334 research outputs found
Metric sparsification and operator norm localization
We study an operator norm localization property and its applications to the
coarse Novikov conjecture in operator K-theory. A metric space X is said to
have operator norm localization property if there exists a positive number c
such that for every r>0, there is R>0 for which, if m is a positive locally
finite Borel measure on X, H is a separable infinite dimensional Hilbert space
and T is a bounded linear operator acting on L^2(X,m) with propagation r, then
there exists an unit vector v satisfying with support of diameter at most R and
such that |Tv| is larger or equal than c|T|. If X has finite asymptotic
dimension, then X has operator norm localization property. In this paper, we
introduce a sufficient geometric condition for the operator norm localization
property. This is used to give many examples of finitely generated groups with
infinite asymptotic dimension and the operator norm localization property. We
also show that any sequence of expanding graphs does not possess the operator
norm localization property
Asymptotic pseudodifferential calculus and the rescaled bundle
By following a groupoid approach to pseudodifferential calculus developed by
Van erp and Yuncken, we study the parallel theory on the rescaled bundle and
show that the rescaled bundle gives a geometric characterization to asymptotic
pseudodifferential calculus on spinor bundles by Block and Fox
Effects of Aeration Treatment on γ
To explore the optimum condition of γ-aminobutyric acid (GABA) accumulation in germinated tartary buckwheat, effects of some factors including aeration treatment, physiological indexes, air flow rate, culture temperature, and pH value of cultivating solution under hypoxia on GABA in germinated tartary buckwheat were investigated. The results showed that the dark cultures with distilled water at 30°C, 2 days, and aeration stress with 1.0 L/min air flow rate at 30°C were optimal for GABA accumulation. Under these conditions, the predicted content of GABA was up to 371.98 μg/g DW. The analysis of correlation indicated that there was a significant correlation (P<0.01) between GABA accumulation and physiological indexes. Box-Behnken experimental analysis revealed that optimal conditions with aeration treatment for GABA accumulation in germinated tartary buckwheat were air flow rate of 1.04 L/min, culture temperature of 31.25°C, and a pH value of 4.21. Under these conditions, the GABA content was predicted as high as 386.20 μg/g DW, which was close to the measured value (379.00±9.30 μg/g DW). The variance analysis and validation test suggested that this established regression model could predict GABA accumulation in tartary buckwheat during germination
Low-Latency Strategies for Service Migration in Fog Computing Enabled Cellular Networks
This chapter presents a fog computing enabled cellular network (FeCN), in which the high user-mobility feature brings critical challenges for service continuity under stringent service requirements. Service migration is promising to fulfill the service continuity during mobility. However, service migration cannot be completed immediately and may lead to situations where the user-experience degrades. For this, a quality-of-service aware service migration strategy is proposed. The method is based on existing handover procedures with newly introduced distributed fog computing resource management scheme to minimize the potential negative effects induced by service migration. The performance of the proposed schemes is evaluated by a case study, where realistic vehicular mobility pattern in the metropolitan network of Luxembourg is used. Results show that low end-to-end latency for vehicular communication can be achieved. During service migration, both the traffic generated by migration and the other traffic (e.g., control information, video) are transmitted via mobile backhaul networks. To balance the performance of the two kinds of traffic, a delay-aware bandwidth slicing scheme is proposed. Simulation results show that, with the proposed method, migration data can be transmitted successfully within a required time threshold, while the latency and jitter for nonmigration traffic with different priorities can be reduced significantly
General approach to tunable critical phases with two coupled chains
Critical phase (CP) with multifractal wave functions has attracted much
attention in the past decades. However, the underlying mechanism for this phase
is still ambiguous. Here we propose that the coupling between the localized and
the extended states in their overlapped spectra can provide a general recipe
for this phase with tunable structures. We demonstrate this picture using two
models. In the first model, we show that the CP can be realized in the
overlapped spectra with quasiperiodic potential, in which the CP regime can be
tailored by the offset between the two chains, yielding tunable CP. This phase
is insensitive to the forms of inter-chain couplings and quasiperiodic
potentials. In the second model, we consider the CP by a disordered flat bands
coupling with an extended band. We show that the localized states in the flat
bands turn into critical too. Finally, we account for the emergence of this
phase as a result of unbounded potential, which yields singular continuous
spectra and excludes the extended states. Our approach opens a remarkable
avenue for various CPs with tailored structures, which have wide applications
in higher-dimensional single-particle CPs and many-body CPs.Comment: 7+11 pages, 5+13 figure
Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation
Segmentation stands at the forefront of many high-level vision tasks. In this
study, we focus on segmenting finger bones within a newly introduced
semi-supervised self-taught deep learning framework which consists of a student
network and a stand-alone teacher module. The whole system is boosted in a
life-long learning manner wherein each step the teacher module provides a
refinement for the student network to learn with newly unlabeled data.
Experimental results demonstrate the superiority of the proposed method over
conventional supervised deep learning methods.Comment: IEEE BHI 2019 accepte
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