9,117 research outputs found
Kernelized Sparse Self-Representation for Clustering and Recommendation
Sparse models have demonstrated substantial success in applications for data analysis such as clustering, classification and denoising. However, most of the current work is built upon the assumption that data is distributed in a union of subspaces, whereas limited work has been conducted on nonlinear datasets where data reside in a union of manifolds rather than a union of subspaces. To understand data nonlinearity using sparse models, in this paper, we propose to exploit the self-representation property of nonlinear data in an implicit feature space using kernel methods. We propose a kernelized sparse self-representation model, denoted as KSSR, and a novel Kernelized Fast Iterative Soft-Thresholding Algorithm, denoted as K-FISTA, to recover the underlying nonlinear structure among the data. We evaluate our method for clustering problems on both synthetic and real-world datasets, and demonstrate its superior performance compared to the other state-of-the-art methods. We also apply our method for collaborative filtering in recommender systems, and demonstrate its great potential for novel applications beyond clustering
Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems
There is a trend towards using wireless technologies in networked control
systems. However, the adverse properties of the radio channels make it
difficult to design and implement control systems in wireless environments. To
attack the uncertainty in available communication resources in wireless control
systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS)
scheme is developed, which takes advantage of the co-design of control and
wireless communications. By exploiting cross-layer design, CLAFS adjusts the
sampling periods of control systems at the application layer based on
information about deadline miss ratio and transmission rate from the physical
layer. Within the framework of feedback scheduling, the control performance is
maximized through controlling the deadline miss ratio. Key design parameters of
the feedback scheduler are adapted to dynamic changes in the channel condition.
An event-driven invocation mechanism for the feedback scheduler is also
developed. Simulation results show that the proposed approach is efficient in
dealing with channel capacity variations and noise interference, thus providing
an enabling technology for control over WLAN.Comment: 17 pages, 12 figures; Open Access at
http://www.mdpi.org/sensors/papers/s8074265.pd
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
2-Chloro-4-(3,3-dichloroallyloxy)-1-nitrobenzene
In the crystal structure of the title compound, C9H6Cl3NO3, molecules are connected by C—H⋯O hydrogen bonds, forming chains along the b axis. The dihedral angle between the benzene ring and the plane of the nitro group is 16.2 (1)° and that between the benzene ring and the plane of the dichloroallyl group is 10.2 (1)°
The Schur concavity, Schur multiplicative and harmonic convexities of the second dual form of the Hamy symmetric function with applications
AbstractFor x=(x1,x2,…,xn)∈R+n, the second dual form of the Hamy symmetric function is defined by Hn∗∗(x,r)=Hn∗∗(x1,x2,…,xn;r)=∏1≤i1<i2<⋯<ir≤n(∑j=1rxij)1r, where r∈{1,2,…,n} and i1,i2,…,in are positive integers.In this paper, we prove that Hn∗∗(x,r) is Schur concave, and Schur multiplicatively and harmonic convex in R+n. Some applications in inequalities and reliability theory are presented
Ozone and haze pollution weakens net primary productivity in China
Atmospheric pollutants have both beneficial and detrimental effects on carbon uptake by land ecosystems. Surface ozone (O3) damages leaf photosynthesis by oxidizing plant cells, while aerosols promote carbon uptake by increasing diffuse radiation and exert additional influences through concomitant perturbations to meteorology and hydrology. China is currently the world’s largest emitter of both carbon dioxide and short-lived air pollutants. The land ecosystems of China are estimated to provide a carbon sink, but it remains unclear whether air pollution acts to inhibit or promote carbon uptake. Here, we employ Earth system modeling and multiple measurement datasets to assess the separate and combined effects of anthropogenic O3 and aerosol pollution on net primary productivity (NPP) in China. In the present day, O3 reduces annual NPP by 0.6 Pg C (14 %) with a range from 0.4 Pg C (low O3 sensitivity) to 0.8 Pg C (high O3 sensitivity). In contrast, aerosol direct effects increase NPP by 0.2 Pg C (5 %) through the combination of diffuse radiation fertilization, reduced canopy temperatures, and reduced evaporation leading to higher soil moisture. Consequently, the net effects of O3 and aerosols decrease NPP by 0.4 Pg C (9 %) with a range from 0.2 Pg C (low O3 sensitivity) to 0.6 Pg C (high O3 sensitivity). However, precipitation inhibition from combined aerosol direct and indirect effects reduces annual NPP by 0.2 Pg C (4 %), leading to a net air pollution suppression of 0.8 Pg C (16 %) with a range from 0.6 Pg C (low O3 sensitivity) to 1.0 Pg C (high O3 sensitivity). Our results reveal strong dampening effects of air pollution on the land carbon uptake in China today. Following the current legislation emission scenario, this suppression will be further increased by the year 2030, mainly due to a continuing increase in surface O3. However, the maximum technically feasible reduction scenario could drastically relieve the current level of NPP damage by 70 % in 2030, offering protection of this critical ecosystem service and the mitigation of long-term global warming
Big networks : a survey
A network is a typical expressive form of representing complex systems in terms of vertices and links, in which the pattern of interactions amongst components of the network is intricate. The network can be static that does not change over time or dynamic that evolves through time. The complication of network analysis is different under the new circumstance of network size explosive increasing. In this paper, we introduce a new network science concept called a big network. A big networks is generally in large-scale with a complicated and higher-order inner structure. This paper proposes a guideline framework that gives an insight into the major topics in the area of network science from the viewpoint of a big network. We first introduce the structural characteristics of big networks from three levels, which are micro-level, meso-level, and macro-level. We then discuss some state-of-the-art advanced topics of big network analysis. Big network models and related approaches, including ranking methods, partition approaches, as well as network embedding algorithms are systematically introduced. Some typical applications in big networks are then reviewed, such as community detection, link prediction, recommendation, etc. Moreover, we also pinpoint some critical open issues that need to be investigated further. © 2020 Elsevier Inc
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