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This paper addresses the problem of anomaly detection for high-dimensional sensing data. The one-class support vector machine (OCSVM) is one of the most popular unsupervised methods for anomaly detection. When data are high dimensional and large scale, however, the efficiency of OCSVM-based methods in anomaly detection suffers. Although dimensionality-reduction tools, such as deep belief networks, can be applied to compress the high-dimensional data to alleviate the problem, the accuracy and timely detection are still hard to improve due to the inherent features of OCSVM. In this paper, we propose a new form of OCSVM model based on the structure of the compressed data and the characteristics of OCSVM. Based on the new model, we design both optimal and approximate methods for model training and testing. We evaluate the performance of our methods with extensive experiments on four real-world datasets. The experimental results demonstrate that our new methods, both optimal and approximate ones, not only significantly outperform the state-of-the-art in accuracy and efficiency, but also achieve the good performance without the need of manual parameter tuning. In addition, our approximate training and testing mechanism can reduce the computing time by three orders of magnitude with a negligible loss in accuracy.

This article aims to stabilize probabilistic Boolean networks (PBNs) via a novel pinning control strategy. In a PBN, the state evolution of each gene switches among a collection of candidate Boolean functions with preassigned probability distributions, which govern the activation frequency of each Boolean function. Due to the existence of stochasticity, the mode-independent pinning controller might be disabled. Thus, both mode-independent and mode-dependent pinning controller are required here. Moreover, a criterion is derived to determine whether mode-independent controllers are applicable while the pinned nodes are given. It is worth pointing out that this pinning control is based on the nx n network structure rather than 2ⁿ x 2ⁿ state transition matrix. Therefore, compared with the existing results, this pinning control strategy is more practicable and has the ability to handle large-scale networks, especially sparsely connected networks. To demonstrate the effectiveness of the designed control scheme, a PBN that describes the mammalian cell-cycle encountering a mutated phenotype is discussed as a simulation.

Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. However, in general clustering tasks, the graph structure of data does not exist such that the strategy to construct a graph is crucial for performance. Therefore, how to extend graph convolution networks into general clustering tasks is an attractive problem. In this paper, we propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs. The adaptive process is designed to induce the model to exploit the high-level information behind data and utilize the non-Euclidean structure sufficiently. We further design a novel mechanism with rigorous analysis to avoid the collapse caused by the adaptive construction. Via combining the generative model for network embedding and graph-based clustering, a graph auto-encoder with a novel decoder is developed such that it performs well in weighted graph used scenarios. Extensive experiments prove the superiority of our model., Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence

Triangle count is a critical parameter in mining relationships among people in social networks. However, directly publishing the findings obtained from triangle counts may bring potential privacy concern, which raises great challenges and opportunities for privacy-preserving triangle counting. In this paper, we choose to use differential privacy to protect triangle counting for large scale graphs. To reduce the large sensitivity caused in large graphs, we propose a novel graph projection method that can be used to obtain an upper bound for sensitivity in different distributions. In particular, we publish the triangle counts satisfying the node-differential privacy with two kinds of histograms: the triangle count distribution and the cumulative distribution. Moreover, we extend the research on privacy preserving triangle counting to one of its applications, the local clustering coefficient. Experimental results show that the cumulative distribution can fit the real statistical information better, and our proposed mechanism has achieved better accuracy for triangle counts while maintaining the requirement of differential privacy.

In the era of the Internet of Things, the construction of edge computing platform has become more and more important, which has led lots of object detection applications being deployed on embedded devices. However, traditional object detection algorithms require lots of engery and a large amount of well labeled samples for training. The time spent on model training and data labeling also slows down the upgrade iteration of applications. Therefore, an object detection algorithm that requires only few energy and a few samples to update parameters could help the long-term benign development of IoT technology. In this paper, we propose an effective object detection method based on the few-shot learning, which could achieve considerable performance with few data for novel(new) classes. Our well-designed strategies could alleviate the impact of scale variation in support set under few-shot setting. Through extensive experiments, we prove that our model is superior to well-recognized baselines on few-shot object detection task.

We outline recent trends in capacity-based aggregation in large universes. Capacities (fuzzy measures) model dependencies among the inputs, and aggregation by the discrete Choquet, Sugeno and other fuzzy integrals accounts for synergies and redundancies. For large number of inputs the exponential complexity of all interactions is a major obstacle. We exemplify the need for aggregation of a large number of dependent inputs on several applications and discuss the challenges and approaches to reducing the complexity of capacity-based aggregation. We also state which mathematical and computational tools are required for large scale capacity modelling.

Sparse multi-objective optimization problems have become increasingly important in many applications in recent years, e.g., the search for lightweight deep neural networks and high-dimensional feature selection. However, little attention has been paid to sparse large-scale multi-objective optimization problems, whose Pareto optimal sets are sparse, i.e., with many decision variables equal to zero. To address this issue, this paper proposes an enhanced competitive swarm optimization algorithm assisted by a strongly convex sparse operator. A tri-competition mechanism is introduced into competitive swarm optimization, aiming to strike a better balance between exploration and exploitation. In addition, the strongly convex sparse operator is embedded in the position updating of the particles to generate sparse solutions. Our simulation results show that the proposed algorithm outperforms the state-of-the-art methods on both sparse test problems and application examples.

The Support Vector Machine (SVM) is a state-of-the-art classifier that for large datasets is very slow and requires much memory. To solve this defficiency, we propose the Fast Support Vector Classifier (FSVC) that includes: 1) an efficient closed-form training without numerical procedures; 2) a small collection of class prototypes instead of support vectors; and 3) a fast method that selects the spread of the radial basis function kernel directly from data. Its storage requirements are very low and can be adjusted to the available memory, being able to classify any dataset of arbitrarily large sizes (31 millions of patterns, 30,000 inputs and 131 classes in less than 1.5 hours). The FSVC spends 12 times less memory than Liblinear, that fails on the 4 largest datasets by lack of memory, being one and two orders of magnitude faster than Liblinear and Libsvm, respectively. Comparing performance, FSVC is 4.1 points above Liblinear and only 6.7 points below Libsvm. The time spent by FSVC only depends on the dataset size (610^-7 sec. per pattern, input and class) and can be accurately estimated for new datasets, while for Libsvm and Liblinear depends on the dataset difficulty. Code is provided.

Producción Científica, We consider the context of social choice in which individual evaluations of the alternatives are expressed over an ordered qualitative scale. We propose to extend the framework of Majority Judgment to the case of non-uniform ordered qualitative scales, described by an ordinal proximity measure. The central construct in our model is a weak order defined over multisets of ordinal proximity degrees. On the basis of this weak order, each alternative profile is represented by an ordinal mean which balances the multisets of ordinal proximity degrees associated with the upper and lower ordinal evaluations. The procedure for ranking the alternative profiles consists primarily in comparing means, plus a tie-breaking scheme in which the weak order plays once more an important role., Ministerio de Economía, Industria y Competitividad (project ECO2016-77900-P)

This work is concerned with the design of a two-step distributed state estimation scheme for large-scale systems in the presence of unknown-but-bounded disturbances and noise. The set-membership approach is employed to construct a compact set containing the states consistent with system measurements and bounded noise and disturbances. The tightened feasible region is then provided to a moving horizon estimator that determines the optimal state estimates. Partitioning of the overall problem and coordination of the resulting subproblems are achieved using decomposition of the optimality conditions and community detection. The proposed strategy is tested on a case study based on a reactor–separator system widely used in the literature. Its performance is compared to those of centralized and distributed (without set-membership) implementations, allowing to highlight its effectiveness.

Multi-agent reinforcement learning (MARL) based methods for adaptive traffic signal control (ATSC) have shown promising potentials to solve the heavy traffic problems. The existing MARL methods adopt centralized or distributed strategies. The former only models the environment as an agent and suffers from the exponential growth of action and state space. The latter extends the independent reinforcement learning methods, such as DQN, to multiple interactions directly or propagates information, such as state and policy, without taking their qualities into account. In this paper, we propose a multi-agent transfer reinforcement learning method to enhance the performance of MARL for ATSC, which is termed as multi-agent transfer soft actor-critic with the multi-view encoder (MT-SAC). The MT-SAC combines centralized and distributed strategies. In MT-SAC, we propose a multi-view state encoder and a transfer learning paradigm with guidance. The encoder processes input states from multiple perspectives and uses an attention mechanism to weigh the neighborhood information. While the paradigm enables the agents to handle different conditions for improving generalization abilities by transfer learning. Experimental studies on different scale road networks show that the MT-SAC outperforms the state-of-the-art algorithms and makes the traffic signal controllers more collaborative and robust.

This article studies the distributed linear minimum mean square error (LMMSE) estimation problem for large-scale systems with local information (LSLI). Large-scale systems are composed of numerous subsystems. Each subsystem only transmits information to its neighbors. Thus, only the local information is available to each subsystem. This implies that the information available to different subsystems is different. Using local information to design an LMMSE estimator, the gains of the estimator must satisfy the sparse structure constraint, which makes the estimator design challenging and complicates the boundedness analysis of the estimation error covariance (EEC). In this article, a framework of the distributed LMMSE estimation for LSLI is established. The gains of the LMMSE estimator are effectively constructed by solving linear matrix equations. A gradient descent algorithm is exploited to design the gains of the LMMSE estimator numerically. Sufficient conditions are derived to ensure the boundedness of the EEC. Also, a gradient-based search algorithm is developed to verify whether the sufficient conditions hold or not. Finally, an example is used to illustrate the effectiveness of the proposed results.

The evaluation performance of PDP (Policy Decision Point) plays a key role in the operation of a system. In order to solve bottlenecks of improving the PDP evaluation performance for large-scale policy sets, we propose an evaluation engine based on artificial neural networks, namely ANNPDP. We transform rules in a large-scale policy set described in the XACML (eXtensible Access Control Markup Language) into numerical rules. Evaluation networks are established and trained by the numerical rules. In order to ensure the accuracy, a misjudgment set is constructed for error corrections and stored by hash indexes. By simulating the arrival of requests, ANNPDP is compared with the Sun PDP, HPEngine, XEngine and SBA-XACML. The experiment results show that ANNPDP has: 1) high performance: if the number of requests reaches 10,000, the evaluation time of ANNPDP on the large-scale policy set with 100,000 rules is approximately 0.46%, 0.93%, 0.71% and 1.43% of that of the Sun PDP, HPEngine, XEngine and SBA-XACML, respectively, and 2) stability: as the size of the large-scale policy set and the number of requests increase, the evaluation time of ANNPDP grows linearly. ANNPDP can satisfy the requirements of an authorization system with large-scale policy sets

It is challenging to deploy check-in nodes optimally in a complex network so as to perform specific check-in like services, e.g., fuel supplements etc., but crucial in many real-world applications. In this article, we propose and study the new optimization problem of placing check-in nodes with the minimum cost, i.e., the problem of finding the minimum-cost check-in nodes (MCCN), which is of great interests for real-world application situations, but even more difficult. We motivate the new algorithms through three typical worse cases by the often-used greedy-type algorithms, i.e., One-to-Many, Many-to-One, and Duplicate-Overrides. With this respect, the proposed novel optimization algorithms utilize a novel metric of contribution density for selecting check-in nodes iteratively, which successfully avoid the occurrence of the two worse cases of One-to-Many and Many-to-One. We also introduce an extra backward extraction step in one of new algorithms, which overcomes the crucial worse case of ``duplicate overrides'' and largely improves the algorithmic performance in solving the introduced optimization problem of MCCN. Meanwhile, we extend the new contribution density metric to a more general class of functions and study their effectiveness to eliminate the two worse cases of One-to-Many and Many-to-One; also, a detailed analysis on complexity and performance of the proposed algorithms is presented to show their numerical efficiency and accuracy. Extensive experiments over two classical artificial networks, i.e., BA network and ER network, and ten real-world networks, under two typical cost setups, show the proposed algorithms significantly outperform the four state-of-the-art algorithms. We also demonstrate that our proposed algorithms are much reliable and robust with different experiment settings of deployment cost and network-type and scale.

Clustering is a fundamental primitive in manifold applications. In order to achieve valuable results in exploratory clustering analyses, parameters of the clustering algorithm have to be set appropriately, which is a tremendous pitfall. We observe multiple challenges for large-scale exploration processes. On the one hand, they require specific methods to efficiently explore large parameter search spaces. On the other hand, they often exhibit large runtimes, in particular when large datasets are analyzed using clustering algorithms with super-polynomial runtimes, which repeatedly need to be executed within exploratory clustering analyses. We address these challenges as follows: First, we present LOG-Means and show that it provides estimates for the number of clusters in sublinear time regarding the defined search space, i.e., provably requiring less executions of a clustering algorithm than existing methods. Second, we demonstrate how to exploit fundamental characteristics of exploratory clustering analyses in order to significantly accelerate the (repetitive) execution of clustering algorithms on large datasets. Third, we show how these challenges can be tackled at the same time. To the best of our knowledge, this is the first work which simultaneously addresses the above-mentioned challenges. In our comprehensive evaluation, we unveil that our proposed methods significantly outperform state-of-the-art methods, thus especially supporting novice analysts for exploratory clustering analyses in large-scale exploration processes., Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, Bundesministerium für Bildung und Forschung, Projekt DEAL

In this paper, MUSE, an algorithm enabled by backscattering tag-to-tag network (BTTN) is presented to accomplish simultaneous 2-D localization of large-scale (10m 10m) massive (20~50) passive UHF RFIDs. In BTTNs, the most intractable problem is the high-frequency loss of range measurements. In a particular case of 30 tags to be located with maximum communication range being 3m, the rate is nearly up to 85.75%. In the proposed framework, we utilize relevant knowledge in the theory of graphs to obtain underlying subsets in which tags can communicate with each other and then assemble them stage by stage to achieve overall localization. Theoretical analysis shows that multistage assembly imparts extraordinary characteristics to MUSE: Assembling rectifies fragment maps given some condition, and in later stages prevents errors flowing down into the next stage. Experimental analysis shows that the condition is easy to satisfy. Furthermore, an analytical expression for the Cramr-Rao lower bound is also derived as a benchmark to evaluate the localization performance. Extensive simulations demonstrate that MUSE outperforms existing algorithms for simultaneous localization.

The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of the direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, this paper proposes a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)-sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.

This article investigates the decentralized optimal linear quadratic regulation (LQR) control for large-scale systems. The large-scale system is defined over a weakly connected graph. Assume that the information is transmitted along the edges in the graph, and one sampling period is required for the information to travel across an edge. Under the above setup, the LQR control problem for the strongly connected graph case has been fully solved. However, for the weakly connected graph case, the existing results fail. In this article, a new decomposition approach for noise history is proposed. Then, the LQR control problem of large-scale systems defined over a weakly connected graph can be well solved. In addition, the decentralized realization of the control input is derived based on the decentralized information hierarchy graph (DIHG). The DIHG construction algorithm is also provided. Finally, the effectiveness of the developed design scheme is illustrated by a numerical example.

Coin mixing can be used to protect the identity privacy of Bitcoin owners, by engaging a set of middlepersons (i.e., ${Mix}$ ) to temporarily hold on to the transacting Bitcoins and remove the linkage between the transacting parties. However, existing schemes are generally not scalable due to limitations associated with the anonymity set, and self-credibility. In this paper, we propose an efficient coin mixing scheme (CoinLayering). To achieve strong anonymity, CoinLayering randomly selects two sets of middlepersons to respectively execute Bitcoin holding and Bitcoin trading. The seller can also select lower-loaded sets of middlepersons in the shortest time possible. We also design two coin mixing protocols (CoinLayering-PA and CoinLayering-PB) to mitigate the risk due to misbehaving middlepersons and ${Supervisor}$ . We then mathematically prove that CoinLayering achieves both strong anonymity and self-credibility, and evaluate its performance to demonstrate its scalability.

Recently, face hallucination, also termed face super-resolution (SR), has been widely studied and achieved significant progress. The algorithm based on manifold learning is one of the primary methods for SR. However, when recovering the high-resolution (HR) counterparts from extremely low-resolution (LR) images, the basic assumption of manifold consistence between LR/HR spaces is doubtful and vulnerable. To address this issue, some algorithms usually employ the cascaded models in line with the inherent magnification factors, such as 2, 4 and 8, to maintain the manifold consistence. As a simple cascade mechanism, the inherent factors cannot ensure the optimal performance without the relationship between the manifold relevance and the down-sampling scale. In this paper, we explore the relevance with the groups of gradually down-sampled training sets and divide the scales into different classes for robust manifold consistence. And then, to map the optimal coefficients from LR spaces to HR target ones better, we introduce the weight-mapping neighbour embedding model. Qualitative and quantitative evaluations demonstrate that the enhancing of the manifold relevance can promote effective face hallucination. Based on the weight-mapping and scale clustering, our algorithm achieves better results compared with the state-of-the-art methods.

This article presents a data-driven method for computing reachable sets where active learning (AL) is used to reduce the computational burden. Set-based methods used to estimate reachable sets typically do not scale well with the state-space dimension, or rely heavily on the existence of a model. If such a model is not available, it is simple to generate state trajectory data by numerically simulating black-box oracles of systems (whose dynamics are unknown) from sampled initial conditions. Using these data samples, the estimation of reachable sets can be posed as a classification problem, wherein AL can intelligently select samples that are most informative and least similar to previously labeled samples. By exploiting submodularity, the actively learned samples can be selected efficiently, with bounded suboptimality. Our proposed framework is illustrated by estimating the domains of attractions of model predictive controllers (MPCs) and reinforcement learners. We also consider a scenario where there are two oracles that differ with respect to evaluation costs and labeling accuracy. We propose a framework to reduce the dependency of the expensive oracle in labeling samples using disagreement-based AL (DBAL). The potential of the DBAL algorithm is demonstrated on a solver selection problem for real-time MPC.

Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed choices can be a very challenging and time-consuming task. This paper seeks to help modellers by leveraging the Bayesian framework and the concept of automatic relevance determination (ARD), in order to automatically determine an optimal utility function specification from an exponentially large set of possible specifications in a purely data-driven manner. Based on recent advances in approximate Bayesian inference, a doubly stochastic variational inference is developed, which allows the proposed DCM-ARD model to scale to very large and high-dimensional datasets. Using semi-artificial choice data, the proposed approach is shown to very accurately recover the true utility function specifications that govern the observed choices. Moreover, when applied to real choice data, DCM-ARD is shown to be able discover high quality specifications that can outperform previous ones from the literature according to multiple criteria, thereby demonstrating its practical applicability., 21 pages, 2 figures, 11 tables

For solving large-scale multi-objective problems (LSMOPs), the transformation-based methods have shown promising search efficiency, which varies the original problem as a new simplified problem and performs the optimization in simplified spaces instead of the original problem space. Owing to the useful information provided by the simplified searching space, the performance of LSMOPs has been improved to some extent. However, it is worth noting that the original problem has changed after the variation, and there is thus no guarantee of the preservation of the original global or near-global optimum in the newly generated space. In this paper, we propose to solve LSMOPs via a multi-variation multifactorial evolutionary algorithm. In contrast to existing transformation-based methods, the proposed approach intends to conduct an evolutionary search on both the original space of the LSMOP and multiple simplified spaces constructed in a multi-variation manner concurrently. In this way, useful traits found along the search can be seamlessly transferred from the simplified problem spaces to the original problem space toward efficient problem-solving. Besides, since the evolutionary search is also performed in the original problem space, preserving the original global optimal solution can be guaranteed. To evaluate the performance of the proposed framework, comprehensive empirical studies are carried out on a set of LSMOPs with 2-3 objectives and 500-5000 variables. The experiment results highlight the efficiency and effectiveness of the proposed method compared to the state-of-the-art methods for large-scale multi-objective optimization.

A software system evolves overtime to meet the needs of users. Understanding a program is the most important step to apply new requirements. Clustering techniques by dividing a program into small and meaningful parts make it possible to understand the program. In general, clustering algorithms are classified into two categories: hierarchical and non-hierarchical algorithms (such as search-based approaches). While clustering problems generally tend to be NP-hard, search-based algorithms produce acceptable clustering, but have time and space constraints and hence they are inefficient in large-scale software systems. Most algorithms currently used in software clustering fields do not scale well when applied to large and very large applications. In this paper, we present a new and fast clustering algorithm, named FCA, that can overcome space and time constraints of existing algorithms by performing operations on the dependency matrix and extracting other matrices based on a set of features. The experimental results on ten small-sized applications, ten folders with different functionalities from Mozilla Firefox, a large-sized application (namely ITK), and a very large-sized application (namely Chromium) demonstrate that the proposed algorithm achieves higher quality modularization compared with hierarchical algorithms. It can also compete with search-based algorithms and a clustering algorithm based on subsystem patterns. But the running time of the proposed algorithm is much shorter than that of the hierarchical and non-hierarchical algorithms. The source code of the proposed algorithm can be accessed at https://github.com/SoftwareMaintenanceLab.

High-quality magnetic resonance (MR) images afford more detailed information for reliable diagnoses and quantitative image analyses. Given low-resolution (LR) images, the deep convolutional neural network (CNN) has shown its promising ability for image super-resolution (SR). The LR MR images usually share some visual characteristics: structural textures of different sizes, edges with high correlation, and less informative background. However, multi-scale structural features are informative for image reconstruction, while the background is more smooth. Most previous CNN-based SR methods use a single receptive field and equally treat the spatial pixels (including the background). It neglects to sense the entire space and get diversified features from the input, which is critical for high-quality MR image SR. We propose a wide weighted attention multi-scale network (W2AMSN) for accurate MR image SR to address these problems. On the one hand, the features of varying sizes can be extracted by the wide multi-scale branches. On the other hand, we design a non-reduction attention mechanism to recalibrate feature responses adaptively. Such attention preserves continuous cross-channel interaction and focuses on more informative regions. Meanwhile, the learnable weighted factors fuse extracted features selectively. The encapsulated wide weighted attention multi-scale block (W2AMSB) is integrated through a recurrent framework and global attention mechanism. Extensive experiments and diversified ablation studies show the effectiveness of our proposed W2AMSN, which surpasses state-of-the-art methods on most popular MR image SR benchmarks quantitatively and qualitatively. And our method still offers superior accuracy and adaptability on real MR images.

Communication between people and machines has been extended in the last two decades. Corresponding techniques have been founded to cover the need of voice understanding, including speech and speaker recognition on a large-scale. In this paper, the authors propose a simplified deep-learning approach to accomplish the large-scale speaker identification task using as little training data as possible. Fisher speech corpus has been explored to select the recordings of unique speakers having sufficient data. The authors are using the MFCC method to represent the feature vectors of a large set of more than 4 k speakers with about 343 h of speech signals. The solution includes omitting the pre-processing and considering longer segments of the voice signals. Various portions of training datasets have been tested, as well as dedicating larger percentages of the used data for testing. Bidirectional LSTM neural networks provided up to 76.9% accuracy rate for individual voice segments, and 99.5% when considering the segments of each speaker as a bundle. Doubling the amount of the training data yielded a perfect accuracy rate of 100%.

Mass spectrometry (MS) based omics data analysis require significant time and resources. To date, few parallel algorithms have been proposed for deducing peptides from mass spectrometry-based data. However, these parallel algorithms were designed, and developed when the amount of data that needed to be processed was smaller in scale. In this paper, we prove that the communication bound that is reached by the \emph{existing} parallel algorithms is $\Omega(mn+2r\frac{q}{p})$, where $m$ and $n$ are the dimensions of the theoretical database matrix, $q$ and $r$ are dimensions of spectra, and $p$ is the number of processors. We further prove that communication-optimal strategy with fast-memory $\sqrt{M} = mn + \frac{2qr}{p}$ can achieve $\Omega({\frac{2mnq}{p}})$ but is not achieved by any existing parallel proteomics algorithms till date. To validate our claim, we performed a meta-analysis of published parallel algorithms, and their performance results. We show that sub-optimal speedups with increasing number of processors is a direct consequence of not achieving the communication lower-bounds. We further validate our claim by performing experiments which demonstrate the communication bounds that are proved in this paper. Consequently, we assert that next-generation of \emph{provable}, and demonstrated superior parallel algorithms are urgently needed for MS based large systems-biology studies especially for meta-proteomics, proteogenomic, microbiome, and proteomics for non-model organisms. Our hope is that this paper will excite the parallel computing community to further investigate parallel algorithms for highly influential MS based omics problems., Comment: 20 pages, 6 figures, preprint

Many realistic mobile social networks can be characterized by evolving bipartite graphs [1], [2], in which dynamically added elements are divided into two entities and connected by links between these two entities, such as users and items in recommendation networks, followees and followers in Twitter networks, authors and scientific topics in scholarly networks, etc. However, given the fact that connections between two entities are often weighted, how to mathematically model such weighted evolving bipartite relationships, along with quantitative characterizations, remains unexplored. Motivated by this, we develop a novel Evolving Bipartite Model (EBM), which, based on empirically validated power-law distribution on multiple realistic mobile social networks, discloses that the distribution of total weights of incoming and outgoing edges in networks is determined by the weighting scale and bounded by certain ceilings and floors. Based on these theoretical results, for evolving bipartite networks whose degree follows power-law distribution, their overall weights of vertices can be predicted by EBM. To illustrate, in recommendation networks, the evaluation of items, i.e., total rating scores, can be estimated through the given bounds; in Twitter social networks, the influence of users can be roughly measured; in scholarly networks

The $F_{10.7cm}$ radio flux and the Sunspot Number are the most widely used long-term indices of solar activity. They are strongly correlated, which led to the publication of many proxy relations allowing to convert one index onto the other. However, those existing proxies show significant disagreements, in particular at low solar activity. Our aim is to bring a global clarification of those many issues. We compute new polynomial regressions up to degree 4, in order to obtain a more accurate proxy. We also study the role of temporal averaging on the regression, and we investigate the issue of the all-quiet $F_{10.7}$ background flux. Finally, we check for any change in the $F_{10.7}$ -- sunspot number relation over the entire period 1947 -- 2015. We find that, with a $4^{th}$-degree polynomial, we obtain a more accurate proxy relation than all previous published ones, and we derive a formula giving standard errors. The relation is different for daily, monthly and yearly mean values, and it proves to be fully linear for raw non-averaged daily data. By a simple two-component model for daily values, we show how temporal averaging leads to non-linear proxy relations. We also show that the quiet-Sun $F_{10.7}$ background is not absolute and actually depends on the duration of the spotless periods. Finally, we find that the $F_{10.7cm}$ time series is inhomogeneous, with an abrupt 10.5% upward jump occurring between 1980 and 1981. Our new proxy relations show the importance of temporal scale for choosing the appropriate proxy and the $F_{10.7}$ quiet-Sun background level. From historical evidence, we conclude that the 1981 jump is most likely due to a unique change in the $F_{10.7}$ scientific team and the data processing, and that the newly re-calibrated sunspot number (version2) will probably provide the only possible reference to correct this inhomogeneity., 51 pages, 27 figures, 10 tables

This chapter describes the concept of paper space. Covered are the primary reasons for using paper space, how to get in/out of paper space, and how to set up title blocks (layouts), viewports and their properties, viewport scale assignments, controlling what is visible in viewports, setting up text and dim styles in viewports, and the annotative concept.

Novel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering.

Motivation Long-read phasing has been used for reconstructing diploid genomes, improving variant calling and resolving microbial strains in metagenomics. However, the phasing blocks of existing methods are broken by large Structural Variations (SVs), and the efficiency is unsatisfactory for population-scale phasing. Results This article presents a novel algorithm, LongPhase, which can simultaneously phase single nucleotide polymorphisms (SNPs) and SVs of a human genome in 10–20 min, 10× faster than the state-of-the-art WhatsHap, HapCUT2 and Margin. In particular, co-phasing SNPs and SVs produces much larger haplotype blocks (N50 = 25 Mbp) than those of existing methods (N50 = 10–15 Mbp). We show that LongPhase combined with Nanopore ultra-long reads is a cost-effective and highly contiguous solution, which can produce between one and 26 blocks per chromosome arm without the need for additional trios, chromosome-conformation and strand-seq data. Availabilityand implementation LongPhase is freely available at https://github.com/twolinin/LongPhase/. Supplementary information Supplementary data are available at Bioinformatics online.

Detectors based on deep learning tend to detect multi-scale objects on a single input image for efficiency. Recent works, such as FPN and SSD, generally use feature maps from multiple layers with different spatial resolutions to detect objects at different scales, e.g., high-resolution feature maps for small objects. However, we find that objects at all scales can also be well detected with features from a single layer of the network. In this paper, we carefully examine the factors affecting detection performance across a large range of scales, and conclude that the balance of training samples, including both positive and negative ones, at different scales is the key. We propose a group sampling method which divides the anchors into several groups according to the scale, and ensure that the number of samples for each group is the same during training. Our approach using only one single layer of FPN as features is able to advance the state-of-the-arts. Comprehensive analysis and extensive experiments have been conducted to show the effectiveness of the proposed method. Moreover, we show that our approach is favorably applicable to other tasks, such as object detection on COCO dataset, and to other detection pipelines, such as YOLOv3, SSD and R-FCN. Our approach, evaluated on face detection benchmarks including FDDB and WIDER FACE datasets, achieves state-of-the-art results without bells and whistles.

Offspring generation plays an important role in evolutionary multiobjective optimization. However, generating promising candidate solutions effectively in high-dimensional spaces is particularly challenging. To address this issue, we propose an adaptive offspring generation method for large-scale multiobjective optimization. First, a preselection strategy is proposed to select a balanced parent population, and then these parent solutions are used to construct direction vectors in the decision spaces for reproducing promising offspring solutions. Specifically, two kinds of direction vectors are adaptively used to generate offspring solutions. The first kind takes advantage of the dominated solutions to generate offspring solutions toward the Pareto optimal set (PS) for convergence enhancement, while the other kind uses those nondominated solutions to spread the solutions over the PS for diversity maintenance. The proposed offspring generation method can be embedded in many existing multiobjective evolutionary algorithms (EAs) for large-scale multiobjective optimization. Experiments are conducted to reveal the mechanism of our proposed adaptive reproduction strategy and validate its effectiveness. Experimental results on some large-scale multiobjective optimization problems have demonstrated the competitive performance of our proposed algorithm in comparison with five state-of-the-art large-scale EAs.

The $t/k$ t / k -diagnosability and $h$ h -extra connectivity are regarded as two important indicators to improve the network reliability. The $t/k$ t / k -diagnosis strategy can significantly improve the self-diagnosing capability of a network at the expense of no more than $k$ k fault-free nodes being mistakenly diagnosed as faulty. The $h$ h -extra connectivity can tremendously improve the real fault tolerability of a network by insuring that each remaining component has no fewer than $h+1$ h + 1 nodes. However, there is few result on the inherent relationship between these two indicators. In this article, we investigate the reason that caused the serious flawed results in (Liu, 2020), and we propose a diagnosis algorithm to establish the $t/k$ t / k -diagnosability for a large-scale connected network $G$ G under the PMC model by considering its $h$ h -extra connectivity. Let $\kappa _h(G)$ κ h ( G ) be the $h$ h -extra connectivity of $G$ G . Then, we can deduce that $G$ G is $\kappa _h(G)/h$ κ h ( G ) / h -diagnosable under the PMC model with some basic conditions. All $\kappa _h(G)$ κ h ( G ) faulty nodes can be correctly diagnosed in the large-scale connected network $G$ G and at most $h$ h fault-free nodes would be misdiagnosed as faulty. The complete fault tolerant method adopts combinatorial properties and linearly many fault analysis to conquer the core of our proofs. We will apply the newly found relationship to directly obtain the $\kappa _h(G)/h$ κ h ( G ) / h -diagnosability of a series of well known networks, including hypercubes, folded hypercubes, balanced hypercubes, dual-cubes, BC graphs, star graphs, Cayley graphs generated by transposition trees, bubble-sort star graphs, alternating group graphs, split-star networks, $k$ k -ary $n$ n -cubes and $(n,k)$ ( n , k ) -star graphs.

In this letter, a batch authentication protocol called as accurate bloom-filter-based batch authentication (ABBA) protocol is presented for large-scale radio-frequency identification (RFID) systems. Similar to its predecessor BBA, ABBA can estimate the number of invalid tags contained in an unknown tag set in one communication round by exploiting a bloom filter (BF) vector constructed distributively by the tags. Whereas different from BBA, ABBA adopts a novel BF vector construction method where the randomness caused by valid tags is removed, and thus the estimation is guaranteed to be more accurate and stable than BBA. Both theoretical analysis and extensive simulations confirm the superiority of ABBA over BBA.

We introduce the concept of a fuzzy measure μ on a set X. We discuss some of the properties of a fuzzy measure. We provide some notable examples of fuzzy measures. We discuss the important application of using fuzzy measure to provide information about an uncertain variable V. Here the measure of a subset A indicates the anticipation of finding the value of V in A. Our interest here is in finding the best course of action, alternative, in this uncertain environment where each alternative is modeled as a payoff function that associates with an element in X a payoff. Here because of the uncertainty associated with the value of V our concept of best course of action is captured for each alternative by an average like value of its payoff function with respect to the measure μ. Our particular concern here is with the case where the payoffs for each alternative are drawn from a scale, which rather then being numeric, is just ordinal. Here then we become interested in finding an aggregated value of a collection of uncertain ordinal values where the uncertainty is modeled by a measure.

Huge amounts of spatio-textual objects are being generated at an unprecedented scale, leading to a variety of applications such as location-based recommendation and sponsored search. Many of these applications need to support moving top-k spatio-textual subscriptions. Unfortunately, existing methods that monitor the results of spatio-textual subscriptions support only static top-k subscriptions or moving boolean subscriptions. In this paper, we propose a novel system, called Lamps (Location-Aware Moving Top-k Pub/Sub), which continuously monitors the top-k most relevant spatio-textual objects for a large number of moving top-k spatio-textual subscriptions simultaneously. As with existing works on continuous moving top-k subscription processing, Lamps employs the concept of a safe region to monitor top-k results. However, unlike with existing works that assume static objects, top-k result updates may be triggered by newly generated objects. To continuously monitor the top-k results for massive moving subscriptions efficiently, we propose SQ-Tree, a novel index based on safe regions, to filter subscriptions whose top-k results do not change. Moreover, to reduce the expensive cost of safe region re-evaluation, we develop a novel approximation technique for safe region construction. Our experimental results on real datasets show that Lamps achieves higher performance than baseline approaches.

Monitoring ionospheric scintillation on a global scale requires introducing a network of widely distributed geodetic receivers, which call for a special type of scintillation index due to the low sampling rate of such receivers. ROTI, as a scintillation index with great potential being applied in geodetic receivers globally, lacks extensive verification in the high-latitude region. Taking the phase scintillation index ( σ ϕ ) provided by ionospheric scintillation monitoring receivers as the reference, this paper analyses data collected at 8 high-latitude GNSS stations to validate the performance of ROTI statistically. The data is evaluated against 4 parameters: 1, the detected daily scintillation occurrence rate; 2, the ability to detect the daily occurrence pattern of ionospheric scintillation; 3, the correlation between the detected scintillation and the space weather parameters, including the 10.7 cm solar flux, Ap, the H component of longitudinally asymmetric and polar cap north indices; 4, the overall distribution of the scintillation magnitude. Results reveal that the scintillation occurrence rates, the occurrence patterns of ionospheric scintillations and the correlations provided by ROTI are generally consistent with those given by σ ϕ , particularly in the middle-high-latitude region. However, the analysis on the distribution of σ ϕ for different ranges of ROTI shows ROTI cannot achieve accurate scintillation monitoring at the epoch level in all selected stations. The main outcomes of this paper are of importance in guiding the reasonable application area of ROTI and developing a high-latitude ionospheric scintillation model based on geodetic receivers.

We study a model where evaluation consists of multiple components of different nature and (cardinal) performances in all components are aggregated into a summary index between 0 and 1. We propose what we call the normalizer-based aggregation rules and characterize them by individual separability, monotonicity, anonymity, and component independence. Each member in this family is distinguished by three types of parameters: (i) a profile of weights that determines the relative importance of each component; (ii) a profile of “individual normalizers” that converts an agent's performance in each component into a raw score (for that component) in the normalized scale of [ 0 , 1 ] ; and (iii) a profile of “group normalizers” that adjusts a raw score for each component relative to all agents' performances. Given these parameters, the overall evaluation, or score, of an agent is obtained as a weighted average of his adjusted scores for all components produced by individual and group normalizers.

As one of the crucial topics in the development of rough set, attribute reduction has received extensive attentions because it is practical and interpretable for us to perform dimensional reduction or feature selection. Currently, to further improve the efficiency of searching reducts, many researchers have devoted themselves to designing various accelerative mechanisms. Among these existing results, it should be pointed out that the accelerators designed by reducing the scale of samples strongly depend on the distribution of data. To fill such a gap, an accelerator based on the random sampling is developed. The superiorities of our accelerator are: (1) randomly selecting samples without considering the distribution of data; (2) guidance-based evaluations and selections of attributes; (3) easily to be combined with other popular searching strategies. By comparing with 5 state-of-the-art accelerators over 24 UCI data sets: (1) our accelerator may significantly reduce the time consumption of deriving reducts and then the average speed-up ratio can exceed 10; (2) the reduct derived by our accelerator can offer competent performance in classification task.

In this letter, we introduce MappingConvSeg, a continuous convolution network for semantic segmentation of large-scale point clouds. In particular, a conceptually simple, end-to-end learnable, and continuous convolution operator is proposed for learning spatial correlation of unstructured 3-D point clouds. For each local point set, the unstructured point features are first mapped onto a series of learned kernel points based on the spatial relationship, and the continuous convolution is then applied to capture specific local geometrical patterns. Taking the proposed mapping convolution operation as the building block, a hierarchical network is then built for large-scale point cloud semantic segmentation. Experimental results conducted on two public benchmarks, including Toronto-3D and Stanford large-scale 3-D Indoor Spaces (S3DIS) dataset, demonstrate the superiority of the proposed method.

Single image dehazing is a challenging ill-posed problem that has drawn significant attention in the last few years. Recently, convolutional neural networks have achieved great success in image dehazing. However, it is still difficult for these increasingly complex models to recover accurate details from the hazy image. In this paper, we pay attention to the feature extraction and utilization of the input image itself. To achieve this, we propose a Multi-scale Topological Network (MSTN) to fully explore the features at different scales. Meanwhile, we design a Multi-scale Feature Fusion Module (MFFM) and an Adaptive Feature Selection Module (AFSM) to achieve the selection and fusion of features at different scales, so as to achieve progressive image dehazing. This topological network provides a large number of search paths that enable the network to extract abundant image features as well as strong fault tolerance and robustness. In addition, ASFM and MFFM can adaptively select important features and ignore interference information when fusing different scale representations. Extensive experiments are conducted to demonstrate the superiority of our method compared with state-of-the-art methods.

In space-based observations, there are large numbers of targets with large size ranges and intensities in a given image. However, the current detection studies mostly emphasize the saliency of targets but neglect their distribution characteristics, which may result in incorrect or missed detection results. In this letter, an effective detection algorithm based on target characteristics is proposed to pursue good multitarget detection performance. First, a space target model is studied to obtain its characteristics. Next, operators are applied to reduce the influence and search for suspected target-center points. Then, local regions around these points are extracted and normalized to reduce the intensity differences among targets and the influence of light conditions. Finally, three features are designed to confirm these suspected regions and their scale information. Furthermore, the threshold setting process is intuitive and significant while keeping the developed method robust in different cases. Compared with other current algorithms, the proposed algorithm achieves superior detection performance in terms of the number of detected targets, accuracy rate, false alarm rate, and computational cost.

Sparse regression based on spectral libraries has become a promising alternative for addressing the hyperspectral unmixing problem. However, the actual endmembers of a scene are usually inconsistent with the corresponding spectral signatures in the spectral library, which largely limits the performance of sparse regression approaches. In this letter, a new sparse regression algorithm considering spectral library mismatch is proposed, which allows the spectral signatures in the spectral library to independently scale in each band and regularizes the differential of the scaling factors to be sparse. Moreover, a guided filter (GF)-based regularizer is introduced to explore the spatial-contextual information. The spectral library sparse scaling and GF constraints are combined to mitigate the impact of the spectral library mismatch. Experimental results on both synthetic and real data show that the proposed algorithm outperforms other methods that address the spectral library mismatch problem.

MotivationGenome-wide association studies show that variants in individual genomic loci alone are not sufficient to explain the heritability of complex, quantitative phenotypes. Many computational methods have been developed to address this issue by considering subsets of loci that can collectively predict the phenotype. This problem can be considered a challenging instance of feature selection in which the number of dimensions (loci that are screened) is much larger than the number of samples. While currently available methods can achieve decent phenotype prediction performance, they either do not scale to large datasets or have parameters that require extensive tuning.ResultsWe propose a fast and simple algorithm, Macarons, to select a small, complementary subset of variants by avoiding redundant pairs that are in linkage disequilibrium. Our method features two interpretable parameters that control the time/performance trade-off without requiring parameter tuning. In our computational experiments, we show that Macarons consistently achieves similar or better prediction performance than state-of-the-art selection methods while having a simpler premise and being at least 2 orders of magnitude faster. Overall, Macarons can seamlessly scale to the human genome with ~107 variants in a matter of minutes while taking the dependencies between the variants into account.ConclusionMacarons can offer a reasonable trade-off between phenotype predictivity, runtime and the complementarity of the selected subsets. The framework we present can be generalized to other high-dimensional feature selection problems within and beyond biomedical applications.AvailabilityMacarons is implemented in Matlab and the source code is available at: https://github.com/serhan-yilmaz/macarons

In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a fading multiple access channel, subject to central and local differential privacy (DP/LDP) constraints. It has been shown that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong DP guarantees for the users. Specifically, the central DP privacy leakage has been shown to scale as $\mathcal {O}(1/K^{1/2})$ , where $K$ is the number of users. It has also been shown that user sampling coupled with orthogonal transmission can enhance the central DP privacy leakage with the same scaling behavior. In this work, we show that, by jointly incorporating both wireless aggregation and user sampling, one can obtain even stronger privacy guarantees. We propose a private wireless gradient aggregation scheme, which relies on independently randomized participation decisions by each user. The central DP leakage of our proposed scheme scales as $\mathcal {O}(1/K^{3/4})$ . In addition, we show that LDP is also boosted by user sampling. We also present analysis for the convergence rate of the proposed scheme and study the tradeoffs between wireless resources, convergence, and privacy theoretically and empirically for two scenarios when the number of sampled participants are $(a)$ known, or $(b)$ unknown at the parameter server.

The visible infrared radiometer (VIRR) is the first and the most historic instrument equipped on the Fengyun (FY) polar-orbiting satellites. After re-processing of the historic data, all the participating VIRR instruments can provide a global data record over 20 years on a consistent radiometric scale, which offers the capacity to study climate change. Because of the lack of an onboard calibration system for VIRR, the reflective solar bands must be vicariously calibrated. This study outlines the multi-site vicarious approach for the consistent calibration of the VIRR visible and near-infrared data, which produced calibration coefficients for the five instruments on the satellites FY-1C/D and FY-3A/B/C, as well as an evaluation of the record. The reflectance predicted using the radiative transfer model over multiple invariant desert and ocean targets was used to derive the calibration slope via a weighted fitting scheme, in which the weights were the inverse of the variance from a radiative transfer simulation evaluation with reference to Aqua MODIS. The sensor-specific calibration coefficients were derived on a daily basis using piecewise polynomials. The calibration reference of the VIRR solar band record was further traced to the Aqua MODIS Collection 6.1 reference calibration using a systematic correction derived from the Libya4 desert. The VIRR record was compared to the Aqua MODIS C6.1 calibration over the polar region using simultaneous nadir overpass observations. The lifetime relative biases for each sensor are within 2.7% and 4.4% for channel 1 and channel 2, respectively. Invariant deserts were also employed to evaluate the stability and consistency of the VIRR record. In general, the means of the directional and spectral corrected reflectance for each sensor are within 1% of the 20 year average, which implies that the VIRR reflectance of the invariant targets is consistent to within 1% among the sensors for channels 1 and 2.

Variability assessment of qualitative data has an important role in diverse areas such as sociology, quality engineering, healthcare, decision making, genetics, metrology in chemistry and many others. To test the homogeneity hypothesis, we use two main categorical factors. Using a cross-balanced design, we provide a decomposition theorem of the sample total-variation into ‘intra’ (within) component and ‘inter’ (between) component for any scale. Building on this, we provide a way for decomposing the ‘inter’ component itself into variation components contributed by the two factors and their interaction (when we consider more than one replicated test in the cross-design). Moreover, focusing on an ordinal scale, we offer segregation power indices for testing the significate effects of the two factors and their interaction. A two-way ORDANOVA calculator is provided to display the empirical distributions of the segregation power indices and to find critical points of the test statistics distributions at the given confidence level. Some examples are given to demonstrate the proposed method.

We present a search for transient radio sources on timescales of 2 to 9 yr at 150 MHz. This search is conducted by comparing the first Alternative Data Release of the TIFR GMRT Sky Survey (TGSS ADR1) and the second data release of the LOFAR Two-metre Sky Survey (LoTSS DR2). The overlapping survey area covers 5570 $\rm{deg}^2$ on the sky, or 14 per cent of the total sky. We introduce a method to compare the source catalogues that involves a pair match of sources, a flux density cutoff to meet the survey completeness limit and a newly developed compactness criterion. This method is used to identify both transient candidates in the TGSS source catalogue that have no counterpart in the LoTSS catalogue and transient candidates in LoTSS without a counterpart in TGSS. We find that imaging artefacts and uncertainties and variations in the flux density scales complicate the transient search. Our method to search for transients by comparing two different surveys, while taking into account imaging artefacts around bright sources and misaligned flux scales between surveys, is universally applicable to future radio transient searches. No transient sources were identified, but we are able to place an upper limit on the transient surface density of $, 14 pages, 11 figures