Closing the loop: assisting archival appraisal and information retrieval in one sweep

In this article, we examine the similarities between the concept of appraisal, a process that takes place within the archives, and the concept of relevance judgement, a process fundamental to the evaluation of information retrieval systems. More specifically, we revisit selection criteria proposed as result of archival research, and work within the digital curation communities, and, compare them to relevance criteria as discussed within information retrieval's literature based discovery. We illustrate how closely these criteria relate to each other and discuss how understanding the relationships between the these disciplines could form a basis for proposing automated selection for archival processes and initiating multi-objective learning with respect to information retrieval

Cell assembly dynamics of sparsely-connected inhibitory networks: a simple model for the collective activity of striatal projection neurons

Striatal projection neurons form a sparsely-connected inhibitory network, and this arrangement may be essential for the appropriate temporal organization of behavior. Here we show that a simplified, sparse inhibitory network of Leaky-Integrate-and-Fire neurons can reproduce some key features of striatal population activity, as observed in brain slices [Carrillo-Reid et al., J. Neurophysiology 99 (2008) 1435{1450]. In particular we develop a new metric to determine the conditions under which sparse inhibitory networks form anti-correlated cell assemblies with time-varying activity of individual cells. We found that under these conditions the network displays an input-specific sequence of cell assembly switching, that effectively discriminates similar inputs. Our results support the proposal [Ponzi and Wickens, PLoS Comp Biol 9 (2013) e1002954] that GABAergic connections between striatal projection neurons allow stimulus-selective, temporally-extended sequential activation of cell assemblies. Furthermore, we help to show how altered intrastriatal GABAergic signaling may produce aberrant network-level information processing in disorders such as Parkinson's and Huntington's diseases.Comment: 22 pages, 9 figure

Learning what they think vs. learning what they do: The micro-foundations of vicarious learning

Vicarious learning is a vital component of organizational learning. We theorize and model two fundamental processes underlying vicarious learning: observation of actions (learning what they do) vs. belief sharing (learning what they think). The analysis of our model points to three key insights. First, vicarious learning through either process is beneficial even when no agent in a system of vicarious learners begins with a knowledge advantage. Second, vicarious learning through belief sharing is not universally better than mutual observation of actions and outcomes. Specifically, enabling mutual observability of actions and outcomes is superior to sharing of beliefs when the task environment features few alternatives with large differences in their value and there are no time pressures. Third, symmetry in vicarious learning in fact adversely affects belief sharing but improves observational learning. All three results are shown to be the consequence of how vicarious learning affects self-confirming biased beliefs

Unsupervised learning of overlapping image components using divisive input modulation

This paper demonstrates that nonnegative matrix factorisation is mathematically related to a class of neural networks that employ negative feedback as a mechanism of competition. This observation inspires a novel learning algorithm which we call Divisive Input Modulation (DIM). The proposed algorithm provides a mathematically simple and computationally efficient method for the unsupervised learning of image components, even in conditions where these elementary features overlap considerably. To test the proposed algorithm, a novel artificial task is introduced which is similar to the frequently-used bars problem but employs squares rather than bars to increase the degree of overlap between components. Using this task, we investigate how the proposed method performs on the parsing of artificial images composed of overlapping features, given the correct representation of the individual components; and secondly, we investigate how well it can learn the elementary components from artificial training images. We compare the performance of the proposed algorithm with its predecessors including variations on these algorithms that have produced state-of-the-art performance on the bars problem. The proposed algorithm is more successful than its predecessors in dealing with overlap and occlusion in the artificial task that has been used to assess performance

That Others May Learn: Three Views on Vicarious Learning in Organizations.

Vicarious learning, the process by which an individual learns from another’s experience, has long been recognized as a source of development and performance improvement in organizations, at both individual and collective levels. Yet existing perspectives on this critical learning process have been fairly limited, typically casting vicarious learning as a simple process of observation and imitation, enabled by formal organizational knowledge-transfer conduits. Largely absent from prior approaches is a consideration of the interpersonal dynamics underlying vicarious learning, leaving unexplored important questions related to 1) the actual behaviors unfolding when individuals interact to learn from one another’s experience, 2) how people coordinate efforts to enact and facilitate these vicarious learning interactions, and 3) the performance impact of different patterns of engagement in these interactions. In this dissertation, I advance a perspective on vicarious learning that views it as relationally co-created, emergently organized, and dyadically reciprocal, exploring the issues identified above in three distinct chapters. First, I present a theoretical model of what I term coactive vicarious learning, integrating theories of experiential learning and symbolic interactionism to articulate a co-construction process of vicarious learning, arising from individuals’ discussion and shared meaning-making. I unpack the antecedents and underlying behaviors of these discursive vicarious learning interactions, and theorize that they not only increase individuals’ knowledge, but also build individual and relational capacity for future learning. Second, I present a qualitative study of how these vicarious learning interactions manifest at work, inductively exploring the organizing processes used to facilitate vicarious learning in air medical transport teams. I advance a view of vicarious learning not as wholly determined by formal structures, but rather as an emergently organized phenomenon, enacted through interpersonal storytelling and facilitated by the coalescence of informal practices and formal structures. Third, I present a quantitative examination of different distributions of vicarious learning in work teams. Specifically, I examine what leads individuals to engage in reciprocal vicarious learning relationships (where each individual learns from the other, in contrast to the prevailing view of vicarious learning as one-way information transfer) and demonstrate that greater reciprocation of vicarious learning within a team enhances performance.PhDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113410/1/cgmyers_1.pd

Human-Agent Teamwork in Cyber Operations: Supporting Co-evolution of Tasks and Artifacts with Luna

Abstract. In this article, we outline the general concept of coactive emergence, an iterative process whereby joint sensemaking and decision-making activities are undertaken by analysts and software agents. Then we explain our rationale for the development of the Luna software agent framework. In particular, we focus on how we use capabilities for comprehensive policy-based governance to ensure that key requirements for security, declarative specification of task-work, and built-in support for joint activity within mixed teams of humans and agents are satisfied

Society-in-the-Loop: Programming the Algorithmic Social Contract

Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, `SITL = HITL + Social Contract.'Comment: (in press), Ethics of Information Technology, 201

A comprehensive study on disease risk predictions in machine learning

Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. Comprehensive survey on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavours have been shifted