Search

DC corona discharge has been widely used for the application of volatile organic compounds (VOCs) degradation. In this article, the microdischarge was found in an improved wire-plate corona discharge reactor, which could extremely promote the toluene degradation efficiency. Due to the porous insulating materials attached to the grounded electrode, microdischarge could be easily induced by the accumulated charge on the insulating layer during corona discharge. An influence of parameters of microdischarge was systematically investigated, such as volume resistivity of insulating materials, layer thickness ($d$), the number of pores ($n$), and pore size ($s$). It is found that a better toluene degradation could be obtained when a porous insulating layer with low volume resistivity and small pore was coated on the grounded electrode of the corona discharge reactor. With the polytetrafluoroethylene (PTFE) insulating layer of $d =5 0\,\,\mu \text{m}$ , n $= 50\,\,\mu \text{m}$ , and s $= 10\,\,\mu \text{m}$ , the degradation efficiency of toluene reached 54.2% when the discharge power was 24 W, while the efficiency is only 40.8% compared to the negative dc corona discharge alone. Besides, the concentration of CO2 and CO produced during the degradation of toluene was also increased by 189 and 293 ppm due to the induction of microdischarge. The investigations on the microdischarge-enhanced corona plasma reactor could not only offer an effective way for toluene degradation but also provide new insight on the improvement of plasma reactors for VOCs’ decomposition. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process (e.g., local computations and global communications with the server) incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL in mobile edge networks that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. We establish the analytical relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different optimization metrics. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for different optimization metrics for various datasets and heterogeneous system and statistical settings. [ABSTRACT FROM AUTHOR]

The global alignment of biological networks (GABN) aims to find an optimal alignment between proteins across species, such that both the biological structures and the topological structures of the proteins are maximally conserved. The research on GABN has attracted great attention due to its applications on species evolution, orthology detection and genetic analyses. Most of the existing methods for GABN are difficult to obtain a good tradeoff between the conservation of the biological structures and topological structures. In this paper, we propose a multi-neighborhood learning method for solving GABN (called as CLMNA). CLMNA first models GABN as an optimization of a weighted similarity which evaluates the conserved biological and topological similarities of an alignment, and then it combines a first-proximity, second-proximity and individual-aware proximity learning algorithm to solve the modeled problem. Finally, systematic experiments on 10 pairs of biological networks across 5 species show the superiority of CLMNA over the state-of-the-art network alignment algorithms. They also validate the effectiveness of CLMNA as a refinement method on improving the performance of the compared algorithms. [ABSTRACT FROM AUTHOR]

Mobile edge computing provides the opportunity for wireless users to exploit the power of cloud computing without a large communication delay. To serve data-intensive applications (e.g., video analytics, machine learning tasks) from the edge, we need, in addition to computation resources, storage resources for storing server code and data as well as network bandwidth for receiving user-provided data. Moreover, due to time-varying demands, the code and data placement needs to be adjusted over time, which raises concerns of system stability and operation cost. In this paper, we address these issues by proposing a two-time-scale framework that jointly optimizes service (code and data) placement and request scheduling, while considering storage, communication, computation, and budget constraints. First, by analyzing the hardness of various cases, we completely characterize the complexity of our problem. Next, we develop a polynomial-time service placement algorithm by formulating our problem as a set function optimization, which attains a constant-factor approximation under certain conditions. Furthermore, we develop a polynomial-time request scheduling algorithm by computing the maximum flow in a carefully constructed auxiliary graph, which satisfies hard resource constraints and is provably optimal in the special case where requests have homogeneous resource demands. Extensive synthetic and trace-driven simulations show that the proposed algorithms achieve 90% of the optimal performance. [ABSTRACT FROM AUTHOR]

Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over distributed data. Although viewed as a proxy of the original dataset, each coreset is only designed to approximate the cost function of a specific machine learning problem, and thus different coresets are often required to solve different machine learning problems, increasing the communication overhead. We resolve this dilemma by developing robust coreset construction algorithms that can support a variety of machine learning problems. Motivated by empirical evidence that suitably-weighted $k$ -clustering centers provide a robust coreset, we harden the observation by establishing theoretical conditions under which the coreset provides a guaranteed approximation for a broad range of machine learning problems, and developing both centralized and distributed algorithms to generate coresets satisfying the conditions. The robustness of the proposed algorithms is verified through extensive experiments on diverse datasets with respect to both supervised and unsupervised learning problems. [ABSTRACT FROM AUTHOR]

The rapid development of network function virtualization (NFV) enables a communication network to provide in-network services using virtual network functions (VNFs) deployed on general IT hardware. While existing studies on NFV focused on how to provision VNFs from the provider’s perspective, little is done about how to validate the provisioned resources from the user’s perspective. In this work, we take a first step towards this problem by developing an inference framework designed to “look into” the NFV network. Our framework infers the structure and state of the overlay formed by VNF instances, ingress/egress points of measurement flows, and critical points on their paths (branching/joining points). Our solution only uses external observations such as the required service chains and the end-to-end performance measurements. Besides the novel application scenario, our work also fundamentally advances the state of the art on topology inference by considering (i) general topologies with general measurement paths, and (ii) information of service chains. Our evaluations show that the proposed solution significantly improves both the reconstruction accuracy and the inference accuracy over existing solutions, and service chain information is critical in revealing the structure of the underlying topology. [ABSTRACT FROM AUTHOR]