Parafoveal-foveal overlap can facilitate ongoing word identification during reading: evidence from eye movements.

Readers continuously receive parafoveal information about the upcoming word in addition to the foveal information about the currently fixated word. Previous research (Inhoff, Radach, Starr, & Greenberg, 2000) showed that the presence of a parafoveal word that was similar to the foveal word facilitated processing of the foveal word. We used the gaze-contingent boundary paradigm (Rayner, 1975) to manipulate the parafoveal information that subjects received before or while fixating a target word (e.g., news) within a sentence. Specifically, a reader's parafovea could contain a repetition of the target (news), a correct preview of the posttarget word (once), an unrelated word (warm), random letters (cxmr), a nonword neighbor of the target (niws), a semantically related word (tale), or a nonword neighbor of that word (tule). Target fixation times were significantly lower in the parafoveal repetition condition than in all other conditions, suggesting that foveal processing can be facilitated by parafoveal repetition. We present a simple model framework that can account for these effects

Ground state fluctuations in finite Fermi and Bose systems

We consider a small and fixed number of fermions (bosons) in a trap. The ground state of the system is defined at T=0. For a given excitation energy, there are several ways of exciting the particles from this ground state. We formulate a method for calculating the number fluctuation in the ground state using microcanonical counting, and implement it for small systems of noninteracting fermions as well as bosons in harmonic confinement. This exact calculation for fluctuation, when compared with canonical ensemble averaging, gives considerably different results, specially for fermions. This difference is expected to persist at low excitation even when the fermion number in the trap is large.Comment: 20 pages (including 1 appendix), 3 postscript figures. An error was found in one section of the paper. The corrected version is updated on Sep/05/200

On the Microcanonical Entropy of a Black Hole

It has been suggested recently that the microcanonical entropy of a system may be accurately reproduced by including a logarithmic correction to the canonical entropy. In this paper we test this claim both analytically and numerically by considering three simple thermodynamic models whose energy spectrum may be defined in terms of one quantum number only, as in a non-rotating black hole. The first two pertain to collections of noninteracting bosons, with logarithmic and power-law spectra. The last is an area ensemble for a black hole with equi-spaced area spectrum. In this case, the many-body degeneracy factor can be obtained analytically in a closed form. We also show that in this model, the leading term in the entropy is proportional to the horizon area A, and the next term is ln A with a negative coefficient.Comment: 15 pages, 1 figur

A molecular perspective on the limits of life: Enzymes under pressure

From a purely operational standpoint, the existence of microbes that can grow under extreme conditions, or "extremophiles", leads to the question of how the molecules making up these microbes can maintain both their structure and function. While microbes that live under extremes of temperature have been heavily studied, those that live under extremes of pressure have been neglected, in part due to the difficulty of collecting samples and performing experiments under the ambient conditions of the microbe. However, thermodynamic arguments imply that the effects of pressure might lead to different organismal solutions than from the effects of temperature. Observationally, some of these solutions might be in the condensed matter properties of the intracellular milieu in addition to genetic modifications of the macromolecules or repair mechanisms for the macromolecules. Here, the effects of pressure on enzymes, which are proteins essential for the growth and reproduction of an organism, and some adaptations against these effects are reviewed and amplified by the results from molecular dynamics simulations. The aim is to provide biological background for soft matter studies of these systems under pressure.Comment: 16 pages, 8 figure

Counterfactual Explanations for Neural Recommenders

Understanding why specific items are recommended to users can significantly increase their trust and satisfaction in the system. While neural recommenders have become the state-of-the-art in recent years, the complexity of deep models still makes the generation of tangible explanations for end users a challenging problem. Existing methods are usually based on attention distributions over a variety of features, which are still questionable regarding their suitability as explanations, and rather unwieldy to grasp for an end user. Counterfactual explanations based on a small set of the user's own actions have been shown to be an acceptable solution to the tangibility problem. However, current work on such counterfactuals cannot be readily applied to neural models. In this work, we propose ACCENT, the first general framework for finding counterfactual explanations for neural recommenders. It extends recently-proposed influence functions for identifying training points most relevant to a recommendation, from a single to a pair of items, while deducing a counterfactual set in an iterative process. We use ACCENT to generate counterfactual explanations for two popular neural models, Neural Collaborative Filtering (NCF) and Relational Collaborative Filtering (RCF), and demonstrate its feasibility on a sample of the popular MovieLens 100K dataset

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach