Neural Measures of Pitch Processing in EEG Responses to Running Speech

Linearized encoding models are increasingly employed to model cortical responses to running speech. Recent extensions to subcortical responses suggest clinical perspectives, potentially complementing auditory brainstem responses (ABRs) or frequency-following responses (FFRs) that are current clinical standards. However, while it is well-known that the auditory brainstem responds both to transient amplitude variations and the stimulus periodicity that gives rise to pitch, these features co-vary in running speech. Here, we discuss challenges in disentangling the features that drive the subcortical response to running speech. Cortical and subcortical electroencephalographic (EEG) responses to running speech from 19 normal-hearing listeners (12 female) were analyzed. Using forward regression models, we confirm that responses to the rectified broadband speech signal yield temporal response functions consistent with wave V of the ABR, as shown in previous work. Peak latency and amplitude of the speech-evoked brainstem response were correlated with standard click-evoked ABRs recorded at the vertex electrode (Cz). Similar responses could be obtained using the fundamental frequency (F0) of the speech signal as model predictor. However, simulations indicated that dissociating responses to temporal fine structure at the F0 from broadband amplitude variations is not possible given the high co-variance of the features and the poor signal-to-noise ratio (SNR) of subcortical EEG responses. In cortex, both simulations and data replicated previous findings indicating that envelope tracking on frontal electrodes can be dissociated from responses to slow variations in F0 (relative pitch). Yet, no association between subcortical F0-tracking and cortical responses to relative pitch could be detected. These results indicate that while subcortical speech responses are comparable to click-evoked ABRs, dissociating pitch-related processing in the auditory brainstem may be challenging with natural speech stimuli.

APPLIED DEEP LEARNING FOR SLENDER MARINE STRUCTURE DYNAMIC ANALYSIS

Non-linear finite element models (FEM) are commonly used to perform analysis in the time domain to simulate a limited number of stochastic loading scenarios that a slender marine structure may undergo, requiring a high computational time effort. Analytical equations and frequency domain analysis can be used to speed up these simulations, but they are not a convenient choice when high non-linearities are present in the dynamic system. Alternative models can be developed to reduce the simulation time while maintaining a good accuracy level of the system's response. This work proposes different strategies to develop artificial neural networks (ANN) architectures, based on deep learning algorithms, which can predict multiple structural node responses at once, in time and space, significantly reducing the total training time when a great number of structural nodes are considered. A novel classification concept of ANN-based models is introduced for this application: the NodeNet and the LengthNet class types. In the first approach, the model predictor focuses on a single structural node, while in the latter the model focuses on a length (segment) comprising many structural nodes. The work also extends the response predictions of such marine structures from the top region down to the Touchdown Zone (TDZ).

Promotion Scenario Based Sales Prediction on E-Retail Groceries Using Data Mining

Mostly in many business cases, sales prediction plays an important role. Production planning is a good example. One aspect which affecting sales forecasting is promotion schedule. Since using promotion is commonly done nowadays, especially in internet business, it is hardly seen a day without promotion in Indonesian e-commerce. Thus, this study discusses about forecasting future sales based on promotion scenario data with main objective is to discover the best machine learning algorithm and model to forecast future sales. Promotion mechanism which employed in this study are price cut, buy 1-get 1, and product bundling. We use 577 data from January 2018 to July 2019 as dataset. We compare kNN, GLM, and SVR as the model predictor to forecast number of transactions in a day. From the experiment k-NN yielded the highest performance ability with squared correlation of 0.938. the worst model predictor for this case is GLM with squared correlation of 0.507. We also determine the best parameter input for each parameter using grid optimization method. We discover 2 is the best k value of kNN and Manhattan distance is the best distance calculation for this case

Spiritual Crisis as a Unique Causal Predictor of Emotional and Characterological Impairment in Atheists and Agnostics: Numinous Motivations as Universal Psychological Qualities

While there is a tremendous literature documenting the positive value of religious and spiritual (R/S) constructs on an array of psychosocial outcomes (e.g., health, resilience, coping ability), Spiritual Crisis (SC) reflects the negative side of the numinous in a way that stresses the value and importance of R/S constructs for psychological functioning. This study examined whether numinous constructs are more relevant to theists or represent universal psychological qualities. Using an MTurk-based sample comprising both theists and atheists (N = 1399; 800 women and 595 men, four gave no response), the predictive ability of SC on both affective and characterological distress was examined using both regression and SEM analyses. The results indicated that both theists and atheists understood the numinous in similar ways and that scores on SC were of equal incremental predictiveness for both groups. SEM analyses supported the causal model that understood SC to be a unique and independent (from the personality domains of the Five-Factor Model) predictor of these clinical outcomes. These findings stress the value of the numinous for understanding all clients and that psychological assessment needs to systematically address numinous constructs in order to ensure comprehensive treatment of psychological impairment.

Model-based Predictive Control of an HVAC System

This paper presents the application of two model-based predictive control (MPC) algorithms on the cooling system of an office building. The two strategies discussed are a simple MPC, and an adaptive MPC algorithm connected to a model predictor. The cooling method used represents the air-conditioning unit of an HVAC system. The temperature of the building’s three rooms is controlled with fan coil units, based on the reference temperature and with different constraints applied. Furthermore, the building model is affected by dynamically changing interior and exterior heat sources, which we introduced into the controller as disturbances.