Topological Problems Research Articles

Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, F-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.

Risk-Aware Identification of Highly Suspected COVID-19 Cases in Social IoT: A Joint Graph Theory and Reinforcement Learning Approach.

The recent outbreak of the coronavirus disease 2019 (COVID-19) has rapidly become a pandemic, which calls for prompt action in identifying suspected cases at an early stage through risk prediction. To suppress its further spread, we exploit the social relationships between mobile devices in the Social Internet of Things (SIoT) to help control its propagation by allocating the limited protective resources to the influential so-called high-degree individuals to stem the tide of precipitated spreading. By exploiting the so-called differential contact intensity and the infectious rate in susceptible-exposed-infected-removed (SEIR) epidemic model, the resultant optimization problem can be transformed into the minimum weight vertex cover (MWVC) problem of graph theory. To solve this problem in a high-dynamic random network topology, we propose an adaptive scheme by relying on the graph embedding technique during the state representation and reinforcement learning in the training phase. By relying on a pair of real-life datasets, the results demonstrate that our scheme can beneficially reduce the epidemiological reproduction rate of the infection. This technique has the potential of assisting in the early identification of COVID-19 cases.

Approximation algorithms for minimum power k backbone node r-connected subgraph problem in wireless sensor networks

The minimum power assignment problem in a Wireless Sensor Network (WSN) is to assign transmission range to the nodes of the network with specified connectivity constraints minimizing the total power. This problem is termed as Strong Minimum Energy Topology (SMET) problem. Fault tolerance addresses the issue of a node or link failure in a WSN. So, with an objective of achieving high connectivity, we consider SMET problem with an additional property, i.e., the resultant network must satisfy the property of two or high connectivity. Minimum Power [Formula: see text]-Connected Subgraph (MP[Formula: see text]CS) problem is to contrive an [Formula: see text]-connected network with minimum power. Minimum Power [Formula: see text]-Connected Subgraph (MP2CS) problem is proved to be NP -hard. Minimum Power [Formula: see text] Backbone node [Formula: see text]-Connected Subgraph (MP[Formula: see text]B[Formula: see text]CS) problem is a special case of MP[Formula: see text]CS problem, which seeks a power assignment satisfying [Formula: see text]-connectivity with [Formula: see text] backbone nodes. We study MP[Formula: see text]B[Formula: see text]CS problem for [Formula: see text] backbone nodes, [Formula: see text]-connectivity and derive the approximation ratios. We also propose an algorithm of approximation ratio [Formula: see text], for Minimum Power [Formula: see text] Backbone node 3-Connected Subgraph (MP[Formula: see text]B3CS) problem for [Formula: see text] that runs in [Formula: see text] time and generalize for the case [Formula: see text] for any [Formula: see text] and [Formula: see text].

DNA Topoisomerases of Leishmania Parasites; Druggable Targets for Drug Discovery.

DNA topoisomerases (Top) are a group of isomerase enzymes responsible for controlling the topological problems caused by DNA double helix in the cell during the processes of replication, transcription and recombination. Interestingly, these enzymes have been known since long to be key molecular machines in several cellular processes through overwinding or underwinding of DNA in all living organisms. Leishmania, a trypanosomatid parasite responsible for causing fatal diseases mostly in impoverished populations of low-income countries, has a set of six classes of Top enzymes. These are placed in the nucleus and the single mitochondrion and can be deadly targets of suitable drugs. Given the fact that there are clear differences in structure and expression between parasite and host enzymes, numerous studies have reported the therapeutic potential of Top inhibitors as antileishmanial drugs. In this regard, numerous compounds have been described as Top type IB and Top type II inhibitors in Leishmania parasites, such as camptothecin derivatives, indenoisoquinolines, indeno-1,5- naphthyridines, fluoroquinolones, anthracyclines and podophyllotoxins. The aim of this review is to highlight several facts about Top and Top inhibitors as potential antileishmanial drugs, which may represent a promising strategy for the control of this disease of public health importance.

A Wideband and Low-Sidelobe Series-Fed Patch Array at 5.8 GHz for Radar Applications

In this letter, a novel series-fed patch array working in the 5.8 GHz industrial, scientific, and medical (ISM) band for healthcare radar monitoring applications has been proposed. The single patch has been opportunely shaped to solve two critical problems of this antenna topology: reduced bandwidth and highsidelobe level (SLL). The bandwidth enhancement has been obtained with a dual-band structure, resulting from the superposition of two tapered patches with different lengths. For the sidelobes, a nonconventional technique, based on the modulation of the power transferred by one patch to the following one, has been proposed. An electrical two-port model of the single patch has been developed to significantly speed up the design process of longer series-fed arrays. The model impedance matrix shows a good agreement with the one extracted from electromagnetic simulations. The designed antenna shows a fractional bandwidth of 5.92% (more than twice the available one at 5.8 GHz) and an SLL of about -20 dB inside all the bandwidth. A prototype has been realized, and both reflection coefficient and radiation pattern at 5.8 GHz have been measured showing an excellent agreement with simulation results.

Topological structure in the space of (weighted) composition operators on weighted Banach spaces of holomorphic functions

We consider the topological structure problem for the space of composition operators as well as the space of weighted composition operators on weighted Banach spaces with sup-norm. For the first space, we prove that the set of all composition operators that differ from the given one by a compact operator is path connected; however, in general, it is not always a component. Furthermore, we show that the set of compact weighted composition operators is path connected, but it is not a component in the second space.

A Review on Routing in Internet of Things

Recently, internet of things has drawn attention among the academicians, governments and engineers from various sectors. There are many critical issues in IoT such as security, scalability, big data analytics. Availability, interoperability, performance, mobility. This paper focus on providing optimal routing among the various networking environment such as wired wireless and sensors in the IoT. In this paper the major research issues involved in the existing routing protocols to meet the requirements of internet of things are reviewed the major issues involved in the design of a routing protocol and the different classification of routing protocols are studied. The major challenges of the internet of things, must address the problem of dynamic topology, scalability, mobility of nodes and limited bandwidth. The review covers different kinds of routing protocols such as reactive, proactive, hybrid, location aware, hybrid, multicast, multipath, Geocast, power aware and hierarchical existing geometric routing protocols have been sensibly studied which involves discussion on routing techniques, the advantages and disadvantages of the existing work area analysed in order to help the future researchers. The analysis of existing routing protocols is carried out based on shortest path, least transmission time. Finally, a research open challenge in routing which needs to be addressed by the research and academic community are discussed.

Checkpoint inhibition of origin firing prevents DNA topological stress.

A universal feature of DNA damage and replication stress in eukaryotes is the activation of a checkpoint-kinase response. In S-phase, the checkpoint inhibits replication initiation, yet the function of this global block to origin firing remains unknown. To establish the physiological roles of this arm of the checkpoint, we analyzed separation of function mutants in the budding yeast Saccharomyces cerevisiae that allow global origin firing upon replication stress, despite an otherwise normal checkpoint response. Using genetic screens, we show that lack of the checkpoint-block to origin firing results in a dependence on pathways required for the resolution of topological problems. Failure to inhibit replication initiation indeed causes increased DNA catenation, resulting in DNA damage and chromosome loss. We further show that such topological stress is not only a consequence of a failed checkpoint response but also occurs in an unperturbed S-phase when too many origins fire simultaneously. Together we reveal that the role of limiting the number of replication initiation events is to prevent DNA topological problems, which may be relevant for the treatment of cancer with both topoisomerase and checkpoint inhibitors.

A sliceness criterion for odd free knots

The problem of concordance and cobordism of knots is a well- known classical problem in low-dimensional topology. The purpose of this paper is to show that for odd free knots, that is, free knots with all intersections odd, the question of whether the knot is slice (concordant to a trivial knot) can be answered effectively by analysing pairing of the chords in a knot diagram. Bibliography: 8 titles.

3d modularity

We find and propose an explanation for a large variety of modularity-related symmetries in problems of 3-manifold topology and physics of 3d mathcal{N} = 2 theories where such structures a priori are not manifest. These modular structures include: mock modular forms, SL(2,ℤ) Weil representations, quantum modular forms, non-semisimple modular tensor categories, and chiral algebras of logarithmic CFTs.

Topoisomerase II Is Crucial for Fork Convergence during Vertebrate Replication Termination

SUMMARYTermination of DNA replication occurs when two replication forks converge upon the same stretch of DNA. Resolution of topological stress by topoisomerases is crucial for fork convergence in bacteria and viruses, but it is unclear whether similar mechanisms operate during vertebrate termination. Using Xenopus egg extracts, we show that topoisomerase II (Top2) resolves topological stress to prevent converging forks from stalling during termination. Under these conditions, stalling arises due to an inability to unwind the final stretch of DNA ahead of each fork. By promoting fork convergence, Top2 facilitates all downstream events of termination. Converging forks ultimately overcome stalling independently of Top2, indicating that additional mechanisms support fork convergence. Top2 acts throughout replication to prevent the accumulation of topological stress that would otherwise stall converging forks. Thus, termination poses evolutionarily conserved topological problems that can be mitigated by careful execution of the earlier stages of replication.

P-Torus: wavelength-based switching in packet granularity for intra-data-center networks

In this work we introduce P-Torus, a wavelength switching architecture for intra-data-center networks that exploits optical-packet-based transport and is based on 2D torus interconnected clusters. P-Torus does not require optical buffers, as all buffering takes place at the end hosts. The proposed architecture combines torus connectivity richness, time-slotted operation, and efficient wavelength routing in order to provide single-hop, contentionless, and low-latency interconnection among all top-of-rack switches. Wavelength switching is performed in wavelength or waveband granularity with the use of fast wavelength-tunable transmitters, in compliance with the optimal solution of the routing and wavelength assignment problem of the generic torus topology. Numerical simulations are carried out to evaluate the latency and throughput performance of the proposed architecture. Results show that P-Torus exhibits low end-to-end latency and high throughput, even under a high load, in uniform and hot-spot traffic scenarios. This work also includes cost and power consumption analyses showing that P-Torus has significant benefits over other contemporary wavelength-based switching architectures. Comparative simulations and cost analysis validate that P-Torus provides a very good trade-off between performance and cost compared to other wavelength-based switching architectures targeting small to medium data centers, up to 10,000 servers.

Dx.doi.org/10.18081/2333-5106/016-7/253-264

DNA topoisomerases are ubiquitous, enzymes needed to control topological problems encountered during DNA replication, transcription, recombination and maintenance of genomic stability. They proved to be valuable targets for cancer chemotherapy. The seeds alkaloids extract of Rue plant (Peganum harmala L. Zygophyllaceae) were fractioned by high performance liquid chromatography as a suitable technique, HPLC waters company, gradient, Nova-Pak, C18*, 4µm, 3.9x150mm, mobile phase ACN-water pH10.7. Thirty-four separate solution depending on the retention time of eluents. The aim of this study is to investigate the trace compounds relatively and used as inhibitors for the topoisomerase II enzyme, the study the poison and suppressor model inhibitions and the synergy and antagonism activity of fractions. The visicinone, harmine, visicine and harmaline, were (2.86, 0.75, 13.14, and 16.62 mg/100mg of plant powder. The harmine ˃vicicinone˃ harmaline˃ vicicine as enzyme inhibitors. The unknown trace compounds gave the best inhibition relatively, the alkaloid fractions gave higher inhibitory units of fraction effective (FE). Results of seeds alkaloid fractions showed that the FE in state of fraction mixture was 36% inhibitory units, while summation of FE of each fraction alone gave higher level than their mixture, where sum of FE of them was 1229% inhibitory units. The suppressor inhibition model according to highest percent of the remain supercoiled DNA plasmid than production of linear and open nicked circular with high value in F8 (84%), the poisons inhibition model activity according to the highest percent of linear and nicked open circular DNA plasmid with high level in F31 (88%). There are a trace compounds within some fraction but that the effectiveness of inhibitory relatively higher than known compounds (harmine, vicisinone, harmaline, and vicisine), this can be purified and used as somewhat better inhibitors. There is antagonism activity among seed alkaloids fractions. The all fractions showed a poison and a suppressor model inhibition action in varies percentage, the suppressor inhibition appear in high value in F8 (84%), while the poisons inhibition model gave high level in F31 (88%).

Controllability of continuum ensemble of formation systems over directed graphs

We propose in the paper a novel framework about using a common control input to simultaneously steer an infinite ensemble of networked control systems. We address the problem of co-designing information flow topology and network dynamics of every individual networked system so that a continuum ensemble of such systems is controllable. To keep the problem tractable, we focus in the paper on a special class of ensembles systems, namely ensembles of multi-agent formation systems. Specifically, we consider an ensemble of formation systems indexed by a parameter in a compact real, analytic manifold. Every individual formation system in the ensemble is composed of N agents. These agents evolve in Rn and can access relative positions of their neighbors. The information flow topology within every individual formation system is, by convention, described by a directed graph where the vertices correspond to the N agents and the directed edges indicate the information flow. For simplicity, we assume in the paper that all the individual formation systems share the same information flow topology described by a common digraph G. Amongst other things, we establish a sufficient condition for approximate path-controllability of the continuum ensemble of formation systems. We show that if the digraph G is strongly connected and the number N of agents in each individual formation system is greater than (n+1), then every such system in the ensemble is simultaneously approximately path-controllable over a path-connected, open dense subset.

Definable Transformation to Normal Crossings over Henselian Fields with Separated Analytic Structure

We are concerned with rigid analytic geometry in the general setting of Henselian fields K with separated analytic structure, whose theory was developed by Cluckers–Lipshitz–Robinson. It unifies earlier work and approaches of numerous mathematicians. Separated analytic structures admit reasonable relative quantifier elimination in a suitable analytic language. However, the rings of global analytic functions with two kinds of variables seem not to have good algebraic properties such as Noetherianity or excellence. Therefore, the usual global resolution of singularities from rigid analytic geometry is no longer at our disposal. Our main purpose is to give a definable version of the canonical desingularization algorithm (the hypersurface case) due to Bierstone–Milman so that both of these powerful tools are available in the realm of non-Archimedean analytic geometry at the same time. It will be carried out within a category of definable, strong analytic manifolds and maps, which is more flexible than that of affinoid varieties and maps. Strong analytic objects are those definable ones that remain analytic over all fields elementarily equivalent to K. This condition may be regarded as a kind of symmetry imposed on ordinary analytic objects. The strong analytic category makes it possible to apply a model-theoretic compactness argument in the absence of the ordinary topological compactness. On the other hand, our closedness theorem enables application of resolution of singularities to topological problems involving the topology induced by valuation. Eventually, these three results will be applied to such issues as the existence of definable retractions or extending continuous definable functions. The established results remain valid for strictly convergent analytic structures, whose classical examples are complete, rank one valued fields with the Tate algebras of strictly convergent power series. The earlier techniques and approaches to the purely topological versions of those issues cannot be carried over to the definable settings because, among others, non-Archimedean geometry over non-locally compact fields suffers from lack of definable Skolem functions.

Crash Model Order Reduction for Efficient Topology Optimization Using the ESLs Method

This paper presents an efficient structural topology optimization strategy for crashworthiness. The proposed method combines the equivalent static loads (ESLs) method with the crash model order reduction method. The ESLs method transforms crashworthiness topology optimization problems into a series of linear static topology problems. The crash model order reduction scheme is applicable to speed up the crash simulation. Therefore, the crashworthiness topology optimization is performed in a reduction manner and the efficiency is improved. The topology optimization results are filtered into a black-white design for the crash simulation to avoid the element distortion problem. The process is repeated until the convergence criteria is satisfied. The effectiveness of proposed method is demonstrated by investing a numerical example. The results show that, the proposed method can efficiently solve structural topology optimization problems for crashworthiness.

Three convolutional neural network models for facial expression recognition in the wild

Facial expression recognition (FER) in the wild is a novel and challenging topic in the field of human emotion perception. Different kinds of convolutional neural network (CNN) approaches have been applied to this topic, but few of them ever considered what kind of architecture was better for the FER research. In this paper, we proposed three novel CNN models with different architectures. The first one is a shallow network, named the Light-CNN, which is a fully convolutional neural network consisting of six depthwise separable residual convolution modules to solve the problem of complex topology and over-fitting. The second one is a dual-branch CNN which extracts traditional LBP features and deep learning features in parallel. The third one is a pre-trained CNN which is designed by transfer learning technique to overcome the shortage of training samples. Extensive evaluations on three popular datasets (public CK+, multi-view BU-3DEF and FER2013 datasets) demonstrated that our models were competitive and representative in the field of FER in the wild research. We achieved significant better results with comparisons to plenty of state-of-the-art approaches. Moreover, we provided discussions on the effectiveness and practicability of CNNs with different feature types and architectures for FER in the wild as well.

Twist and Turn-Topoisomerase Functions in Mitochondrial DNA Maintenance.

Like any genome, mitochondrial DNA (mtDNA) also requires the action of topoisomerases to resolve topological problems in its maintenance, but for a long time, little was known about mitochondrial topoisomerases. The last years have brought a closer insight into the function of these fascinating enzymes in mtDNA topology regulation, replication, transcription, and segregation. Here, we summarize the current knowledge about mitochondrial topoisomerases, paying special attention to mammalian mitochondrial genome maintenance. We also discuss the open gaps in the existing knowledge of mtDNA topology control and the potential involvement of mitochondrial topoisomerases in human pathologies. While Top1mt, the only exclusively mitochondrial topoisomerase in mammals, has been studied intensively for nearly a decade, only recent studies have shed some light onto the mitochondrial function of Top2β and Top3α, enzymes that are shared between nucleus and mitochondria. Top3α mediates the segregation of freshly replicated mtDNA molecules, and its dysfunction leads to mtDNA aggregation and copy number depletion in patients. Top2β, in contrast, regulates mitochondrial DNA replication and transcription through the alteration of mtDNA topology, a fact that should be acknowledged due to the frequent use of Topoisomerase 2 inhibitors in medical therapy.

Towards attack tolerant networks: Concurrent multipath routing and the butterfly network.

It is crucial for large-scale communication networks such as the internet to be resilient against attacks such as censorship and surveillance, which pose a threat to free expression and free association. Self-organized networks such as the internet’s router network typically have heavy-tailed degree distributions, making them highly vulnerable to targeted attacks against central nodes. While cryptographic solutions exist, they fail to address the underlying topological problem, and remain vulnerable to man-in-the-middle attacks and coercion. Coercion-resistant, topological approaches to attack tolerance are needed to address the current vulnerability of communications infrastructure to censorship and surveillance. We present a novel concurrent multipath routing (CMR) algorithm for the wraparound butterfly network topology, as well as a highly attack-tolerant Structured Multipath Fault Tolerance (SMFT) architecture which incorporates the butterfly CMR algorithm. We also identify a previously unexplored relationship between network topology, trust transitivity, and attack-tolerance, and provide a framework for further exploration of this relationship. Our work is the first theoretical demonstration of a point-to-point communication network architecture that can resist coercion and other non-technical attacks, without requiring infinitely transitive trust. To address cases where the network structure cannot be fully controlled, we demonstrate how a snapshot of the internet’s router network can be partially rewired for greater attack-tolerance. More broadly, we hope that this work will serve as a starting point for the evelopment of additional topology-based attack-tolerant communication architectures to guard against the dangers of censorship and surveillance.

An Efficient Topology Optimization Strategy for Structural Crashworthiness Using Model and ESLs Reduction Method

This paper presents an efficient structural topology optimization strategy for crashworthiness. Although equivalent static loads (ESLs) provides a relatively effective way to solve nonlinear dynamic response topology optimization problems, efficiency is still required to improve. To overcome above disadvantage, the proposed method combines the ESLs with model reduction (MR) method. The ESLs method transforms crashworthiness topology optimization problems into a series of linear static topology problems. The model reduction scheme is applicable to reduce the linear matrix equation and ESLs to interface degrees of freedom (DOFs), by selecting interface DOFs between design and non-design space as master DOFs. Therefore, the linear static topology optimization is performed in a reduction manner and the efficiency is improved. The topology optimization results are filtered into a black-white design for the crash simulation to avoid the element distortion problem. The process is repeated until the convergence criteria is satisfied. A numerical example is used to verify the reliability of the proposed method. The results are compared with the dynamic response optimization method using ESLs (ESLSO). The results show that, the proposed method can efficiently solve structural topology optimization problems for crashworthiness.