The lexical nature of alpha-beta oscillations in context-driven word production

In context-driven word production, picture naming is faster following constrained than neutral sentential contexts (e.g., “The farmer milked the… [picture]” vs. “The child drew a… [picture]”, followed by the picture of a cow), suggesting conceptual-lexical pre-activation of the target response. Power decreases in the alpha-beta oscillatory band (8–25 Hz) are consistently found for constrained relative to neutral contexts prior to picture onset, when conceptual and lexical retrieval is ongoing. However, it remains a matter of debate whether the alpha-beta power decreases reflect (low-level) expectations of the visual input, conceptual and lexical retrieval, or motor preparation. The present study aimed at investigating the lexical-semantic nature of alpha-beta oscillations. Participants performed context-driven picture naming with constrained and neutral contexts. In addition, an auditory distractor word was presented before picture onset. Distractors were either semantically related (e.g., “goat”) or unrelated (e.g., “bean”) to the picture to be named. Picture naming was faster with constrained than neutral contexts. Distractor type did not affect naming latencies nor the behavioural context effect. In the oscillatory brain responses, the context-related alpha-beta power decreases were observed throughout the pre-picture interval when distractors were semantically unrelated to the picture, in line with previous findings. However, with semantically related distractors, the context effect was delayed until a period after distractor processing. Thus, alpha-beta power seems to be sensitive to the semantic relationship between the distractor word and the picture to be named. We interpret these results as suggesting that alpha-beta power decreases in context-driven word production reflect lexical-semantic retrieval mechanisms

Detecting Addiction, Anxiety, and Depression by Users Psychometric Profiles

Detecting and characterizing people with mental disorders is an important task that could help the work of different healthcare professionals. Sometimes, a diagnosis for specific mental disorders requires a long time, possibly causing problems because being diagnosed can give access to support groups, treatment programs, and medications that might help the patients. In this paper, we study the problem of exploiting supervised learning approaches, based on users' psychometric profiles extracted from Reddit posts, to detect users dealing with Addiction, Anxiety, and Depression disorders. The empirical evaluation shows an excellent predictive power of the psychometric profile and that features capturing the post's content are more effective for the classification task than features describing the user writing style. We achieve an accuracy of 96% using the entire psychometric profile and an accuracy of 95% when we exclude from the user profile linguistic features

Experimental observations of tsunami induced scour at onshore structures

Tsunami inundation of the coastal environment can induce scour at structure foundations leading to failure. A series of experiments are made using a unique Pneumatic Long Wave Generator to generate tsunami wave periods of 25 - 147 s equating to 3 - 17.3 mins at 1:50 Froude scale. The waves propagate over a sloping bathymetry and impinge upon a square structure founded onshore in a flat sediment bed. Flow velocity, height and scour are recorded as a function of time during tsunami inundation. The rate of scour is observed to be time dependent. Equilibrium, which is not attained, is argued to be an inappropriate measure for time-dependent transient flows such as tsunami in which the flow velocity, depth and direction are variable. The maximum scour depth is recorded and critically is observed not to be equal to the final depth due to significant sediment slumping when flow velocities reduce in the latter stages of inundation. Current and wave scour predictor equations over predict the scour, while the ASCE 7-16 method under predicts. Comparisons with available data in the literature show longer inundation durations increase the amount of scour

SHARPER-enhanced benchtop NMR: improving SNR by removing couplings and approaching natural linewidths

We present a signal enhancement strategy for benchtop NMR that produces SNR increases on the order of 10 to 30 fold by collapsing the target resonance into an extremely narrow singlet. Importantly, the resultant signal is amenable to quantitative interpretation and therefore can be applied to analytical applications such as reaction monitoring

How can new technologies help us with earthquake reconnaissance?

Earthquake reconnaissance missions have been very successful in identifying the specific causes of failure for individual buildings and the deficiencies in building codes or local construction practices that have led to these; however, their ability to capture robust statistics on patterns of failure is usually beyond their scope. Furthermore, the success of these endeavours in establishing poor construction designs and practices, means that if we are to continue to learn new lessons we will need to gain fresh insights using new data streams. Recent technological advances have the ability to enable us to both increase the amount of data collected and to improve on the precision of these measurements. Furthermore, social media has the potential to provide entirely new data streams and to significantly add value to collected data by harnessing an army of data manipulators and interpreters. Howto do this in a reliable way however, is the subject of much debate. In this paper, we explore the potential for a number of trialled and potential technologies to collect better and new information in earthquake reconnaissance, including virtual damage surveying - where results from damage surveys completed in the field, are compared to omnidirectional images collected during the mission and interpreted by a virtual surveyor based in the UK, data collected through aerial images taken by UAVs and 3D models created from a series of drone or other images. Finally, we describe the potential of social media such as Twitter to collect data streams on damage and other impacts. Examples of impact data such as road closures, landslips and infrastructure service failures collected for flooding and landslide will be presented to show the potential of this technology for earthquakes

The radio emission pattern of air showers as measured with LOFAR - a tool for the reconstruction of the energy and the shower maximum

The pattern of the radio emission of air showers is finely sampled with the Low-Frequency ARray (LOFAR). A set of 382 measured air showers is used to test a fast, analytic parameterization of the distribution of pulse powers. Using this parameterization we are able to reconstruct the shower axis and give estimators for the energy of the air shower as well as the distance to the shower maximum.Comment: 15 pages, 10 figures, accepted for publication in JCA

A low power and low signal 4 bit 50MS/s double sampling pipelined ADC for Monolithic Active Pixel Sensors

International audienceA 4 bit very low power and low incoming signal analog to digital converter (ADC) using a double sampling switched capacitor technique, designed for use in CMOS monolithic active pixels sensor readout, has been implemented in 0.35μm CMOS technology. A non-resetting sample and hold stage is integrated to amplify the incoming signal by 4. This first stage compensates both the amplifier offset effect and the input common mode voltage fluctuations. The converter is composed of a 2.5 bit pipeline stage followed by a 2 bit flash stage. This prototype consists of 4 ADC double-channels; each one is sampling at 50MS/s and dissipates only 2.6mW at 3.3V supply voltage. A bias pulsing stage is integrated in the circuit. Therefore, the analog part is switched OFF or ON in less than 1μs. The size for the layout is 80μm*0.9mm. This corresponds to the pitch of 4 pixel columns, each one is 20μm wide