179 research outputs found
Is There a Focal Meaning of Being in Aristotle?
At the beginning of Metaphysics Ī Aristotle claims that there is a science which is concerned with being qua being. \u27Beingā is said in many senses. Different beings are not said to be purely homonymous, but rather to be ārelated to one thing (ĻĻĻĻ į¼Ī½)ā(1003a33- 4). G.E.L Owen translates this ĻĻĻĻĻ į¼Ī½ formula as focal meaning , and in his paraphrase, it means that all the āsenses [of ābeingā] have one focus, one common elementā, or āa central senseā, so that āall its senses can be explained in terms of substance and of the sense of ābeingā that is appropriate to substance.ā According to Owen, āfocal meaningā is new and revolutionary in Meta.Ī, and introduces a ānew treatment of to on and other cognate expressionsā, which consists mainly in the following two thesis: (1) The āfocal meaningā idea contradicts and replaces Aristotleās earlier view in the Organon, EE and others that beings differ in different categories, and ābeingā has various distinct senses. (2) The āfocal meaningā idea makes it possible for Aristotle to establish a universal science of being qua being in Meta. Ī, which contradicts and replaces his earlier view that because beings differ, a universal science of being is impossible. The influence of Owenās interpretation on Aristotelian scholarship cannot be exaggerated. The notion of the āfocal meaningā has been widely adopted as a technical term and Owenās above two theses continue to be embraced in their fundamentals.
In this paper, I try to provide an alternative account of the ĻĻĻĻ ĪµĪ½, which shows that the ĻĻĪæĻ ĪµĪ½ of being in Meta. Ī2 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 and () 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 and (), then the payoff to the
player who chooses the small value will be and the payoff to the other
player will be . 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
. However, in previous behavioral studies, players in TD game typically
select values that are much larger than , and the average selected value
exhibits an inverse relationship with . 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 until 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 Ī, 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
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