Storage of Natural Language Sentences in a Hopfield Network

This paper look at how the Hopfield neural network can be used to store and recall patterns constructed from natural language sentences. As a pattern recognition and storage tool, the Hopfield neural network has received much attention. This attention however has been mainly in the field of statistical physics due to the model's simple abstraction of spin glass systems. A discussion is made of the differences, shown as bias and correlation, between natural language sentence patterns and the randomly generated ones used in previous experiments. Results are given for numerical simulations which show the auto-associative competence of the network when trained with natural language patterns.Comment: latex, 10 pages with 2 tex figures and a .bib file, uses nemlap.sty, to appear in Proceedings of NeMLaP-

A Survey of Cellular Automata: Types, Dynamics, Non-uniformity and Applications

Cellular automata (CAs) are dynamical systems which exhibit complex global behavior from simple local interaction and computation. Since the inception of cellular automaton (CA) by von Neumann in 1950s, it has attracted the attention of several researchers over various backgrounds and fields for modelling different physical, natural as well as real-life phenomena. Classically, CAs are uniform. However, non-uniformity has also been introduced in update pattern, lattice structure, neighborhood dependency and local rule. In this survey, we tour to the various types of CAs introduced till date, the different characterization tools, the global behaviors of CAs, like universality, reversibility, dynamics etc. Special attention is given to non-uniformity in CAs and especially to non-uniform elementary CAs, which have been very useful in solving several real-life problems.Comment: 43 pages; Under review in Natural Computin

Delay line based passive radio frequency identification tags

This work describes the concept, design, fabrication, and characterization of delay-based radio frequency identification (RFID) tags and RFID-based sensor tags, representing a novel RFID technology. The presented delay-based RFID concept is based on the LC-delay-line and transmission-delay-line based approaches. The proposed concept allows the realization of RFIDs and RFID-based sensor tags at any allowed radio frequency, with the limitation of realizing delay elements capable of producing required delays. The RFID configurations presented in this work are for operation at 915 MHz. Simulations are used to design and optimize components and devices that constitute the tags, and to integrate them to realize tags of different configuration. A set of fabrication processes has been developed for the realization of the tag. Characterization and field testing of these tags show that delay-based RFID approach can be used to make passive tags at ultra high frequency (UHF) and other allowed frequencies. Delay-based tags have the advantages of time domain operation, and the feasibility of complying with FCC regulations. However, size, need of isolators and circulator, and design constraints in producing higher number of bits are some of the concerns that need to be further addressed. In summary, this dissertation work presents a viable alternative RFID approach based on the delay line concept. The results obtained show great promise for further development and optimization of this approach for a wide range of commercial applications

Interpretable Categorization of Heterogeneous Time Series Data

Understanding heterogeneous multivariate time series data is important in many applications ranging from smart homes to aviation. Learning models of heterogeneous multivariate time series that are also human-interpretable is challenging and not adequately addressed by the existing literature. We propose grammar-based decision trees (GBDTs) and an algorithm for learning them. GBDTs extend decision trees with a grammar framework. Logical expressions derived from a context-free grammar are used for branching in place of simple thresholds on attributes. The added expressivity enables support for a wide range of data types while retaining the interpretability of decision trees. In particular, when a grammar based on temporal logic is used, we show that GBDTs can be used for the interpretable classi cation of high-dimensional and heterogeneous time series data. Furthermore, we show how GBDTs can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply GBDTs to analyze the classic Australian Sign Language dataset as well as data on near mid-air collisions (NMACs). The NMAC data comes from aircraft simulations used in the development of the next-generation Airborne Collision Avoidance System (ACAS X).Comment: 9 pages, 5 figures, 2 tables, SIAM International Conference on Data Mining (SDM) 201

How Do Gestures Influence Thinking and Speaking? The Gesture-for-Conceptualization Hypothesis.

Peer reviewedPostprin

The deterrent effect of voting against minarets: identity utility and foreigners' location choice

This paper uses the vote on the Swiss minaret initiative as a natural experiment to identify the causal effect of negative attitudes towards immigrants on foreigners' location choices and thus indirectly on their utility. Based on a regression discontinuity design with unknown discontinuity points and administrative data on the population of foreigners, we find that the probability of their moving to a municipality that unexpectedly expressed strong reservations decreases initially by about 60 percent. The effect levels off over a period of about 5 months. Consistent with a reduction in the identity utility for immigrants in general, the reaction is not confined to Muslims, whereby high-skilled foreigners seem to be most sensitive to the newly revealed reservations