Discovering information flow using a high dimensional conceptual space

This paper presents an informational inference mechanism realized via the use of a high dimensional conceptual space. More specifically, we claim to have operationalized important aspects of G?rdenforss recent three-level cognitive model. The connectionist level is primed with the Hyperspace Analogue to Language (HAL) algorithm which produces vector representations for use at the conceptual level. We show how inference at the symbolic level can be implemented by employing Barwise and Seligmans theory of information flow. This article also features heuristics for enhancing HAL-based representations via the use of quality properties, determining concept inclusion and computing concept composition. The worth of these heuristics in underpinning informational inference are demonstrated via a series of experiments. These experiments, though small in scale, show that informational inference proposed in this article has a very different character to the semantic associations produced by the Minkowski distance metric and concept similarity computed via the cosine coefficient. In short, informational inference generally uncovers concepts that are carried, or, in some cases, implied by another concept, (or combination of concepts)

Exploration of applying a theory-based user classification model to inform personalised content-based image retrieval system design

© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published at http://dl.acm.org/citation.cfm?id=2903636To better understand users and create more personalised search experiences, a number of user models have been developed, usually based on different theories or empirical data study. After developing the user models, it is important to effectively utilise them in the design, development and evaluation of search systems to improve users’ overall search experiences. However there is a lack of research has been done on the utilisation of the user models especially theory-based models, because of the challenges on the utilization methodologies when applying the model to different search systems. This paper explores and states how to apply an Information Foraging Theory (IFT) based user classification model called ISE to effectively identify user’s search characteristics and create user groups, based on an empirically-driven methodology for content-based image retrieval (CBIR) systems and how the preferences of different user types inform the personalized design of the CBIR systems

Investigating Bell Inequalities for Multidimensional Relevance Judgments in Information Retrieval

Relevance judgment in Information Retrieval is influenced by multiple factors. These include not only the topicality of the documents but also other user oriented factors like trust, user interest, etc. Recent works have identified and classified these various factors into seven dimensions of relevance. In a previous work, these relevance dimensions were quantified and user's cognitive state with respect to a document was represented as a state vector in a Hilbert Space, with each relevance dimension representing a basis. It was observed that relevance dimensions are incompatible in some documents, when making a judgment. Incompatibility being a fundamental feature of Quantum Theory, this motivated us to test the Quantum nature of relevance judgments using Bell type inequalities. However, none of the Bell-type inequalities tested have shown any violation. We discuss our methodology to construct incompatible basis for documents from real world query log data, the experiments to test Bell inequalities on this dataset and possible reasons for the lack of violation

Document generality: its computation for ranking

The increased variety of information makes it critical to retrieve documents which are not only relevant but also broad enough to cover as many different aspects of a certain topic as possible. The increased variety of users also makes it critical to retrieve documents that are jargon free and easy-to-understand rather than the specific technical materials. In this paper, we propose a new concept namely document generality computation. Generality of document is of fundamental importance to information retrieval. Document generality is the state or quality of docu- ment being general. We compute document general- ity based on a domain-ontology method that analyzes scope and semantic cohesion of concepts appeared in the text. For test purposes, our proposed approach is then applied to improving the performance of doc- ument ranking in bio-medical information retrieval. The retrieved documents are re-ranked by a combined score of similarity and the closeness of documents’ generality to that of a query. The experiments have shown that our method can work on a large scale bio-medical text corpus OHSUMED (Hersh, Buckley, Leone & Hickam 1994), which is a subset of MEDLINE collection containing of 348,566 medical journal references and 101 test queries, with an encouraging performance

Constitutive Modeling Of Viscoplastic Porous Single Crystals And Polycrystals: Macroscopic Response And Evolution Of The Microstructure

\noindent Porosity can have a significant effect on the overall constitutive behavior of many materials, especially when it serves to relax kinematic constraints imposed by the underlying matrix behavior. In this study, we investigate the multiscale, finite-strain response of viscoplastic porous single crystals and porous polycrystals. For these materials, the presence of voids leads to highly nonlinear dilatational behavior for loads with a large hydrostatic component, even though the matrix material itself is essentially incompressible. \vspace*{15pt} \noindent In this study, we employ the recently developed ``fully optimized second-order homogenization approach, along with an iterated homogenization procedure, to obtain accurate estimates for the effective behavior of porous single crystals and porous polycrystals with fixed states of the microstructure. The method makes use of the effective properties of a ``linear comparison composite, whose local properties are chosen according to a suitably designed variational principle, to generate the corresponding estimates for the actual nonlinear porous materials. Additionally, consistent homogenization estimates for the average strain-rate and spin fields in the phases are used to develop approximate evolution equations for the microstructures. The model is quite general, and applies for viscoplastic porous single crystals and polycrystals with general crystallographic texture, general ellipsoidal voids, and general ellipsoidal grains, which are subjected to general loading conditions. The model is used to study both the instantaneous response and the evolution of the microstructure for porous FCC and HCP single crystals and polycrystals. It is found that the intrinsic anisotropy of the matrix phase---either due to the local crystallography in single crystals or to the texture of polycrystals---has significant effects on the porosity evolution, as well as on the overall hardening/softening behavior of the porous materials. In particular, the predictions of the model for porous single crystals are found to be in fairly good agreement with the full-field, numerical results available in the literature. The results for porous polycrystals suggest that the macroscopic behavior is controlled by porosity growth at high stress triaxialities, while it is controlled by texture evolution of the underlying matrix at low triaxialities

Old receptors learn new tricks : biasing anti-IGF1R cancer therapy through the GPCR system

As cancers progress, tumor cells exploit the extracellular signals generated from plasma membrane receptors for cell growth, migration, and anti-apoptosis. G protein-coupled receptors (GPCR) and receptor tyrosine kinases (RTKs) are two important families of plasm membrane receptors, controlling multiple biological functions via their downstream signaling. IGF1R, one of the major RTKs involved in developing the malignant phenotype, plays a critical role in the tumorigenesis of multiple cancers. Thus, anti-IGF1R antibodies and inhibitors soon became attractive stars in cancer treatment. Disappointingly, IGF1R lost its “glory” in clinical trials with a promising start but no happy end. However, lessons from those clinical trials led us to explore the underlying mechanisms behind anti-IGF1R cancer therapy. One of the outcomes is that IGF1R interacts with GPCR downstream modulators (G proteins, GRKs, β-arrestins), which are vital in coordinating IGF1R downstream signaling. This thesis aims to refine the concept of IGF1R targeting through GPCR components and translate it into clinical application. In Study 1, we investigated the potential therapeutic mechanisms of the IGF1R/ β-arrestin /p53 axis in conjunctival melanoma (CM). This research revealed the targeted therapeutic strategy of controlling IGF1R and p53 pro/suppressor tumorigenic signals via β-arrestin1/MDM2, thus reducing tumor growth and the risk of metastasis. In Study 2, we studied the molecular mechanism of inhibiting IGF1R through “system bias”. Our work highlights unbiased downregulation of IGF1R via GRK2 inhibition in Ewing’s sarcoma. These findings reveal the molecular and biological roles of biased signaling downstream of IGF1R and its potential therapeutic application in clinical settings. In Study 3, we investigated the therapeutic strategies of targeting IGF1R via “system bias” in colorectal cancer. This work demonstrated that paroxetine (PX) could downregulate both IGF1R and the epidermal growth factor receptor (EGFR), resulting in inhibition of cancer cell viability. When combining PX with MAPK and PI3K inhibitors, the combination treatment showed an additive inhibition effect on tumor growth and metastasis. This study revealed a strategy of controlling signaling pathways residual to system bias inhibition. In Study 4, we studied the involvement of G proteins in the IGF1R system. We revealed that G protein signaling regulates IGF-induced cell growth in both in vivo and in vitro experiments and their inhibition induces receptor internalization via the GRK/β-arrestin system. This study expanded the RTK-GPCR dualism paradigm of the IGF1R and explored the concept of G-protein signaling targeting in cancer. To summarize, our studies highlight the potential of targeting IGF1R via the GRK/β-arrestin system and suggest the possibility of clinical translation of this novel concept into different cancer types. These findings broaden our understanding of the IGF1R system and open a brand-new chapter in anti-IGF1R cancer therapy

What is Cybercrime?: A Criminology Perspective

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