Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions.Behavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of Data set 1, both assumptions were largely fulfilled. In contrast, the less restrictive paradigm of Data set 2 met neither assumption. Simulations predict that the larger diversity of neuronal response preferences, rather than higher cross-trial reliability, drives the better performance of Data set 1. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context.

The development and online access to an operational global actual evapotranspiration (ETa) is described. The global ETa is generated using the Operational Simplified Surface Energy Balance (SSEBop) model with inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and gridded weather datasets. Global and regional ETa, as well as anomaly graphics and data, are posted at https://earlywarning.usgs.gov/fews at dekadal, monthly, and annual aggregation periods at 1 km spatial resolution since 2003. As part of the convergence of evidence, the Famine Early Warning Systems Network (FEWS NET) consults these products along with precipitation- and vegetation-derived products for drought monitoring and early warning applications to avert food insecurity crises around the world.

Models of human mobility have broad applicability in fields such as mobile computing, urban planning, and ecology. This paper proposes and evaluates WHERE, a novel approach to modeling how large populations move within different metropolitan areas. WHERE takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records (CDRs) from a cellular telephone network, and produces synthetic CDRs for a synthetic population. We have validated WHERE against billions of anonymous location samples for hundreds of thousands of phones in the New York and Los Angeles metropolitan areas. We found that WHERE offers significantly higher fidelity than other modeling approaches. For example, daily range of travel statistics fall within one mile of their true values, an improvement of more than 14 times over a Weighted Random Waypoint model. Our modeling techniques and synthetic CDRs can be applied to a wide range of problems while avoiding many of the privacy concerns surrounding real CDRs.

In 1995, AT&T Research (then within Bell Labs) began work on a software-only automated speech recognition system named Watson(TM). The goal was ambitious; Watson was to serve as a single code base supporting applications ranging from PC-desktop command and control through to scaleable telephony interactive voice services. Furthermore, the software was to be the new code base for the research group, allowing fast deployment of new algorithmic advances from the lab into the field. A set of C++ objects has been developed which support these objectives. This paper gives an overview of the Watson automatic speech recognizer software architecture, describes the algorithms employed, and provides performance numbers for some sample tasks.