7,561 research outputs found
Quantum Transports in Two-Dimensions with Long Range Hopping: Shielding, Localization and the Extended Isolated State
We investigate the effects of disorder and shielding on quantum transports in
a two dimensional system with all-to-all long range hopping. In the weak
disorder, cooperative shielding manifests itself as perfect conducting channels
identical to those of the short range model, as if the long range hopping does
not exist. With increasing disorder, the average and fluctuation of conductance
are larger than those in the short range model, since the shielding is
effectively broken and therefore long range hopping starts to take effect. Over
several orders of disorder strength (until times of nearest
hopping), although the wavefunctions are not fully extended, they are also
robustly prevented from being completely localized into a single site. Each
wavefunction has several localization centers around the whole sample, thus
leading to a fractal dimension remarkably smaller than 2 and also remarkably
larger than 0, exhibiting a hybrid feature of localization and delocalization.
The size scaling shows that for sufficiently large size and disorder strength,
the conductance tends to saturate to a fixed value with the scaling function
, which is also a marginal phase between the typical metal
() and insulating phase (). The all-to-all coupling expels
one isolated but extended state far out of the band, whose transport is
extremely robust against disorder due to absence of backscattering. The bond
current picture of this isolated state shows a quantum version of short circuit
through long hopping.Comment: 15 pages, 8 figure
Computer and Mathematical Modeling: Translational Research and Economics in Clinical Diagnostics
The computer and modeling approach has begun to be used extensively in clinical intelligence diagnosis, we have refined the necessary techniques related to intelligence medicine, and we have performed economics-directional analysis of models and structures of artificial intelligence in the translational medicine sense.At the same time, the development of clinical diagnostic techniques is also the result of constant innovation, and we propose the necessary strategy for a cross-disciplinary approach to clinical diagnostics and computer and mathematical modeling, with the authors reporting in conjunction with the results of the study
Severity and prevalence of behavioral and psychological symptoms among patients of different dementia stages in Taiwan
Background To better understand the trends of behavioral and psychological symptoms of dementia (BPSD) over the disease progression is important to provide psychoeducation for dementia caregivers. Objective This study examined the severity and occurrence of BPSD across the various degrees of the disease. Methods This study was a cross-sectional design. Patients (N = 276) who had dementia from July 2001 to October 2008 were surveyed and assessed for dementia stage, using the clinical dementia rating scale (CDR). BPSD was evaluated using the Neuropsychiatric Inventory (NPI). We examined the differences between the severities and occurrence of the individual’s BPSD among various CDR stages with the Kruskal-Wallis test and Chi-square test. Results Delusion (p = 0.01), agitation/aggression (p = 0.033), apathy/indifference (p = 0.009), aberrant motor behavior (p < 0.001), nighttime behavior disturbances (p < 0.001), and eating abnormalities (p = 0.001) were significantly different among stages of dementia. The severity of BPSD became exacerbated over the course of the disease, and was highest in moderate (CDR = 2) or severe (CDR = 3) dementia. The occurrence of BPSD was highest when the CDR equaled 2 (97.5%). Discussion The association of global (or certain) BPSD, across different stages of dementia, is a non-linear relationship. These findings suggest the importance of taking into account clinical dementia stage for managing BPSD
Orbital Magnetization under an Electric Field and Orbital Magnetoelectric Polarizabilty for a Bilayer Chern System
In the the real space formalism of orbital magnetization (OM) for a Chern
insulator without an external electric field, it is correct to average the
local OM either over the bulk region or over the whole sample. However for a
layered Chern insulator in an external electric field, which is directly
related to the nontrivial nature of orbital magnetoelectric coupling, the role
of boundaries remains ambiguous in this formalism. Based on a bilayer model
with an adjustable Chern number at half filling, we numerically investigate the
OM with the above two different average types under a nonzero perpendicular
electric field. The result shows that in this case, the nonzero Chern number
gives rise to a gauge shift of the OM with the bulk region average, while this
gauge shift is absent for the OM with the whole sample average. This indicates
that only the whole sample average is reliable to calculate the OM under a
nonzero electric field for Chern insulators. On this basis, the orbital
magnetoelectric polarizablity (OMP) and the Chern-Simons orbital
magnetoelectric polarizablity (CSOMP) with the whole sample average are
studied. We also present the relationship between the OMP (CSOMP) and the
response of Berry curvature to the electric field. The stronger the response of
Berry curvature to electric field, the stronger is the OMP (CSOMP). Besides
clarify the calculation methods, our result also provides an effective method
to enhance OMP and CSOMP of materials.Comment: 11 pages, 11 figure
Omnidirectional Information Gathering for Knowledge Transfer-based Audio-Visual Navigation
Audio-visual navigation is an audio-targeted wayfinding task where a robot
agent is entailed to travel a never-before-seen 3D environment towards the
sounding source. In this article, we present ORAN, an omnidirectional
audio-visual navigator based on cross-task navigation skill transfer. In
particular, ORAN sharpens its two basic abilities for a such challenging task,
namely wayfinding and audio-visual information gathering. First, ORAN is
trained with a confidence-aware cross-task policy distillation (CCPD) strategy.
CCPD transfers the fundamental, point-to-point wayfinding skill that is well
trained on the large-scale PointGoal task to ORAN, so as to help ORAN to better
master audio-visual navigation with far fewer training samples. To improve the
efficiency of knowledge transfer and address the domain gap, CCPD is made to be
adaptive to the decision confidence of the teacher policy. Second, ORAN is
equipped with an omnidirectional information gathering (OIG) mechanism, i.e.,
gleaning visual-acoustic observations from different directions before
decision-making. As a result, ORAN yields more robust navigation behaviour.
Taking CCPD and OIG together, ORAN significantly outperforms previous
competitors. After the model ensemble, we got 1st in Soundspaces Challenge
2022, improving SPL and SR by 53% and 35% relatively.Comment: ICCV 202
Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing
To address the challenging task of instance-aware human part parsing, a new
bottom-up regime is proposed to learn category-level human semantic
segmentation as well as multi-person pose estimation in a joint and end-to-end
manner. It is a compact, efficient and powerful framework that exploits
structural information over different human granularities and eases the
difficulty of person partitioning. Specifically, a dense-to-sparse projection
field, which allows explicitly associating dense human semantics with sparse
keypoints, is learnt and progressively improved over the network feature
pyramid for robustness. Then, the difficult pixel grouping problem is cast as
an easier, multi-person joint assembling task. By formulating joint association
as maximum-weight bipartite matching, a differentiable solution is developed to
exploit projected gradient descent and Dykstra's cyclic projection algorithm.
This makes our method end-to-end trainable and allows back-propagating the
grouping error to directly supervise multi-granularity human representation
learning. This is distinguished from current bottom-up human parsers or pose
estimators which require sophisticated post-processing or heuristic greedy
algorithms. Experiments on three instance-aware human parsing datasets show
that our model outperforms other bottom-up alternatives with much more
efficient inference.Comment: CVPR 2021 (Oral). Code: https://github.com/tfzhou/MG-HumanParsin
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