A simple and reliable instance selection for fast training support vector machine: Valid Border Recognition

Support vector machines (SVMs) are powerful statistical learning tools, but their application to large datasets can cause time-consuming training complexity. To address this issue, various instance selection (IS) approaches have been proposed, which choose a small fraction of critical instances and screen out others before training. However, existing methods have not been able to balance accuracy and efficiency well. Some methods miss critical instances, while others use complicated selection schemes that require even more execution time than training with all original instances, thus violating the initial intention of IS. In this work, we present a newly developed IS method called Valid Border Recognition (VBR). VBR selects the closest heterogeneous neighbors as valid border instances and incorporates this process into the creation of a reduced Gaussian kernel matrix, thus minimizing the execution time. To improve reliability, we propose a strengthened version of VBR (SVBR). Based on VBR, SVBR gradually adds farther heterogeneous neighbors as complements until the Lagrange multipliers of already selected instances become stable. In numerical experiments, the effectiveness of our proposed methods is verified on benchmark and synthetic datasets in terms of accuracy, execution time and inference time.

Diffusion capacity of single and interconnected networks

Understanding diffusive processes in networks is a significant challenge in complexity science. Networks possess a diffusive potential that depends on their topological configuration, but diffusion also relies on the process and initial conditions. This article presents Diffusion Capacity, a concept that measures a node's potential to diffuse information based on a distance distribution that considers both geodesic and weighted shortest paths and dynamical features of the diffusion process. Diffusion Capacity thoroughly describes the role of individual nodes during a diffusion process and can identify structural modifications that may improve diffusion mechanisms. The article defines Diffusion Capacity for interconnected networks and introduces Relative Gain, which compares the performance of a node in a single structure versus an interconnected one. The method applies to a global climate network constructed from surface air temperature data, revealing a significant change in diffusion capacity around the year 2000, suggesting a loss of the planet's diffusion capacity that could contribute to the emergence of more frequent climatic events.

Physician scheduling problem in Mobile Cabin Hospitals of China during Covid-19 outbreak

In this paper, we investigate a novel physician scheduling problem in the Mobile Cabin Hospitals (MCH) which are constructed in Wuhan, China during the outbreak of the Covid-19 pandemic. The shortage of physicians and the surge of patients brought great challenges for physicians scheduling in MCH. The purpose of the studied problem is to get an approximately optimal schedule that reaches the minimum workload for physicians on the premise of satisfying the service requirements of patients as much as possible. We propose a novel hybrid algorithm integrating particle swarm optimization (PSO) and variable neighborhood descent (VND) (named as PSO-VND) to find the approximate global optimal solution. A self-adaptive mechanism is developed to choose the updating operators dynamically during the procedures. Based on the special features of the problem, three neighborhood structures are designed and searched in VND to improve the solution. The experimental comparisons show that the proposed PSO-VND has a significant performance increase than the other competitors.

A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability

Safeguarding natural resources and energy is essential to ensure food security for future generations. Given the increase of published papers in the agricultural field applying Data Envelopment Analysis (DEA), this review seeks to address the special requirements of this methodology when implemented in the agricultural sector as well as to classify papers under sustainability aspects (economic, environmental, social). More specifically, 120 papers from Scopus and Web of Science databases were included in this review by using PRISMA methodology, and they were tested in the following groups (i) General information, (ii) DEA implementation, (iii) DEA extensions, (iv) Data type, (v) Data collection and processing, and (vi) Sustainability dimensions. Results indicate that there is a great need for weights use when performing DEA in the agricultural sector, to acquire results with greater explanatory power. Moreover, systematic data collection of multiple factors could lead to the implementation of complex methodologies, providing feasible solutions to the involved stakeholders. Lastly, the social aspect is the least represented dimension out of the three aspects of sustainability, indicating the need for the integration of social factors in such analyses, especially when DEA is used to create a policy framework in a specific area.

Optimization methods for large-scale vaccine supply chains: a rapid review

Global vaccine revenues are projected at $59.2 billion, yet large-scale vaccine distribution remains challenging for many diseases in countries around the world. Poor management of the vaccine supply chain can lead to a disease outbreak, or at worst, a pandemic. Fortunately, a large number of those challenges, such as decision-making for optimal allocation of resources, vaccination strategy, inventory management, among others, can be improved through optimization approaches. This work aims to understand how optimization has been applied to vaccine supply chain and logistics. To achieve this, we conducted a rapid review and searched for peer-reviewed journal articles, published between 2009 and March 2020, in four scientific databases. The search resulted in 345 articles, of which 25 unique studies met our inclusion criteria. Our analysis focused on the identification of article characteristics such as research objectives, vaccine supply chain stage addressed, the optimization method used, whether outbreak scenarios were considered, among others. Approximately 64% of the studies dealt with vaccination strategy, and the remainder dealt with logistics and inventory management. Only one addressed market competition (4%). There were 14 different types of optimization methods used, but control theory, linear programming, mathematical model and mixed integer programming were the most common (12% each). Uncertainties were considered in the models of 44% of the studies. One resulting observation was the lack of studies using optimization for vaccine inventory management and logistics. The results provide an understanding of how optimization models have been used to address challenges in large-scale vaccine supply chains.

Traffic-Driven Epidemic Spreading in Networks: Considering the Transition of Infection From Being Mild to Severe

Realistic epidemic spreading is usually driven by traffic flow in networks, which is not captured in classic diffusion models. Moreover, the progress of a node's infection from mild to severe phase has not been particularly addressed in previous epidemic modeling. To address these issues, we propose a novel traffic-driven epidemic spreading model by introducing a new epidemic state, that is, the severe state, which characterizes the serious infection of a node different from the initial mild infection. We derive the dynamic equations of our model with the tools of individual-based mean-field approximation and continuous-time Markov chain. We find that, besides infection and recovery rates, the epidemic threshold of our model is determined by the largest real eigenvalue of a communication frequency matrix we construct. Finally, we study how the epidemic spreading is influenced by representative distributions of infection control resources. In particular, we observe that the uniform and Weibull distributions of control resources, which have very close performance, are much better than the Pareto distribution in suppressing the epidemic spreading.

Noise-Tolerant Techniques for Decomposition-Based Multiobjective Evolutionary Algorithms

Over the last few decades, the decomposition-based multiobjective evolutionary algorithms (DMOEAs) have became one of the mainstreams for multiobjective optimization. However, there is not too much research on applying DMOEAs to uncertain problems until now. Usually, the uncertainty is modeled as additive noise in the objective space, which is the case this paper concentrates on. This paper first carries out experiments to examine the impact of noisy environments on DMOEAs. Then, four noise-handling techniques based upon the analyses of empirical results are proposed. First, a Pareto-based nadir point estimation strategy is put forward to provide a good normalization of each objective. Next, we introduce two adaptive sampling strategies that vary the number of samples used per solution based on the differences among neighboring solutions and their variance to control the tradeoff between exploration and exploitation. Finally, a mixed objective evaluation strategy and a mixed repair mechanism are proposed to alleviate the effects of noise and remedy the loss of diversity in the decision space, respectively. These features are embedded in two popular DMOEAs (i.e., MOEA/D and DMOEA- [Formula: see text]), and DMOEAs with these features are called noise-tolerant DMOEAs (NT-DMOEAs). NT-DMOEAs are compared with their various variants and four noise-tolerant multiobjective algorithms, including the improved NSGA-II, the classical algorithm Bayesian (1+1)-ES (BES), and the state-of-the-art algorithms MOP-EA and rolling tide evolutionary algorithm to show the superiority of proposed features on 17 benchmark problems with different strength levels of noise. Experimental studies demonstrate that two NT-DMOEAs, especially NT-DMOEA- [Formula: see text], show remarkable advantages over competitors in the majority of test instances.

Interdependent Networks: A Data Science Perspective

Traditionally, networks have been studied in an independent fashion. With the emergence of novel smart city technologies, coupling among networks has been strengthened. To capture the ever-increasing coupling, we explain the notion of interdependent networks, i.e., multi-layered networks with shared decision-making entities, and shared sensing infrastructures with interdisciplinary applications. The main challenge is how to develop data analytics solutions that are capable of enabling interdependent decision making. One of the emerging solutions is agent-based distributed decision making among heterogeneous agents and entities when their decisions are affected by multiple networks. We first provide a big picture of real-world interdependent networks in the context of smart city infrastructures. We then provide an outline of potential challenges and solutions from a data science perspective. We discuss potential hindrances to ensure reliable communication among intelligent agents from different networks. We explore future research directions at the intersection of network science and data science.

This article provides a holistic overview of interdependent cyber-physical-societal networks. We envision the subsequent research directions that require contribution of the data science community as well as interdisciplinary collaboration with network scientists, social scientists, computer scientists, and engineers to tackle the emerging problems raised by the notion of interdependent networks: (1) developing novel algorithms for data analytics and enabling interdependent decision making, (2) proposing holistic models that are capable of capturing the interdependence among human-centered multi-layer critical infrastructures, and (3) developing efficient solutions that are capable of finding globally optimum solutions using information from each network as well as modeling the interdependent information exchange. In addition to these directions, we outline policy and access-control issues, including conflict of interest among stakeholders and operators of each network. Successful implementation and development of an interdependent data analytics framework and its required algorithms will improve the quality of life of citizens by enabling globally optimum decision making, increasing efficiency, preserving privacy of intelligent agents, and reducing operational cost of interdependent networks. Further reading: Sustainable Interdependent Networks book series (interdependentnetworks.com) and Optimization, Learning, and Control for Interdependent Complex Networks (edited by M.H. Amini).

Extended vertical lists for temporal pattern mining from multivariate time series

In this paper, the problem of mining complex temporal patterns in the context of multivariate time series is considered. A new method called the Fast Temporal Pattern Mining with Extended Vertical Lists is introduced. The method is based on an extension of the level-wise property, which requires a more complex pattern to start at positions within a record where all of the subpatterns of the pattern start. The approach is built around a novel data structure called the Extended Vertical List that tracks positions of the first state of the pattern inside records and links them to appropriate positions of a specific subpattern of the pattern called the prefix. Extensive computational results indicate that the new method performs significantly faster than the previous version of the algorithm for Temporal Pattern Mining; however, the increase in speed comes at the expense of increased memory usage.

Effects of government regulations on Manufacturer's behaviors under carbon emission reduction

This paper shifts the discussion of low-carbon technology from science to the economy, especially the reactions of a manufacturer to government regulations. One major concern in this paper is uncertainty about the effects of government regulation on the manufacturing industry. On the trust side, will manufacturers trust the government's commitment to strictly supervise carbon emission reduction? Will a manufacturer that is involved in traditional industry consciously follow a low-carbon policy? On the profit side, does equilibrium between a manufacturer and a government exist on deciding which strategy to undertake to meet a profit maximization objective under carbon emission reduction? To identify the best solutions to these problems, this paper estimates the economic benefits of manufacturers associated with policy regulations in a low-carbon technology market. The problem of an interest conflict between the government and the manufacturer is formalized as a game theoretic model, and a mixed strategy Nash equilibrium is derived and analyzed. The experiment results indicate that when the punishment levied on the manufacturer or the loss to the government is sizable, the manufacturer will be prone to developing innovative technology and the government will be unlikely to supervise the manufacturer.

Assessing diversity in multiplex networks

Diversity, understood as the variety of different elements or configurations that an extensive system has, is a crucial property that allows maintaining the system's functionality in a changing environment, where failures, random events or malicious attacks are often unavoidable. Despite the relevance of preserving diversity in the context of ecology, biology, transport, finances, etc., the elements or configurations that more contribute to the diversity are often unknown, and thus, they can not be protected against failures or environmental crises. This is due to the fact that there is no generic framework that allows identifying which elements or configurations have crucial roles in preserving the diversity of the system. Existing methods treat the level of heterogeneity of a system as a measure of its diversity, being unsuitable when systems are composed of a large number of elements with different attributes and types of interactions. Besides, with limited resources, one needs to find the best preservation policy, i.e., one needs to solve an optimization problem. Here we aim to bridge this gap by developing a metric between labeled graphs to compute the diversity of the system, which allows identifying the most relevant components, based on their contribution to a global diversity value. The proposed framework is suitable for large multiplex structures, which are constituted by a set of elements represented as nodes, which have different types of interactions, represented as layers. The proposed method allows us to find, in a genetic network (HIV-1), the elements with the highest diversity values, while in a European airline network, we systematically identify the companies that maximize (and those that less compromise) the variety of options for routes connecting different airports.

Traffic congestion and the lifetime of networks with moving nodes

For many power-limited networks, such as wireless sensor networks and mobile ad hoc networks, maximizing the network lifetime is the first concern in the related designing and maintaining activities. We study the network lifetime from the perspective of network science. In our model, nodes are initially assigned a fixed amount of energy moving in a square area and consume the energy when delivering packets. We obtain four different traffic regimes: no, slow, fast, and absolute congestion regimes, which are basically dependent on the packet generation rate. We derive the network lifetime by considering the specific regime of the traffic flow. We find that traffic congestion inversely affects network lifetime in the sense that high traffic congestion results in short network lifetime. We also discuss the impacts of factors such as communication radius, node moving speed, routing strategy, etc., on network lifetime and traffic congestion.

Does Economic Crisis Force to Consumption Changes Regarding Fruits and Vegetables?

This study focuses on consumers' behaviour towards Fruits and Vegetables (FVs) under economic crisis. The implementation of both factor analysis and logistic regression reveals discrete consumer groups, affected and not affected by the ongoing economic crisis. Interviewees were selected randomly. In total, 250 questionnaires were completed and 238 of them were used for computations. There are two consumer groups, one affected by the crisis and one which did not. For the former, the price criterion prevails, while for the latter parameters like locality of production and heath concerns lead them to purchasing decisions. The economic crisis has reduced the quantities of FVs being consumed, and the retail chain stores fail to meet the criteria of locality and secure traceability procedures about the origin of the products. Nevertheless, educated consumers with higher incomes prefer to visit super markets, while elderly people with low incomes prefer grocery stores and open markets.

Space pruning monotonic search for the non-unique probe selection problem

Identification of targets, generally viruses or bacteria, in a biological sample is a relevant problem in medicine. Biologists can use hybridisation experiments to determine whether a specific DNA fragment, that represents the virus, is presented in a DNA solution. A probe is a segment of DNA or RNA, labelled with a radioactive isotope, dye or enzyme, used to find a specific target sequence on a DNA molecule by hybridisation. Selecting unique probes through hybridisation experiments is a difficult task, especially when targets have a high degree of similarity, for instance in a case of closely related viruses. After preliminary experiments, performed by a canonical Monte Carlo method with Heuristic Reduction (MCHR), a new combinatorial optimisation approach, the Space Pruning Monotonic Search (SPMS) method, is introduced. The experiments show that SPMS provides high quality solutions and outperforms the current state-of-the-art algorithms.

Data mining in psychiatric research

Mathematical sciences and computational methods have found new applications in fields like medicine over the last few decades. Modern data acquisition and data analysis protocols have been of great assistance to medical researchers and clinical scientists. Especially in psychiatry, technology and science have made new computational methods available to assist the development of predictive modeling and to identify diseases more accurately. Data mining (or knowledge discovery) aims to extract information from large datasets and solve challenging tasks, like patient assessment, early mental disease diagnosis, and drug efficacy assessment. Accurate and fast data analysis methods are very important, especially when dealing with severe psychiatric diseases like schizophrenia. In this paper, we focus on computational methods related to data analysis and more specifically to data mining. Then, we discuss some related research in the field of psychiatry.

Towards a Model-Centric Approach for Developing Enterprise Information Systems

This chapter aims to present a new modeling paradigm that promises to significantly increase the efficiency of developing enterprise information systems. Currently, the software industry faces considerable challenges as it tries to build larger, more complex, software systems with fewer resources. Although modern programming languages such as C++ and Java have in general improved the software development process, they have failed to significantly increase developer’s productivity. Thus, developers are considering other paths to address this issue. One of the potential paths is designing, developing and deploying enterprise information systems using the Model Driven Architecture (MDA). MDA is a model-centric approach that allows for modeling the overall business of an enterprise and capturing requirements to developing, deploying, integrating, and managing different kinds of software components without considering any particular implementation technology. At the center of this approach are models; the software development process is driven by constructing models representing the software under development. Code that expresses the implementation of the model in a certain underlying technology is obtained as a result of model transformation. Thus, the intellectual investment spent in developing the business model of an enterprise is not jeopardized by the continuous changes of the implementation technologies. Currently there are two main approaches trying to implement MDA-based tools. One of the approaches is based on the Object Constraint Language and the other on Action Language. An example of designing, developing and deploying an application using this new modeling paradigm is presented. The MDA approach to software development is considered as the biggest shift since the move from Assembler to the first high level languages.

A robust spike and wave algorithm for detecting seizures in a genetic absence seizure model

Animal Models are used extensively in basic epilepsy research. In many studies, there is a need to accurately score and quantify all epileptic spike and wave discharges (SWDs) as captured by electroencephalographic (EEG) recordings. Manual scoring of long term EEG recordings is a time-consuming and tedious task that requires inordinate amount of time of laboratory personnel and an experienced electroencephalographer. In this paper, we adapt a SWD detection algorithm, originally proposed by the authors for absence (petit mal) seizure detection in humans, to detect SWDs appearing in EEG recordings of Fischer 334 rats. The algorithm is robust with respect to the threshold parameters. Results are compared to manual scoring and the effect of different threshold parameters is discussed.

Real-time differentiation of nonconvulsive status epilepticus from other encephalopathies using quantitative EEG analysis: a pilot study

PURPOSE

Distinguishing nonconvulsive status epilepticus (NCSE) from some nonepileptic encephalopathies is a challenging problem. In many situations, NCSE and nonepileptic encephalopathies are indistinguishable by clinical symptoms and can produce very similar electroencephalography (EEG) patterns. Misdiagnosis or delay to diagnosis of NCSE may increase the rate of morbidity and mortality.

METHODS

We developed a fast-differentiating algorithm using quantitative EEG analysis to distinguish NCSE patients from patients with toxic/metabolic encephalopathy (TME). EEG recordings were collected from 11 patients, including 6 with NCSE and 5 with TME. Three nonlinear dynamic measures were used in the proposed algorithm: the maximum short-term Lyapunov exponent (STLmax), phase of attractor (phase/angular frequency), and approximate entropy (ApEn). A further refined metric derived from STLmax and phase of attractor (the mean distance to EEG epoch samples from their centroid in the feature space) was also utilized as a criterion. Paired t tests were carried out to further clarify the separation between the EEG patterns of NCSE and TME.

RESULTS

Computational results showed that the performance of the proposed algorithm was sufficient to distinguish NCSE from TME. The results were consistent in all subjects in our study.

CONCLUSIONS

The study presents evidence that the maximum short-term Lyapunov exponents (STLmax) and phase of attractors (phase/angular frequency) can be useful in assisting clinical diagnosis of NCSE. Findings presented in this article provide a promising indication that the proposed algorithm may correctly distinguish NCSE from TME. Although the exact mechanism of this association remains unknown, the authors suggest that epileptic activity is highly associated with and can be modeled by dynamic systems.

Antiepileptic drug intervention decouples electroencephalogram (EEG) signals: a case study in Unverricht-Lundborg Disease

Change in severity of myoclonus as an outcome measure of antiepileptic drug (AED) treatment in patients with Unverricht-Lundborg Disease (ULD) has been estimated by utilizing the Unified Myoclonus Rating Scale (UMRS). In this study, we measure treatment effects through EEG analysis using mutual information approach to quantify interdependence/coupling strength among different electrode sites. Mutual information is known to have the ability to capture linear and non-linear dependencies between EEG time series with superior performance over the traditional linear measures. One subject with ULD participated in this study and 1-hour EEG recordings were acquired before and after treatment of AED. Our results indicate that the mutual information is significantly lower after taking the add-on AED for four weeks at least. This finding could lead to a new insight for developing a new outcome measure for patient with ULD, when UMRS could potentially fail to detect a significant difference.

An investigation of EEG dynamics in an animal model of temporal lobe epilepsy using the maximum Lyapunov exponent

Analysis of intracranial electroencephalographic (iEEG) recordings in patients with temporal lobe epilepsy (TLE) has revealed characteristic dynamical features that distinguish the interictal, ictal, and postictal states and inter-state transitions. Experimental investigations into the mechanisms underlying these observations require the use of an animal model. A rat TLE model was used to test for differences in iEEG dynamics between well-defined states and to test specific hypotheses: 1) the short-term maximum Lyapunov exponent (STL(max)), a measure of signal order, is lowest and closest in value among cortical sites during the ictal state, and highest and most divergent during the postictal state; 2) STL(max) values estimated from the stimulated hippocampus are the lowest among all cortical sites; and 3) the transition from the interictal to ictal state is associated with a convergence in STL(max) values among cortical sites. iEEGs were recorded from bilateral frontal cortices and hippocampi. STL(max) and T-index (a measure of convergence/divergence of STL(max) between recorded brain areas) were compared among the four different periods. Statistical tests (ANOVA and multiple comparisons) revealed that ictal STL(max) was lower (p<0.05) than other periods, STL(max) values corresponding to the stimulated hippocampus were lower than those estimated from other cortical regions, and T-index values were highest during the postictal period and lowest during the ictal period. Also, the T-index values corresponding to the preictal period were lower than those during the interictal period (p<0.05). These results indicate that a rat TLE model demonstrates several important dynamical signal characteristics similar to those found in human TLE and support future use of the model to study epileptic state transitions.

Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains

Epilepsy is a brain disorder characterized clinically by temporary but recurrent disturbances of brain function that may or may not be associated with destruction or loss of consciousness and abnormal behavior. Human brain is composed of more than 10 to the power 10 neurons, each of which receives electrical impulses known as action potentials from others neurons via synapses and sends electrical impulses via a sing output line to a similar (the axon) number of neurons. When neuronal networks are active, they produced a change in voltage potential, which can be captured by an electroencephalogram (EEG). The EEG recordings represent the time series that match up to neurological activity as a function of time. By analyzing the EEG recordings, we sought to evaluate the degree of underlining dynamical complexity prior to progression of seizure onset. Through the utilization of the dynamical measurements, it is possible to classify the state of the brain according to the underlying dynamical properties of EEG recordings. The results from two patients with temporal lobe epilepsy (TLE), the degree of complexity start converging to lower value prior to the epileptic seizures was observed from epileptic regions as well as non-epileptic regions. The dynamical measurements appear to reflect the changes of EEG's dynamical structure. We suggest that the nonlinear dynamical analysis can provide a useful information for detecting relative changes in brain dynamics, which cannot be detected by conventional linear analysis.