The Structure Transfer Machine Theory and Applications

Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. We propose a new representation learning method, termed Structure Transfer Machine (STM), which enables feature learning process to converge at the representation expectation in a probabilistic way. We theoretically show that such an expected value of the representation (mean) is achievable if the manifold structure can be transferred from the data space to the feature space. The resulting structure regularization term, named manifold loss, is incorporated into the loss function of the typical deep learning pipeline. The STM architecture is constructed to enforce the learned deep representation to satisfy the intrinsic manifold structure from the data, which results in robust features that suit various application scenarios, such as digit recognition, image classification and object tracking. Compared to state-of-the-art CNN architectures, we achieve the better results on several commonly used benchmarks\footnote{The source code is available. https://github.com/stmstmstm/stm }

Analysis on the Significance of Effects from Operational Conditions on the Performances of Ultrasonic Atomization Dehumidifier with Liquid Desiccant

AbstractIn this work, simulations were carried out based on a L18×L8 cross-product orthogonal array to investigate the significance of the effects from inlet operational conditions on the performances of the ultrasonic atomization liquid desiccant dehumidification system (UADS), where dehumidification effectiveness was adopted as the performance indicator. Taguchi method was employed to analyze the results. It was found that though all of the inlet operational parameters revealed direct effects on the performances of UADS, the significance of their effects was quite different among which, the desiccant flow rate was the most sensible factor in improving DE while air humidity ratio exhibited the least significance. The results presented in this work may help in achieving the optimal running of the liquid desiccant dehumidification system

Protective effects of ischemic postconditioning on intestinal mucosa barrier function in rabbits with crush injury of hind limb: an experimental study

AbstractObjectiveTo explore the protective effects of two types of ischemic postconditioning (IP) on intestinal mucosa barrier in rabbits with crush injury of the hind limb.MethodsThis study was conducted between August and December 2008 in the Department of Trauma Surgery, Daping Hospital, Third Military Medical University, Chongqing, China. The model of crush injury to the hind limb of rabbits was firstly developed by a 25 kg object with the right hind limbs fixed by wooden splints, and then two types of IP were established, including occluding/opening the common iliac artery and vein alternatively (traditional IP, IP A) and binding/loosening the proximum of the injured hind limb alternatively (modified IP, IP B). Thirty-six male New Zealand white rabbits were randomly divided into three groups: IP A group, IP B group and control group, with 12 rabbits in each group. The serum levels of diamine oxidase (DAO) and intestinal fatty acid-binding protein (I-FABP) were detected at 2, 6, 12 and 24 hours after injury. Pathological changes of ileum were examined at 24 hours after injury.ResultsThe serum levels of I-FABP at 2, 6, 12 and 24 hours after injury in both IP A and IP B groups had a significant decrease, compared with control group. DAO levels also showed the same change trend at 2 and 6 hours after injury, but showed no significant difference between two IP groups. No difference in pathological changes of ileum was found among the three groups.ConclusionsIP can protect intestinal mucosa barrier function on the model of hind limb crush injury in rabbits. Meanwhile the modified IP B shows the same protection as the traditional IP A, and is worth applying in clinic

Building a Context World for Dynamic Service Composition

Dynamic service composition requires responding and adapting to changes in the computing environment when orchestrating existing services into one or more new services that fit better to a composite application. This paper abstracts the changes of the environment as a context world to store the physical contexts of the computing environment, user profiles and computed results of services as well. We use ontology techniques to model the domain concepts of application contexts. Context Condition/Effect Description Language is designed to describe the dynamic semantics of the requirements and capabilities of goals and services in a concise and editable manner. Goal-driven and planning techniques are used to dynamically implement the service composition according to the domain knowledge and facts in the context world. ?2010 IEEE.EI

Goal-Driven Context-aware Service Composition

Two important aspects are associated with service composition. One is to understand the needs and constraints for a new added-value composite service, and otherwise it would lead to an ad-hoc effort for service composition. The second is to reflect the changes of computing environment to the service composition to catch up the on-demand of users. This paper introduces a goal-driven approach to specify the user requirements and demands that guides the service composition, and proposes context awareness to adapt to a dynamically changing environment. Computing contexts, including physical context, user profile and computed results, are gathered by various services, and imported into an ontology based a context repository. A Goal Description Language, Context Condition/Effect are designed to describe the dynamic semantics of goal requirements and service capability. A planner is designed and implemented to dynamically compose services based on the current contexts, and a service runner is designed and implemented to invoke proper services based on the contexts and interactions with users. ?2010 IEEE.EI

Binarized attributed network embedding

© 2018 IEEE. Attributed network embedding enables joint representation learning of node links and attributes. Existing attributed network embedding models are designed in continuous Euclidean spaces which often introduce data redundancy and impose challenges to storage and computation costs. To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation. Specifically, we define a new Weisfeiler-Lehman proximity matrix to capture data dependence between node links and attributes by aggregating the information of node attributes and links from neighboring nodes to a given target node in a layer-wise manner. Based on the Weisfeiler-Lehman proximity matrix, we formulate a new Weisfiler-Lehman matrix factorization learning function under the binary node representation constraint. The learning problem is a mixed integer optimization and an efficient cyclic coordinate descent (CCD) algorithm is used as the solution. Node classification and link prediction experiments on real-world datasets show that the proposed BANE model outperforms the state-of-the-art network embedding methods

CAF: Cluster Algorithm and A-Star with Fuzzy Approach for Lifetime Enhancement in Wireless Sensor Networks

Energy is a major factor in designing wireless sensor networks (WSNs). In particular, in the real world, battery energy is limited; thus the effective improvement of the energy becomes the key of the routing protocols. Besides, the sensor nodes are always deployed far away from the base station and the transmission energy consumption is index times increasing with the increase of distance as well. This paper proposes a new routing method for WSNs to extend the network lifetime using a combination of a clustering algorithm, a fuzzy approach, and an A-star method. The proposal is divided into two steps. Firstly, WSNs are separated into clusters using the Stable Election Protocol (SEP) method. Secondly, the combined methods of fuzzy inference and A-star algorithm are adopted, taking into account the factors such as the remaining power, the minimum hops, and the traffic numbers of nodes. Simulation results demonstrate that the proposed method has significant effectiveness in terms of balancing energy consumption as well as maximizing the network lifetime by comparing the performance of the A-star and fuzzy (AF) approach, cluster and fuzzy (CF)method, cluster and A-star (CA)method, A-star method, and SEP algorithm under the same routing criteria

The virulence factor regulator and quorum sensing regulate the type I-F CRISPR-Cas mediated horizontal gene transfer in Pseudomonas aeruginosa

Pseudomonas aeruginosa is capable of thriving in diverse environments due to its network of regulatory components for effective response to stress factors. The survival of the bacteria is also dependent on the ability to discriminate between the acquisition of beneficial and non-beneficial genetic materials via horizontal gene transfer (HGT). Thus, bacteria have evolved the CRISPR-Cas adaptive immune system for defense against the deleterious effect of phage infection and HGT. By using the transposon mutagenesis approach, we identified the virulence factor regulator (Vfr) as a key regulator of the type I-F CRISPR-Cas system in P. aeruginosa. We showed that Vfr influences the expression of the CRISPR-Cas system through two signaling pathways in response to changes in calcium levels. Under calcium-rich conditions, Vfr indirectly regulates the CRISPR-Cas system via modulation of the AHL-QS gene expression, which could be vital for defense against phage infection at high cell density. When encountering calcium deficiency, however, Vfr can directly regulate the CRISPR-Cas system via a cAMP-dependent pathway. Furthermore, we provide evidence that mutation of vfr reduces the CRISPR-Cas spacer acquisition and interference of HGT. The results from this study add to the regulatory network of factors controlling the CRISPR-Cas system in response to abiotic factors in the environment. The findings may facilitate the design of effective and reliable phage therapies against P. aeruginosa infections, as targeting Vfr could prevent the development of the CRISPR-Cas mediated phage resistance