two institutional values: the establishment of legal security and the protection of the “universal interest” of the state. Sometimes, these two values come into conflict. In these moments, established legal rules give way to fundamental state-interests. With Hegel, we can say that it is inevitable that political reasons periodically override legal reasons. In deep legal change, the fundamentally political character of law becomes visible. While reminiscent of Carl Schmitt and the literature on emergency powers, this theory of law is much closer to the reason-of-state tradition. Against the background of several historical case studies and contemporary scholarship, I demonstrate the novelty, coherence, and explanatory power of a Hegelian account of deep legal change. (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)

Deep Self views of moral responsibility have been criticized for positing mysterious concepts, making nearly paradoxical claims about the ownership of one’s mental states, and promoting self-deceptive moral evasion. I defend Deep Self views from these pervasive forms of skepticism by arguing that some criticism is hasty and stems from epistemic injustice regarding testimonies of experiences of alienation, while other criticism targets contingent features of Deep Self views that ought to be abandoned. To aid in this project, (...) I provide original naturalistic analyses of “Self” and “internality” that replace the view’s metaphorical language with common-sensical concepts that make clear their usefulness. (shrink)

The ever-evolving ways attacker continues to improve their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an (...) adaptable framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection. (shrink)

In his 2014 article “Motivations for Relativism as a Solution to Disagreements”, Steven Hales argues that relativism is a plausible disagreement resolution strategy for epistemically irresolvable disagreements. I argue that his relativistic strategy is not adequate for disagreements of this kind, because it demands an impossible doxastic state for disputants to resolve the disagreement. Contrarily, Fogelin’s :1–8, 1985) theory of deep disagreement does not run into the same problems. Deep disagreements, according to Fogelin, cannot be resolved through (...) argumentation because the conditions for argumentation are lacking in such contexts. I advance the view that deep disagreements arise due to differences in disputants’ mutually supporting interrelated beliefs. This view avoids the hurdles caused by the tiered structure of support found at the heart of Hales’s view on disagreement: the assumption that belief and perspective can be separated, and that disagreement is located. (shrink)

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose (...) their vulnerabilities and future research direction. For better analysis and observation, we split machine learning techniques into Bayesian, non-Bayesian, and deep learning. We reviewed the most recent advances in Bayesian and non-Bayesian-based classifiers before exploiting their cor- responding weaknesses to indicate future research direction. While exploiting weaknesses in both Bayesian and non-Bayesian classifiers, we also compared each performance with a deep learning classifier. For a proper review of deep learning-based classifiers, we looked at Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTMs). We did an empirical analysis to evaluate the performance of each classifier along with many of the proposed state-of-the-art anti-phishing techniques to identify future research directions, we also made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model. (shrink)

Deep learning models, particularly deep neural networks (DNNs), have achieved state-of-the-art results in various fields such as image recognition, natural language processing, and speech processing. However, one of the major challenges of deep learning is the need for large datasets to effectively train these models. In real-world applications, acquiring vast amounts of labeled data can be expensive, time-consuming, or simply infeasible. This paper explores techniques and strategies for training deep neural networks with limited data, aiming (...) to overcome the limitations of data scarcity. The paper delves into a range of approaches designed to enhance the performance of deep learning models when limited data is available, including transfer learning, data augmentation, semi-supervised learning, and few-shot learning. Transfer learning allows models to leverage knowledge from related tasks, while data augmentation techniques artificially increase the size of the dataset by modifying existing data. Semi-supervised learning and few-shot learning, on the other hand, enable models to learn from a small amount of labeled data and a larger amount of unlabeled data. In addition to exploring these techniques, the paper discusses the challenges associated with training deep neural networks with limited data. These include issues such as overfitting, model generalization, and the difficulty of selecting the best model architecture when data is scarce. The paper also presents case studies and practical applications where these techniques have been successfully applied to real-world problems. The objective of this research is to provide insights into how deep learning models can be trained effectively with limited data, while identifying key challenges and offering solutions for overcoming these hurdles. This work aims to contribute to the broader adoption and application of deep neural networks in domains with limited labeled data. (shrink)

"Illusory Self Framework," a novel model integrating insights from predictive brain and contemplative practices. The framework proposes a hierarchical cognitive architecture grounded in the "all-ground," the foundational space of pure awareness. Within this architecture, neuronal packets, knowledge domains, and thoughtseeds interact, leading to an emergent self—a dynamic Markov blanket modulating conscious experience. This framework explores the dissolution of the illusory self through contemplative practices drawn from Indo-Tibetan traditions, the insights of J. Krishnamurti and recent research on minimal phenomenal experiences (MPEs) (...) and self-models. It posits that by modulating attention, reducing self-referential processing, and cultivating stillness, one can weaken the emergent self and reveal the underlying all-ground of pure awareness. An illustrative mathematical model formalizes these concepts, for emergence and the dissolving process of the illusory self. Furthermore, the framework elucidates the unfolding of MPEs by proposing a 5-stage lucidity pathway for advanced meditators. This pathway begins with lucid dreaming and culminates in an attentive sleepful state, highlighting the progressive dissolution of the illusory self and the deepening of non-dual awareness. The framework also offers testable predictions about the sleep architecture of advanced meditators using signal-validated lucidity tracking measures and how it might change with progression in these practices. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance (...) of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features . (shrink)

Contemporary versions of natural rights libertarianism trace their locus classicus to Robert Nozick’s Anarchy, State, and Utopia. But although there have been many criticisms of the version of political libertarianism put forward by Nozick, many of these fail objections to meet basic methodological desiderata. Thus, Nozick’s libertarianism deserves to be re-examined. In this paper I develop a new argument which meets these desiderata. Specifically, I argue that the libertarian conception of self-ownership, the view’s foundation, implies what I call the (...) Asymmetrical Value Claim: a dubious claim about the importance of choice relative to other valuable capacities. I argue that this misunderstands what is really valuable in life, and show how it causes libertarianism to generate counterintuitive public policy recommendations. (shrink)

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...) solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI. (shrink)

The State of Nirvana Explained Through the Universal Formula -/- Nirvana, a concept often associated with Buddhism, represents a state of ultimate peace, liberation, and detachment from suffering. Traditionally, it is understood as the cessation of desires and the end of the cycle of birth and rebirth. However, using my universal formula, Nirvana can be understood more precisely as a state of perfect balance, where the system of human consciousness operates free from defects and external disturbances. By (...) examining Nirvana through the universal law of balance in nature, the law of karma as a system principle, and the feedback mechanism of conscious minds, we can explain it as the highest achievable state of homeostasis in human decision-making and awareness. -/- Nirvana as Perfect Balance in Nature -/- According to the universal law of balance in nature, all systems—whether they are physical, biological, or cognitive—must maintain equilibrium to function optimally. Any deviation from balance results in dysfunction, suffering, or destruction. In this context, the state of Nirvana is the ultimate form of equilibrium in the human mind, where all forms of psychological and emotional turbulence have been neutralized. -/- Human suffering often arises from internal imbalances caused by uncontrolled desires, false beliefs, and external influences. When a person reaches Nirvana, their mind no longer fluctuates between extremes such as attachment and aversion, pleasure and pain, or hope and fear. Instead, they attain a stable and self-regulating mental state that is in perfect harmony with the natural laws governing reality. -/- Karma and the System of Consciousness -/- The law of karma, as understood in my universal formula, does not merely refer to the traditional idea of moral cause and effect but extends to the principle of systems functioning without defects. Any system, whether mechanical, biological, or cognitive, must be free of errors to operate effectively. If an engine has faulty components, it will not run efficiently; if the human body accumulates toxins, it will suffer disease. Similarly, if the mind is filled with ignorance, false assumptions, or emotional disturbances, it will create suffering. -/- Nirvana, in this sense, is the final correction of the system of consciousness. It is a state where all mental defects—such as irrational fears, harmful attachments, and misperceptions—have been eliminated. The individual no longer reacts to external influences in a way that creates further imbalance or suffering. They achieve a fully optimized and self-sustaining mental state, free from unnecessary disturbances. -/- Is Nirvana a Permanent or Dynamic State? -/- From the perspective of the universal law of balance in nature, Nirvana is a dynamic state rather than a fixed or permanent one. No system in nature remains absolutely static; instead, it continuously adjusts to maintain balance in response to external and internal influences. Even though Nirvana is often described as the cessation of suffering, it does not mean the individual mind ceases to function or engage with reality. Rather, it reaches a level of self-correction and adaptability, where it can maintain balance despite changing conditions. -/- A person in Nirvana does not freeze in a single state but dynamically interacts with reality while ensuring that no imbalance disturbs their equilibrium. Just as homeostasis in the human body requires constant regulation to maintain stability, the state of Nirvana involves continuous awareness and adjustment to preserve its balance. Therefore, rather than being a fixed endpoint, Nirvana can be understood as a sustained and dynamic process of perfect self-regulation. -/- The Feedback Mechanism of Conscious Minds -/- Beyond the individual, all conscious beings exist in a collective feedback system, where their decisions and emotions continuously influence one another. Human society operates as an interconnected web of minds, shaping each other’s beliefs, actions, and responses. Most people remain trapped in this cycle, where external influences trigger internal disturbances, leading to suffering and further imbalance. -/- However, a person who has reached Nirvana no longer contributes to this cycle of mental disturbances. Instead, they exist as a neutral force within the feedback loop, unaffected by external pressures and incapable of generating suffering for themselves or others. Their dynamic state of equilibrium allows them to respond to the world without being influenced by negative forces, ensuring that they do not create further disturbances in the system of human consciousness. -/- How to Achieve and Regulate the State of Nirvana -/- Since Nirvana is a dynamic state, it is not achieved in a single moment but rather through a continuous process of self-regulation. Based on the universal law of balance in nature and the law of karma, an individual must take specific steps to eliminate mental defects and maintain an optimized state of consciousness. -/- 1. Understanding the Nature of Imbalance – The first step is recognizing how ignorance, false beliefs, and uncontrolled desires create internal imbalances. This requires deep introspection and an awareness that suffering is the result of flawed decision-making processes. -/- 2. Eliminating False Systems of Thought – Mental defects arise from incorrect assumptions about reality. An individual must adopt rational thinking based on natural laws, ensuring that their thoughts align with objective truth rather than societal conditioning, superstitions, or emotional biases. -/- 3. Regulating Desires and Attachments – Desires create imbalance when they control the individual, leading to continuous dissatisfaction. To maintain Nirvana, one must learn to regulate desires, keeping them within a balanced framework where they do not dominate decision-making. -/- 4. Practicing Continuous Feedback Adjustment – Since all minds exist within a collective feedback system, an individual must remain conscious of how external influences affect their state of balance. This means developing a high level of self-awareness to prevent unnecessary emotional reactions to external events. -/- 5. Achieving Self-Correcting Homeostasis – Just as a healthy body automatically corrects imbalances (such as regulating temperature and pH levels), the mind in Nirvana must develop a self-correcting mechanism. This means consistently checking whether one’s thoughts, emotions, and actions remain in harmony with the natural law of balance. -/- 6. Avoiding Sources of Mental Disturbance – External influences such as misinformation, emotional manipulation, and toxic environments can disrupt mental balance. A person striving for Nirvana must carefully filter external inputs to ensure that their internal equilibrium remains undisturbed. -/- 7. Integrating Nirvana into Daily Life – Achieving Nirvana is not about retreating from reality but about engaging with it in a state of complete balance. This means making decisions based on reason, clarity, and natural law, ensuring that every action contributes to a stable and harmonious existence. -/- The Balance Between Social Interaction and Privacy -/- While privacy plays an important role in maintaining Nirvana, it is also essential to strike a balance between privacy and social interaction. A person striving for Nirvana must recognize that while solitude and self-reflection are necessary for maintaining mental equilibrium, human beings are inherently social creatures. Healthy social interaction can be a source of joy, support, and growth, and should not be completely avoided in the pursuit of Nirvana. -/- The key to maintaining Nirvana in social settings lies in setting boundaries and choosing relationships that foster peace and balance. Too much social interaction, especially in environments filled with conflict, negativity, or manipulation, can lead to disturbances in mental stability. On the other hand, complete isolation can lead to a lack of social harmony and neglect of collective well-being. -/- Therefore, an individual in Nirvana must be able to navigate between these two aspects of life—engaging in meaningful social interactions while protecting their personal space when needed. This balance is vital in maintaining the mental clarity and emotional stability required to preserve Nirvana in an ever-changing world. By carefully choosing social environments and establishing healthy boundaries, an individual can prevent external disturbances from disturbing their equilibrium. -/- The Importance of Privacy in Maintaining the State of Nirvana -/- In the pursuit of Nirvana, privacy plays a crucial role in preserving mental balance and maintaining the integrity of the individual’s self-regulation process. In a world filled with external pressures, distractions, and unsolicited influences, privacy serves as a protective barrier that allows an individual to maintain personal space and autonomy. -/- Without privacy, the individual is subject to constant interference from external sources—be it through social media, public scrutiny, or external judgments. These external influences can create mental disturbances that disrupt the equilibrium necessary for Nirvana. Privacy allows individuals to maintain their mental clarity, free from the noise of others’ expectations, and to reflect deeply on their own thoughts and feelings without being swayed by outside opinions. -/- By protecting their privacy, an individual can control the flow of information they are exposed to, ensuring that their mental state is not disturbed by harmful or irrelevant stimuli. Privacy enables one to determine their own internal environment, cultivating a space where personal growth and introspection can occur without interference. -/- Furthermore, privacy is integral to emotional regulation. It allows individuals to process their emotions and experiences in a safe environment, enabling them to reach the state of Nirvana without the pressure of external forces. This protection helps avoid unnecessary emotional reactions and preserve mental peace, ensuring that one remains in a state of dynamic balance amidst the complexities of daily life. -/- Nirvana as the Ultimate State of Homeostasis -/- In conclusion, Nirvana, when analyzed through my universal formula, can be understood as the highest state of homeostasis in human consciousness. It is the point at which a person has fully optimized their decision-making process, eliminated all mental defects, and achieved perfect balance with their internal and external reality. However, rather than being a permanent, unchanging state, Nirvana is a dynamic equilibrium, requiring continuous self-regulation to maintain balance. -/- The role of privacy in achieving and maintaining Nirvana cannot be overstated. In a world filled with external distractions and emotional disturbances, privacy is a key factor in preserving mental equilibrium and allowing the individual to maintain the dynamic self-regulation needed to stay in Nirvana. -/- By understanding the steps to achieve and regulate Nirvana, individuals can cultivate mental stability, rational decision-making, and emotional resilience. This explanation provides a scientific and systematic approach to Nirvana, making it not only a spiritual ideal but a practical model for individual and societal well-being. The path to Nirvana is not just a personal journey but a necessary step toward achieving universal equilibrium in human civilization. -/- . (shrink)

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often (...) appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain. (shrink)

Several state-of-the-art machine and deep learning models in the mode of adversarial training, input transformation, self adaptive training, adversarial purification, zero-shot, one- shot, and few-shot meta learning had been proposed as a possible solution to an out-of-distribution problems by applying them to wide arrays of benchmark dataset across different research domains with varying degrees of performances, but investigating their performance on previously unseen out-of- distribution malware attack remains elusive. Having evaluated the poor performances of these state-of-the-art approaches (...) in our previous research on an out-of-distribution attack. In this research, we dived deeper to understand why they works better for other domain dataset but with poor performance on available benchmark malware dataset like Malimg, Malevis, Sorel, and Avast CTU malware dataset. We explored the both the embedding and vector spaces in datasets and compare them with that from other research domain, and find a surprising wide variation between the embedding and vector spaces in malware datasets. We assert that current state-of-the-art machine and deep learning models does not address the wide variation of embedding and vector spaces which are peculiar to malware dataset, hence their poor performance on out-of-distribution attack classification, and so concluded that addressing this variation in embedding and vector spaces will bring about substantial increase in detection of previously unseen out-of-distribution attack. (shrink)

Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a shallow neural network (...) with maximum of two connected layers to overcome overfitting from the scratch 4) building a Bayesian inference based computation algorithm that intertwine with connected network and is able to create new and adjust existing data points in response to an exposure to new out-of-distribution variants of existing or new malware family which determines the extent at which model weight is adjusted which in turn triggers update on the gradient. Preliminary result of our proposed framework gave an accuracy of 81\% in the successful classification of a novel out-of-distribution malware attack, something that could not be achieve by any of the state-of-the-art algorithms on novel malware classification. (shrink)

Ethical analyses of the effects of neural interventions commonly focus on changes to personality and behavior, interpreting these changes in terms of authenticity and identity. These phenomena have led to debate among ethicists about the meaning of these terms for ethical analysis of such interventions. While these theoretical approaches have different criteria for ethical significance, they agree that patients’ reports are concerning because a sense of self is valuable. In this paper, I question this assumption. I propose that the Buddhist (...) theory of no‐self offers a novel approach to making ethical sense of patients’ claims following deep brain stimulation. This alternative approach is based on the value of insight into patterns of cause and effect among mental states and actions. (shrink)

This article examines the causes of the internal war in Peru during the 1980s, focusing on the interplay between state neglect, economic inequalities, and social apathy. Through a mixed-methods approach, including interviews and statistical analysis, the study identifies key factors that contributed to the rise of the Shining Path insurgency. The findings reveal that the Peruvian state's inability to govern rural areas, combined with deep-seated economic inequalities and widespread political disengagement, created the conditions for the conflict. The (...) study concludes with recommendations for strengthening state presence, addressing economic disparities, and fostering political engagement to prevent future conflicts in similar contexts. These insights contribute to the broader understanding of internal conflicts and the challenges of achieving lasting peace in fragile states. (shrink)

Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning techniques have been proposed (...) by diverse research groups in the recent past. However, yet effective and comprehensive deep ensemble neural network-based classification model with high accuracy classification results is not available in the literature. In this thesis, we review ways and mechanisms to use deep learning techniques to research on multi-disease computer-aided detection about gastrointestinal and identify these images. We re-trained five state-of-the-art neural network architectures, VGG16, ResNet, MobileNet, Inception-v3, and Xception on the Kvasir dataset to classify eight categories that include an anatomical landmark (pylorus, z-line, cecum), a diseased state (esophagitis, ulcerative colitis, polyps), or a medical procedure (dyed lifted polyps, dyed resection margins) in the Gastrointestinal Tract. Our models have showed results with a promising accuracy which is a remarkable performance with respect to the state-of-the-art approaches. The resulting accuracies achieved using VGG, ResNet, MobileNet, Inception-v3, and Xception were 98.3%, 92.3%, 97.6%, 90% and 98.2%, respectively. As it appears, the most accurate result has been achieved when retraining VGG16 and Xception neural networks with accuracy reache to 98% due to its high performance on training on ImageNet dataset and internal structure that support classification problems. (shrink)

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance (...) of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. -/- We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features. (shrink)

Bone Fractures are a prevalent clinical condition that necessitates and timely diagnosis. Conventional manual identification of fractures from X-ray images is slow and error-prone. In response to these issues, this paper introduces a deep learning approach for automated detection and classification of bone fractures. A Convolutional Neural Network (CNN) model was implemented and trained on an X-ray image dataset, using data augmentation methods to improve generalization and avoid overfitting. The suggested model was tested with various experimental configurations, such as (...) cross-validation and showed better performance than current state-of-the-art techniques. Adam optimizer helped achieve better classification accuracy than traditional methods. Additional improvements, including sophisticated augmentation methods, transfer learning and ensemble models, may enhance predication accuracy. This research underscores the potential of deep learning in medical imaging for precise and computerized fracture identification, lowering diagnosis time and enhancing patient outcomes. (shrink)

Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a shallow neural network (...) with maximum of two connected layers to overcome overfitting from the scratch 4) building a Bayesian inference based computation algorithm that intertwine with connected network and is able to create new and adjust existing data points in response to an exposure to new out-of-distribution variants of existing or new malware family which determines the extent at which model weight is adjusted which in turn triggers update on the gradient. Preliminary result of our proposed framework gave an accuracy of 81\% in the successful classification of a novel out-of-distribution malware attack, something that could not be achieve by any of the state-of-the-art algorithms on novel malware classification. (shrink)

Samkhya Philosophy: The Ancient Wisdom for Attaining Profound Tranquillity and Deep Peace Samkhya philosophy, one of the six orthodox systems of Indian thought, offers a profound framework for achieving tranquillity and deep peace through self-realization and liberation. Rooted in ancient Vedic traditions, Samkhya provides a dualistic metaphysics, distinguishing between Purusha (consciousness) and Prakriti (matter), and elucidates the path to transcend suffering by understanding their interplay. This paper explores Samkhya’s core principles, including the three gunas (sattva, rajas, tamas), the (...) twenty-five tattvas (elements of existence), and the process of discriminative knowledge (viveka). By discerning the eternal Purusha from the transient Prakriti, individuals can attain kaivalya (liberation), a state of profound peace free from material entanglements. The philosophy’s emphasis on introspection, meditation, and self-awareness aligns with modern mindfulness practices, making it relevant for contemporary seekers of inner calm. This study highlights how Samkhya’s timeless wisdom fosters mental clarity, emotional balance, and spiritual freedom, offering practical insights for navigating life’s complexities. By integrating its teachings, individuals can cultivate enduring tranquillity, transcending the fluctuations of the material world. (shrink)

Despite an increasing role of machine learning in science, there is a lack of results on limits of empirical exploration aided by machine learning. In this paper, we construct one such limit by proving undecidability of learnability of state spaces of physical systems. We characterize state spaces as binary hypothesis classes of the computable Probably Approximately Correct learning framework. This leads to identifying the first limit for learnability of state spaces in the agnostic setting. Further, using the (...) fact that finiteness of the combinatorial dimension of hypothesis classes is undecidable, we derive undecidability for learnability of state spaces as well. Throughout the paper, we try to connect our formal results with modern neural networks. This allows us to bring the limits close to the current practice and make a first step in connecting scientific exploration aided by machine learning with results from learning theory. (shrink)

Pranayamic breathing, defined as a manipulation of breath movement, has been shown to contribute to a physiologic response characterized by the presence of decreased oxygen consumption, decreased heart rate, and decreased blood pressure, as well as increased theta wave amplitude in EEG recordings, increased parasympathetic activity accompanied by the experience of alertness and reinvigoration. The mechanism of how pranayamic breathing interacts with the nervous system affecting metabolism and autonomic functions remains to be clearly understood. It is our hypothesis that voluntary (...) slow deep breathing functionally resets the autonomic nervous system through stretchinduced inhibitory signals and hyperpolarization currents propagated through both neural and non-neural tissue which synchronizes neural elements in the heart, lungs, limbic system and cortex. During inspiration, stretching of lung tissue produces inhibitory signals by action of slowly adapting stretch receptors (SARs) and hyperpolarization current by action of fibroblasts. Both inhibitory impulses and hyperpolarization current are known to synchronize neural elements leading to the modulation of the nervous system and decreased metabolic activity indicative of the parasympathetic state. In this paper we propose pranayama’s physiologic mechanism through a cellular and systems level perspective,involving both neural and non-neural elements. This theoretical description describes a common physiological mechanism underlying pranayama and elucidate the role of the respiratory and cardiovascular system on modulating the autonomic nervous system. Along with facilitating the design of clinical breathing techniques for the treatment of autonomic nervous system and other disorders, this model will also validate pranayama as a topic requiring more research. (shrink)

The main claim of this chapter is that planetary defense against asteroids cannot be implemented under a decentralized model of democratic global governance, as espoused elsewhere in this book. All relevant indices point to the necessity of establishing a centralized global political authority with legitimate coercive powers. It remains to be seen, however, whether such a political system can be in any recognizable sense democratic. It seems unconvincing that planetary-wide physical-threat, all-comprehensive macrosecuritization, coupled with deep transformations of international law, (...) global centralization of core decision-making powers, de-stigmatization of nuclear weapons and the like can proceed, succeed, and be implemented in a non-hierarchical international system where planetary defense constitutes only one regime among many, and where states basically remain the decisive actors. Although rationally and scientifically robust, the project suffers from oversimplification, as well as naivety with respect to how both international and domestic politics works. Among other topics, this chapter discusses problems associated with the rule of law and constituent powers, political representation and sources of legitimacy, conditions of multilevel collective action, or limits of theoretical idealization. The general message is that the planetary defense community needs to be more aware of the social and political context of its own enterprise. (shrink)

Twitter is one of the biggest social media sites, where users may share their thoughts, ideas, and opinions as well as discuss current events and live tweets. In the subject of opinion mining, creating reliable and effective algorithms for sarcasm detection on Twitter is an intriguing task. Sarcasm is the use of positive language to convey depressing emotions while speaking in opposition to one’s own intentions. Sarcasm is frequently employed on social networking and micro blogging platforms, where users can offend (...) others and find it difficult to express their true feelings. The deep learning technique utilised in the current algorithms to identify these sarcastic tweets has the limitation of not being able to predict continuous variables. A novel deep learning algorithm is proposed to identify both positive and negative terms as well as sarcasm in comments. Deep neural networks are used to classify the comments into positive and negative word categories. Customers’ opinions are mined using sentiment analysis to find and extract information from the text. Sarcastically stated statements from social networking sites can be quickly categorised and recognised by using VADER (Valence Aware Dictionary and Sentiment Reasoner). (shrink)

Since its proposition, the Knowledge Argument has been the center of debate in the Philosophy of the Mind, and many philosophers have proposed their rejections of it. This paper briefly looked at what Philip Goff characterized as the no-compromise response and the no propositional knowledge response before delving deep into a critique of Brian Loar's response to the Knowledge Argument. This paper accepts Brian Loar's critique of the semantic premise and his analysis of phenomenal concepts. However, after examining the (...) implications of Loar's response through conscious experience, this paper contends those implications conflict with simple intuitions and an analysis of the role of basic experiences in type-demonstrative referencing. It leads to new insights into the location of the dialectic of the Knowledge Argument being external to the brain. For the new dialectic of the Knowledge argument, I argue that a simpler Knowledge Argument provides an account of the referent of phenomenal concepts to be phenomenal properties (qualia), showing that Loar's response did not adequately reject the Knowledge Argument. (shrink)

A number of recent articles have invoked the republican ideal of non-domination to justify either open borders, and/or the reduction of states’ discretionary powers to unilaterally determine immigration policy. In this paper, I show that such arguments are one-sided, as they fail to fully account for the deep ambiguity of the very ideal which they invoke. In fact, non-domination lends just as powerful support to maintaining state border controls as it does to dismantling them. There are only two (...) exceptions to the rule. It is well established that promoting non-domination demands, one the one hand, that refugees be admitted, and second, that all migrants have a right to contest decisions concerning their own admission. But aside from these things, the policy implications of the ideal are unclear. In itself, therefore, it is insufficient to justify either open borders, or the reduction of states said discretionary powers. Such arguments will have to rely on other, additional moral criteria. -/- . (shrink)

Neural machine translation (NMT) is one of the success stories of deep learning and artificial intelligence. Revolutionary innovations in the computational architectures made in 2015–2017 have led to dramatic improvements in the quality of machine translation (MT) and changed the field forever. Some professional translators welcome these changes with enthusiasm, others less so. But everyone has to deal with them. Historically, the relationship between human translation and MT has been uneasy and complicated, but an increasing number of players in (...) both fields are now coming to view it as synergistic. -/- OPUS-CAT is a collection of machine translation software based on a Windows implementation of Marian NMT—a stable production-ready neural machine translation framework used in many industrial and research applications. It can be installed on your PC, fine-tuned to specialized translation domains, and integrated into CAT tools. (shrink)

Companies such as Toyota, Samsung, Alibaba, and Lenovo are already innovating with start-ups in the deep-tech field—a group of emerging technologies based on scientific discoveries or meaningful engineering innovations, offering a substantial advance over established technologies, and seeking to tackle some of the world’s fundamental challenges. Currently, this group usually encompasses artificial intelligence, advanced materials, biotechnology, blockchain, robotics and drones, photonics and electronics, and quantum computing. This phenomenon is increasing at speed. In past years, deep-tech startups have received (...) increased attention among corporations, entrepreneurs, investors, and media. Investment in deep-tech start-ups has more than quadrupled over a five-year period, from $15 billion in 2016 to more than $60 billion in 2020, approximately. The average disclosed amount per private investment event for these start-ups and scale-ups has grown 3.4 times between 2016 and 2020. In East and Southeast Asia, some of the corporate venturing activity is concentrated in nine regions: mainland China, Hong Kong, Indonesia, Japan, South Korea, Singapore, Thailand, Taiwan, and Vietnam. On average, this region has a higher adoption rate of corporate venturing (57%), compared to that of Latin American countries (40%), while having room for growth compared to the adoption rate in other regions such as the United States (90%). (shrink)

Background: Health care practitioners are recognized to have a large influence in shaping uptake of vaccine in new borns, children, adolescents, as well as adults. Parents remain more secure in their decisions when health care practitioners communicate successfully with them about vaccine dangers and benefits, the value as well as necessity for vaccinations, as well as vaccine safety. Thus, immunization remain the foundation of the primary health care system, an indisputable human right as well as a global health and development (...) success story, saving millions of lives yearly. Recently, we have vaccines to prevent more than 20 life threatening diseases, helping people of all ages live longer, healthier lives. Yet despite tremendous progress, far too many people around the world, including nearly 20 million infants yearly have insufficient access to vaccines. In some countries, progress has stalled or even reversed, and there is a real risk that complacency will undermine past achievements. Purpose: The current study aimed to explores vaccine hesitancy, its barriers and impact studies regarding COVID-19 decision impacts. Also, to provides policy and decision makers and operational staff with evidence to inform decisions to promote vaccine uptake across Bayelsa State. Methods: A literature review tried to do a deep dive by using a variety of search engines such as Scopus, Research Gate, Mendeley, Summon, PubMed, Google Scholar, Hinari, Dimension, Academia, CAB Abstract, OARE Abstract, SSRN search strategy to retrieve research publications, “grey literature” and expert working group reports, including author’s field experience. Findings: Absence of uniform methods of organization in the various health care facilities upon which we were obliged to rely. Thus affecting the overall immunization programme and health system. Hence policy must urgently address these challenges with emphasis on policy clarity while continuously improving infrastructure. Conclusions: Bayelsa State is categorized as low/poor performing as the findings suggest that the involvement of community-based leaders can improve community participation and acceptance while enhancing and strengthening integrated disease surveillance and Adverse Events Following Immunization (AEFI) monitoring and reporting systems; and conducting integrated advocacy and communications activities to promote demand for vaccination as part of increasing overall demand and acceptability of all essential Primary Health Centers (PHC) services, thus, breaking barriers of vaccine hesitancy. (shrink)

-/- An assessment of the Cultural Mentality of the United States Using the Three Universal Laws -/- By Angelito Malicse -/- The United States, as one of the most powerful and influential nations in modern history, presents a complex and often contradictory cultural landscape. By applying the Three Universal Laws—(1) the Law of Karma (systems and cause-effect), (2) the Law of Balance in Nature, and (3) the Law of Feedback Mechanism—we can evaluate the cultural mentality of the U.S. with objectivity (...) grounded in natural principles. Each dimension is rated on a 1–10 scale to reflect alignment or deviation. -/- 1. The Law of Karma (Systems, Cause and Effect) -/- The U.S. has one of the most sophisticated institutional frameworks in the world, built on the principles of rule of law, separation of powers, and constitutional rights. However, it is also home to some of the most deeply entrenched systemic flaws: racial disparities in the criminal justice system, unequal access to healthcare, and a political system heavily influenced by corporate lobbying. -/- Despite these issues, U.S. civil society retains tools for accountability, including an independent judiciary, investigative journalism, and civic activism. These tools uphold the principle of cause and effect—though inconsistently. -/- Score: 6/10 -/- 2. The Law of Balance in Nature -/- The American lifestyle—rooted in high consumption, fossil fuel dependence, and unchecked economic growth—reflects a profound imbalance with nature. The U.S. has historically been the largest cumulative contributor to global carbon emissions. The prioritization of profit, convenience, and consumerism often overrides ecological and social equilibrium. -/- There are efforts to restore balance—renewable energy programs, conservation efforts, and public awareness campaigns—but these are fragmented and sometimes undercut by political and corporate interests. -/- Score: 4/10 -/- 3. The Law of Feedback Mechanism -/- The feedback mechanisms in the U.S. are structurally strong: elections, public opinion polls, journalism, and social media all allow individuals to express dissatisfaction or influence change. However, these mechanisms are increasingly compromised by misinformation, ideological polarization, and low civic literacy. Many Americans live in media echo chambers, weakening their ability to engage in constructive feedback and correction. -/- Educational gaps and rising distrust in institutions reduce the society’s ability to self-correct on a large scale. -/- Score: 6/10 -/- Final Evaluation: A Cultural Score of 5.3/10 -/- With scores of 6 (Karma), 4 (Balance), and 6 (Feedback), the overall average cultural alignment of the United States with the Three Universal Laws is 5.3 out of 10. The U.S. shows functional institutional capacity and potential for systemic integrity but remains vulnerable to imbalance and chaotic feedback, largely due to misinformation and fragmented values. -/- Conclusion: A Nation at a Crossroads -/- The United States is a society of paradoxes—great innovation alongside deep inequality, democratic freedoms alongside social division. If it can realign with the laws of nature—through sustainable living, systemic accountability, and enhanced critical feedback—it can lead by example on the global stage. Education reform that embeds these universal principles is the key to a cultural transformation. -/- References -/- 1. Pew Research Center. (2022). Public Trust in Government: 1958–2022. -/- 2. Transparency International. (2023). Corruption Perceptions Index. -/- 3. U.S. Environmental Protection Agency (EPA). (2022). Inventory of U.S. Greenhouse Gas Emissions and Sinks. -/- 4. World Inequality Report. (2022). Income Inequality in the United States. -/- 5. American Bar Association. (2023). Racial Disparities in the U.S. Criminal Justice System. -/- 6. Brookings Institution. (2021). The Role of Corporate Money in U.S. Politics. -/- 7. MIT Media Lab. (2018). The Spread of True and False News Online. Science, 359(6380), 1146–1151. -/- 8. National Assessment of Educational Progress (NAEP). (2022). Civic and Critical Thinking Skills in U.S. Schools. -/- . (shrink)

In the late 19th century Husserl studied our internal sense of time passing, maintaining that its deep connections into experience represent prima facie evidence for it as the basis for all investigations in the sciences: Phenomenology was born. Merleau-Ponty focused on perception pointing out that any theory of experience must in accord with established aspects of biology i.e. embodied. Recent analyses suggest that theories of experience require non-reductive, integrative information, together with a specific property connecting them to experience. Here (...) we elucidate a new class of information states with just such properties found at the loci of control of complex biological systems, including nervous systems. Complexity biology concerns states satisfying self-organized criticality. Such states are located at critical instabilities, commonly observed in biological systems, and thought to maximize information diversity and processing, and hence to optimize regulation. Major results for biology follow: why organisms have unusually low entropies; and why they are not merely mechanical. Criticality states form singular self-observing systems, which reduce wave packets by processes of perfect self-observation associated with feedback gain g=1. Analysis of their information properties leads to identification of a new kind of information state with high levels of internal coherence, and feedback loops integrated into their structure. The major idea presented here is that the integrated feedback loops are responsible for our ‘sense of self’, and also the feeling of continuity in our sense of time passing. Long-range internal correlations guarantee a unique kind of non-reductive, integrative information structure enabling such states to naturally support phenomenal experience. Being founded in complexity biology, they are ‘embodied’; they also fulfill the statement that ‘The self is a process’, a singular process. High internal correlations and RenéThom-style catastrophes support non-digital forms of information, gestalt cognition, and information transfer via quantum teleportation. Criticality in complexity biology can ‘embody’ cognitive states supporting gestalts, and phenomenology’s senses of ‘self,’ time passing, existence and being. (shrink)

We contest the unsubstantiated assumption of both materialists and non-materialist that the ontological status they propose applies to all humans and that the competing claim is false for all - ie we reject both the claim of non-materialists that all humans share the same fundamental aspect of having a "non-material consciousness" (nmc), as well as the contrasting claim of materialists that none do (being fully material as according to eliminative materialists/reductive physicalists etc). Instead, the basic proposition of this paper, our (...) ‘ontological conecture’ (OC) - an updated version of our 1998 website article “Mindless Materialists” - is that the central tenets of proponents on both sides are true, but only regarding themselves. A signature feature of nmc is that brains associated to it are capable of knowing of its existence directly, and as a corollary we would propose that if a sophisticated brain does not know it is associated to nmc then most likely this is because it is in fact not so associated. Thus, in accordance with our OC we will consider a brain’s statements on this issue (that it does or does not possess nmc) as not merely its ‘philosophical position’ but as an authoritative statement, a reflection of an ontological fact. Furthermore, we propose that only those who possess nmc are capable of understanding what it is, so that although they are well-qualified to know directly that they themselves possess it, in contrast those who lack nmc and possess only material consciousness (mc) cannot even comprehend what non-materiality means - they will understandably tend to consider it a non-existent absurdity, which contention would be quite correct in a purely-material reality, such as they effectively inhabit. -/- Terminology reflecting ontological status: Since according to our OC those brains which categorize themselves as non-materialists are presumed to indeed be associated to nmc, for usefulness in phraseology we’ll refer to people whose brain is so associated as being nmc’s (not merely “espousing nmc”). Materialists say they are conscious but that theirs is a material consciousness (abbrev: “mc”) , and since according to our OC we accept this self-determination we will refer to them as being “mc’s” or being materialist (not merely “espousing materialism”). -/- Are materialists mindless or are non-materialists delusional? Notoriously, it is impossible to prove that one possesses nmc (which is a sort of corollary to the fact that it is directly self-known) - being non-material, nmc cannot be detected via the scientific method. However just as those with nmc cannot prove they possess it so too one cannot prove or determine via measurement that materialists do not possess it. Nevertheless we feel that our OC is the simplest solution to the conundrum of how there are materialists if a brain can directly sense its associated nmc. In sum, although of course it is impossible to prove that any particular person possesses or lacks nmc, given all the above our OC considers - as stated by the title of this paper - a brain’s self-identification as "materialist" or “non-materialist” (dualist, panpsychist, idealist etc) as reflecting the absence or presence of an associated real non-material awareness/consciousness, rather than merely as a statement of a philosophical stance. An alternative solution is implicit in the above - that all humans possess nmc just that materialists are those whose brains lack the awareness of theirs, or that the brain-aspect which communicates to others has no access to it. -/- Towards developing more constructive dialogue between mc’s/nmc’s, and greater self-confidence and independence among nmc’s in the face of materialist dominance of the intellectual-climate: A large part of the overall paper is devoted to not just pointing out the futility of communication of the sort usually engaged in - ie based on the erroneous underlying assumption that both sides of the debate are ontologically the same - but also suggesting ways to make the debate less frustrating by a recognition of the OC. Greater clarity in discussions can be achieved partly via a deeper understanding of the different meanings the same term might have to nmc’s/mc’s and suggestions relevant the construction of a sort of translation algorithm to utilize in discussions (as a simple example, substituting ‘higher-level cognitive material brain-processes’ for ‘mind’ when that term is used by an mc); by the frank statement by nmc’s engaged in dialogue with mc’s that the existence of nmc is not up for discussion; by learning how the existence of our nmc colors the meaning we give to certain terms in ways we were not aware of, meanings which are absent for mc’s; and in general via the recognition by the nmc that the dialogue is with a person lacking nmc. -/- Perhaps materialists are right: In the interest of ‘reciprocity’, the paper also offers an alternative/opposing view to the central proposition, to the effect that the materialist claim is correct, and it is a defect of brain wiring or structure which is the source of an active illusion of "self-awareness" underlying the philosophical claims of non-materialists. A prospective source of this illusion is offered - an analog of the sense of presence experienced during ‘sleep paralysis’. (shrink)

Recently, philosophers with an interest in consciousness have turned their attention towards “fringe states of consciousness”. Examples include dreams, trances, and meditative states. Teetering between wakefulness and non-consciousness, fringe states illuminate the limits and boundaries of consciousness. This paper aims to give a coherent conceptualisation of deep meditative states, focussing in particular on phenomenal temporality during meditation. Advanced meditators overwhelmingly describe deep states of meditation as atemporal and timeless; however, they also report being continuously alert while meditating. I (...) intend to give a coherent interpretation of this apparent contradiction. After introducing some candidate interpretations, I shall argue that during (deepest) meditation, the subject experiences ‘pure duration’ without temporal structure. This, I argue, explains best why meditators describe deep meditation as ongoing but timeless awareness. A central part of the paper will expand on an account of phenomenal duration without phenomenal succession. The conclusion points towards some further avenues of research. (shrink)

State violence against disabled people and Indigenous people as well as disabled Indigenous people has long been endemic in the US. Recent scholarship in philosophy of disability and disability studies rarely addresses the underlying issue that causes such state violence: settler-colonial conceptions of land. The aim of this article is to begin filling this gap in the literature. We detail settler colonial epistemologies and argue that the property relation underwrites operative concepts of accessibility dominant across disability theory. We (...) show how such concepts of accessibility are Lockean and thereby defined terms of the project of settler colonialism. We instead offer an Indigenized account of access, which we term deep access, that does not rely on the notion of Lockean property and that provides a coalitional path for Indigenous futures and disability justice. On our account, decolonization is and must be a deep access measure. (shrink)

The vertiginous rise of right-wing populism, especially in its “nationalist, xenophobic and conservative form”, and some “racist, anti-Semitic, homophobic and sexist” drifts associated with this phenomenon – whether real or perceived as such – make the mainstream media play a double role. On the one hand, the mainstream media reflect the struggle for political hegemony between different vested interests; on the other hand, they engage in the fight against right-wing populism blasting both right-wing populist candidates and their voters or supporters. (...) Many mainstream journalists ask citizens to realize a “sanitary cordon”, a “wall” or a “republican front” to block far-right populism and preserve liberal democracy. Moreover, they urge people to be wary of all attempts to “dediabolize” or “normalize” some tokens of right-wing populism. The main idea of this article is that right-wing populism is more harmless than is generally believed and, if excessive, negative media coverage doesn’t baffle but feed it. Populism is essentially a latent side effect of liberal democracy. Populism rises and becomes obtrusive only if a significant part of society perceives a regime of illiberal majoritarianism instead of one of liberal democracy. Right-wing populists are chiefly frustrated “ci-devants” who feel dispossessed of their past, identity, properties, qualities, privileges or titles. Inasmuch as the causes of collective frustration are many and varied (e.g. the real or just perceived corruption of the elites, the “system”, the deep state, the relocation of jobs, immigration, national sovereignty, national identity, communitarianism, radical Islamism, the status of some traditional institutions, some chapters of official history, etc.), there will always be right-wing populists, whether they are self-declared or covert. By adopting David G. Hackett’s thesis that the media are “agents of hegemony”, we applied the critical analysis of discourse to a set of 346 media articles in order to reveal the discursive sources of power, domination, inequality and partiality. The articles appeared in The New York Times and Le Monde during the period 2016-2017, at the time when the presidential elections took place in the United States and France. The articles were selected according to the occurrence of the keywords “Donald Trump” – “populism” and “Marine Le Pen” – “populisme” within the titles. The analysis of these articles reveals a divisive discursive structure that correspond to a real political cleavage in society. It is true that populism presents a “Manichean outlook”, in which there are only friends and foes” and no compromise is possible. It is also true that mainstream media reinforce this Manichean perspective on society and make populists feel marginalized and politically disempowered. The mainstream media may appease right-wing populism if they treat its followers as legitimate and equal political actors. For this, they have to give up the narrative structures that underscore insurmountable divergences and irreconcilable interests. In a liberal democracy, mass media should chiefly play the role of mediator. They may not aim to defeat or re-educate certain categories of citizens just because they advocate “wrong” political solutions. (shrink)

The profound changes in personality, mood, and other features of the self that neural interventions can induce can be disconcerting to patients, their families, and caregivers. In the neuroethical debate, these concerns are often addressed in the context of possible threats to the narrative self. In this paper, I argue that it is necessary to consider a dimension of impacts on the narrative self which has so far been neglected: neural interventions can lead to a loss of meaning of actions, (...) feelings, beliefs, and other intentional elements of our self-narratives. To uphold the coherence of the self-narrative, the changes induced by neural interventions need to be accounted for through explanations in intentional or biochemical terms. However, only an explanation including intentional states delivers the content to directly ascribe personal meaning, i.e., subjective value to events. Neural interventions can deprive events of meaning because they may favor a predominantly biochemical account. A loss of meaning is not inherently negative but it can be problematic, particularly if events are affected one was not prepared or willing to have stripped of meaning. The paper further examines what it is about neural interventions that impacts meaning by analyzing different methods. To which degree the pull towards a biochemical view occurs depends on the characteristics of the neural intervention. By comparing Deep Brain Stimulation, Prozac, Ritalin, psychedelics, and psychotherapy, the paper identifies some main factors: the rate of change, the transparency of the causal chain, the involvement of the patient, and the presence of an acute phenomenological experience. (shrink)

I argue for the existence of imaginative beliefs: mental states that are imaginative in format and doxastic in attitude. I advance two arguments for this thesis. First, there are imaginings that play the functional roles of belief. Second, there are imaginings that play the epistemic roles of belief. These arguments supply both descriptive and normative grounds for positing imaginative beliefs. I also argue that this view fares better than alternatives that posit distinct imaginative and doxastic states to account for the (...) same phenomena. Along the way, I demonstrate the theoretical significance of imaginative beliefs by showing how they pose a challenge for standard taxonomies of the mind, clarify the sense in which imagination is occurrent and voluntary, explain the justificatory role of the imagination, and point to a deep but underappreciated symmetry between imagistic and non-imagistic thought. (shrink)

Our conscious experience seemingly involves the subjective sense or feeling of the passage of time. However, in recent years, several authors have denied that such an aspect or feature of experience can be found. If the experience of the flow of time exists, it remains elusive and intangible. My aim here is to try to pin it down. For this purpose, I will investigate acute disturbances of normal temporal experience that accompany deep meditative and psychedelic states. I will argue (...) that these experiences of altered temporality provide phenomenal contrast cases to ordinary passage phenomenology, allowing us to establish the existence of the latter and making it amenable to theorizing. (shrink)

The United States Fish and Wildlife Service in Alaska, USA, conducted a species status assessment for a petition to list the Alexander Archipelago wolf (Canis lupus ligoni) under the Endangered Species Act in 2020-2022. This federal undertaking could not be adequately prepared without including the knowledge of Indigenous People who have a deep cultural connection with the subspecies. Our objective is to communicate the authoritative expertise and voice of the Indigenous People who partnered on the project by demonstrating how (...) their knowledge contributed to the species status assessment. The Indigenous knowledge applied in the assessment is the cultural and intellectual property of those who have shared it. We employed rapid appraisal research to expeditiously develop a preliminary and qualitative understanding of Indigenous People's cultural and ecological knowledge of Alexander Archipelago wolves. We used semi-directed interviewing and inductive coding from grounded theory for text analysis. Indigenous knowledge contributed to the agency's understanding of the Alexander Archipelago wolf in Southeast Alaska and helped the agency with their classification decision. Indigenous research partners explained the rich cultural significance and position of wolves in Tlingit society and described human–wolf relationships and ecological interactions. The agency used a single-species assessment approach based in species ecology and conservation biology, whereas the Indigenous wolf experts applied a multi-species, community ecology approach based in a sociocultural context of balance and respect. The Indigenous wolf experts successfully addressed knowledge gaps identified by the agency. The partners were challenged by a short regulatory timeframe that did not allow for comprehensive study of Indigenous knowledge and constrained review and feedback by Indigenous experts. The United States Fish and Wildlife Service learned that its assessment framework was not designed to account for an Indigenous worldview. To level the playing field, the agency and Indigenous experts should discuss how to co-develop an assessment framework that equitably applies both perspectives. (shrink)

Claims about whether or not infertility is a disease are sometimes invoked to defend or criticize the provision of state-funded treatment for infertility. In this paper, I suggest that this strategy is problematic. By exploring infertility through key approaches to disease in the philosophy of medicine, I show that there are deep theoretical disagreements regarding what subtypes of infertility qualify as diseases. Given that infertility's disease status remains unclear, one cannot uncontroversially justify or undermine its claim to medical (...) treatment by claiming that it is or is not a disease. Instead of focusing on disease status, a preferable strategy to approach the debate about state-funded treatment is to explicitly address the specific ethical considerations raised by infertility. I show how this alternative strategy can be supported by a recent theoretical framework in the philosophy of medicine, which avoids the problems associated with the concepts of health and disease. (shrink)

The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, linguistic (...) competence in this framework was regarded as being innate, rule-governed, domain-specific, and fundamentally different from performance, i.e., idiosyncrasies and factors governing linguistic behavior. I analyze state-of-the-art connectionist, deep learning models of natural language processing, most notably large language models, to see what they can tell us about linguistic competence. Deep learning is a statistical technique for the classification of patterns through which artificial intelligence researchers train artificial neural networks containing multiple layers that crunch a gargantuan amount of textual and/or visual data. I argue that these models suggest that linguistic competence should be construed as stochastic, pattern-based, and stemming from domain-general mechanisms. Moreover, I distinguish syntactic from semantic competence, and I show for each the ramifications of the endorsement of a connectionist research program as opposed to the traditional symbolic cognitive science and transformational-generative grammar. I provide a unifying front, consisting of usage-based theories, a construction grammar approach, and an embodied approach to cognition to show that the more multimodal and diverse models are in terms of architectural features and training data, the stronger the case is for the connectionist linguistic competence. I also propose to discard the competence vs. performance distinction as theoretically inferior so that a novel and integrative account of linguistic competence originating in connectionism and empiricism that I propose and defend in the dissertation could be put forward in scientific and philosophical literature. (shrink)

Evaluator-relative consequentialists frequently endorse the traditional doing-allowing distinction. Yet their endorsement of this traditional distinction only serves to clear the way for their argument against a more fundamental doing-allowing distinction, an argument that one never ought to do something when this will allow something worse to happen. Unlike the case against its more traditional counterpart, the case against this deeper doing-allowing distinction can draw for support upon widely held “state of affairs centered” accounts of attitudes, actions, reasons and value, (...) accounts upon which desires are (all) attitudes towards states of affairs, practical reasons are all reasons to promote states of affairs, and to act is to make something happen -- to promote, produce, or bring about some states of affairs. Moreover, to reject this deeper distinction is to accept the centerpiece of consequentialism – that the evaluation of actions is determined through appeal to the evaluation of states of affairs. Only once the nature of the deep doing-allowing distinction, and these grounds for its rejection, come into view, does it also become clear both what an adequate response to such arguments for its rejection will look like, and that many elements of such a response have already been proposed. (shrink)

How should liberal democratic states respond to cultural practices and arrangements that run afoul of liberal norms and laws? This article argues for a reframing of the challenges posed by traditional or nonliberal cultural minorities. The author suggests that viewed from up close, such dilemmas are revealed to be primarily intracultural rather than intercultural conflicts, and reflect the political and practical interests of factions of communities much more than deep moral differences. Using the example of the reform of customary (...) marriage laws in post-apartheid South Africa, this article makes the case for a more pragmatic, politically focused approach to resolving conflicts of culture that it is argued is both more democratic and effective than alternatives recently advanced by liberals and deliberative democracy theorists. (shrink)

It was Hume's concept of knowledge that marked Masaryk's philosophy more than influential Kant. Masaryk devoted Hume his inaugural lecture The Probability Calculus and Hume's Scepticism at Prague University in 1882. He tried to face his scepticism concerning causation and induction by a formula P = n: or P = : where P means the increasing probability that an event that had happened n times in the past will happen again. Hume stresses that there is an essential difference between probability (...) and certainty, and we have no logical reason to presuppose that the past experience is legitimate for expectation that the future will resemble to the past. Only our belief, not knowledge, leads us to that. Although Masaryk did not accept Hume's scepticism, he had deep understanding for his way of thinking. Reflection of Hume's influence on Masaryk in Czech philosophy is unsatisfactory, which is stated in this article. (shrink)

Alexis karpouzos is a visioner in the development of post-history sense of cosmic unity and the integral consciousness. For him, spirituality is not just about personal enlightenment but is deeply connected to moral action. He view ethical living as a natural outgrowth of spiritual awareness. In his worldview, the divine is not something distant or abstract but is present in every human being, and this awareness should lead to moral behavior that reflects love, justice, and equality. In many of his (...) writings, Alexis karpouzos calls for the realization of a moral society, one that is based on mutual respect, compassion, and the recognition of the divine essence in all people. His commitment to social reform, education, and the improvement of human welfare can be seen as an extension of his spiritual values, grounded in the belief that the divine resides within every person, regardless of their social or economic status. Alexis karpouzos believe that the process of spiritual growth was a deeply personal journey. He often spoke of the importance of inner freedom and self-realization. For him, true liberation was not something that could be granted from outside, but a state of consciousness that could only be attained through deep self-awareness and connection to the universe. This self-realization, however, did not mean renouncing the social and historical world; rather, it involved seeing the universal consciousness in all aspects of life and acting in alignment with it. (shrink)

This essay seeks to see if one side or the other in the hunting debate gets more purchase if we first ask what gives the state the moral right to promote sport hunting when the practice is in deep decline. We look at the dominant economic and political reasons for state support, none of which settle the moral matter. We then look at various state appeals to moral justification (ethical hunting, the right to hunt, the value (...) of heritage, etc.) and determine that they beg the same prior questions that hunters beg--questions about the new scientific understanding of animal cognition and about the ideal individual and society in the 21st C. Neither side in the philosophical debate is helped by state promotion of hunting, given that anti-hunters also often dispense with the deeper questions and the relevant science. Conclusion: the debate should be based on the new scientific understanding of animals' capacities and on current ideals of the individual and society. State policy should reflect that. -/- thehe state should see that it is on slippery ground when it devises programs to gratify hunters and to encourage the innocent, the reluctant, and the lapsed to take up the practice. (shrink)

The third volume of “Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond” presents an in-depth exploration of the cutting-edge developments in uncertain combinatorics and set theory. This comprehensive collection highlights innovative methodologies such as graphization, hyperization, and uncertainization, which enhance combinatorics by incorporating foundational concepts from fuzzy, neutrosophic, soft, and rough set theories. These advancements open new mathematical horizons, offering novel approaches to managing uncertainty within complex systems. Combinatorics, a discipline focused on counting, arrangement, (...) and structure, often faces challenges when uncertainty is present. Set theory, which underpins combinatorial problems, has evolved to tackle these challenges. The introduction of fuzzy and neutrosophic sets has expanded the toolkit for modeling uncertainty by incorporating elements of truth, indeterminacy, and falsehood into decision-making processes. These innovations seamlessly intersect with graph theory, providing new ways to represent uncertain structures through "graphized" forms such as hypergraphs and superhypergraphs. This volume also introduces advanced concepts like Neutrosophic Oversets, Undersets, and Offsets, which push the boundaries of classical graph theory and offer deeper insights into the mathematical and practical challenges posed by real-world systems. By blending combinatorics, set theory, and graph theory, the authors have created a robust framework for addressing uncertainty in both mathematical systems and their real-world applications. This foundation sets the stage for future breakthroughs in combinatorics, set theory, and related fields. Each chapter in this volume contributes both theoretical foundations and practical applications, demonstrating the power of integrating graph theory, set theory, and uncertainty models. The new ideas, algorithms, and mathematical tools presented here will drive the future of combinatorial research and its applications in uncertain environments. In the first chapter, “Introduction to Upside-Down Logic: Its Deep Relation to Neutrosophic Logic and Applications”, the authors present Upside-Down Logic, a novel logical framework that systematically transforms truths into falsehoods and vice versa, based on contextual shifts. Introduced by F. Smarandache, this paper provides a mathematical definition of Upside-Down Logic, including applications related to the Japanese language. The chapter also introduces Contextual Upside-Down Logic, an extension that adjusts logical connectives alongside flipped truth values, as well as Indeterm-Upside-Down Logic and Certain Upside-Down Logic to address indeterminacy. A simple algorithm is also proposed to demonstrate the computational aspects of this logic. In the second chapter, “Local-Neutrosophic Logic and Local-Neutrosophic Sets: Incorporating Locality with Applications”, the authors introduce Local-Neutrosophic Logic and Local-Neutrosophic Sets, which integrate the concept of locality into Neutrosophic Logic. By defining locality as the influence of immediate surroundings on an object or system, this chapter explores how it affects indeterminacy in real-world problems. The paper also examines potential applications and provides mathematical definitions for these new concepts. The third chapter, “A Review of Fuzzy and Neutrosophic Offsets: Connections to Some Set Concepts and Normalization Function”, extends the concept of offsets in uncertain set-theoretic frameworks, such as Fuzzy Sets, Neutrosophic Sets, and Plithogenic Sets. This chapter introduces several advanced types of offsets, including Nonstationary Fuzzy Offset, Multi-valued Plithogenic Offset, and Subset-valued Neutrosophic Offset, offering deeper insights into handling uncertainty in mathematical models. In the fourth chapter, “Review of Plithogenic Directed, Mixed, Bidirected, and Pangene OffGraph”, the authors build upon Plithogenic Graphs to propose extensions such as Plithogenic Directed OffGraph, Plithogenic BiDirected OffGraph, and Plithogenic Mixed OffGraph. These new concepts, including the Plithogenic Pangene OffGraph, are explored in detail, with a focus on their mathematical properties and potential applications in uncertain graph theory. The fifth chapter, “Short Note on Neutrosophic Closure Matroids”, explores the extension of matroid concepts into Neutrosophic and Turiyam Neutrosophic set theories, introducing Neutrosophic closure matroids. This concept integrates uncertainty, indeterminacy, and liberal states into matroid theory, enhancing its applicability in optimization and combinatorial problems. In the sixth chapter, “Some Graph Parameters for Superhypertree-width and Neutrosophic Tree-width”, the authors discuss graph parameters such as Superhypertree-width and Neutrosophic tree-width. These parameters play a crucial role in the study of graph characteristics, particularly in algorithms and real-world applications. The chapter explores the generalization of hypergraphs to SuperHyperGraphs and examines how these concepts extend tree-width parameters within the context of Neutrosophic logic. (shrink)