Asymmetric Network Research Articles

Irreversible energy transfer, localization and non-reciprocity in weakly coupled, nonlinear lattices with asymmetry

A semi-infinite network of two coupled semi-infinite nonlinear lattices is studied, having the capacity for passive irreversible energy transfer (redirection) from an “excited lattice” (forced by an impulse) to an “absorbing lattice” (that is not directly forced). This is achieved by breaking the symmetry through a spatial variation of the grounding stiffnesses of the excited lattice from the second unit cell onwards, and assuming weak coupling. We show analytically that irreversible energy transfer is caused by the macroscopic analogue (in space) of the Landau–Zener tunneling (LZT) effect, which originated in quantum mechanics in the context of linear parametric oscillators (in time). By constructing a reduced-order model (ROM) of two coupled oscillators it is possible to theoretically model the LZT-induced irreversible energy redirection from the excited to the absorbing lattice. A computational study reveals that the LZT effect is realized only in a critical band of energy, while outside this band there occurs energy localization. The lower and upper energy bounds of this critical band are theoretically approximated by constructing appropriate ROMs of the coupled lattices and studying the bifurcations in their dynamics with energy. This analysis sheds physical insight on the different regimes of the acoustics, and theoretical predictions agree well with direct numerical simulations of the full lattice network. Finally, we show that the LZT effect induces strong non-reciprocity in the dynamics of the finite asymmetric lattice network, in the sense that the impulsive response of this network depends crucially on the forcing-measurement locations and energy. The implications and the possible applications of these results are discussed.

Transmission line fault location based on three-phase state estimation framework considering measurement chain error model

In this paper, a wide-area integrated method including a number of algorithms for transmission lines fault location, faulty line discrimination, fault type and faulted phase(s) identification is suggested. The proposed method is based on extension and modification of state estimation formulation. Thus, the method is applicable to both symmetrical and asymmetrical networks as well as all fault types including symmetrical and asymmetrical ones. The method exploites the capacities of the state estimation formulation and the solution algorithm of weighted least squares (WLS) to reduce the effect of inherent error measurement chain on the fault location accuracy and detection and elimination of bad data in the measurement vectors. For this purpose, an error model of the measurement chain including instrument transformers and PMUs is proposed. This model is used to design measuring errors covariance matrix in the state estimation formulation. The performance of the proposed method has been investigated through numerous fault events simulated on different locations of all transmission lines of the 39-bus IEEE test system.

Survival Scale: Marketplace Lending and Asymmetric Network Effects

Using data on 988 peer-to-peer lending platforms in China, we examine cross-side network effects (CNEs)---arguably the most important factor for multi-sided marketplaces---throughout platforms’ lifecycle in a dynamic industry characterized by entries, exits, and network externalities. We find that unlike borrowers’ symmetric CNEs, lenders’ CNEs are lower on declining, more established, or smaller platforms than on growing, new, or larger platforms. Borrowers’ CNEs are also larger than lenders’ CNEs, especially for declining or sub-scale platforms. We rationalize the asymmetries in a model of financial platforms incorporating endogenous failures and their empirically motivated distinguishing features: lenders’ portfolio diversification, failures’ differential impacts on agents, and borrowers’ stickiness. The model further predicts that lenders’ CNEs and platforms’ ranks predict the platforms’ survival likelihood, among others, which the data corroborate. Our findings provide novel economic insights on multi-sided platforms and inform FinTech practitioners and regulators.

Reply to “Comments on ‘A Theorem on Asymmetric Structure-Based Rat-Race Coupler”’

The theorem proposed in the original article ( IEEE Microw. Wireless Compon. Lett. , vol. 25, no. 3, pp. 145–147, Mar. 2015.) regarding the replacement of six quarter-wavelength lines of conventional rat-race coupler (RRC) using asymmetric two-port networks (TNs) is absolutely correct. This letter will further illustrate the concept of single-frequency equivalence of the conventional RRC using asymmetric TNs.

Gene co-expression network analysis reveals pathways associated with graft healing by asymmetric profiling in tomato

BackgroundThe ability of severed rootstocks and shoots to re-establish vascular connections is used to generate grafted plants that combine desirable traits from both scions and rootstocks. Clarifying the mechanisms of graft healing is essential for its further application. We performed RNA sequencing of internodes near the cut position, making a distinction between separated or grafted tissues above and below the cut, in order to obtain a genetic description of graft union formation.ResultsUsing weighted gene co-expression analysis, variable transcripts were clustered into 10 distinct co-expression networks (modules) based on expression profiles, and genes with the most “hubness” (“hub” genes show the most connections in a network) within each module were predicted. A large proportion of modules were related to Position, and represent asymmetric expression networks from different pathways. Expression of genes involved in auxin and sugar transport and signaling, and brassinosteroid biosynthesis was increased above the cut, while stress response genes were up-regulated below the cut. Some modules were related to graft union formation, among which oxidative detoxification genes were co-expressed along with both wounding response and cell wall organization genes.ConclusionsThe present work provides a comprehensive understanding of graft healing-related gene networks in tomato. Also, the candidate pathways and hub genes identified here will be valuable for future studies of grafting in tomato.

A strategic analysis of stationary radiation portal monitors and mobile detection systems in border monitoring

Abstract Radiation Portal Monitors (RPMs) are our primary border defense against nuclear smuggling, but are they still the best way to spend limited funds? The purpose of this research is to strategically compare RPM defense at the border with state-side mobile detectors. Limiting the problem to a comparison of two technologies, a decision-maker can prioritize how to best allocate resources, by reinforcing the border with stationary overt RPMs, or by investing in Mobile Radiation Detection Systems (MRDs) which are harder for an adversary to detect but may have other weaknesses. An abstract, symmetric network was studied to understand the impact of initial conditions on a network. An asymmetric network, loosely modeled on a state transportation system, is then examined for the technology that will maximally suppress the adversary's success rate. We conclude that MRDs, which have the advantage of discrete operation, outperform RPMs deployed to a border. We also conclude that MRDs maintain this strategic advantage if they operate with one-tenth the relative efficiency of their stationary counter-parts or better.

Resource Allocation Among Attractor Vertices in Asymmetric Regular Resource Networks

In this paper, asymmetric regular resource networks with several attractor vertices are considered. It is demonstrated that the resource surplus ΔW = W − T above a threshold value W = T has the same allocation in such a network as in the corresponding absorbing network, which is obtained from the asymmetric one by eliminating the outbound edges of attractors. But there exist corrections depending on the capacities of the outbound edges of attractors and also on the initial resource allocation. Upper bounds of these corrections are derived. The initial states that lead to the exact limit states without any adjustments are determined.

Document-level Sentiment Analysis Based on Domain-specific Sentiment Words

Aiming at the problem that the relationship between non-contiguous words in a sentence cannot be effectively captured, and the existing sentiment lexicon has poor adaptability in the field, a model for document-level sentiment analysis based on domain-specific sentiment words is constructed. We reconstructed word vectors using attention mechanism to capture the relationship between non-contiguous words in word vectors; Words are synthesized using Asymmetric Convolutional Neural Network. Sentences are synthesized by Bidirectional Gated Recurrent Neural Network based on attention mechanism to form document vector features; we used CNN to construct a domain-specific sentiment dictionary to generate emotional vector features; Document vector features and emotional vector features are combined using a linear binding layer to form document features that facilitate document classification. By comparing the performance of this method with other methods through experiments, the results show that there is a big advantage in classification accuracy, and can be widely used in various specific fields such as public health.

Resilient Clock Synchronization Using Power Grid Voltage

Many clock synchronization protocols based on message passing, e.g., the Network Time Protocol (NTP), assume symmetric network delays to estimate the one-way packet transmission time as half of the round-trip time. As a result, asymmetric network delays caused by either network congestion or malicious packet delays can cause significant synchronization errors. This article exploits sinusoidal voltage signals of an alternating current (AC) power grid to limit the impact of the asymmetric network delays on these clock synchronization protocols. Our extensive measurements show that the voltage signals at geographically distributed locations in a city are highly synchronized. Leveraging calibrated voltage phases, we develop a new clock synchronization protocol that we call Grid Time Protocol (GTP), which allows direct measurement of one-way packet transmission times between its slave and master nodes, subject to an analytic condition that can be easily verified in practice. The direct measurements render GTP resilient against asymmetric network delays under this condition. A prototype implementation of GTP maintains sub-millisecond synchronization accuracy for two nodes tens of kilometers apart in the presence of malicious packet delays. The result has been demonstrated for both Singapore and Hangzhou, China. Simulations driven by real network delay measurements between Singapore and Hangzhou under both normal and congested network conditions also show the synchronization accuracy improvement by GTP. We believe that GTP is suitable for grid-connected distributed systems that are currently served by NTP but desire higher resilience against unfavorable network dynamics and packet delay attacks.

AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification

Background and objectivesThe multi-label Human Protein Atlas (HPA) classification can yield a better understanding of human diseases and help doctors to enhance the automatic analysis of biomedical images. The existing automatic protein recognition methods have been limited to single pattern. Therefore, an automatic multi-label human protein atlas recognition system with satisfactory performance should be conducted. This work aims to build an automatic recognition system for multi-label human protein atlas classification based on deep learning. MethodsIn this work, an automatic feature extraction and multi-label classification framework is proposed. Specifically, an asymmetric and multi-scale convolutional neural network is designed for HPA classification. Furthermore, this work introduces a combined loss that consists of the binary cross-entropy and F1-score losses to improve identification performance. ResultsRigorous experiments are conducted to estimate the proposed system. In particular, unlike the current automatic identification systems, which focus on a limited number of patterns, the proposed method is capable of classifying mixed patterns of proteins in microscope images and can handle the subcellular multi-label protein classification task including 28 subcellular localization patterns. The proposed framework based on deep convolutional neural network outperformed the existing approaches with a F1-score of 0.823, which illustrates the robustness and effectiveness of the proposed system. ConclusionThis study proposed a high-performance recognition system for protein atlas classification based on deep learning, and it achieved an automatic multi-label human protein atlas identification framework with superior performance than previous studies.

Back-End Electronics Based on an Asymmetric Network for Low Background and Medium- Scale Physics Experiments

The detector readout architecture introduced in this paper is intended for small to medium size physics experiments that have moderate bandwidth needs and applications that only tolerate an ultimately low background radioactivity for the parts close to the detector. The first idea to simplify the readout system and minimize the material budget is to use a common fan-out structure to transport from off-detector back-end electronics to the front ends all the traffic required for the synchronization, configuration, and readout. The second idea is to use between each front-end card and the back-end electronics a point-to-point link that runs at the relatively low speed that suffices for the target application. This broadens the possible choices of physical media, e.g., glass fiber, plastic optical fiber, or copper. This paper presents a communication protocol adequate for the proposed asymmetric network. It shows the design of a back-end unit capable of controlling 32 front-end units at up to 12.8 Gb/s of aggregate bandwidth. This back-end unit uses an inexpensive commercial field-programmable gate array (FPGA) module where a large number of regular I/O pins interface to the front-end links, while the few available multigigabit per second capable transceivers are assigned to the communication with the upper stage of the data acquisition (DAQ) system.

Weaponized Interdependence: How Global Economic Networks Shape State Coercion

Abstract Liberals claim that globalization has led to fragmentation and decentralized networks of power relations. This does not explain how states increasingly “weaponize interdependence” by leveraging global networks of informational and financial exchange for strategic advantage. The theoretical literature on network topography shows how standard models predict that many networks grow asymmetrically so that some nodes are far more connected than others. This model nicely describes several key global economic networks, centering on the United States and a few other states. Highly asymmetric networks allow states with (1) effective jurisdiction over the central economic nodes and (2) appropriate domestic institutions and norms to weaponize these structural advantages for coercive ends. In particular, two mechanisms can be identified. First, states can employ the “panopticon effect” to gather strategically valuable information. Second, they can employ the “chokepoint effect” to deny network access to adversaries. Tests of the plausibility of these arguments across two extended case studies that provide variation both in the extent of U.S. jurisdiction and in the presence of domestic institutions—the SWIFT financial messaging system and the internet—confirm the framework's expectations. A better understanding of the policy implications of the use and potential overuse of these tools, as well as the response strategies of targeted states, will recast scholarly debates on the relationship between economic globalization and state coercion.

PCA-ANN Based Algorithm for the Determination of Asymmetrical Network Failures of Network-Connected Induction Generators

PCA-ANN Based Algorithm for the Determination of Asymmetrical Network Failures of Network-Connected Induction Generators

Hydrodynamic Microparticle Separation Mechanism Using Three-Dimensional Flow Profiles in Dual-Depth and Asymmetric Lattice-Shaped Microchannel Networks.

We herein propose a new hydrodynamic mechanism of particle separation using dual-depth, lattice-patterned asymmetric microchannel networks. This mechanism utilizes three-dimensional (3D) laminar flow profiles formed at intersections of lattice channels. Large particles, primarily flowing near the bottom surface, frequently enter the shallower channels (separation channels), whereas smaller particles flowing near the microchannel ceiling primarily flow along the deeper channels (main channels). Consequently, size-based continuous particle separation was achieved in the lateral direction in the lattice area. We confirmed that the depth of the main channel was a critical factor dominating the particle separation efficiencies, and the combination of 15-μm-deep separation channels and 40-μm-deep main channels demonstrated the good separation ability for 3–10-μm particles. We prepared several types of microchannels and successfully tuned the particle separation size. Furthermore, the input position of the particle suspension was controlled by adjusting the input flow rates and/or using a Y-shaped inlet connector that resulted in a significant improvement in the separation precision. The presented concept is a good example of a new type of microfluidic particle separation mechanism using 3D flows and may potentially be applicable to the sorting of various types of micrometer-sized objects, including living cells and synthetic microparticles.

DeepACLSTM: deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction

BackgroundProtein secondary structure (PSS) is critical to further predict the tertiary structure, understand protein function and design drugs. However, experimental techniques of PSS are time consuming and expensive, and thus it’s very urgent to develop efficient computational approaches for predicting PSS based on sequence information alone. Moreover, the feature matrix of a protein contains two dimensions: the amino-acid residue dimension and the feature vector dimension. Existing deep learning based methods have achieved remarkable performances of PSS prediction, but the methods often utilize the features from the amino-acid dimension. Thus, there is still room to improve computational methods of PSS prediction.ResultsWe propose a novel deep neural network method, called DeepACLSTM, to predict 8-category PSS from protein sequence features and profile features. Our method efficiently applies asymmetric convolutional neural networks (ACNNs) combined with bidirectional long short-term memory (BLSTM) neural networks to predict PSS, leveraging the feature vector dimension of the protein feature matrix. In DeepACLSTM, the ACNNs extract the complex local contexts of amino-acids; the BLSTM neural networks capture the long-distance interdependencies between amino-acids. Furthermore, the prediction module predicts the category of each amino-acid residue based on both local contexts and long-distance interdependencies. To evaluate performances of DeepACLSTM, we conduct experiments on three publicly available datasets: CB513, CASP10 and CASP12. Results indicate that the performance of our method is superior to the state-of-the-art baselines on three publicly datasets.ConclusionsExperiments demonstrate that DeepACLSTM is an efficient predication method for predicting 8-category PSS and has the ability to extract more complex sequence-structure relationships between amino-acid residues. Moreover, experiments also indicate the feature vector dimension contains the useful information for improving PSS prediction.

Asymmetric Residual Neural Network for Accurate Human Activity Recognition

Human activity recognition (HAR) using deep neural networks has become a hot topic in human–computer interaction. Machines can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of automatic learning, we propose a novel asymmetric residual network, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, while still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of the ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared the results with some conventional and state-of-the-art learning-based methods. We discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets.

Environmental friendly synthesis of silver nanomaterials from the promising Streptomyces parvus strain Al-Dhabi-91 recovered from the Saudi Arabian marine regions for antimicrobial and antioxidant properties

The therapeutic molecules recovered from the marine biological origin are widely used for the treatment of diverse levels of infections caused by microbial pathogens. In addition, the eco-friendly preparations of nanomaterials together with the secondary metabolites' producing active microbial strains effectively suppress the spreading of the pathogenic bacteria. Considering their importance, the present study evaluated the environmental friendly synthesis of Silver nitrate nanomaterials (SNM) from the active marine Streptomyces strain Al-Dhabi-91 isolated from the Dammam region of Saudi Arabia. The obtained SNM was chemically characterized by various spectroscopic techniques such as UV, XRD, FTIR, SEM, TEM and EDAX; and its biological applications such as antimicrobial properties and antioxidant potential were recorded by DPPH methods. Biochemical and micromorphological studies together with the molecular techniques confirmed that the isolate Al-Dhabi-91 belonged to Streptomyces species. The characterization techniques confirmed that the UV spectrum showed maximum absorption peak at 305 nm indicating the plasmodium characteristics. SEM and TEM analyses evidenced 5–2 nm which are agglomerated, cool to form porous asymmetrical networks. Additionally, the FTIR spectrum showed maximum peak at 1194 cm−1 and 1394 cm−1, confirming the presence of aromatic CH bending and aromatic CC bending in the SNM. SNM exhibited prolific antibacterial activity against Gram negative pathogens, K. pneumoniae (28.33 mm) and E. coli (21.66 mm) respectively. The MIC values of SNM were significant with respect to E. faecalis (125 μg/ml), S. aureus (250 μg/ml), P. aeruginosa (125 μg/ml), K. pneumoniae (500 μg/ml) and E. coli (250 μg/ml) respectively. In addition, the antioxidant potential of the SNM was another value added importance. Especially 50 μg/ml of the nanoparticles showed 33% antioxidant potential; similarly in nitric oxide radical inhibition assay the concentration of 50 μg/ml nanoparticles showed 33% of inhibition potential. Overall, the eco-friendly synthesis of SNM from the marine Streptomyces strain Al-Dhabi-91 was an ideal active source for the treatment of infectious disease and health associated disorders.

Molecular dynamics simulations on apo ADP/ATP carrier shed new lights on the featured motif of the mitochondrial carriers

The ADP/ATP carrier (AAC) is a transporter responsible for the equal molar exchange of cytosolic ADP and ATP synthesized within mitochondrial matrix across the mitochondrial membrane. Its primary structure consists of three homologous repeats, and each repeat contains a conserved motif that is shared by all members of the mitochondrial carrier family (MCF). Although these MCF motif residues cluster together in the crystal structure of AAC, detailed analyses on the interactions among the motif residues are still limited. In the present study, all-atom molecular dynamics (MD) simulations of up to 10 μs have been carried out on AAC, and interactions and structural dynamics of the MCF motif residues have been specifically investigated. Our simulations have revealed: i) a very asymmetrical electrostatic network at the bottom of the pocket of apo AAC, ii) the asymmetrical interactions between the Pro kink region and the [YWF][KR] G motif in three repeats, iii) the role of the conserved Arg residues in stabilizing the C-ends of the odd-numbered helices, iv) the structural change of the [YWF][KR] G motif and its potential involvement in substrate translocation process. Our results highlight the asymmetry of the MCF residues in the three repeats, which might contribute to the ability of the carriers to transport the asymmetrical substrates. Our observations provide microscopic basis for further research on the translocation mechanism of mitochondrial carriers.

DualCheXNet: dual asymmetric feature learning for thoracic disease classification in chest X-rays

Recently, deep convolutional neural networks (DCNNs) such as the famous ResNet and DenseNet have achieved significant improvements in the field of automatic analysis of chest X-rays (CXRs). However, we observe that a wider network can combine characteristics from different DCNNs to improve the ability of object recognition compared with the single networks. In this paper, we focus on the cooperation and complementarity of dual asymmetric DCNNs and present a novel dual asymmetric feature learning network named DualCheXNet for multi-label thoracic disease classification in CXRs. Correspondingly, two asymmetric subnetworks based on the ResNet and DenseNet are combined to adaptively capture more discriminative features of different abnormalities from the raw CXRs. Specifically, the proposed method enables two different feature fusion operations, such as feature-level fusion (FLF) and decision-level fusion (DLF), which exactly form the complementary feature learning embedded in DualCheXNet. Moreover, an iterative training strategy is designed to integrate the loss contribution of the involved classifiers into a unified loss, and optimize the process of complementary features learning in an alternative way. Extensive experiments on the ChestX-ray14 dataset clearly substantiate the effectiveness of the proposed method as compared with the state-of-the-art baselines.

Measurement-device-independent quantum secret sharing and quantum conference based on Gaussian cluster state

Cluster state is the basic resource for one-way quantum computation and a valuable resource for establishing quantum network, because it has a flexible and varied composition form. We present measurement-device-independent quantum secret sharing (QSS) and quantum conference (QC) schemes based on continuous variable (CV) four-mode cluster state with different structures. The users of the protocol prepare their own Einstein-Podolsky-Rosen (EPR) states, respectively. One mode of these EPR states is sent to an untrusted relay where a generalized Bell measurement creates different types of CV cluster states among four users, while the other mode is kept at their own station. We show that a shared secret key for QSS and QC schemes is distilled based on the shared quantum correlation among four users. QC and four users QSS are implemented based on the star shape CV cluster state. QSS with three users are implemented based on the linear or square shape CV cluster states. The results show that the secure transmission distance for an asymmetric network, where the transmission distances between the users and relay are different, is longer than that of a symmetric network, where the transmission distances between the users and relay are the same. The presented schemes provide concrete references for establishing quantum network with the CV cluster state.