Semi-Automated Nasal PAP Mask Sizing using Facial Photographs

We present a semi-automated system for sizing nasal Positive Airway Pressure (PAP) masks based upon a neural network model that was trained with facial photographs of both PAP mask users and non-users. It demonstrated an accuracy of 72% in correctly sizing a mask and 96% accuracy sizing to within 1 mask size group. The semi-automated system performed comparably to sizing from manual measurements taken from the same images which produced 89% and 100% accuracy respectively.Comment: 4 pages, 3 figures, 4 tables, IEEE Engineering Medicine and Biology Conference 201

Affective Facial Expression Processing via Simulation: A Probabilistic Model

Understanding the mental state of other people is an important skill for intelligent agents and robots to operate within social environments. However, the mental processes involved in `mind-reading' are complex. One explanation of such processes is Simulation Theory - it is supported by a large body of neuropsychological research. Yet, determining the best computational model or theory to use in simulation-style emotion detection, is far from being understood. In this work, we use Simulation Theory and neuroscience findings on Mirror-Neuron Systems as the basis for a novel computational model, as a way to handle affective facial expressions. The model is based on a probabilistic mapping of observations from multiple identities onto a single fixed identity (`internal transcoding of external stimuli'), and then onto a latent space (`phenomenological response'). Together with the proposed architecture we present some promising preliminary resultsComment: Annual International Conference on Biologically Inspired Cognitive Architectures - BICA 201

Explicit tracking of uncertainty increases the power of quantitative rule-of-thumb reasoning in cell biology

"Back-of-the-envelope" or "rule-of-thumb" calculations involving rough estimates of quantities play a central scientific role in developing intuition about the structure and behaviour of physical systems, for example in so-called `Fermi problems' in the physical sciences. Such calculations can be used to powerfully and quantitatively reason about biological systems, particularly at the interface between physics and biology. However, substantial uncertainties are often associated with values in cell biology, and performing calculations without taking this uncertainty into account may limit the extent to which results can be interpreted for a given problem. We present a means to facilitate such calculations where uncertainties are explicitly tracked through the line of reasoning, and introduce a `probabilistic calculator' called Caladis, a web tool freely available at www.caladis.org, designed to perform this tracking. This approach allows users to perform more statistically robust calculations in cell biology despite having uncertain values, and to identify which quantities need to be measured more precisely in order to make confident statements, facilitating efficient experimental design. We illustrate the use of our tool for tracking uncertainty in several example biological calculations, showing that the results yield powerful and interpretable statistics on the quantities of interest. We also demonstrate that the outcomes of calculations may differ from point estimates when uncertainty is accurately tracked. An integral link between Caladis and the Bionumbers repository of biological quantities further facilitates the straightforward location, selection, and use of a wealth of experimental data in cell biological calculations.Comment: 8 pages, 3 figure

Cotenants Trumping Cotenants: The Eighth Circuit Takes a Diverse Stance on Cotenants\u27 Authority under the Fourth Amendment

Reluctantly, John Adams mailed the envelope addressed to his wife, Abigail, knowing the contents could bring about his death. This letter, mailed to his dear friend, contained a description of his pleas for independence to the Continental Congress, a description that if located by the British, would most certainly subject him to charges of treason. Immediately after Mr. Adams dispatched his letter, he was approached by a British intelligence officer requesting to review the letter. Mr. Adams denied the officer\u27s request and sent him on his way. Later, when the letter arrived to the unsuspecting Abigail, it was accompanied by a British officer who asked if he could examine the letter. Ignorant as to the letter\u27s contents, Abigail consented to the request and the officer discovered the treasonous materials, resulting in the seizure of the letter and the subsequent arrest of Mr. Adams. Would our founding fathers have considered this particular exercise of police power beyond reproach? While this fictional illustration is distinguishable from the more disturbing factual scenario presented in United States v. Hudspeth, it nevertheless embodies the same question: If two individuals have common authority over a piece of property, can government officials purposely ignore one party\u27s express refusal to search and instead accept the consent of the other party? Hudspeth asks this question in the unforgiving light of the despicable acts of a pedophile; where a computer containing child pornography takes the place of John Adams\u27 rebellious letter. In light of its deplorable factual setting, Hudspeth is a case which must be viewed with an objective eye. In doing so, it is helpful to keep the analogy of John Adams\u27s letter in mind, as one may be, albeit unconsciously, predisposed to the persecution of pedophiles. Because Hudspeth is a case which not only implicates the rights of a pedophile, but the rights of all citizens who wish to object to governmental searches and seizures of their property, objectivity is essential to arriving at the correct conclusion

Induction of defeasible logic theories in the legal domain

The market for intelligent legal information systems remains relatively untapped and while this might be interpreted as an indication that it is simply impossible to produce a system that satisfies the needs of the legal community, an analysis of previous attempts at producing such systems reveals a common set of deficiencies that in-part explain why there have been no overwhelming successes to date. Defeasible logic, a logic with proven successes at representing legal knowledge, seems to overcome many of these deficiencies and is a promising approach to representing legal knowledge. Unfortunately, an immediate application of technology to the challenges in this domain is an expensive and computationally intractable problem. So, in light of the benefits, we seek to find a practical algorithm that uses heuristics to discover an approximate solution. As an outcome of this work, we have developed an algorithm that integrates defeasible logic into a decision support system by automatically deriving its knowledge from databases of precedents. Experiments with the new algorithm are very promising - delivering results comparable to and exceeding other approaches

An algorithm for the induction Of defeasible logic theories from databases

Defeasible logic is a non-monotonic logic with applications in rule-based domains such as law. To ease the development and improve the accuracy of expert systems based on defeasible logic, it is desirable to automatically induce a theory of the logic from a training set of precedent data. Empirical evidence suggests that minimal theories that describe the training set tend to be more faithful representations of reality. We show via transformation from the hitting set problem that this global minimization problem is intractable, belonging to the class of NP optimisation problems. Given the inherent difficulty of finding the optimal solution, we instead use heuristics and demonstrate that a best-first, greedy, branch and bound algorithm can be used to find good theories in short time. This approach displays significant improvements in both accuracy and theory size as compared to recent work in the area that post-processed the output of an Aprori association rule-mining algorithm, with comparable execution times

Entangled subspaces and generic local state discrimination with pre-shared entanglement

Walgate and Scott have determined the maximum number of generic pure quantum states in multipartite space that can be unambiguously discriminated by an LOCC measurement [Journal of Physics A: Mathematical and Theoretical, 41:375305, 08 2008]. In this work, we determine this number in a more general setting in which the local parties have access to pre-shared entanglement in the form of a resource state. We find that, for an arbitrary pure resource state, this number is equal to the Krull dimension of (the closure of) the set of pure states obtainable from the resource state by SLOCC. This dimension is known for several resource states, for example the GHZ state. Local state discrimination is closely related to the topic of entangled subspaces, which we study in its own right. We introduce $r$-entangled subspaces, which naturally generalize previously studied spaces to higher multipartite entanglement. We use algebraic geometric methods to determine the maximum dimension of an $r$-entangled subspace, and present novel explicit constructions of such spaces. We obtain similar results for symmetric and antisymmetric $r$-entangled subspaces, which correspond to entangled subspaces of bosonic and fermionic systems, respectively.Comment: 32 pages, feedback welcom