A quantum geometric model of similarity

No other study has had as great an impact on the development of the similarity literature as that of Tversky (1977), which provided compelling demonstrations against all the fundamental assumptions of the popular, and extensively employed, geometric similarity models. Notably, similarity judgments were shown to violate symmetry and the triangle inequality, and also be subject to context effects, so that the same pair of items would be rated differently, depending on the presence of other items. Quantum theory provides a generalized geometric approach to similarity and can address several of Tversky’s (1997) main findings. Similarity is modeled as quantum probability, so that asymmetries emerge as order effects, and the triangle equality violations and the diagnosticity effect can be related to the context-dependent properties of quantum probability. We so demonstrate the promise of the quantum approach for similarity and discuss the implications for representation theory in general

English-Hindi transliteration using context-informed PB-SMT: the DCU system for NEWS 2009

This paper presents English—Hindi transliteration in the NEWS 2009 Machine Transliteration Shared Task adding source context modeling into state-of-the-art log-linear phrase-based statistical machine translation (PB-SMT). Source context features enable us to exploit source similarity in addition to target similarity, as modelled by the language model. We use a memory-based classification framework that enables efficient estimation of these features while avoiding data sparseness problems.We carried out experiments both at character and transliteration unit (TU) level. Position-dependent source context features produce significant improvements in terms of all evaluation metrics

Streaming Similarity Self-Join

We introduce and study the problem of computing the similarity self-join in a streaming context (SSSJ), where the input is an unbounded stream of items arriving continuously. The goal is to find all pairs of items in the stream whose similarity is greater than a given threshold. The simplest formulation of the problem requires unbounded memory, and thus, it is intractable. To make the problem feasible, we introduce the notion of time-dependent similarity: the similarity of two items decreases with the difference in their arrival time. By leveraging the properties of this time-dependent similarity function, we design two algorithmic frameworks to solve the sssj problem. The first one, MiniBatch (MB), uses existing index-based filtering techniques for the static version of the problem, and combines them in a pipeline. The second framework, Streaming (STR), adds time filtering to the existing indexes, and integrates new time-based bounds deeply in the working of the algorithms. We also introduce a new indexing technique (L2), which is based on an existing state-of-the-art indexing technique (L2AP), but is optimized for the streaming case. Extensive experiments show that the STR algorithm, when instantiated with the L2 index, is the most scalable option across a wide array of datasets and parameters

T cell cross-reactivity between coxsackievirus and glutamate decarboxylase is associated with a murine diabetes susceptibility allele.

Limited regions of amino acid sequence similarity frequently occur between microbial antigens and host proteins. It has been widely anticipated that during infection such sequence similarities could induce cross-reactive T cell responses, thereby initiating T cell-mediated autoimmune disease. However, the nature of major histocompatibility complex (MHC)-restricted antigen presentation confers a number of constraints that should make this type of T cell cross-reactivity a rare, MHC allele-dependent event. We tested this prediction using two insulin-dependent diabetes mellitus (IDDM)-associated antigens, coxsackievirus P2-C (Cox P2-C) protein and glutamate decarboxylase (GAD65), which share a prototypic sequence similarity of six consecutive amino acids within otherwise unrelated proteins. We surveyed a panel of 10 murine MHC class II alleles that encompass the spectrum of standard alleles for the ability to cross-reactively present Cox P2-C and GAD65. Out of the 10 restriction elements tested, the sequence similarity regions were both dominant determinants and were cross-reactively displayed after the natural processing of whole antigens, only in the context of I-Anod. These data show that cross-reactive T cell recognition of sequence similarity regions in unrelated proteins is confined to certain MHC alleles, which may explain MHC association with autoimmune disease. It is striking that these two diabetes-associated antigens were cross-reactively recognized only in the context of a diabetes susceptibility allele. Since the human and the murine class II alleles associated with IDDM share conserved features, cross-reactive T cell recognition of GAD65 and Cox P2-C may contribute to the pathogenesis of human IDDM and account for the epidemiological association of coxsackievirus with IDDM

Exploring cultural factors in human-robot interaction: A matter of personality?

This paper proposes an experimental study to investigate task-dependence and cultural-background dependence of the personality trait attribution on humanoid robots. In Human-Robot Interaction, as well as in Human-Agent Interaction research, the attribution of personality traits towards intelligent agents has already been researched intensively in terms of the social similarity or complementary rule. These two rules imply that humans either tend to like others with similar personality traits or complementary personality traits more. Even though state of the art literature suggests that similarity attraction happens for virtual agents, and complementary attraction for robots, there are many contradictions in the findings. We assume that searching the explanation for personality trait attribution in the similarity and complementary rule does not take into account important contextual factors. Just like people equate certain personality types to certain professions, we expect that people may have certain personality expectations depending on the context of the task the robot carries out. Because professions have different social meaning in different national culture, we also expect that these task-dependent personality preferences differ across cultures. Therefore suggest an experiment that considers the task-context and the cultural background of users

CoSimLex : A Resource for Evaluating Graded Word Similarity in Context

State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists. Standard tasks and datasets for intrinsic evaluation of embeddings are based on judgements of similarity, but ignore context; standard tasks for word sense disambiguation take account of context but do not provide continuous measures of meaning similarity. This paper describes an effort to build a new dataset, CoSimLex, intended to fill this gap. Building on the standard pairwise similarity task of SimLex-999, it provides context-dependent similarity measures; covers not only discrete differences in word sense but more subtle, graded changes in meaning; and covers not only a well-resourced language (English) but a number of less-resourced languages. We define the task and evaluation metrics, outline the dataset collection methodology, and describe the status of the dataset so far.Peer reviewe