Eye movement patterns during the recognition of three-dimensional objects: Preferential fixation of concave surface curvature minima

This study used eye movement patterns to examine how high-level shape information is used during 3D object recognition. Eye movements were recorded while observers either actively memorized or passively viewed sets of novel objects, and then during a subsequent recognition memory task. Fixation data were contrasted against different algorithmically generated models of shape analysis based on: (1) regions of internal concave or (2) convex surface curvature discontinuity or (3) external bounding contour. The results showed a preference for fixation at regions of internal local features during both active memorization and passive viewing but also for regions of concave surface curvature during the recognition task. These findings provide new evidence supporting the special functional status of local concave discontinuities in recognition and show how studies of eye movement patterns can elucidate shape information processing in human vision

Visual processing of words in a patient with visual form agnosia: A behavioural and fMRI study

Patient D.F. has a profound and enduring visual form agnosia due to a carbon monoxide poisoning episode suffered in 1988. Her inability to distinguish simple geometric shapes or single alphanumeric characters can be attributed to a bilateral loss of cortical area LO, a loss that has been well established through structural and functional fMRI. Yet despite this severe perceptual deficit, D.F. is able to “guess” remarkably well the identity of whole words. This paradoxical finding, which we were able to replicate more than 20 years following her initial testing, raises the question as to whether D.F. has retained specialized brain circuitry for word recognition that is able to function to some degree without the benefit of inputs from area LO. We used fMRI to investigate this, and found regions in the left fusiform gyrus, left inferior frontal gyrus, and left middle temporal cortex that responded selectively to words. A group of healthy control subjects showed similar activations. The left fusiform activations appear to coincide with the area commonly named the visual word form area (VWFA) in studies of healthy individuals, and appear to be quite separate from the fusiform face area. We hypothesize that there is a route to this area that lies outside area LO, and which remains relatively unscathed in D.F

Control Theoretic Analysis of Human Brain Networks

The brain is a complex system with complicated structures and entangled dynamics. Among the various approaches to investigating the brain\u27s mechanics, the graphical method provides a successful framework for understanding the topology of both the structural and functional networks, and discovering efficient diagnostic biomarkers for cognitive behaviors, brain disorders and diseases. Yet it cannot explain how the structure affects the functionality and how the brain tunes its transition among multiple states to manipulate the cognitive control. In my dissertation, I propose a novel framework of modeling the mechanics of the cognitive control, which involves in applying control theory to analyzing the brain networks and conceptually connecting the cognitive control with the engineering control. First, I examine the energy distribution among different states via combining the energetic and structural constraints of the brain\u27s state transition in a free energy model, where the interaction between regions is explicitly informed by structural connectivity. This work enables the possibility of achieving a whole view of the brain\u27s energy landscape and preliminarily indicates the feasibility of control theory to model the dynamics of cognitive control. In the following work, I exploit the network control theory to address two questions about how the large-scale circuitry of the human brain constrains its dynamics. First, is the human brain theoretically controllable? Second, which areas of the brain are most influential in constraining or facilitating changes in brain state trajectories? Further, I seek to examine the structural effect on the control actions through solving the optimal control problem under different boundary conditions. I quantify the efficiency of regions in terms of the energy cost for the brain state transition from the default mode to task modes. This analysis is extended to the perturbation analysis of trajectories and is applied to the comparison between the group with mild traumatic brain injury(mTBI) and the healthy group. My research is the first to demonstrate how control theory can be used to analyze human brain networks

Probing the Lorentz Symmetry Violation Using the First Image of Sagittarius A*: Constraints on Standard-Model Extension Coefficients

Thanks to unparalleled near-horizon images of the shadows of Messier 87* (M87*) and Sagittarius A* (Sgr A*) delivered by the Event Horizon Telescope (EHT), two amazing windows opened up to us for the strong-field test of the gravity theories as well as fundamental physics. Information recently published from EHT about the Sgr A*'s shadow lets us have a novel possibility of exploration of Lorentz symmetry violation (LSV) within the Standard-Model Extension (SME) framework. Despite the agreement between the shadow image of Sgr A* and the prediction of the general theory of relativity, there is still a slight difference which is expected to be fixed by taking some fundamental corrections into account. We bring up the idea that the recent inferred shadow image of Sgr A* is explicable by a minimal SME-inspired Schwarzschild metric containing the Lorentz violating (LV) terms obtained from the post-Newtonian approximation. The LV terms embedded in Schwarzschild metric are dimensionless spatial coefficients ${\bar s}^{jk}$ associated with the field responsible for LSV in the gravitational sector of the minimal SME theory. In this way, one can control Lorentz invariance violation in the allowed sensitivity level of the first shadow image of Sgr A*. Actually, using the bounds released within $1\sigma$ uncertainty for the shadow size of Sgr A* and whose fractional deviation from standard Schwarzschild, we set upper limits for the two different combinations of spatial diagonal coefficients and the time-time coefficient of the SME, as well. The best upper bound is at the $10^{-2}$ level, which should be interpreted differently from those constraints previously extracted from well-known frameworks since unlike standard SME studies it is not obtained from a Sun-centered celestial frame but comes from probing the black hole horizon scale.Comment: 16 pages, 5 figures. v3:discussion improved, references added, figures revised; matches the version accepted for publication in PR

Near-wall dynamics of inertial particles in dilute turbulent channel flows

This investigation considers the effect of the Stokes number on the near-wall particle dynamics of two-phase (solid-fluid) turbulent channel flows. The spectral element method-based direct numerical simulation code Nek5000 is used to model the fluid phase at a shear Reynolds number, Reτ = 180. Dispersed particles are tracked using a Lagrangian approach with one-way coupling. Eulerian fluid and particle statistics are gathered and analyzed to determine the effect of the Stokes number, first on macroscopic statistics. Previous work of this nature indicates that mean streamwise particle velocities and root-mean-square velocity fluctuations are reduced in the bulk and increased very close to the wall, an effect which is stronger with increased particle Stokes number or inertial particles. This phenomenon has important consequences for mechanisms such as particle deposition and preferential concentration, and so for the first time, this work aims to elucidate the dynamics of this effect through rigorous analysis on various scales. An in-depth force analysis indicates the importance of the lift force, even at increased Stokes numbers, in predicting particle motion in the buffer layer and log-law regions. It is also observed that pressure gradient and virtual mass forces are significant close to the wall. Alongside bulk velocity and acceleration statistics, microscopic behavior is analyzed by considering region-based particle dynamics. Probability density functions are used to determine the effect of the Stokes number on particle motion in three near-wall regions, as well as within the bulk flow. It is observed that at higher Stokes numbers, the viscous sublayer contains particles with dynamic properties similar to those present in the buffer layer. This suggests rapid interlayer migration in the wall direction, causing increased particle turbulence intensities in near-wall regions. A local flow topology classification method is also used to correlate particle behavior with near-wall coherent turbulent structures, and a mechanism for particle sweep toward the wall is suggested. Finally, low-speed streak accumulation and interlayer particle fluxes are considered and the extent of mixing for low and high Stokes numbers is discussed

A content-based music recommender system

Music recommenders have become increasingly relevant due to increased accessibility provided by various music streaming services. Some of these streaming services, such as Spotify, include a recommender system of their own. Despite many advances in recommendation techniques, recommender systems still often do not provide accurate recommendations. This thesis provides an overview of the history and developments of music information retrieval from a more content-based perspective. Furthermore, this thesis describes recommendation as a problem and the methods used for music recommendation with special focus on content-based recommendation by providing detailed descriptions on the audio content features and content-based similarity measures used in content-based music recommender systems. Some of the presented features are used in our own content-based music recommender. Both objective and subjective evaluation of the implemented recommender system further confirm the findings of many researchers that music recommendation based solely on audio content does not provide very accurate recommendations

Has financial attitude impacted the trading activity of retail investors during the COVID-19 pandemic?

Financial attitude influences the financial behavior of retail investors. Although the extant research has acknowledged and examined this relationship, the measures of financial attitude and behavior still vary widely and are generally posed as a series of questions rather than statements. In addition to this, there is insufficient knowledge regarding retail investors' behavior in the face of a health crisis, such as the current COVID-19 pandemic. This study addresses these gaps in the prior literature by examining the relative influence of six dimensions of financial attitude, namely, financial anxiety, optimism, financial security, deliberative thinking, interest in financial issues, and needs for precautionary savings, on the trading activity of retail investors during the pandemic. Data were collected from 404 respondents and analyzed using the artificial neural network (ANN) method. The results revealed that all six dimensions had a positive influence on trading activity, with interest in financial issues exerting the strongest influence, followed by deliberative thinking. The study thus contributes important inferences for researchers and managers.publishedVersio

Has financial attitude impacted the trading activity of retail investors during the COVID-19 pandemic?

Financial attitude influences the financial behavior of retail investors. Although the extant research has acknowledged and examined this relationship, the measures of financial attitude and behavior still vary widely and are generally posed as a series of questions rather than statements. In addition to this, there is insufficient knowledge regarding retail investors' behavior in the face of a health crisis, such as the current COVID-19 pandemic. This study addresses these gaps in the prior literature by examining the relative influence of six dimensions of financial attitude, namely, financial anxiety, optimism, financial security, deliberative thinking, interest in financial issues, and needs for precautionary savings, on the trading activity of retail investors during the pandemic. Data were collected from 404 respondents and analyzed using the artificial neural network (ANN) method. The results revealed that all six dimensions had a positive influence on trading activity, with interest in financial issues exerting the strongest influence, followed by deliberative thinking. The study thus contributes important inferences for researchers and managers.publishedVersio