An Italian perspective on the development of financial mathematics from 1992 to 2008

AbstractThis paper is intended to be a survey of the development of financial mathematics as seen through the events that I organised, and partly co-organised, between 1992 and 2008. These events all took place in Italy between 1992 and 2003, while in 2008 I was involved in the organisation of an entire special semester in Linz (Austria); this semester is included here because it marks quite well the state-of-the-art of the period just before the so-called big financial crisis that lasted from, roughly, 2008 to 2012. Even if the survey may be affected by my personal views, it can still be seen as reflecting the actual global development since what I am going to describe here concerns major occurrences. For completeness, I also mention, although only briefly, some events that took place in Italy during the given period, but where I was not personally involved.

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A Review of Charge Pump Topologies for the Power Management of IoT Nodes

Electronics ◽

10.3390/electronics8050480 ◽

2019 ◽

Vol 8 (5) ◽

pp. 480 ◽

Cited By ~ 18

Author(s):

Andrea Ballo ◽

Alfio Dario Grasso ◽

Gaetano Palumbo

Keyword(s):

Internet Of Things ◽

Power Management ◽

Energy Harvester ◽

State Of The Art ◽

Charge Pump ◽

The State ◽

Data Driven ◽

Design Specifications ◽

The Given ◽

Insight Into

With the aim of providing designer guidelines for choosing the most suitable solution, according to the given design specifications, in this paper a review of charge pump (CP) topologies for the power management of Internet of Things (IoT) nodes is presented. Power management of IoT nodes represents a challenging task, especially when the output of the energy harvester is in the order of few hundreds of millivolts. In these applications, the power management section can be profitably implemented, exploiting CPs. Indeed, presently, many different CP topologies have been presented in literature. Finally, a data-driven comparison is also provided, allowing for quantitative insight into the state-of-the-art of integrated CPs.

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Target-Aspect-Sentiment Joint Detection for Aspect-Based Sentiment Analysis

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6447 ◽

2020 ◽

Vol 34 (05) ◽

pp. 9122-9129

Author(s):

Hai Wan ◽

Yufei Yang ◽

Jianfeng Du ◽

Yanan Liu ◽

Kunxun Qi ◽

...

Keyword(s):

Sentiment Analysis ◽

High Performance ◽

State Of The Art ◽

Language Model ◽

The State ◽

Experimental Results ◽

Joint Detection ◽

Sentiment Detection ◽

Novel Method ◽

The Given

Aspect-based sentiment analysis (ABSA) aims to detect the targets (which are composed by continuous words), aspects and sentiment polarities in text. Published datasets from SemEval-2015 and SemEval-2016 reveal that a sentiment polarity depends on both the target and the aspect. However, most of the existing methods consider predicting sentiment polarities from either targets or aspects but not from both, thus they easily make wrong predictions on sentiment polarities. In particular, where the target is implicit, i.e., it does not appear in the given text, the methods predicting sentiment polarities from targets do not work. To tackle these limitations in ABSA, this paper proposes a novel method for target-aspect-sentiment joint detection. It relies on a pre-trained language model and can capture the dependence on both targets and aspects for sentiment prediction. Experimental results on the SemEval-2015 and SemEval-2016 restaurant datasets show that the proposed method achieves a high performance in detecting target-aspect-sentiment triples even for the implicit target cases; moreover, it even outperforms the state-of-the-art methods for those subtasks of target-aspect-sentiment detection that they are competent to.

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Knowledge-Grounded Chatbot Based on Dual Wasserstein Generative Adversarial Networks with Effective Attention Mechanisms

Applied Sciences ◽

10.3390/app10093335 ◽

2020 ◽

Vol 10 (9) ◽

pp. 3335 ◽

Cited By ~ 1

Author(s):

Sihyung Kim ◽

Oh-Woog Kwon ◽

Harksoo Kim

Keyword(s):

State Of The Art ◽

The State ◽

Generative Adversarial Networks ◽

External Knowledge ◽

Adversarial Networks ◽

Proposed Model ◽

Context Knowledge ◽

The Given ◽

Internal Knowledge

A conversation is based on internal knowledge that the participants already know or external knowledge that they have gained during the conversation. A chatbot that communicates with humans by using its internal and external knowledge is called a knowledge-grounded chatbot. Although previous studies on knowledge-grounded chatbots have achieved reasonable performance, they may still generate unsuitable responses that are not associated with the given knowledge. To address this problem, we propose a knowledge-grounded chatbot model that effectively reflects the dialogue context and given knowledge by using well-designed attention mechanisms. The proposed model uses three kinds of attention: Query-context attention, query-knowledge attention, and context-knowledge attention. In our experiments with the Wizard-of-Wikipedia dataset, the proposed model showed better performances than the state-of-the-art model in a variety of measures.

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Lexical Simplification with Pretrained Encoders

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6389 ◽

2020 ◽

Vol 34 (05) ◽

pp. 8649-8656

Author(s):

Jipeng Qiang ◽

Yun Li ◽

Yi Zhu ◽

Yunhao Yuan ◽

Xindong Wu

Keyword(s):

State Of The Art ◽

The State ◽

Experimental Results ◽

Parallel Corpus ◽

Complex Word ◽

Original Sentence ◽

Complex Words ◽

The Given ◽

Linguistic Databases

Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. We present a simple LS approach that makes use of the Bidirectional Encoder Representations from Transformers (BERT) which can consider both the given sentence and the complex word during generating candidate substitutions for the complex word. Specifically, we mask the complex word of the original sentence for feeding into the BERT to predict the masked token. The predicted results will be used as candidate substitutions. Despite being entirely unsupervised, experimental results show that our approach obtains obvious improvement compared with these baselines leveraging linguistic databases and parallel corpus, outperforming the state-of-the-art by more than 12 Accuracy points on three well-known benchmarks.

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Towards Topic-to-Question Generation

Computational Linguistics ◽

10.1162/coli_a_00206 ◽

2015 ◽

Vol 41 (1) ◽

pp. 1-20 ◽

Cited By ~ 16

Author(s):

Yllias Chali ◽

Sadid A. Hasan

Keyword(s):

Latent Dirichlet Allocation ◽

State Of The Art ◽

Automatic Generation ◽

The State ◽

The Body ◽

Question Generation ◽

Named Entity ◽

Series Of Experiments ◽

The Given ◽

Dirichlet Allocation

This paper is concerned with automatic generation of all possible questions from a topic of interest. Specifically, we consider that each topic is associated with a body of texts containing useful information about the topic. Then, questions are generated by exploiting the named entity information and the predicate argument structures of the sentences present in the body of texts. The importance of the generated questions is measured using Latent Dirichlet Allocation by identifying the subtopics (which are closely related to the original topic) in the given body of texts and applying the Extended String Subsequence Kernel to calculate their similarity with the questions. We also propose the use of syntactic tree kernels for the automatic judgment of the syntactic correctness of the questions. The questions are ranked by considering both their importance (in the context of the given body of texts) and syntactic correctness. To the best of our knowledge, no previous study has accomplished this task in our setting. A series of experiments demonstrate that the proposed topic-to-question generation approach can significantly outperform the state-of-the-art results.

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Practical Picture Processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051700 ◽

1974 ◽

Vol 32 ◽

pp. 338-339

Author(s):

T. A. Welton

Keyword(s):

Radiation Damage ◽

Coherence Length ◽

Spatial Information ◽

State Of The Art ◽

Coherent Radiation ◽

The State ◽

Energy Spread ◽

Electron Micrograph ◽

Picture Processing ◽

Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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