, mechanisms that cooperatively support temporal representation and long-term consolidation of information. T he first human electroencephalographic (EEG) pattern described was an 8 to 12 Hz rhythm, the alpha waves of Berger (1), followed by a barrage of intensive clinical and basic research. From scalp

more involved in face than object processing by subtracting the activation when subjects passively viewed non-face common objects from that obtained when viewing faces (localizer task 6 ). This led to the selection of two 8 ´8 voxel (each voxel, 1.3 ´1.7 mm; see Methods) regions of interest (ROIs

Neurons in the cortex of behaving animals show temporally irregular spiking patterns. The origin of this irregularity and its implications for neural processing are unknown. The hypothesis that the temporal variability in the firing of a neuron results from an approximate balance between its

communication patterns and similar malicious activity patterns. These hosts are thus bots in the monitored network. We have implemented our BotMiner prototype system and evaluated it using many real network traces. The results show that it can detect real-world botnets (IRC-based, HTTP-based, and P2P botnets

in terms of the blending dynamics defined among a set of temporally overlapping active units; the relative timing of speech gestures is formulated in terms of the serial dynamics that shape the temporal patterning of onsets and offsets in unit activations. Implications of this approach for certain

-sided activation i the frontal and anterior temporal regions compared with the happy condition. Happi-ness was accompanied byleft-sided activation i the anterior temporal region compared with disgust. No differences in asymmetry were found between emotions in the central and parietal regions. When data aggregated

e for processing faces than the control groups, but for these analyses, the effect was now on the left side. Greater ITG activation was the pattern found in both control groups during object processing. Conclusions: Individuals with autism spectrum disorders demonstrate a pattern of brain activity

, sophisticated social bots, and how their presence can endanger online ecosystems and our society. We then discuss current efforts aimed at detection of social bots in Twitter. Characteristics related to content, network, sentiment, and temporal pat-terns of activity are imitated by bots but at the same time can

an understanding of probabilistic relationships among temporal patterns of activity, informational goals, and classes of query refinement. We construct Bayesian networks that predict search behavior, with a focus on the progression of queries over time. We review a methodology for abstracting and tagging user

Cognitively demanding tasks that evoke activation in the brain's central-executive network (CEN) have been consistently shown to evoke decreased activation (deactivation) in the default-mode network (DMN). The neural mechanisms underlying this switch between activation and deactivation