Davidson’s Wittgenstein

Although the later Wittgenstein appears as one of the most influential figures in Davidson’s later works on meaning, it is not, for the most part, clear how Davidson interprets and employs Wittgenstein’s ideas. In this paper, I will argue that Davidson’s later works on meaning can be seen as mainly a manifestation of his attempt to accommodate the later Wittgenstein’s basic ideas about meaning and understanding, especially the requirement of drawing the seems right/is right distinction and the way this requirement must be met. These ideas, however, are interpreted by Davidson in his own way. I will then argue that Davidson even attempts to respect Wittgenstein’s quietism, provided that we understand this view in the way Davidson does. Having argued for that, I will finally investigate whether, for Davidson at least, his more theoretical and supposedly explanatory projects, such as that of constructing a formal theory of meaning and his use of the notion of triangulation, are in conflict with this Wittgensteinian quietist view

The Manifestation Challenge: The Debate between McDowell and Wright

In this paper, we will discuss what is called the “Manifestation Challenge” to semantic realism, which was originally developed by Michael Dummett and has been further refined by Crispin Wright. According to this challenge, semantic realism has to meet the requirement that knowledge of meaning must be publically manifested in linguistic behaviour. In this regard, we will introduce and evaluate John McDowell’s response to this anti-realistic challenge, which was put forward to show that the challenge cannot undermine realism. According to McDowell, knowledge of undecidable sentences’ truth-conditions can be properly manifested in our ordinary practice of asserting such sentences under certain circumstances, and any further requirement will be redundant. Wright’s further objection to McDowell’s response will be also discussed and it will be argued that this objection fails to raise any serious problem for McDowell’s response and that it is an implausible objection in general

A Discrete-time Dynamical Model for Optimal Dispatching and Rebalancing of Autonomous Mobility-on-Demand Systems

Autonomous vehicles are rapidly evolving and will soon enable the application of large-scale mobility-on-demand (MoD) systems. Managing the fleets of available vehicles, commonly known as "rebalancing," is crucial to ensure that vehicles are distributed properly to meet customer demands. This paper presents an optimal control approach to optimize vehicle scheduling and rebalancing in an autonomous mobility-on-demand (AMoD) system. We use graph theory to model a city partitioned into virtual zones. Zones represent small areas of the city where vehicles can stop and pick up/drop off customers, whereas links denote corridors of the city along which autonomous vehicles can move. They are considered vertices and edges in the graph. Vehicles employed in the AMoD scheme are autonomous, and rebalancing can be executed by dispatching available empty vehicles to areas undersupplied. Rebalancing is performed on the graph's vertices, i.e., between city areas. We propose a linear, discrete-time model of an AMoD system using a transformed network. After acquiring the model, the desired number of rebalancing vehicles for the AMoD model is derived through an optimization problem. Moreover, the well-posedness of the model is illustrated. To leverage the proposed model, we implemented the model predictive control (MPC) framework to find the optimal rebalancing and scheduling policy. We show the MPC's effectiveness and how the MPC framework can be implemented in real-time for a real-world case study. The numerical results show that the MPC with a linear cost function and linear reference, which it tracks, is effective, outperforming other MPC-based and state-of-the-art algorithms across all evaluation criteria

Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems

Reinforcement learning (RL) is a class of artificial intelligence algorithms being used to design adaptive optimal controllers through online learning. This paper presents a model-free, real-time, data-efficient Q-learning-based algorithm to solve the H$_{\infty}$ control of linear discrete-time systems. The computational complexity is shown to reduce from $\mathcal{O}(\underline{q}^3)$ in the literature to $\mathcal{O}(\underline{q}^2)$ in the proposed algorithm, where $\underline{q}$ is quadratic in the sum of the size of state variables, control inputs, and disturbance. An adaptive optimal controller is designed and the parameters of the action and critic networks are learned online without the knowledge of the system dynamics, making the proposed algorithm completely model-free. Also, a sufficient probing noise is only needed in the first iteration and does not affect the proposed algorithm. With no need for an initial stabilizing policy, the algorithm converges to the closed-form solution obtained by solving the Riccati equation. A simulation study is performed by applying the proposed algorithm to real-time control of an autonomous mobility-on-demand (AMoD) system for a real-world case study to evaluate the effectiveness of the proposed algorithm

MaskTune: Mitigating Spurious Correlations by Forcing to Explore

A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting spurious input features. This work proposes MaskTune, a masking strategy that prevents over-reliance on spurious (or a limited number of) features. MaskTune forces the trained model to explore new features during a single epoch finetuning by masking previously discovered features. MaskTune, unlike earlier approaches for mitigating shortcut learning, does not require any supervision, such as annotating spurious features or labels for subgroup samples in a dataset. Our empirical results on biased MNIST, CelebA, Waterbirds, and ImagenNet-9L datasets show that MaskTune is effective on tasks that often suffer from the existence of spurious correlations. Finally, we show that MaskTune outperforms or achieves similar performance to the competing methods when applied to the selective classification (classification with rejection option) task. Code for MaskTune is available at https://github.com/aliasgharkhani/Masktune.Comment: Accepted to NeurIPS 202

The Effect of Dexamethasone on the incidence of laryngospasm in pediatric patients after Tonsillectomy

Background and Aims: Laryngospasm and vomiting occurring after tracheal extubation in children is potentially dangerous. The aim of this study was to investigate the effects of preoperative 0.5 mg/kg i.v. Dexamethasone on the incidence of postextubation laryngospasm, and vomiting in children after tonsillectomy. Material and Methods : This study was performed at the Ilam Imam Khomeini hospital, IR, during the year 2009. In a randomized, double-blind trial, 66 pediatric patients 4-12 years (Dexamethasone group, n=33- placebo group , n=33) undergoing tonsillectomy received IV placebo (saline) or Dexamethasone , 0.5mg/kg IV after the induction of anesthesia before surgery. The incidence of postextubation laryngospasm and vomiting was recorded by the an investigator. All collected data were analyzed with using the statistical software (SPSS, Ver.16). Results : Mean age in Dexamethasone group 6.4±2.2, placebo group 6.1±2.8. Mean weight in Dexamethasone group 19.2±5.3, placebo group 20.3± 6.8 (p>0.05). Mean duration of anesthesia in Dexamethasone group 57.4 ±7.4 min, placebo group 55.6±4.6min. Mean duration of surgery in Dexamethasone group 40.7±6.7min , placebo group 42.3 ±8.4min (p>0.05). The incidence of postextubation laryngospasm in Dexamethasone group (6) was lower than that in the placebo group (30) (

Evaluation of the quality of nursing work life and its association with job burnout in Isfahan University of Medical Sciences

Background and aims: Nurses are particularly susceptible to burnout. Nursing staffs are in face to a relatively stressful work environment, high mental and physical pressure, irregular scheduling or shifting, limited job promotion, and socio-emotional pressures in connection with the patients and partners. This study aimed to assess the quality of work life (QWL) and its association wit

The Relationship Between Individual Stock Trading And Returns: The Case Of An Emerging Market

This research investigates the relationship between stock trading of individual investors and returns in short horizon in an emerging market. The results indicate that the individuals would like to invest in stocks after declining in the preceding month prices and they would like to sell after increasing in prices. Moreover, we find that there are positive abnormal returns in the month after high buying by individuals and there are negative abnormal returns following high individuals selling. The result is consistent with the literature that the individuals play roles of liquidity providers because they can meet the institutional need of immediacy

Working from Within: The Nature and Development of Quine’s Naturalism

Working from Within: The Nature and Development of Quine's Naturalism. By Verhaegh Sander

Davidson's Wittgensteinian Metaphilosophy

In this short paper, I am going to discuss an often neglected aspect of Davidson's philosophy, his metaphilosophy. Metaphilosophy is traditionally defined as the philosophy of philosophy. This definition, however, is not illuminating. I think metaphilosophy aims at a disclosure of the nature of philosophical questions, what they are and how to approach them. ..