Classical logic, argument and dialectic

A well studied instantiation of Dung's abstract theory of argumentation yields argumentation-based characterisations of non-monotonic inference over possibly inconsistent sets of classical formulae. This provides for single-agent reasoning in terms of argument and counter-argument, and distributed non-monotonic reasoning in the form of dialogues between computational and/or human agents. However, features of existing formalisations of classical logic argumentation (Cl-Arg) that ensure satisfaction of rationality postulates, preclude applications of Cl-Arg that account for real-world dialectical uses of arguments by resource-bounded agents. This paper formalises dialectical classical logic argumentation that both satisfies these practical desiderata and is provably rational. In contrast to standard approaches to Cl-Arg we: 1) draw an epistemic distinction between an argument's premises accepted as true, and those assumed true for the sake of argument, so formalising the dialectical move whereby arguments\u2019 premises are shown to be inconsistent, and avoiding the foreign commitment problem that arises in dialogical applications; 2) provide an account of Cl-Arg suitable for real-world use by eschewing the need to check that an argument's premises are subset minimal and consistent, and identifying a minimal set of assumptions as to the arguments that must be constructed from a set of formulae in order to ensure that the outcome of evaluation is rational. We then illustrate our approach with a natural deduction proof theory for propositional classical logic that allows measurement of the \u2018depth\u2019 of an argument, such that the construction of depth-bounded arguments is a tractable problem, and each increase in depth naturally equates with an increase in the inferential capabilities of real-world agents. We also provide a resource-bounded argumentative characterisation of non-monotonic inference as defined by Brewka's Preferred Subtheories

Meta-Argumentation Frameworks for Multi-party Dialogues

Postprin

Black String Perturbations in RS1 Model

We present a general formalism for black string perturbations in Randall-Sundrum 1 model (RS1). First, we derive the master equation for the electric part of the Weyl tensor $E_{\mu\nu}$. Solving the master equation using the gradient expansion method, we give the effective Teukolsky equation on the brane at low energy. It is useful to estimate gravitational waves emitted by perturbed rotating black strings. We also argue the effect of the Gregory-Laflamme instability on the brane using our formalism.Comment: 14 pages, Based on a talk presented at ACRGR4, the 4th Australasian Conference on General Relativity and Gravitation, Monash University, Melbourne, January 2004. To appear in the proceedings, in General Relativity and Gravitatio

The Quark Gluon Pion Plasma

While it is commonly believed that there is a {\it direct} transition from the hadronic to a quark gluon phase at high temperature, it would be prejudicial to rule out a sequence of dynamically generated intermediate scales. Using as guide, an effective lagrangian with unconfined gluons and constituent quarks, interacting with a chiral multiplet, we examine a scenario in which the system undergoes first-order transitions at $T_{comp}$, the compositeness scale of the pions, at $T_{\chi}$, the scale for spontaneous chiral symmetry breaking, and at $T_c$, the confinement temperature. We find that at current energies, it is likely that the formation temperature of the plasma, $T_0 < T_{comp}$, and that this is therefore a quark gluon pion plasma (QGPP) rather than the usual quark gluon plasma (QGP). We propose some dilepton-related signatures of this scenario.Comment: Rewritten, new figure

Search for Global Metric Anisotropy in Type Ia Supernova Data

We examine the Type 1a supernova data in order to determine if it shows any signal of large scale anisotropy. The anisotropy is modelled by an extended G\"{o}del metric, which incorporates expansion along with rotation. The model is smoothly connected to the usual FRW type, while expressing anisotropic metric effects depending on certain parameters. We find no significant signal of anisotropy in the data. We obtain bounds on an anisotropic redshift versus magnitude relationship, and accompanying parameters of the G\"{o}del-Obukhov metric.Comment: 16 pages, 2 figures, minor changes, to be published in Modern Physics Letters

Genetic Deletion of the Clathrin Adaptor GGA3 Reduces Anxiety and Alters GABAergic Transmission

Golgi-localized γ-ear-containing ARF binding protein 3 (GGA3) is a monomeric clathrin adaptor that has been shown to regulate the trafficking of the Beta-site APP-cleaving enzyme (BACE1), which is required for production of the Alzheimer’s disease (AD)-associated amyloid βpeptide. Our previous studies have shown that BACE1 is degraded via the lysosomal pathway and that depletion of GGA3 results in increased BACE1 levels and activity owing to impaired lysosomal trafficking and degradation. We further demonstrated the role of GGA3 in the regulation of BACE1 in vivo by showing that BACE1 levels are increased in the brain of GGA3 null mice. We report here that GGA3 deletion results in novelty-induced hyperactivity and decreased anxiety-like behaviors. Given the pivotal role of GABAergic transmission in the regulation of anxiety-like behaviors, we performed electrophysiological recordings in hippocampal slices and found increased phasic and decreased tonic inhibition in the dentate gyrus granule cells (DGGC). Moreover, we found that the number of inhibitory synapses is increased in the dentate gyrus of GGA3 null mice in further support of the electrophysiological data. Thus, the increased GABAergic transmission is a leading candidate mechanism underlying the reduced anxiety-like behaviors observed in GGA3 null mice. All together these findings suggest that GGA3 plays a key role in GABAergic transmission. Since BACE1 levels are elevated in the brain of GGA3 null mice, it is possible that at least some of these phenotypes are a consequence of increased processing of BACE1 substrates

Quantum Statistical Entropy and Minimal Length of 5D Ricci-flat Black String with Generalized Uncertainty Principle

In this paper, we study the quantum statistical entropy in a 5D Ricci-flat black string solution, which contains a 4D Schwarzschild-de Sitter black hole on the brane, by using the improved thin-layer method with the generalized uncertainty principle. The entropy is the linear sum of the areas of the event horizon and the cosmological horizon without any cut-off and any constraint on the bulk's configuration rather than the usual uncertainty principle. The system's density of state and free energy are convergent in the neighborhood of horizon. The small-mass approximation is determined by the asymptotic behavior of metric function near horizons. Meanwhile, we obtain the minimal length of the position $\Delta x$ which is restrained by the surface gravities and the thickness of layer near horizons.Comment: 11pages and this work is dedicated to the memory of Professor Hongya Li

Artificial Intelligence for Supply Chain Resilience: Learning from Covid-19

Purpose: Many supply chains have faced disruption during Covid-19. Artificial intelligence (AI) is one mechanism that can be used to improve supply chain resilience by developing business continuity capabilities. This study examines how firms employ AI and considers the opportunities for AI to enhance supply chain resilience by developing visibility, risk, sourcing and distribution capabilities. Design/Methodology: We have gathered rich data by conducting semi-structured interviews with 35 experts from the e-commerce supply chain. We have adopted a systematic approach of coding using open, axial, and selective methods to map and identify the themes that represent the critical elements of AI-enabled supply chain resilience. Findings: The results of the study highlight the emergence of five critical areas where AI can contribute to enhanced supply chain resilience; (i) transparency, (ii) ensuring last-mile delivery, (iii) offering personalized solutions to both upstream and downstream supply chain stakeholders, (iv) minimizing the impact of disruption, and (v) facilitating an agile procurement strategy. Originality: The study presents the dynamic capabilities for supply chain resilience through the employment of AI. AI can contribute to readying supply chains to reduce their risk of disruption through enhanced resilience. Implications: The study offers interesting implications for bridging the theory-practice gap by drawing on contemporary empirical data to demonstrate how enhancing dynamic capabilities via AI technologies further strengthens supply chain resilience. The study also offers suggestions for utilizing the findings and proposes a framework to strengthen supply chain resilience through AI