1,721,082 research outputs found
STEREOSELECTIVITY AND REVERSIBILITY OF ELECTROPHILIC BROMINE ADDITION TO STILBENES IN CHLOROFORM - INFLUENCE OF THE BROMIDE TRIBROMIDE PENTABROMIDE EQUILIBRIUM IN THE COUNTERANION OF THE IONIC INTERMEDIATES
No full textTwo equilibria were found in chloroform solutions of Bu4N+Br- and Br2, leading to tribromide and pentabromide salts. The electronic spectra and formation constants of both (K3 = 2.77 (0.13) x 10(4) M-1 and K5 = 3.51 (0.35) X 10(5) M-2 at 25-degrees-C) were computed from spectrophotometric data. The stability of the Br3- species in chloroform resulted to be at least 3 orders of magnitude lower than in 1,2-dichloroethane. A change from prevalent formation of d,l-1,2-dibromo-1,2-diphenylethane to prevalent formation of meso dibromide, accompanied by a cis-trans isomerization of the unreacted olefin, has been observed in the bromination of cis-stilbene with decreasing reagent concentrations, when the bromide-tribromide-pentabromide equilibrium in the counteranions of the ion pairs intermediates is shifted in favor of the Br- form. The results show that these intermediates are reversibly formed even when the anion is Br-
QueryAGT: Asynchronous global types in co-logic programming[Formula presented]
No full textGlobal types are at the core of communication-based programming. They allow a high-level specification of protocols involving many participants and enforce good safety and liveness properties, such as deadlock freedom, and the absence of locked participants and orphan messages. The present software provides an implementation in co-logic programming of a novel formalism of global types for sessions with asynchronous communications, where we use coinduction to properly handle the coinductive syntax of global types and processes. It also offers a simple query language to write sessions and global types, providing primitives for type checking. (c) 2022 Elsevier B.V. All rights reserved
Asynchronous Global Types in Co-logic Programming
No full textGlobal types are at the core of communication based programming. They allow a high level specification of protocols involving many participants and enforce good safety and liveness properties, such as absence of deadlock, locked participants and orphan messages. In this paper, we describe an implementation of a novel formalism of global types for sessions with asynchronous communications in co-logic programming, where we use coinduction to properly handle the coinductive syntax of global types and processes. We also define a simple query language to write sessions and global types, providing primitives for type checking
Distributed artificial intelligence for edge computing
Full text linkIn an increasingly wide range of work and personal life settings, sensors and micro-devices embedded into objects generate continuous data streams with high volume, velocity and heterogeneity. Such data can be analyzed to detect and infer knowledge about phenomenons and events of interest. By learning from data, Artificial Intelligence (AI) methods enable automating an ever larger amount of activities and decision-making tasks with comparable or better proficiency than human experts. Big Data applications based on pervasive Internet of Things (IoT) deployments are now a well-established reality: they feed large Machine Learning (ML) models, which are trained exploiting the huge computational resources of cloud computing infrastructures to offer increasingly accurate prediction capabilities on fresh data. However, the increasing miniaturization of IoT devices equipped with highly accurate sensors enables novel Cyber-Physical Systems (CPSs) with tight feedback loops coupling computation, communication and control tasks. CPS applications are expanding in sensitive fields like high-precision manufacturing, telemedicine, and self-driving vehicles. Those scenarios require real-time response, high computational and bandwidth efficiency, cost-effectiveness to support business scalability and strict data privacy constraints. For this reason, classical cloud-based approaches are progressively integrated with the Edge Computing (EC) architectural model, which distributes significant processing and storage resources at the edge of the local network, in closer proximity to field devices and sensors. This paradigm allows AI-based IoT applications to scale even more, as models are trained with massive amounts of data generated by large deployments of micro- and nano-devices, and ML inference achieves ever greater accuracy. In this context, the Edge Intelligence paradigm --which promotes the integration of EC and AI-- is increasingly adopted to execute inference on data at the border of local networks, employing models trained in the cloud. The next logical step in Cloud-Edge AI cooperation is to enable training tasks on edge nodes as well. However, as of now flexible approaches to combine Edge Intelligence with cloud infrastructures, allowing dynamic migration of training and inference tasks, are not available yet. This thesis proposes a Cloud-Edge AI microservice architecture, based on Osmotic Computing principles. A full pipeline consisting in data collection from local devices, preprocessing, AI model training and inferencing is supported either on the edge, on the cloud or both, exploiting computational resources opportunistically based on device status, available bandwidth, and application requirements on latency, prediction accuracy, and privacy. To demonstrate the feasibility of the proposed framework, a prototype has been realized with commodity hardware leveraging open-source software technologies. It has been used in a small-scale intelligent manufacturing case study, carrying out experiments on elapsed time and network activity in (i) data gathering, (ii) AI model training and validation, and (iii) prediction tasks. Obtained results validate the key benefits of the approach.
In addition to architectural aspects, open issues still limiting the full applicability of EC include the heterogeneity of devices, services, and information that arise in pervasive contexts, the integrity of the gathered data, and the trustworthiness and dependability of autonomous decisions. The Semantic Web of Things (SWoT), coalescing the Semantic Web and IoT paradigms, has been proposed to overcome these problems. In SWoT environments, the dynamic exchange of knowledge fragments expressed in logic-based formalisms in volatile wireless networks of independent agents enables decentralized collaborative service discovery, autonomous decision and user decision support. A relevant problem in such scenarios consists in evaluating agreements and disagreements about knowledge produced by different interacting agents, in order to possibly reconcile conflicts and determine the best overall outcome to accomplish distributed coordination. In AI, Argumentation is recognized as a powerful formalism to negotiate and solve disagreements within a group of agents, which convey knowledge represented as a constellation of arguments and counterarguments. The argumentation literature provides a wealth of frameworks for agent decision-making and coordination. Nevertheless, few proposals leverage Semantic Web languages and technologies, which can provide a well-known formal model for arguments, well-studied inference algorithms for the assessment of argument relations and approaches to evaluate argument acceptability. This thesis presents a novel Bipolar Weighted Argumentation Framework, where arguments are modeled as Description Logics concept expressions in Web Ontology Language (OWL) 2, and their relations are assessed via semantic matchmaking, leveraging non-standard inference services with logic-based outcome explanation. Argument acceptability is computed via a novel propagation-based ranking semantics, which supports argument cycles and information fading. In order to make the proposal suitable for pervasive semantic agents in resource-constrained devices, optimizations in argument assessment and ranking evaluation are adopted, while a graph simplification approach via pruning is proposed and experimentally tested as a tunable trade-off between computational resource usage and accuracy of results. Validation of the approach has been carried out by means of a prototypical implementation of a player agent for the StarCraft II real-time strategy game, whose environment allows simulating the complexities of real CPS scenarios
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Drop edges and adapt. A fairness enforcing fine-tuning for graph neural networks
Full text linkThe rise of graph representation learning as the primary solution for many different network science tasks led to a surge of interest in the fairness of this family of methods. Link prediction, in particular, has a substantial social impact. However, link prediction algorithms tend to increase the segregation in social networks by disfavouring the links between individuals in specific demographic groups. This paper proposes a novel way to enforce fairness on graph neural networks with a fine-tuning strategy. We Drop the unfair Edges and, simultaneously, we Adapt the model's parameters to those modifications, DEA in short. We introduce two covariance-based constraints designed explicitly for the link prediction task. We use these constraints to guide the optimization process responsible for learning the new 'fair' adjacency matrix. One novelty of DEA is that we can use a discrete yet learnable adjacency matrix in our fine-tuning. We demonstrate the effectiveness of our approach on five real-world datasets and show that we can improve both the accuracy and the fairness of the link prediction tasks. In addition, we present an in-depth ablation study demonstrating that our training algorithm for the adjacency matrix can be used to improve link prediction performances during training. Finally, we compute the relevance of each component of our framework to show that the combination of both the constraints and the training of the adjacency matrix leads to optimal performances
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