Driving forces in free visual search : An ethology

Peer reviewedPostprin

Attentional load interferes with target localization across saccades

Peer reviewedPostprin

Differentiation Versus Denial: Impact of Messaging About Player Transactions On Team Reputation, Ticket Sales, and Sports Channel Subscriptions

Part of a concerted effort to quantitatively test claims made throughout image repair research, this study provides a greater understanding of the effectiveness of the differentiation strategy over simple denial and provides further clarification for related claims made in image repair research. Sports teams who differentiate major roster changes from a team rebuild score higher on measures of reputation, intent to purchase tickets, and intent to subscribe to premium sports channels. On behalf of practitioners, scholars should consider the conditions under which the ability to claim differentiation is plausible with a loyal clientele or customer base

Temporal and spatial dynamics of CO2 air-sea flux in the Gulf of Maine

Ocean surface layer carbon dioxide (CO2) data collected in the Gulf of Maine from 2004 to 2008 are presented. Monthly shipboard observations are combined with additional higher‐resolution CO2 observations to characterize CO2 fugacity ( fCO2) and CO2 flux over hourly to interannual time scales. Observed fCO2 andCO2 flux dynamics are dominated by a seasonal cycle, with a large spring influx of CO2 and a fall‐to‐winter efflux back to the atmosphere. The temporal results at inner, middle, and outer shelf locations are highly correlated, and observed spatial variability is generally small relative to the monthly to seasonal temporal changes. The averaged annual flux is in near balance and is a net source of carbon to the atmosphere over 5 years, with a value of +0.38 mol m−2 yr−1. However, moderate interannual variation is also observed, where years 2005 and 2007 represent cases of regional source (+0.71) and sink (−0.11) anomalies. We use moored daily CO2 measurements to quantify aliasing due to temporal undersampling, an important error budget term that is typically unresolved. The uncertainty of our derived annual flux measurement is ±0.26 mol m−2 yr−1 and is dominated by this aliasing term. Comparison of results to the neighboring Middle and South Atlantic Bight coastal shelf systems indicates that the Gulf of Maine exhibits a similar annual cycle and range of oceanic fCO2 magnitude but differs in the seasonal phase. It also differs by enhanced fCO2 controls by factors other than temperature‐driven solubility, including biological drawdown, fall‐to‐winter vertical mixing, and river runoff

Note and Comment

The Federal Courts and Local Law in Porto Rico; The Investigation of Corporate Monopolies; Compelling the Production of Corporation Books and Papers; Goods Damages by Act of God Because of a Carrier\u27s Negligent Delay; The Effect of Dogmatic Changes Upon the Legal Status of a Church; Bays and Gulfs as Territory of the Adjoining Nation

Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization

Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics (``climate'') can be produced by employing a feedback loop, whereby the model is trained to predict forward one time step, then the model output is used as input for multiple time steps. In the absence of mitigating techniques, however, this technique can result in artificially rapid error growth. In this article, we systematically examine the technique of adding noise to the ML model input during training to promote stability and improve prediction accuracy. Furthermore, we introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training. Our case study uses reservoir computing, a machine-learning method using recurrent neural networks, to predict the spatiotemporal chaotic Kuramoto-Sivashinsky equation. We find that reservoir computers trained with noise or with LMNT produce climate predictions that appear to be indefinitely stable and have a climate very similar to the true system, while reservoir computers trained without regularization are unstable. Compared with other regularization techniques that yield stability in some cases, we find that both short-term and climate predictions from reservoir computers trained with noise or with LMNT are substantially more accurate. Finally, we show that the deterministic aspect of our LMNT regularization facilitates fast hyperparameter tuning when compared to training with noise.Comment: 39 pages, 8 figures, 5 table

Covalent organic frameworks

The first members of covalent organic frameworks (COF) have been designed and successfully synthesized by condensation reactions of phenyl diboronic acid C6H4[B(OH)2]2 and hexahydroxytriphenylene C18H6(OH)6. The high crystallinity of the products (C3H2BO)6 (C9H12)1 (COF-1) and C9H4BO2 (COF-5) has allowed definitive resolution of their structure by powder X-ray diffraction methods which reveal expanded porous graphitic layers that are either staggered (COF-1, P63/mmc) or eclipsed (COF-5, P6/mmm). They exhibit high thermal stability (to temperatures up to 500- to 600-C), permanent porosity, and high surface areas (711 and 1590 m2/g, respectively) surpassing those of related inorganic frameworks. A similar approach has been used for the design of other extended structures