631 research outputs found
Role of Alpha Oscillations During Short Time Memory Task Investigated by Graph Based Partitioning
In this study, we investigate the clustering pattern of alpha band (8 Hz - 12 Hz) electroencephalogram (EEG) oscillations obtained from healthy individuals during a short time memory task with 3 different memory loads. The retention period during which subjects were asked to memorize a pattern in a square matrix is analyzed with a graph theoretical approach. The functional coupling among EEG electrodes are quantified via mutual information in the time-frequency plane. A spectral clustering algorithm followed by bootstrapping is used to parcellate memory related circuits and for identifying significant clusters in the brain. The main outcome of the study is that the size of the significant clusters formed by alpha oscillations decreases as the memory load increases. This finding corroborates the active inhibition hypothesis about alpha oscillations
Investigation of dispersion-relation-preserving scheme and spectral analysis methods for acoustic waves
Important characteristics of the aeroacoustic wave propagation are mostly encoded in their dispersion relations. Hence, a computational aeroacoustic (CAA) algorithm, which reasonably preserves these relations, was investigated. It was derived using an optimization procedure to ensure, that the numerical derivatives preserved the wave number and angular frequency of the differential terms in the linearized, 2-D Euler equations. Then, simulations were performed to validate the scheme and a compatible set of discretized boundary conditions. The computational results were found to agree favorably with the exact solutions. The boundary conditions were transparent to the outgoing waves, except when the disturbance source was close to a boundary. The time-domain data generated by such CAA solutions were often intractable until their spectra was analyzed. Therefore, the relative merits of three different methods were included in the study. For simple, periodic waves, the periodogram method produced better estimates of the steep-sloped spectra than the Blackman-Tukey method. Also, for this problem, the Hanning window was more effective when used with the weighted-overlapped-segment-averaging and Blackman-Tukey methods gave better results than the periodogram method. Finally, it was demonstrated that the representation of time domain-data was significantly dependent on the particular spectral analysis method employed
A Review of Business Models for Shared Mobility and Mobility-as-a-Service (MaaS):A Research Report
The mobility solutions that currently dominate the mobility market have raised global challenges. Specifically, mass car ownership has led to traffic congestion, shortage of parking spaces, and sustainability issues. Recently, mobility solutions driven by technological advancements have emerged to address these issues via more efficient and sustainable use of resources. However, the wide range of mobility offerings has led to a scattered mobility market, and oversight is hard to grasp for travelers. Mobility-as-a-Service (MaaS) platforms aim to address this issue by integrating mobility services into a single platform. However, MaaS providers (operators) struggle to find sustainable business models. Additionally, research on shared mobility business models is limited, and there is little oversight in the scattered business model landscape. This report addresses this issue by summarizing the dominant business models in the mobility market through a systematic review of current initiatives and literature. It provides an overview of active MaaS business models and challenges and opportunities to integrate mobility services into MaaS. The types of mobility services reviewed in this study include bike-sharing, scooter-sharing, car-sharing, e-hailing, and MaaS platform providers. For each mobility service, the dominant operating mode and the main business model actors are identified and represented using the Service-Dominant Business Model Radar (SDBM/R). Furthermore, the value exchanges between the actors are mapped in Value Capture Diagrams. The report concludes with a discussion on the challenges and opportunities related to synthesizing shared mobility modes into MaaS and the expectations for its future
Recommended from our members
Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank
In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment is unfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) images to the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics (heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factors including acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage (i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of the stacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slice motion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastolic cardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involved in UKBB CMR acquisition and for the ones who use the dataset for research purposes
Chaos in Matrix Gauge Theories with Massive Deformations
Starting from an matrix quantum mechanics model with massive
deformation terms and by introducing an ansatz configuration involving fuzzy
four- and two-spheres with collective time dependence, we obtain a family of
effective Hamiltonians, and examine
their emerging chaotic dynamics. Through numerical work, we model the variation
of the largest Lyapunov exponents as a function of the energy and find that
they vary either as or , where
stand for the energies of the unstable fixed points of the phase
space. We use our results to put upper bounds on the temperature above which
the Lyapunov exponents comply with the Maldacena-Shenker-Stanford (MSS) bound,
, and below which it will eventually be violated.Comment: 17 pages, 2 figures, 5 tables, Talk given by S.
K\"{u}rk\c{c}\"{u}o\v{g}lu at the workshop on "Quantum Geometry, Field Theory
and Gravity", Corfu Summer Institute 202
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution
In this work, we investigate the value of uncertainty modeling in 3D
super-resolution with convolutional neural networks (CNNs). Deep learning has
shown success in a plethora of medical image transformation problems, such as
super-resolution (SR) and image synthesis. However, the highly ill-posed nature
of such problems results in inevitable ambiguity in the learning of networks.
We propose to account for intrinsic uncertainty through a per-patch
heteroscedastic noise model and for parameter uncertainty through approximate
Bayesian inference in the form of variational dropout. We show that the
combined benefits of both lead to the state-of-the-art performance SR of
diffusion MR brain images in terms of errors compared to ground truth. We
further show that the reduced error scores produce tangible benefits in
downstream tractography. In addition, the probabilistic nature of the methods
naturally confers a mechanism to quantify uncertainty over the super-resolved
output. We demonstrate through experiments on both healthy and pathological
brains the potential utility of such an uncertainty measure in the risk
assessment of the super-resolved images for subsequent clinical use.Comment: Accepted paper at MICCAI 201
- …