Is There a Focal Meaning of Being in Aristotle?

At the beginning of Metaphysics Γ Aristotle claims that there is a science which is concerned with being qua being. \u27Being’ is said in many senses. Different beings are not said to be purely homonymous, but rather to be “related to one thing (πρόσ ἕν)”(1003a33- 4). G.E.L Owen translates this ττρός ἕν formula as focal meaning , and in his paraphrase, it means that all the “senses [of ‘being’] have one focus, one common element”, or “a central sense”, so that “all its senses can be explained in terms of substance and of the sense of ‘being’ that is appropriate to substance.” According to Owen, “focal meaning” is new and revolutionary in Meta.Γ, and introduces a “new treatment of to on and other cognate expressions”, which consists mainly in the following two thesis: (1) The “focal meaning” idea contradicts and replaces Aristotle’s earlier view in the Organon, EE and others that beings differ in different categories, and ‘being’ has various distinct senses. (2) The “focal meaning” idea makes it possible for Aristotle to establish a universal science of being qua being in Meta. Γ, which contradicts and replaces his earlier view that because beings differ, a universal science of being is impossible. The influence of Owen’s interpretation on Aristotelian scholarship cannot be exaggerated. The notion of the “focal meaning” has been widely adopted as a technical term and Owen’s above two theses continue to be embraced in their fundamentals. In this paper, I try to provide an alternative account of the πρός εν, which shows that the προς εν of being in Meta. Γ2 is neither new nor revolutionary. Consequently, I will reject, respectfully, both claims made by Owen

Evolution of cooperation in spatial traveler's dilemma game

Traveler's dilemma (TD) is one of social dilemmas which has been well studied in the economics community, but it is attracted little attention in the physics community. The TD game is a two-person game. Each player can select an integer value between $R$ and $M$ ($R < M$) as a pure strategy. If both of them select the same value, the payoff to them will be that value. If the players select different values, say $i$ and $j$ ($R \le i < j \le M$), then the payoff to the player who chooses the small value will be $i+R$ and the payoff to the other player will be $i-R$. We term the player who selects a large value as the cooperator, and the one who chooses a small value as the defector. The reason is that if both of them select large values, it will result in a large total payoff. The Nash equilibrium of the TD game is to choose the smallest value $R$. However, in previous behavioral studies, players in TD game typically select values that are much larger than $R$, and the average selected value exhibits an inverse relationship with $R$. To explain such anomalous behavior, in this paper, we study the evolution of cooperation in spatial traveler's dilemma game where the players are located on a square lattice and each player plays TD games with his neighbors. Players in our model can adopt their neighbors' strategies following two standard models of spatial game dynamics. Monte-Carlo simulation is applied to our model, and the results show that the cooperation level of the system, which is proportional to the average value of the strategies, decreases with increasing $R$ until $R$ is greater than the threshold where cooperation vanishes. Our findings indicate that spatial reciprocity promotes the evolution of cooperation in TD game and the spatial TD game model can interpret the anomalous behavior observed in previous behavioral experiments

The Unity of Aristotle's Metaphysics

This paper discusses the three ancient commentaries on Book E of Aristotle's Metaphysics, that have been handed down to us. It aims to demonstrate the fundamental part played by their particular interpretation of Aristotle's doctrines in the birth of the traditional interpretation of his Metaphysics, according to which all the books comprising the work were written as a function of Book Λ, containing the well-known doctrine of the unmoved mover. Among the main elements supporting this assumption there is Aristotle's distinction between three types of science - the theoretical, the practical and the productive - and his claiming the primacy of metaphysics as a theological science. According to the ancient commentators, the remainder of Book E would belong to the unitary project of the Metaphysics, since it would indicate what is not encompassed in the object of metaphysics. This would mean that Aristotle's treatment of accidental being, being as truth and not-being as falsity, and being potentially and actually would take on a negative function. The theological interpretation of Aristotle's Metaphysics thus retains its ultimate foundations in premises contained in the Aristotelian text itsel

Exploring Asymmetric Tunable Blind-Spots for Self-supervised Denoising in Real-World Scenarios

Self-supervised denoising has attracted widespread attention due to its ability to train without clean images. However, noise in real-world scenarios is often spatially correlated, which causes many self-supervised algorithms based on the pixel-wise independent noise assumption to perform poorly on real-world images. Recently, asymmetric pixel-shuffle downsampling (AP) has been proposed to disrupt the spatial correlation of noise. However, downsampling introduces aliasing effects, and the post-processing to eliminate these effects can destroy the spatial structure and high-frequency details of the image, in addition to being time-consuming. In this paper, we systematically analyze downsampling-based methods and propose an Asymmetric Tunable Blind-Spot Network (AT-BSN) to address these issues. We design a blind-spot network with a freely tunable blind-spot size, using a large blind-spot during training to suppress local spatially correlated noise while minimizing damage to the global structure, and a small blind-spot during inference to minimize information loss. Moreover, we propose blind-spot self-ensemble and distillation of non-blind-spot network to further improve performance and reduce computational complexity. Experimental results demonstrate that our method achieves state-of-the-art results while comprehensively outperforming other self-supervised methods in terms of image texture maintaining, parameter count, computation cost, and inference time

Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning

Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients manipulate their updates to affect the global model. Although various methods exist for detecting those clients in FL, identifying malicious clients requires sufficient model updates, and hence by the time malicious clients are detected, FL models have been already poisoned. Thus, a method is needed to recover an accurate global model after malicious clients are identified. Current recovery methods rely on (i) all historical information from participating FL clients and (ii) the initial model unaffected by the malicious clients, leading to a high demand for storage and computational resources. In this paper, we show that highly effective recovery can still be achieved based on (i) selective historical information rather than all historical information and (ii) a historical model that has not been significantly affected by malicious clients rather than the initial model. In this scenario, while maintaining comparable recovery performance, we can accelerate the recovery speed and decrease memory consumption. Following this concept, we introduce Crab, an efficient and certified recovery method, which relies on selective information storage and adaptive model rollback. Theoretically, we demonstrate that the difference between the global model recovered by Crab and the one recovered by train-from-scratch can be bounded under certain assumptions. Our empirical evaluation, conducted across three datasets over multiple machine learning models, and a variety of untargeted and targeted poisoning attacks reveals that Crab is both accurate and efficient, and consistently outperforms previous approaches in terms of both recovery speed and memory consumption

A Survey on Federated Unlearning: Challenges, Methods, and Future Directions

In recent years, the notion of ``the right to be forgotten" (RTBF) has evolved into a fundamental element of data privacy regulations, affording individuals the ability to request the removal of their personal data from digital records. Consequently, given the extensive adoption of data-intensive machine learning (ML) algorithms and increasing concerns for personal data privacy protection, the concept of machine unlearning (MU) has gained considerable attention. MU empowers an ML model to selectively eliminate sensitive or personally identifiable information it acquired during the training process. Evolving from the foundational principles of MU, federated unlearning (FU) has emerged to confront the challenge of data erasure within the domain of federated learning (FL) settings. This empowers the FL model to unlearn an FL client or identifiable information pertaining to the client while preserving the integrity of the decentralized learning process. Nevertheless, unlike traditional MU, the distinctive attributes of federated learning introduce specific challenges for FU techniques. These challenges lead to the need for tailored design when designing FU algorithms. Therefore, this comprehensive survey delves into the techniques, methodologies, and recent advancements in federated unlearning. It provides an overview of fundamental concepts and principles, evaluates existing federated unlearning algorithms, reviews optimizations tailored to federated learning, engages in discussions regarding practical applications, along with an assessment of their limitations, and outlines promising directions for future research

Greenhouse gas emissions from croplands of China

China possesses cropland of 1.33 million km 2. Cultivation of the cropland not only altered the biogeochemical cycles of carbon (C) and nitrogen (N) in the agroecosystems but also affected global climate. The impacts of agroecosystems on global climate attribute to emissions of three greenhouse gases, namely carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O)

Contrastive Continual Multi-view Clustering with Filtered Structural Fusion

Multi-view clustering thrives in applications where views are collected in advance by extracting consistent and complementary information among views. However, it overlooks scenarios where data views are collected sequentially, i.e., real-time data. Due to privacy issues or memory burden, previous views are not available with time in these situations. Some methods are proposed to handle it but are trapped in a stability-plasticity dilemma. In specific, these methods undergo a catastrophic forgetting of prior knowledge when a new view is attained. Such a catastrophic forgetting problem (CFP) would cause the consistent and complementary information hard to get and affect the clustering performance. To tackle this, we propose a novel method termed Contrastive Continual Multi-view Clustering with Filtered Structural Fusion (CCMVC-FSF). Precisely, considering that data correlations play a vital role in clustering and prior knowledge ought to guide the clustering process of a new view, we develop a data buffer with fixed size to store filtered structural information and utilize it to guide the generation of a robust partition matrix via contrastive learning. Furthermore, we theoretically connect CCMVC-FSF with semi-supervised learning and knowledge distillation. Extensive experiments exhibit the excellence of the proposed method