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In this paper we explore ChatGPT’s ability to produce a summary, a precis, and/or an essay on the basis of excerpts from a novel—The Solid Mandala—by Nobel Prize Australian writer Patrick White. We use a number of prompts to test a number of functions related to narrative analysis from the point of view of the “sujet”, the “fable”, and the style. In the paper, we illustrate extensively a number of recurrent semantic mistakes that can badly harm the understanding of the contents of the novel. We made a list of 12 different types of semantic mistakes or parrotting we found GPT made, which can be regarded as typical for stochastic-based generation. We then tested Gemini for the same 12 mistakes and found a marked improvement in all critical key issues. The conclusion for ChatGPT is mostly negative. We formulate an underlying hypothesis for its worse performance, the influence of vocabulary size, which in Gemini is seven times higher than in GPT.

The temporal lobe has been implicated in multiple cognitive domains through lesion studies as well as cognitive neuroimaging research. There has been a recent increased interest in the structural and connective architecture that underlies these functions. However there has not yet been a comprehensive exploration of the patterns of connectivity that appear across the temporal lobe. This article uses a data driven, spectral reordering approach in order to understand the general axes of structural connectivity within the temporal lobe. Two important findings emerge from the study. Firstly, the temporal lobe's overarching patterns of connectivity are organised along two key structural axes: medial to lateral and anteroventral to posterodorsal, mirroring findings in the functional literature. Secondly, the connective organisation of the temporal lobe is graded and transitional; this is reminiscent of the original work of 19th Century neuroanatomists, who posited the existence of some regions which transitioned between one another in a graded fashion. While regions with unique connectivity exist, the boundaries between these are not always sharp. Instead there are zones of graded connectivity reflecting the influence and overlap of shared connectivity. •A graded parcellation identified changes in connectivity across the temporal lobe•Connective organisation of the temporal lobe was graded and transitional•Two axes of organisation were found: medial-lateral and anterovental-posterodorsal•While regions of distinct connectivity exist, their boundaries are not always sharp•Zones of graded connectivity exist reflecting influence of shared connectivity

The popular model checker PRISM has been successfully used for the modeling and analysis of complex probabilistic systems. As one way to tackle the challenging state explosion problem, PRISM supports symbolic storage and manipulation using multi-terminal binary decision diagrams for representing the models and in the computations. However, it lacks automated heuristics for variable reordering, even though it is well known that the order of BDD variables plays a crucial role for compact representations and efficient computations. In this article, we present a collection of extensions to PRISM. First, we provide support for automatic variable reordering within the symbolic engines of PRISM and allow users to manually control the variable ordering at a fine-grained level. Second, we provide extensions in the realm of reward-bounded properties, namely symbolic computations of quantiles in Markov decision processes and, for both the explicit and symbolic engines, the approximative computation of quantiles for continuous-time Markov chains as well as support for multi-reward-bounded properties. Finally, we provide an implementation for obtaining minimal weak deterministic Büchi automata for the obligation fragment of linear temporal logic (LTL), with applications for expected accumulated reward computations with a finite horizon given by a co-safe LTL formula.

Sparse matrix-vector multiplication (SpMxV) is a kernel operation widely used in iterative linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers. Matrices with irregular sparsity patterns make it difficult to utilize cache locality effectively in SpMxV computations. In this work, we investigate single- and multiple-SpMxV frameworks for exploiting cache locality in SpMxV computations. For the single-SpMxV framework, we propose two cache-size--aware row/column reordering methods based on one-dimensional (1D) and two-dimensional (2D) top-down sparse matrix partitioning. We utilize the column-net hypergraph model for the 1D method and enhance the row-column-net hypergraph model for the 2D method. The primary aim in both of the proposed methods is to maximize the exploitation of temporal locality in accessing input vector entries. The multiple-SpMxV framework depends on splitting a given matrix into a sum of multiple nonzero-disjoint matrices. We propose a cache-size--aware splitting method based on 2D top-down sparse matrix partitioning by utilizing the row-column-net hypergraph model. The aim in this proposed method is to maximize the exploitation of temporal locality in accessing both input- and output-vector entries. We evaluate the validity of our models and methods on a wide range of sparse matrices using both cache-miss simulations and actual runs by using OSKI. Experimental results show that proposed methods and models outperform state-of-the-art schemes. [PUBLICATION ABSTRACT]

Information Visualisation strategies can be applied in a variety of domains. In the context of temporal networks, i.e., networks in which interactions between individuals occur throughout time, efforts have been conducted to develop visual approaches that allow finding interaction patterns, anomalies, and other behaviours not previously perceived in the data. This paper presents two case studies involving real-world education networks from a primary school and a high school. For this purpose, we used the Massive Sequence View (MSV) layout with the Community-based Node Ordering (CNO) method, two well established approaches for visual analysis of temporal networks. Our results show that the identified patterns involving students/students and students/teachers represent important information to benefit and support decision making about school management and teaching strategies, especially those related to strategic group formation.

In this study, novel pipelined architectures, optimised in terms of throughput and throughput/area factors, for the SHA-512 cryptographic hash function, are proposed. To achieve this, algorithmic- and circuit-level optimisation techniques such as loop unrolling, re-timing, temporal pre-computation, resource re-ordering and pipeline are applied. All the techniques, except pipeline are applied in the function's transformation round. The pipeline was applied through the development of all the alternative pipelined architectures and implementation in several Xilinx FPGA families and they are evaluated in terms of frequency, area, throughput and throughput/area factors. Compared to the initial un-optimised implementation of SHA-512 function, the introduced five-stage pipelined architecture improves the both the throughput and throughput/area factors by 123 and 61.5%, respectively. Furthermore, the proposed five-stage pipelined architecture outperforms the existing ones both in throughput (3.4× up to 16.9×) and throughput/area (19.5% up to 6.9×) factors.

This chapter contains sections titled: The MacGuffin Plot: A Modernist Search for Meaning Violent Disruption of Narrative Point of View: The Perspective from the Middle The Other Modernism: Does the Story of Death End Narrative or Begin Storytelling? Narrative Time as Passage: Liberating Ironies of Conflicting Desires Works Cited