Odontoameloblastoma with extensive chondroid matrix deposition in a guinea pig

Odontoameloblastomas (previously incorporated within ameloblastic odontomas) are matrix-producing odontogenic mixed tumors and are closely related in histologic appearance to the 2 other types of matrix-producing odontogenic mixed tumors: odontomas and ameloblastic fibro-odontomas. The presence or absence of intralesional, induced non-neoplastic tissue must be accounted for in the diagnosis. Herein we describe a naturally occurring odontoameloblastoma with extensive chondroid cementum deposition in a guinea pig (Cavia porcellus). Microscopically, the mass featured palisading neoplastic odontogenic epithelium closely apposed to ribbons and rings of a pink dental matrix (dentinoid), alongside extensive sheets and aggregates of chondroid cementum. The final diagnosis was an odontoameloblastoma given the abundance of odontogenic epithelium in association with dentinoid but a paucity of pulp ectomesenchyme. Chondroid cementum is an expected anatomical feature of cavies, and its presence within the odontoameloblastoma was interpreted as a response of the ectomesenchyme of the dental follicle to the described neoplasm. Our case illustrates the inductive capabilities of odontoameloblastomas while highlighting species-specific anatomy that has resulted in a histologic appearance unique to cavies and provides imaging and histologic data to aid diagnosis of these challenging lesions

Providing a foundation for interpretable autonomous agents through elicitation and modeling of criminal investigation pathways

Criminal investigations are guided by repetitive and time-consuming information retrieval tasks, often with high risk and high consequence. If Artificial intelligence (AI) systems can automate lines of inquiry, it could reduce the burden on analysts and allow them to focus their efforts on analysis. However, there is a critical need for algorithmic transparency to address ethical concerns. In this paper, we use data gathered from Cognitive Task Analysis (CTA) interviews of criminal intelligence analysts and perform a novel analysis method to elicit question networks. We show how these networks form an event tree, where events are consolidated by capturing analyst intentions. The event tree is simplified with a Dynamic Chain Event Graph (DCEG) that provides a foundation for transparent autonomous investigations

Model reduction and simulation of nonlinear circuits via tensor decomposition

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Pan: conversational agent for criminal investigations

We present an early prototype conversational agent (CA), called Pan, for retrieving information to support criminal investigations. Our approach tackles the issue of algorithmic transparency, which is critical in unpredictable, high risk, and high consequence domains. We present a novel method to flexibly model CA intentions and provide transparency of attributes that is underpinned with human recognition. We propose that Pan can be used for experimentation to probe analyst requirements and to evaluate the effectiveness of our explanation structure

How analysts think: a preliminary study of human needs and demands for AI-based conversational agents

For conversational agents to provide benefit to intelligence analysis they need to be able to recognise and respond to the analysts intentions. Furthermore, they must provide transparency to their algorithms and be able to adapt to new situations and lines of inquiry. We present a preliminary analysis as a first step towards developing conversational agents for intelligence analysis: that of understanding and modeling analyst intentions so they can be recognised by conversational agents. We describe in-depth interviews conducted with experienced intelligence analysts and implications for designing conversational agent intentions using Formal Concept Analysis

Developing conversational agents for use in criminal investigations

The adoption of artificial intelligence (AI) systems in environments that involve high risk and high consequence decision making is severely hampered by critical design issues. These issues include system transparency and brittleness, where transparency relates to (i) the explainability of results and (ii) the ability of a user to inspect and verify system goals and constraints, and brittleness (iii) the ability of a system to adapt to new user demands. Transparency is a particular concern for criminal intelligence analysis, where there are significant ethical and trust issues that arise when algorithmic and system processes are not adequately understood by a user. This prevents adoption of potentially useful technologies in policing environments. In this paper, we present a novel approach to designing a conversational agent (CA) AI system for intelligence analysis that tackles these issues.We discuss the results and implications of three different studies; a Cognitive Task Analysis to understand analyst thinking when retrieving information in an investigation, Emergent Themes Analysis to understand the explanation needs of different system components, and an interactive experiment with a prototype conversational agent. Our prototype conversational agent, named Pan, demonstrates transparency provision and mitigates brittleness by evolving new CA intentions. We encode interactions with the CA with human factors principles for situation recognition and use interactive visual analytics to support analyst reasoning. Our approach enables complex AI systems, such as Pan, to be used in sensitive environments and our research has broader application than the use case discussed

Developing conversational agents for use in criminal investigations

The adoption of artificial intelligence (AI) systems in environments that involve high risk and high consequence decision-making is severely hampered by critical design issues. These issues include system transparency and brittleness, where transparency relates to (i) the explainability of results and (ii) the ability of a user to inspect and verify system goals and constraints; and brittleness, (iii) the ability of a system to adapt to new user demands. Transparency is a particular concern for criminal intelligence analysis, where there are significant ethical and trust issues that arise when algorithmic and system processes are not adequately understood by a user. This prevents adoption of potentially useful technologies in policing environments. In this article, we present a novel approach to designing a conversational agent (CA) AI system for intelligence analysis that tackles these issues. We discuss the results and implications of three different studies; a Cognitive Task Analysis to understand analyst thinking when retrieving information in an investigation, Emergent Themes Analysis to understand the explanation needs of different system components, and an interactive experiment with a prototype conversational agent. Our prototype conversational agent, named Pan, demonstrates transparency provision and mitigates brittleness by evolving new CA intentions. We encode interactions with the CA with human factors principles for situation recognition and use interactive visual analytics to support analyst reasoning. Our approach enables complex AI systems, such as Pan, to be used in sensitive environments, and our research has broader application than the use case discussed

Finite Density Algorithm in Lattice QCD -- a Canonical Ensemble Approach

I will review the finite density algorithm for lattice QCD based on finite chemical potential and summarize the associated difficulties. I will propose a canonical ensemble approach which projects out the finite baryon number sector from the fermion determinant. For this algorithm to work, it requires an efficient method for calculating the fermion determinant and a Monte Carlo algorithm which accommodates unbiased estimate of the probability. I shall report on the progress made along this direction with the Pad\'{e} - Z$_2$ estimator of the determinant and its implementation in the newly developed Noisy Monte Carlo algorithm.Comment: Invited talk at Nankai Symposium on Mathematical Physics, Tianjin, Oct. 2001, 18 pages, 3 figures; expanded and references adde

Algorithmic transparency of conversational agents

A lack of algorithmic transparency is a major barrier to the adoption of artificial intelligence technologies within contexts which require high risk and high consequence decision making. In this paper we present a framework for providing transparency of algorithmic processes. We include important considerations not identified in research to date for the high risk and high consequence context of defence intelligence analysis. To demonstrate the core concepts of our framework we explore an example application (a conversational agent for knowledge exploration) which demonstrates shared human-machine reasoning in a critical decision making scenario. We include new findings from interviews with a small number of analysts and recommendations for future research

A moment-matching scheme for the passivity-preserving model order reduction of indefinite descriptor systems with possible polynomial parts

Passivity-preserving model order reduction (MOR) of descriptor systems (DSs) is highly desired in the simulation of VLSI interconnects and on-chip passives. One popular method is PRIMA, a Krylov-subspace projection approach which preserves the passivity of positive semidefinite (PSD) structured DSs. However, system passivity is not guaranteed by PRIMA when the system is indefinite. Furthermore, the possible polynomial parts of singular systems are normally not captured. For indefinite DSs, positive-real balanced truncation (PRBT) can generate passive reduced-order models (ROMs), whose main bottleneck lies in solving the dual expensive generalized algebraic Riccati equations (GAREs). This paper presents a novel moment-matching MORfor indefinite DSs, which preserves both the system passivity and, if present, also the improper polynomial part. This method only requires solving one GARE, therefore it is cheaper than existing PRBT schemes. On the other hand, the proposed algorithm is capable of preserving the passivity of indefinite DSs, which is not guaranteed by traditional moment-matching MORs. Examples are finally presented showing that our method is superior to PRIMA in terms of accuracy. ©2011 IEEE.published_or_final_versionThe 16th Asia and South Pacific Design Automation Conference (ASP-DAC 2011), Yokohama, Japan, 25-28 January 2011. In Proceedings of the 16th ASP-DAC, 2011, p. 49-54, paper 1C-