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"Topic": models, code, and papers

Coupled Clustering: a Method for Detecting Structural Correspondence

Jul 23, 2001
Zvika Marx, Ido Dagan, Joachim Buhmann

This paper proposes a new paradigm and computational framework for identification of correspondences between sub-structures of distinct composite systems. For this, we define and investigate a variant of traditional data clustering, termed coupled clustering, which simultaneously identifies corresponding clusters within two data sets. The presented method is demonstrated and evaluated for detecting topical correspondences in textual corpora.

* In: C. E. Brodley and A. P. Danyluk (eds.), Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 353-360 
* html with 5 figures 

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Automatic Detection of Text Genre

Jul 08, 1997
Brett Kessler, Geoffrey Nunberg, Hinrich Schuetze

As the text databases available to users become larger and more heterogeneous, genre becomes increasingly important for computational linguistics as a complement to topical and structural principles of classification. We propose a theory of genres as bundles of facets, which correlate with various surface cues, and argue that genre detection based on surface cues is as successful as detection based on deeper structural properties.

* Proceedings ACL/EACL 1997, Madrid, p. 32-38 
* 7 pages 

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Chatbot System Architecture

Jan 17, 2022
Moataz Mohammed, Mostafa M. Aref

The conversational agents is one of the most interested topics in computer science field in the recent decade. Which can be composite from more than one subject in this field, which you need to apply Natural Language Processing Concepts and some Artificial Intelligence Techniques such as Deep Learning methods to make decision about how should be the response. This paper is dedicated to discuss the system architecture for the conversational agent and explain each component in details.

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Artificial Intelligence and Machine Learning in Nuclear Physics

Dec 04, 2021
Amber Boehnlein, Markus Diefenthaler, Cristiano Fanelli, Morten Hjorth-Jensen, Tanja Horn, Michelle P. Kuchera, Dean Lee, Witold Nazarewicz, Kostas Orginos, Peter Ostroumov, Long-Gang Pang, Alan Poon, Nobuo Sato, Malachi Schram, Alexander Scheinker, Michael S. Smith, Xin-Nian Wang, Veronique Ziegler

Advances in artificial intelligence/machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by artificial intelligence and machine learning techniques.

* Comments are welcome 

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Comments on Combining Switched TMAs and FDAs to Synthesize Dot-Shaped Beampatterns

Sep 20, 2021
Mahdi Fartookzadeh

The idea of frequency diverse array (FDA) has been invigorated in the recent years by supposing to have the ability of stopping the electromagnetic wave propagation and producing secretive direct connections between distant points. However, it was based on a theoretical mistake in a series of published papers beginning from 2014. In fact, the issue was not very clear since 2018; hence the researchers should be very careful in relying on the papers of this topic.

* 1 page, 1 figure, Accepted for publication in IEEE Antennas and Wireless Propagation Letters 

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Reinforcement Learning for Ridesharing: A Survey

May 03, 2021
Zhiwei Qin, Hongtu Zhu, Jieping Ye

In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to ridesharing problems. Papers on the topics of rideshare matching, vehicle repositioning, ride-pooling, and dynamic pricing are covered. Popular data sets and open simulation environments are also introduced. Subsequently, we discuss a number of challenges and opportunities for reinforcement learning research on this important domain.

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Neural Machine Translation

Sep 22, 2017
Philipp Koehn

Draft of textbook chapter on neural machine translation. a comprehensive treatment of the topic, ranging from introduction to neural networks, computation graphs, description of the currently dominant attentional sequence-to-sequence model, recent refinements, alternative architectures and challenges. Written as chapter for the textbook Statistical Machine Translation. Used in the JHU Fall 2017 class on machine translation.

* 100+ pages 

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From the Fundamental Theorem of Algebra to Kempe's Universality Theorem

Jul 19, 2015
Gábor Hegedüs, Zijia Li, Josef Schicho, Hans-Peter Schröcker

This article provides a gentle introduction for a general mathematical audience to the factorization theory of motion polynomials and its application in mechanism science. This theory connects in a rather unexpected way a seemingly abstract mathematical topic, the non-unique factorization of certain polynomials over the ring of dual quaternions, with engineering applications. Four years after its introduction, it is already clear how beneficial it has been to both fields.

* Internat. Math. Nachrichten, Nr. 229 (2015), 13-26 

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Scalable Inference for Latent Dirichlet Allocation

Sep 25, 2009
James Petterson, Tiberio Caetano

We investigate the problem of learning a topic model - the well-known Latent Dirichlet Allocation - in a distributed manner, using a cluster of C processors and dividing the corpus to be learned equally among them. We propose a simple approximated method that can be tuned, trading speed for accuracy according to the task at hand. Our approach is asynchronous, and therefore suitable for clusters of heterogenous machines.

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Comparative Discourse Analysis of Parallel Texts

Jul 26, 1994
Pim van der Eijk

A quantitative representation of discourse structure can be computed by measuring lexical cohesion relations among adjacent blocks of text. These representations have been proposed to deal with sub-topic text segmentation. In a parallel corpus, similar representations can be derived for versions of a text in various languages. These can be used for parallel segmentation and as an alternative measure of text-translation similarity.

* 11 pages uuencoded compressed Postscript 

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