3D Radiation hydrodynamics of a dynamical torus

We have developed a new dynamical model of the torus region in active galactic nucleus (AGN), using a three-dimensional radiation hydrodynamics algorithm. These new simulations have the specific aim to explore the role of radiatively-driven outflows, which is hotly debated in current literature as a possible explanation for the observed infrared emission from the polar regions of AGN. In this first paper, we only consider radiative effects induced by the primary radiation from the AGN. The simulations generate a disk & outflow structure that qualitatively agrees with observations, although the outflow is radial rather than polar, likely due to the lack of radiation pressure from hot dust. We find cut-offs between the wind and disk at gas temperatures of 1000 K and dust temperatures of 100 K, producing kinematic signatures that can be used for interpretation of high resolution infrared observations. We also produce line emission maps to aid in the interpretation of recent ALMA observations and future JWST observations. We investigate a number of simulation parameters, and find that the anisotropy of the radiation field is equally important to the Eddington factor, despite the anisotropy often being assumed to have a single sometimes arbitrary form in many previous works. We also find that supernovae can have a small but significant impact, but only at extremely high star formation rates.Comment: 2nd revision, Accepted in Ap

Trust-Based Fusion of Untrustworthy Information in Crowdsourcing Applications

In this paper, we address the problem of fusing untrustworthy reports provided from a crowd of observers, while simultaneously learning the trustworthiness of individuals. To achieve this, we construct a likelihood model of the userss trustworthiness by scaling the uncertainty of its multiple estimates with trustworthiness parameters. We incorporate our trust model into a fusion method that merges estimates based on the trust parameters and we provide an inference algorithm that jointly computes the fused output and the individual trustworthiness of the users based on the maximum likelihood framework. We apply our algorithm to cell tower localisation using real-world data from the OpenSignal project and we show that it outperforms the state-of-the-art methods in both accuracy, by up to 21%, and consistency, by up to 50% of its predictions. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved

«All Aram» and «Upper and Lower Aram»: what the Sefire Inscription suggests us about the Aramaean ethnicity

The Aramaeans are always presented as an “undifferentiated group present from the Lower Khabur to the Mount Lebanon” (Sader 1992), without any ethnic affiliation. The construction of their identity may be given by two opposite viewpoints: their own perspective (internal view) and that perceived by other populations (external view). We will show this through the notion of “all Aram” in the Sefire inscription, and by looking at some passages from Assyrian records and the Bible. The first document is the longest Aramaic inscription (about 200 lines) found 25 km from Aleppo in 1930 and dated to the 8th century. It is a treaty stipulated between the unknown king of KTK, Bargaʼ yah and the king of Arpad Matiʻel. The other inscriptions concern, in particular, the records of Shalmaneser III and Tiglath-pileser III who occupied the Aramaean territories in the 9th-8th centuries, and some letters from Nippur

Dendritic-Cell (DC)-Based Immunotherapy: Tumor Endothelial Marker 8 (TEM8) Gene Expression of DC Vaccines Correlates with Clinical Outcome

ABSTRACT\ud Previous studies have shown that tumor-endothelial markers (TEMs) are upregulated in immunosuppressive, pro-angiogenic dendritic cells (DCs) found in tumor microenvironments. \ud We reported that pro-angiogenic monocyte-derived DCs (Mo-DCs), utilized for therapeutic vaccination of cancer patients upon maturation, markedly differ in their ability to up-regulate tumor-endothelial marker 8 (TEM8) gene\ud expression. A DC vaccination trial of 17 advanced cancer patients (13 melanoma and 4 renal cell carcinoma), carried out at the Cancer Institute of Romagna (I.R.S.T.) in Meldola, highlighted a significant correlation between delayed-type hypersensitivity test (DTH) and overall survival (OS). In the study, relative TEM8 mRNA and protein expression levels (mature (m) vs. immature (i) DCs), in DCs obtained for therapeutic vaccines were evaluated by quantitative real-time RT-PCR and cytofluorimetric analysis, respectively. mDCs from six healthy donors were included for comparison purposes. Eight non-progressing patients, all DTH-positive, had a mean fold increase\ud (mfi) of 1.97 in TEM8 expression. Similarly, a TEM8 mRNA mfi = 2.7 was found in healthy donor mDCs. Conversely, mDCs from nine progressing patients, all but one with negative DTH, had a TEM8 mRNA mfi of 12.88. Thus, mDC TEM8 expression levels would seem to identify (p = 0.0018) patients who could benefit from DC therapeutic vaccination

Reasoning with Categories for Trusting Strangers: a Cognitive Architecture

A crucial issue for agents in open systems is the ability to filter out information sources in order to build an image of their counterparts, upon which a subjective evaluation of trust as a promoter of interactions can be assessed. While typical solutions discern relevant information sources by relying on previous experiences or reputational images, this work presents an alternative approach based on the cognitive ability to: (i) analyze heterogeneous information sources along different dimensions; (ii) ascribe qualities to unknown counterparts based on reasoning over abstract classes or categories; and, (iii) learn a series of emergent relationships between particular properties observable on other agents and their effective abilities to fulfill tasks. A computational architecture is presented allowing cognitive agents to dynamically assess trust based on a limited set of observable properties, namely explicitly readable signals (Manifesta) through which it is possible to infer hidden properties and capabilities (Krypta), which finally regulate agents' behavior in concrete work environments. Experimental evaluation discusses the effectiveness of trustor agents adopting different strategies to delegate tasks based on categorization

Facing Openness with Socio Cognitive Trust and Categories.

Typical solutions for agents assessing trust relies on the circulation of information on the individual level, i.e. reputational images, subjective experiences, statistical analysis, etc. This work presents an alternative approach, inspired to the cognitive heuristics enabling humans to reason at a categorial level. The approach is envisaged as a crucial ability for agents in order to: (1) estimate trustworthiness of unknown trustees based on an ascribed membership to categories; (2) learn a series of emergent relations between trustees observable properties and their effective abilities to fulfill tasks in situated conditions. On such a basis, categorization is provided to recognize signs (Manifesta) through which hidden capabilities (Kripta) can be inferred. Learning is provided to refine reasoning attitudes needed to ascribe tasks to categories. A series of architectures combining categorization abilities, individual experiences and context awareness are evaluated and compared in simulated experiments

Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems

Crowdsourcing systems commonly face the problem of aggregating multiple judgments provided by potentially unreliable workers. In addition, several aspects of the design of efficient crowdsourcing processes, such as defining worker's bonuses, fair prices and time limits of the tasks, involve knowledge of the likely duration of the task at hand. Bringing this together, in this work we introduce a new time--sensitive Bayesian aggregation method that simultaneously estimates a task's duration and obtains reliable aggregations of crowdsourced judgments. Our method, called BCCTime, builds on the key insight that the time taken by a worker to perform a task is an important indicator of the likely quality of the produced judgment. To capture this, BCCTime uses latent variables to represent the uncertainty about the workers' completion time, the tasks' duration and the workers' accuracy. To relate the quality of a judgment to the time a worker spends on a task, our model assumes that each task is completed within a latent time window within which all workers with a propensity to genuinely attempt the labelling task (i.e., no spammers) are expected to submit their judgments. In contrast, workers with a lower propensity to valid labeling, such as spammers, bots or lazy labelers, are assumed to perform tasks considerably faster or slower than the time required by normal workers. Specifically, we use efficient message-passing Bayesian inference to learn approximate posterior probabilities of (i) the confusion matrix of each worker, (ii) the propensity to valid labeling of each worker, (iii) the unbiased duration of each task and (iv) the true label of each task. Using two real-world public datasets for entity linking tasks, we show that BCCTime produces up to 11% more accurate classifications and up to 100% more informative estimates of a task's duration compared to state-of-the-art methods

From Manifesta to Krypta: The Relevance of Categories for Trusting Others

In this paper we consider the special abilities needed by agents for assessing trust based on inference and reasoning. We analyze the case in which it is possible to infer trust towards unknown counterparts by reasoning on abstract classes or categories of agents shaped in a concrete application domain. We present a scenario of interacting agents providing a computational model implementing different strategies to assess trust. Assuming a medical domain, categories, including both competencies and dispositions of possible trustees, are exploited to infer trust towards possibly unknown counterparts. The proposed approach for the cognitive assessment of trust relies on agents' abilities to analyze heterogeneous information sources along different dimensions. Trust is inferred based on specific observable properties (Manifesta), namely explicitly readable signals indicating internal features (Krypta) regulating agents' behavior and effectiveness on specific tasks. Simulative experiments evaluate the performance of trusting agents adopting different strategies to delegate tasks to possibly unknown trustees, while experimental results show the relevance of this kind of cognitive ability in the case of open Multi Agent Systems

Reply With: Proactive Recommendation of Email Attachments

Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message. Analysis of an enterprise email corpus reveals that 35% of the time when users include these items as part of their response, the attachable item is already present in their inbox or sent folder. A modern email client can proactively retrieve relevant attachable items from the user's past emails based on the context of the current conversation, and recommend them for inclusion, to reduce the time and effort involved in composing the response. In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user. As email search systems are commonly available, we constrain the recommendation task to formulating effective search queries from the context of the conversations. The query is submitted to an existing IR system to retrieve relevant items for attachment. We also present a novel strategy for generating labels from an email corpus---without the need for manual annotations---that can be used to train and evaluate the query formulation model. In addition, we describe a deep convolutional neural network that demonstrates satisfactory performance on this query formulation task when evaluated on the publicly available Avocado dataset and a proprietary dataset of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on Information and Knowledge Management. 201