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 Additive Ordered Structure of Natural Numbers with a Beatty Sequence

We have provided a pure model-theoretic proof for the decidability of the additive structure of the natural numbers together with a function {f} sending {x} to {[\phi x]} with {\phi} the golden ratio.Comment: 14 page

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

Adaptive Real-Time Optimal Dispatch of Privately Owned Energy Storage Systems

In this thesis, a real-time optimal dispatching (RTOD) algorithm is developed by formulating a mixed integer linear programming problem to determine charging and discharging power set-points of a privately owned energy storage system (ESS) in a competitive electricity market. The objective of the optimization problem is to generate revenue by exploiting price volatility in the day-ahead/week-ahead market. Moreover, this thesis aims to evaluate and improve the usefulness of publicly available electricity market prices for RTOD of a privately owned ESS in a competitive electricity market by developing a new adaptive technique. The pre-dispatch and the corresponding ex-post hourly Ontario energy prices are employed as the forecasted and actual prices. A compressed air ESS unit is optimally sized and modeled for evaluations. The conventional RTOD algorithm is developed, and its sensitivity to price forecast inaccuracy is evaluated. It is demonstrated that the forecast inaccuracy of publicly available market prices significantly reduces the ESS revenue. Then, a new adaptive algorithm is proposed and evaluated which adapts the objective function of the optimization problem online based on historical market prices. The outcomes reveal that the proposed adaptive RTOD can significantly reduce the adverse impact of the price forecast inaccuracy on the ESS revenue by online calibration of the 24-h-ahead market prices using 24-h-behind market prices. Moreover, the concept of optimal weekly usage of cryogenic energy storage (CES) is introduced and compared with the common daily usage. The results reveal significant benefits of weekly usage of the CES as compared to the daily usage

Partially dissociable roles of OFC and ACC in stimulus-guided and action-guided decision making

Recently, the functional specialization of prefrontal areas of the brain, and, specifically, the functional dissociation of the orbitofrontal cortex (OFC) and the anterior cingulate cortex (ACC), during decision making have become a particular focus of research. A number of neuropsychological and lesion studies have shown that the OFC and ACC have dissociable functions in various dimensions of decision making, which are supported by their different anatomical connections. A recent single-neuron study, however, described a more complex picture of the functional dissociation between these two frontal regions during decision making. Here, I discuss the results of that study and consider alternative interpretations in connection with other findings

Medical Image Segmentation Using Machine Learning

Image segmentation is the most crucial step in image processing and analysis. It can divide an image into meaningfully descriptive components or pathological structures. The result of the image division helps analyze images and classify objects. Therefore, getting the most accurate segmented image is essential, especially in medical images. Segmentation methods can be divided into three categories: manual, semiautomatic, and automatic. Manual is the most general and straightforward approach. Manual segmentation is not only time-consuming but also is imprecise. However, automatic image segmentation techniques, such as thresholding and edge detection, are not accurate in the presence of artifacts like noise and texture. This research aims to show how to extract features and use traditional machine learning methods like a random forest to obtain the most accurate regions of interest in CT images. In addition, this study shows how to use a deep learning model to segment the wound area in raw pictures and then analyze the corresponding area in near-infrared images. This thesis first gives a brief review of current approaches to medical image segmentation and deep learning background. Furthermore, we describe different approaches to build a model for segmenting CT-Scan images and Wound Images. For the results, we achieve 97.4% accuracy in CT-image segmentation and 89.8% F1-Score For wound image segmentation