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Instance Space Analysis (ISA) is a recently developed methodology to (a) support objective testing of algorithms and (b) assess the diversity of test instances. Representing test instances as feature vectors, the ISA methodology extends Rice’s 1976 Algorithm Selection Problem framework to enable visualization of the entire space of possible test instances, and gain insights into how algorithm performance is affected by instance properties. Rather than reporting algorithm performance on average across a chosen set of test problems, as is standard practice, the ISA methodology offers a more nuanced understanding of the unique strengths and weaknesses of algorithms across different regions of the instance space that may otherwise be hidden on average. It also facilitates objective assessment of any bias in the chosen test instances and provides guidance about the adequacy of benchmark test suites. This article is a comprehensive tutorial on the ISA methodology that has been evolving over several years, and includes details of all algorithms and software tools that are enabling its worldwide adoption in many disciplines. A case study comparing algorithms for university timetabling is presented to illustrate the methodology and tools. [ABSTRACT FROM AUTHOR]

Item Response Theory (IRT) has been proposed within the field of Educational Psychometrics to assess student ability as well as test question difficulty and discrimination power. More recently, IRT has been applied to evaluate machine learning algorithm performance on a single classification dataset, where the student is now an algorithm, and the test question is an observation to be classified by the algorithm. In this paper we present a modified IRT-based framework for evaluating a portfolio of algorithms across a repository of datasets, while simultaneously eliciting a richer suite of characteristics - such as algorithm consistency and anomalousness - that describe important aspects of algorithm performance. These characteristics arise from a novel inversion and reinterpretation of the traditional IRT model without requiring additional dataset feature computations. We test this framework on algorithm portfolios for a wide range of applications, demonstrating the broad applicability of this method as an insightful algorithm evaluation tool. Furthermore, the explainable nature of IRT parameters yield an increased understanding of algorithm portfolios. [ABSTRACT FROM AUTHOR]

Mucopolysaccharidosis type II (MPS II, Hunter syndrome) is an X-linked recessive lysosomal disease caused by deficiency of iduronate-2-sulfatase (IDS). The absence of IDS results in the accumulation of the glycosaminoglycans (GAGs) heparan sulfate and dermatan sulfate. Currently, the only approved treatment option for MPS II is enzyme replacement therapy (ERT), Elaprase. However, ERT is demanding for the patient and does not ameliorate neurological manifestations of the disease. Using an IDS-deficient mouse model that phenocopies the human disease, we evaluated hematopoietic stem and progenitor cells (HSPCs) transduced with a lentiviral vector (LVV) carrying a codon-optimized human IDS coding sequence regulated by a ubiquitous MNDU3 promoter (MNDU3-IDS). Mice treated with MNDU3-IDS LVV-transduced cells showed supraphysiological levels of IDS enzyme activity in plasma, peripheral blood mononuclear cells, and in most analyzed tissues. These enzyme levels were sufficient to normalize GAG storage in analyzed tissues. Importantly, IDS levels in the brains of MNDU3-IDS-engrafted animals were restored to 10–20% than that of wild-type mice, sufficient to normalize GAG content and prevent emergence of cognitive deficit as evaluated by neurobehavioral testing. These results demonstrate the potential effectiveness of ex vivo MNDU3-IDS LVV-transduced HSPCs for treatment of MPS II. [ABSTRACT FROM AUTHOR]

When demonstrating the effectiveness of a new algorithm, researchers are traditionally encouraged to compare their algorithm's performance against existing algorithms on well-studied benchmark test suites. In the absence of more nuanced methodologies, algorithm performance is typically summarized on average across the test suite examples. This paper highlights the potential bias of conclusions drawn by analyzing “on average” performance, and the opportunities offered by a recent testing methodology known as instance space analysis. To illustrate, we revisit our 2007 comparative study of algorithms for facial age estimation, and rigorously stress-test to challenge the original conclusions. The case study demonstrates how powerful visualizations offered by instance space analysis enable greater insights into unique strengths and weaknesses, and which algorithm should be used when and why. Inspired by such insights, a new algorithm is proposed, and its unique advantage is demonstrated. The bias often hidden in well-studied datasets, and the ramifications for drawing biased conclusions, are also illustrated in this case study. While focused on facial age estimation, the methodology and lessons learned from the case study are broadly applicable to any study seeking to draw conclusions about algorithm performance based on empirical results. [ABSTRACT FROM AUTHOR]

The subsurface oceans of icy moons in the outer solar system are prime candidates for harboring extra-terrestrial life. Missions to melt through the ice shells of these worlds utilizing an ice probe or "cryobot" to access the oceans have been proposed. To design such a cryobot for a successful mission, the relationship between a cryobot's heat budget and descent speed within any given environment must be understood. Large uncertainties in the ice shell thickness and temperature profile require that the problem be treated via probabilistic techniques, such as a Monte Carlo simulation. By contrast, the speed of a cryobot descending through ice with specified far-field temperature and material properties can, in principle, be quantified with little uncertainty. A model of cryobot performance that can be computed quickly and accurately will save significant computational time for the broader simulation, and allow for the mission's basic thermal needs to be understood. The work reported here builds upon an existing analytic thermal model of ice probes that, while suitable for terrestrial conditions, begins to incur potentially problematic errors in extraterrestrial environments. The new model provides improvement to the previous model for flight-relevant probes in extra-terrestrial ice conditions, predicting the total heat budget to within 1.2% of high-fidelity numerical simulations while requiring a small fraction of the computational time. The model can be non-dimensionalized in a compact form, making it suitable for modeling a broad class of cylindrical ice probes in a probabilistic modeling framework for determining the time required to access the sub-surface ocean. Such models can then provide the high-level requirements for cryobot system architectures that will enable successful ocean-access missions. [Display omitted] • Responds to strong community interest in accessing the oceans of Europa and Enceladus. • Quick-solving quasi-analytic model relates ice probe descent speed to heat budget. • Builds upon previous work to include temperature dependence and a water annulus. • Verified with benchmark high-fidelity numerical cases. • Provides a powerful tool for high-level mission design for accessing sub-surface ocean worlds. [ABSTRACT FROM AUTHOR]

Water is a key ingredient to the potential for life, and several icy moons, such as Europa and Enceladus, are thought to harbor a dynamic subsurface ocean of liquid water. An ice probe or "cryobot" is a proposed technology to penetrate the surrounding ice shell and deliver a life-detection payload to these oceans. As the ice probe descends, it is surrounded by a thin melt film of liquid water. This paper focuses on the fluidics of the melt film for the case of a general ice probe shape, leading to a new approximate equation for the force balance constraints (validated to < 0. 4 % error by numerical model). In addition to the introduction of water by melting, the analysis allows for consideration of additional volumetric flow via a pumped jet of recirculated water. The general force balance equation is explored in more detail for the example of a cylindrical probe geometry with flat circular end caps. The thickness of the melt film below and to the side of the cylindrical probe is constrained by the force balance such that the probe typically operates in one of two asymptotic regimes, moving or stalled. [Display omitted] • Responds to community interest in accessing the oceans of Europa and Enceladus. • Advances models of mechanical and thermal behavior of a descending ice probe. • Generalizes previous work to an arbitrary probe shape. • Identifies two asymptotic regimes of probe behavior, moving or stalled. • Quantifies previously unmodeled inefficiency due to advective heat loss. [ABSTRACT FROM AUTHOR]

· Mean annual stream temperature was found to warm by 1.00 °C over 1992–2021. · Increased air temperatures and urbanization are key drivers of warming stream temperatures. · Air temperature and urbanization together warm streams more than their individual effects alone. · Both local and global solutions are needed to reduce future increases to stream temperature. Climate change and urbanization threaten streams and the biodiversity that rely upon them worldwide. Emissions of greenhouse gases are causing air and sea surface temperatures to increase, and even small areas of urbanization are degrading stream biodiversity, water quality and hydrology. However, empirical evidence of how increasing air temperatures and urbanization together affect stream temperatures over time and their relative influence on stream temperatures is limited. This study quantifies changes in stream temperatures in a region in South-East Australia with an urban-agricultural-forest landcover gradient and where increasing air temperatures have been observed. Using Random Forest models we identify air temperature and urbanization drive increasing stream temperatures and that their combined effects are larger than their individual effects occurring alone. Furthermore, we identify potential mitigation measures useful for waterway managers and policy makers. The results show that both local and global solutions are needed to reduce future increases to stream temperature. [Display omitted] [ABSTRACT FROM AUTHOR]

This article presents a method to generate diverse and challenging new test instances for continuous black-box optimization. Each instance is represented as a feature vector of exploratory landscape analysis measures. By projecting the features into a two-dimensional instance space, the location of existing test instances can be visualized, and their similarities and differences revealed. New instances are generated through genetic programming which evolves functions with controllable characteristics. Convergence to selected target points in the instance space is used to drive the evolutionary process, such that the new instances span the entire space more comprehensively. We demonstrate the method by generating two-dimensional functions to visualize its success, and ten-dimensional functions to test its scalability. We show that the method can recreate existing test functions when target points are co-located with existing functions, and can generate new functions with entirely different characteristics when target points are located in empty regions of the instance space. Moreover, we test the effectiveness of three state-of-the-art algorithms on the new set of instances. The results demonstrate that the new set is not only more diverse than a well-known benchmark set, but also more challenging for the tested algorithms. Hence, the method opens up a new avenue for developing test instances with controllable characteristics, necessary to expose the strengths and weaknesses of algorithms, and drive algorithm development. [ABSTRACT FROM AUTHOR]

• Presenting a set of mathematical models for green flowshop scheduling problems. • Considering common carbon reduction policies and their corresponding scenarios. • Providing analysis of models and insights to design a framework for green flowshops. • Industries can use our cost-effective strategies to meet limits mandated by government. • Government can explore our integrated framework to gauge impacts of carbon policies. In this paper we consider, from an environmental policy-maker perspective, how carbon reduction policies impact the economic competitiveness of the manufacturing sector. Specifically, we focus on flowshop scheduling – which typically aims to minimize makespan for purely economic objectives – and consider how three common carbon reduction policies – namely, taxes on emissions, baselines on emissions, and emissions trading schemes – can create competitive green flowshops that balance minimization of makespan and carbon emissions. The goal is to enable policy-makers to understand how to set policies and control parameters to achieve environmental objectives while ensuring global economic competitiveness of industry. We initially present a set of mixed-integer linear programming (MILP) models for flowshop scheduling problems operating in a regulated environment in which each carbon reduction policy is adopted. We then introduce a bi-objective scheduling framework for the corresponding problem to obtain alternative solutions under each policy. These models and their computational results however, are not the main focus of the study, but are presented as a means to demonstrate how green policies co-exist with economic objectives, with policy-makers in control of the balance. To this end, based on financial data from Australia's carbon emissions profile, we provide a policy-oriented analysis of the models, and some managerial insights into the effect of scheduling strategies on carbon emissions under different reduction policies. These insights offer support to both environmental policy-makers and corporate production and sustainability managers to determine whether it is technically feasible and profitable to replace traditional scheduling strategies with environmentally friendly scheduling strategies. [ABSTRACT FROM AUTHOR]

Objective: Obexelimab is an investigational, bifunctional, noncytolytic monoclonal antibody that binds CD19 and FcyRIIb to inhibit B cells, plasmablasts, and plasma cells. This trial evaluated the efficacy and safety of obexelimab in the treatment of patients with systemic lupus erythematosus (SLE). Methods: During screening, patients with active, non–organ‐threatening SLE received corticosteroid injections to ameliorate symptoms while immunosuppressants were withdrawn (≤10 mg/day prednisone equivalent and ≤400 mg/day hydroxychloroquine allowed). Patients with improved disease activity were randomized 1:1 to obexelimab 5 mg/kg intravenously or placebo once every 2 weeks until week 32 or loss of improvement (LOI). Results: In this study, 104 patients were randomized. Analysis of the primary endpoint, proportion of patients reaching week 32 without LOI, used an efficacy‐evaluable (EE) population defined as patients who completed the study or withdrew for flare or treatment‐related toxicity. This endpoint did not reach statistical significance: 21 of 50 obexelimab‐treated patients (42.0%) versus 12 of 42 patients (28.6%) treated with a placebo (P = 0.183). Time to LOI was increased in obexelimab‐treated patients versus patients treated with a placebo in the EE (hazard ratio [HR] 0.53, P = 0.025) and intention‐to‐treat (HR 0.59, P = 0.062) populations. In obexelimab‐treated patients, B cells decreased approximately 50%, and trough concentration and inclusion in baseline gene expression clusters with high B cell pathway modules were associated with increased time to LOI. Obexelimab was associated with infusion reactions but was generally safe and well‐tolerated. Conclusion: Although the primary endpoint was not reached, secondary analysis showed time to LOI was significantly increased in obexelimab‐treated patients, and analysis of patient subsets defined by gene expression patterns at baseline suggests a responding subpopulation. [ABSTRACT FROM AUTHOR]

This article presents a method for the objective assessment of an algorithm's strengths and weaknesses. Instead of examining the performance of only one or more algorithms on a benchmark set or generating custom problems that maximize the performance difference between two algorithms, ourmethod quantifies both the nature of the test instances and the algorithm performance. Our aim is to gather information about possible phase transitions in performance, that is, the points in which a small change in problem structure produces algorithm failure. The method is based on the accurate estimation and characterization of the algorithm footprints, that is, the regions of instance space in which good or exceptional performance is expected from an algorithm. A footprint can be estimated for each algorithm and for the overall portfolio. Therefore, we select a set of features to generate a common instance space,which we validate by constructing a sufficiently accurate prediction model. We characterize the footprints by their area and density. Our method identifies complementary performance between algorithms, quantifies the common features of hard problems, and locates regions where a phase transition may lie. [ABSTRACT FROM AUTHOR]

Historians have argued that disestablishment liberated American religion and allowed for the proliferation of religious practice and religious freedom, especially individualistic Evangelicalism in the South. This proposition reduced nearly all of southern Protestantism to Revivalist Evangelicalism, and failed to account for the powerful presence of coercive Protestant religiosity in older southern states such as South Carolina. While they shared certain Evangelical particulars with frontier populations, Protestants in South Carolina, especially Presbyterians, rejected individualized religion in favor of religiosity that favored and nurtured activist state protection of both antidemocratic political norms and chattel slavery. This essay argues that ostensibly disestablished Presbyterianism in South Carolina helped intellectually erect and socially perpetuate coercive religious and state power. [ABSTRACT FROM AUTHOR]

Mucopolysaccharidosis type II (Hunter syndrome, MPS II) is an inherited X-linked recessive disease caused by deficiency of iduronate-2-sulfatase (IDS), resulting in the accumulation of the glycosaminoglycans (GAG) heparan and dermatan sulfates. Mouse models of MPS II have been used in several reports to study disease pathology and to conduct preclinical studies for current and next generation therapies. Here, we report the generation and characterization of an immunodeficient mouse model of MPS II, where CRISPR/Cas9 was employed to knock out a portion of the murine IDS gene on the NOD/SCID/Il2rγ (NSG) immunodeficient background. IDS −/− NSG mice lacked detectable IDS activity in plasma and all analyzed tissues and exhibited elevated levels of GAGs in those same tissues and in the urine. Histopathology revealed vacuolized cells in both the periphery and CNS of NSG-MPS II mice. This model recapitulates skeletal disease manifestations, such as increased zygomatic arch diameter and decreased femur length. Neurocognitive deficits in spatial memory and learning were also observed in the NSG-MPS II model. We anticipate that this new immunodeficient model will be appropriate for preclinical studies involving xenotransplantation of human cell products intended for the treatment of MPS II. [ABSTRACT FROM AUTHOR]

This study focuses on the domain of a two-machine robotic cell scheduling problem under three inspection scenarios. We propose the first analytical method for minimizing the partial cycle time of cells with in-process and post-process inspection scenarios, and then we convert this cell into a multi-function robotic cell with in-line inspection scenario. For the first scenario, parts are inspected in one of the production machines using multiple sensors, while the inspection process is performed by an independent inspection machine for the second scenario. Alternatively, the inspection can be performed by a multi-function robot for the third scenario. A distinguishing characteristic of this robot is that it can perform inspection of the part in transit. However, the robot cannot complete the part transition and load it on the next destination machine if it identifies a fault in the part. The stochastic nature of the process prevents us from applying existing deterministic solution methods for corresponding scheduling problems. In the first stage, we present a heuristic method that converts a multiple-sensor inspection system into a single-sensor inspection system. The expected cycle times of two different cycles are derived based on a geometric distribution, and then the maximum expected throughput is pursued for in-process and post-process inspection sensors, respectively. In the second stage, we develop the inspection system into an in-line inspection system using a multi-function robot. Finally, we determine if it is technically feasible and profitable to replace the in-process (or post-process) inspection scenario with the in-line inspection scenario. [ABSTRACT FROM AUTHOR]

Projectors are deployed in increasingly demanding environments. The fidelity of the projected image as seen by a user is compromised when projectors are deployed in dual-planar environments (e.g. corner of a room or an office cubicle), thereby diminishing the richness of the user experience. There are many reasons for this. The focus of this paper is to compensate for the global illumination effects due to inter-reflection of light. In the process we also correct geometry distortion. Our system is built from off-the-shelf components and easily deployable without any elaborated setup. In this paper, we describe two complementary methods to compensate for global illumination effects in dual-planar environments. Our methods are based on the systematic adaptation and interpretation of the classical radiosity equation in the image domain. The technique neither assumes nor computes 3D scene geometry, relying on an implicit inference. The system is calibrated in an off-line mode once; in our first method, corrected images and video are computed in real time, and in our second method, a richer scene is offered with a modest increase in computational time. The corrected images when projected have better contrast and are more appealing to the user. [ABSTRACT FROM AUTHOR]

Herpes simplex virus type 1 (HSV-1) is a significant human pathogen that infects a large portion of the human population. Cells deploy a variety of defenses to limit the extent to which the virus can replicate. One such factor is the promyelocytic leukemia (PML) protein, the nucleating and organizing factor of nuclear domain 10 (ND10). PML responds to a number of stimuli and is implicated in intrinsic and innate cellular antiviral defenses against HSV-1. While the role of PML in a number of cellular pathways is controlled by post-translational modifications, the effects of phosphorylation on its antiviral activity toward HSV-1 have been largely unexplored. Consequently, we mapped phosphorylation sites on PML, mutated these and other known phosphorylation sites on PML isoform I (PML-I), and examined their effects on a number of PML's activities. Our results show that phosphorylation at most sites on PML-I is dispensable for the formation of ND10s and colocalization between PML-I and the HSV-1 regulatory protein, ICP0, which antagonizes PML-I function. However, inhibiting phosphorylation at sites near the SUMO-interaction motif (SIM) of PML-I impairs its ability to respond to HSV-1 infection. Overall, our data suggest that PML phosphorylation regulates its antiviral activity against HSV-1. [ABSTRACT FROM AUTHOR]

Background: Mathematical modeling is an important tool in systems biology to study the dynamic property of complex biological systems. However, one of the major challenges in systems biology is how to infer unknown parameters in mathematical models based on the experimental data sets, in particular, when the data are sparse and the regulatory network is stochastic. Results: To address this issue, this work proposed a new algorithm to estimate parameters in stochastic models using simulated likelihood density in the framework of approximate Bayesian computation. Two stochastic models were used to demonstrate the efficiency and effectiveness of the proposed method. In addition, we designed another algorithm based on a novel objective function to measure the accuracy of stochastic simulations. Conclusions: Simulation results suggest that the usage of simulated likelihood density improves the accuracy of estimates substantially. When the error is measured at each observation time point individually, the estimated parameters have better accuracy than those obtained by a published method in which the error is measured using simulations over the entire observation time period. [ABSTRACT FROM AUTHOR]

This paper considers the challenge of recognizing how the properties of a graph determine the performance of graph coloring algorithms. In particular, we examine how spectral properties of the graph make the graph coloring task easy or hard. We address the question of when an exact algorithm is likely to provide a solution in a reasonable computation time, when we are better off using a quick heuristic, and how the properties of the graph influence algorithm performance. A new methodology is introduced to visualize a collection of graphs in an instance space defined by their properties, with an optimal feature selection approach included to ensure that the instance space preserves the topology of an algorithm performance metric. In this instance space we can reveal how different algorithms perform across a variety of instance classes with various properties. We demonstrate the methodology using a sophisticated branch and bound exact algorithm, and the faster heuristic approaches of integer rounding of a linear programming relaxation of the graph coloring formulation, as well as a greedy degree-saturation heuristic. Our results demonstrate that spectral properties of the graph instances can provide useful descriptions of when each of these algorithms will provide the best solution for a given computational effort. [ABSTRACT FROM AUTHOR]

The article looks at mid-19th century U.S. politics, focusing on the political career of Texas Governor and U.S. Senator Sam Houston in the context of party politics in the Southern states. It looks at how historians have assessed Houston's membership in the American Party, or Know-Nothings, and discusses Houston's views on matters including slavery, temperance, and Southern secession. The author presents a case for the view that Houston's politics reflect the legacy of the Whig party.

Background: Hematopoiesis is a highly orchestrated developmental process that comprises various developmental stages of the hematopoietic stem cells (HSCs). During development, the decision to leave the self-renewing state and selection of a differentiation pathway is regulated by a number of transcription factors. Among them, genes GATA-1 and PU.1 form a core negative feedback module to regulate the genetic switching between the cell fate choices of HSCs. Although extensive experimental studies have revealed the mechanisms to regulate the expression of these two genes, it is still unclear how this simple module regulates the genetic switching. Methods: In this work we proposed a mathematical model to study the mechanisms of the GATA-PU.1 gene network in the determination of HSC differentiation pathways. We incorporated the mechanisms of GATA switch into the module, and developed a mathematical model that comprises three genes GATA-1, GATA-2 and PU.1. In addition, a novel multiple-objective optimization method was designed to infer unknown parameters in the proposed model by realizing different experimental observations. A stochastic model was also designed to describe the critical function of noise, due to the small copy numbers of molecular species, in determining the differentiation pathways. Results: The proposed deterministic model has successfully realized three stable steady states representing the priming and different progenitor cells as well as genetic switching between the genetic states under various experimental conditions. Using different values of GATA-1 synthesis rate for the GATA-1 protein availability in the chromatin sites during the time period of GATA switch, stochastic simulations for the first time have realized different proportions of cells leading to different developmental pathways under various experimental conditions. Conclusions: Mathematical models provide testable predictions regarding the mechanisms and conditions for realizing different differentiation pathways of hematopoietic stem cells. This work represents the first attempt at using a discrete stochastic model to realize the decision of HSC differentiation pathways showing a multimodal distribution. [ABSTRACT FROM AUTHOR]

Abstract: This paper reports a novel direct Gegenbauer (ultraspherical) transcription method (GTM) for solving continuous-time optimal control (OC) problems (CTOCPs) with linear/nonlinear dynamics and path constraints. In (Elgindy et al. 2012) [1], we presented a GTM for solving nonlinear CTOCPs directly for the state and the control variables, and the method was tailored to find the best path for an unmanned aerial vehicle mobilizing in a stationary risk environment. This article extends the GTM to deal further with problems including higher-order time derivatives of the states by solving the CTOCP directly for the control and the highest-order time derivative . The state vector and its derivatives up to the th-order derivative can then be stably recovered by successive integration. Moreover, we present our solution method for solving linear–quadratic regulator (LQR) problems as we aim to cover a wider collection of CTOCPs with the concrete aim of comparing the efficiency of the current work with other classical discretization methods in the literature. The proposed numerical scheme fully parameterizes the state and the control variables using Gegenbauer expansion series. For problems with various order time derivatives of the state variables arising in the cost function, dynamical system, or path/terminal constraints, the GTM seeks to fully parameterize the control variables and the highest-order time derivatives of the state variables. The time horizon is mapped onto the closed interval . The dynamical system characterized by differential equations is transformed into its integral formulation through direct integration. The resulting problem on the finite interval is then transcribed into a nonlinear programming (NLP) problem through collocation at the Gegenbauer–Gauss (GG) points. The integral operations are approximated by optimal Gegenbauer quadratures in a certain optimality sense. The reduced NLP problem is solved in the Gegenbauer spectral space, and the state and the control variables are approximated on the entire finite horizon. The proposed method achieves discrete solutions exhibiting exponential convergence using relatively small-scale number of collocation points. The advantages of the proposed direct GTM over other traditional discretization methods are shown through four well-studied OC test examples. The present work is a major breakthrough in the area of computational OC theory as it delivers significantly more accurate solutions using considerably smaller numbers of collocation points, states and controls expansion terms. Moreover, the GTM produces very small-scale NLP problems, which can be solved very quickly using the modern NLP software. [Copyright &y& Elsevier]

Background: A fundamental issue in systems biology is how to design simplified mathematical models for describing the dynamics of complex biochemical reaction systems. Among them, a key question is how to use simplified reactions to describe the chemical events of multi-step reactions that are ubiquitous in biochemistry and biophysics. To address this issue, a widely used approach in literature is to use one-step reaction to represent the multi-step chemical events. In recent years, a number of modelling methods have been designed to improve the accuracy of the one-step reaction method, including the use of reactions with time delay. However, our recent research results suggested that there are still deviations between the dynamics of delayed reactions and that of the multi-step reactions. Therefore, more sophisticated modelling methods are needed to accurately describe the complex biological systems in an efficient way. Results: This work designs a two-variable model to simplify chemical events of multi-step reactions. In addition to the total molecule number of a species, we first introduce a new concept regarding the location of molecules in the multi-step reactions, which is the second variable to represent the system dynamics. Then we propose a simulation algorithm to compute the probability for the firing of the last step reaction in the multi-step events. This probability function is evaluated using a deterministic model of ordinary differential equations and a stochastic model in the framework of the stochastic simulation algorithm. The efficiency of the proposed two-variable model is demonstrated by the realization of mRNA degradation process based on the experimentally measured data. Conclusions: Numerical results suggest that the proposed new two-variable model produces predictions that match the multi-step chemical reactions very well. The successful realization of the mRNA degradation dynamics indicates that the proposed method is a promising approach to reduce the complexity of biological systems. [ABSTRACT FROM AUTHOR]

The theory of Gegenbauer (ultraspherical) polynomial approximation has received considerable attention in recent decades. In particular, the Gegenbauer polynomials have been applied extensively in the resolution of the Gibbs phenomenon, construction of numerical quadratures, solution of ordinary and partial differential equations, integral and integro-differential equations, optimal control problems, etc. To achieve better solution approximations, some methods presented in the literature apply the Gegenbauer operational matrix of integration for approximating the integral operations, and recast many of the aforementioned problems into unconstrained/constrained optimization problems. The Gegenbauer parameter α associated with the Gegenbauer polynomials is then added as an extra unknown variable to be optimized in the resulting optimization problem as an attempt to optimize its value rather than choosing a random value. This issue is addressed in this article as we prove theoretically that it is invalid. In particular, we provide a solid mathematical proof demonstrating that optimizing the Gegenbauer operational matrix of integration for the solution of various mathematical problems by recasting them into equivalent optimization problems with α added as an extra optimization variable violates the discrete Gegenbauer orthonormality relation, and may in turn produce false solution approximations. [ABSTRACT FROM AUTHOR]

AbstractIntraperitoneal (IP) injection is frequently reported to be as effective as intravenous (IV) injection. Because it allows administering a larger volume with more radioactivity, we have investigated this route and the possibility of using it to circumvent the volume constraint we earlier experienced with pretargeting radiotherapy. Using 99mTc as the label, the pharmacokinetics (PK) of the cMORF effector (a DNA analogue) was evaluated after IP or IV injection in normal mice by necropsy and SPECT/CT imaging. In another experiment, nude mice bearing tumors were used and they received MORF-CC49 pretargeting antibody IV 2 days earlier than labeled cMORF IV or IP. Tumor accumulations of cMORF were measured at 6 hours after its injections. The absorbed radiation doses for 188Re or 90Y pretargeting were estimated using the 99mTc data and a self-absorbed model. Although the absorbed radiation doses to other organs were comparable, the dose to intestines after IP injection was 30-fold higher than IV injection due to the slow entry into the circulation. It had reached such a level as high as the dose to the kidneys that cleared the radioactivity and usually were at the highest level. Nevertheless, the slow entry did not reduce the tumor accumulation. In conclusion, using IP in place of IV led to an unacceptably high absorbed radiation dose to the intestines although the tumor accumulation was not compromised. This effect may be applicable to other radiotherapeutic agents as well. [ABSTRACT FROM AUTHOR]

Abstract: This paper treats definite integrations numerically using Gegenbauer quadratures. The novel numerical scheme introduces the idea of exploiting the strengths of the Chebyshev, Legendre, and Gegenbauer polynomials through a unified approach, and using a unique numerical quadrature. In particular, the developed numerical scheme employs the Gegenbauer polynomials to achieve rapid rates of convergence of the quadrature for the small range of the spectral expansion terms. For a large-scale number of expansion terms, the numerical quadrature has the advantage of converging to the optimal Chebyshev and Legendre quadratures in the -norm and -norm, respectively. The key idea is to construct the Gegenbauer quadrature through discretizations at some optimal sets of points of the Gegenbauer–Gauss (GG) type in a certain optimality sense. We show that the Gegenbauer polynomial expansions can produce higher-order approximations to the definite integrals of a smooth function for the small range by minimizing the quadrature error at each integration point through a pointwise approach. The developed Gegenbauer quadrature can be applied for approximating integrals with any arbitrary sets of integration nodes. Exact integrations are obtained for polynomials of any arbitrary degree if the number of columns in the developed Gegenbauer integration matrix (GIM) is greater than or equal to . The error formula for the Gegenbauer quadrature is derived. Moreover, a study on the error bounds and the convergence rate shows that the optimal Gegenbauer quadrature exhibits very rapid convergence rates, faster than any finite power of the number of Gegenbauer expansion terms. Two efficient computational algorithms are presented for optimally constructing the Gegenbauer quadrature. We illustrate the high-order approximations of the optimal Gegenbauer quadrature through extensive numerical experiments, including comparisons with conventional Chebyshev, Legendre, and Gegenbauer polynomial expansion methods. The present method is broadly applicable and represents a strong addition to the arsenal of numerical quadrature methods. [Copyright &y& Elsevier]

Primary cells are often used to study viral replication and host-virus interactions as their antiviral pathways have not been altered or inactivated; however, their use is restricted by their short lifespan. Conventional methods to extend the life of primary cultures typically utilize viral oncogenes. Many of these oncogenes, however, perturb or inactivate cellular antiviral pathways, including the interferon (IFN) response. It has been previously shown that expression of the telomerase reverse transcriptase (TERT) gene extends the life of certain cell types. The effect that TERT expression has on the innate antiviral response to RNA- and DNA-containing viruses has not been examined. In the current study, we introduced the human TERT (hTERT) gene into a primary human embryonic lung (HEL-299) cell strain, which is known to respond to the type I IFN, IFN-β. We show that the resulting HEL-TERT cell line is capable of replicating beyond 100 population doublings without exhibiting signs of senescence. Treatment with IFN-β resulted in the upregulation of four model IFN stimulated genes (ISGs) in HEL-299 and HEL-TERT cells. Both cell lines supported the replication of herpes simplex virus type 1 (HSV-1) and vesicular stomatitis virus (VSV) and impaired the replication of both viruses upon IFN-β pretreatment. Introduction of the viral oncoprotein, simian virus 40 (SV40) large T-antigen, which is frequently used to immortalize cells, largely negated this effect. Taken together, our data indicate that expression of hTERT does not alter type 1 IFN signaling and/or the growth of two viruses, making this cell line a useful reagent for studying viral replication and virus-cell interactions. [ABSTRACT FROM AUTHOR]

Abstract: We introduce a hybrid Gegenbauer (ultraspherical) integration method (HGIM) for solving boundary value problems (BVPs), integral and integro-differential equations. The proposed approach recasts the original problems into their integral formulations, which are then discretized into linear systems of algebraic equations using Gegenbauer integration matrices (GIMs). The resulting linear systems are well-conditioned and can be easily solved using standard linear system solvers. A study on the error bounds of the proposed method is presented, and the spectral convergence is proven for two-point BVPs (TPBVPs). Comparisons with other competitive methods in the recent literature are included. The proposed method results in an efficient algorithm, and spectral accuracy is verified using eight test examples addressing the aforementioned classes of problems. The proposed method can be applied on a broad range of mathematical problems while producing highly accurate results. The developed numerical scheme provides a viable alternative to other solution methods when high-order approximations are required using only a relatively small number of solution nodes. [Copyright &y& Elsevier]

In recent years, multifidelity expensive black-box (Mf-EBB) methods have received increasing attention due to their strong applicability to industrial design problems. The challenge, however, is that knowledge of the relationship between decisions and objective values is limited to a small set of sample observations of variable quality. In the field of Mf-EBB, a problem instance consists of an expensive yet accurate source of information, and one or more cheap yet less accurate sources of information. The field aims to provide techniques either to accurately explain how decisions affect design outcome, or to find the best decisions to optimise design outcomes. Many techniques that use surrogate models have been developed to provide solutions to both aims. Only in recent years, however, have researchers begun to explore the conditions under which these new techniques are reliable, often focusing on problems with a single low-fidelity function, known as bifidelity expensive black-box (Bf-EBB) problems. This study extends the existing Bf-EBB test instances found in the literature, as well as the features used to determine when the low-fidelity information source should be used. A literature test suite is constructed and augmented with new instances to demonstrate the potentially misleading results that could be reached using only the instances currently found in the literature, and to expose the criticality of a more heterogeneous test suite for algorithm assessment. Addressing the shortcomings of the existing literature, a new set of features is presented, as well as a new instance creation procedure, and a study of their impact on algorithm assessment is conducted. The low-fidelity information source is shown to be valuable if it is often locally accurate, even when its overall accuracy is relatively low. This contradicts the existing literature guidelines, which indicate the low-fidelity information is only useful if it has a high overall accuracy. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms – Continuous. Funding: This work was supported by Australian Research Council [Grant IC200100009] for the ARC Training Centre in Optimisation Technologies, Integrated Methodologies and Applications (OPTIMA), and the University of Melbourne Research Computing Services and Petascale Campus Initiative. N. Andrés-Thió is also supported by a Research Training Program scholarship from the University of Melbourne. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplementary Information [https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1217] or is available from the IJOC GitHub software repository (https://github.com/INFORMSJoC) at [http://dx.doi.org/10.5281/zenodo.6578060]. [ABSTRACT FROM AUTHOR]

The transcription factors PU.1 and GATA-1 are known to be important in the development of blood progenitor cells. Specifically they are thought to regulate the differentiation of progenitor cells into the granulocyte/macrophage lineage and the erythrocyte/megakaryocite lineage. While several mathematical models have been proposed to investigate the interaction between the transcription factors in recent years, there is still debate about the nature of the progenitor state in the dynamical system, and whether the existing models adequately capture new knowledge about the interactions gleaned from experimental data. Further, the models utilise different formalisms to represent the genetic regulation, and it appears that the resulting dynamical system depends upon which formalism is adopted. In this paper we analyse the four existing models, and propose an alternative model which is shown to demonstrate a rich variety of dynamical systems behaviours found across the existing models, including both bistability and tristability required for modelling the undifferentiated progenitors. [ABSTRACT FROM AUTHOR]

Abstract: Herpes simplex virus type 1 (HSV-1) requires the activities of cellular kinases for efficient replication. The host kinase, CK2, has been shown or is predicted to modify several HSV-1 proteins and has been proposed to affect one or more steps in the viral life cycle. Furthermore, potential cellular and viral substrates of CK2 are involved in antiviral pathways and viral counter-defenses, respectively, suggesting that CK2 regulates these processes. Consequently, we tested whether pharmacological inhibitors of CK2 impaired HSV-1 replication, either alone or in combination with the cellular antiviral factor, interferon-β (IFN-β). Our results indicate that the use of CK2 inhibitors results in a minor reduction in HSV-1 replication but enhanced the inhibitory effect of IFN-β on replication. This effect was dependent on the HSV-1 E3 ubiquitin ligase, infected cell protein 0 (ICP0), which impairs several host antiviral responses, including that produced by IFN-β. Inhibitors of CK2 did not, however, impede the ability of ICP0 to induce the degradation of two cellular targets: the promyelocytic leukemia protein (PML) and the DNA-dependent protein kinase catalytic subunit (DNA-PKcs). Notably, this effect was only apparent for HSV-1, as the CK2 inhibitors did not enhance the antiviral effect of IFN-β on either vesicular stomatitis virus or adenovirus type 5. Thus, our data suggest that the activity of CK2 is required for an early function during viral infection that assists the growth of HSV-1 in IFN-β-treated cells. [Copyright &y& Elsevier]

Multilinear subspace analysis (MSA) is a promising methodology for pattern-recognition problems due to its ability in decomposing the data formed from the interaction of multiple factors. The MSA requires a large training set, which is well organized in a single tensor, which consists of data samples with all possible combinations of the contributory factors. However, such a “complete” training set is difficult (or impossible) to obtain in many real applications. The missing-value problem is therefore crucial to the practicality of the MSA but has been hardly investigated up to present. To solve the problem, this paper proposes an algorithm named \M^2\SA, which is advantageous in real applications due to the following: 1) it inherits the ability of the MSA to decompose the interlaced semantic factors; 2) it does not depend on any assumptions on the data distribution; and 3) it can deal with a high percentage of missing values. \M^2\SA is evaluated by face image modeling on two typical multifactorial applications, i.e., face recognition and facial age estimation. Experimental results show the effectiveness of \M^2 \SA even when the majority of the values in the training tensor are missing. [ABSTRACT FROM PUBLISHER]

The suitability of an optimisation algorithm selected from within an algorithm portfolio depends upon the features of the particular instance to be solved. Understanding the relative strengths and weaknesses of different algorithms in the portfolio is crucial for effective performance prediction, automated algorithm selection, and to generate knowledge about the ideal conditions for each algorithm to influence better algorithm design. Relying on well-studied benchmark instances, or randomly generated instances, limits our ability to truly challenge each of the algorithms in a portfolio and determine these ideal conditions. Instead we use an evolutionary algorithm to evolve instances that are uniquely easy or hard for each algorithm, thus providing a more direct method for studying the relative strengths and weaknesses of each algorithm. The proposed methodology ensures that the meta-data is sufficient to be able to learn the features of the instances that uniquely characterise the ideal conditions for each algorithm. A case study is presented based on a comprehensive study of the performance of two heuristics on the Travelling Salesman Problem. The results show that prediction of search effort as well as the best performing algorithm for a given instance can be achieved with high accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Most work on multi-biometric fusion is based on static fusion rules. One prominent limitation of static fusion is that it cannot respond to the changes of the environment or the individual users. This paper proposes context-aware multi-biometric fusion, which can dynamically adapt the fusion rules to the real-time context. As a typical application, the context-aware fusion of gait and face for human identification in video is investigated. Two significant context factors that may affect the relationship between gait and face in the fusion are considered, i.e., view angle and subject-to-camera distance. Fusion methods adaptable to these two factors based on either prior knowledge or machine learning are proposed and tested. Experimental results show that the context-aware fusion methods perform significantly better than not only the individual biometric traits, but also those widely adopted static fusion rules including SUM, PRODUCT, MIN, and MAX. Moreover, context-aware fusion based on machine learning shows superiority over that based on prior knowledge. [Copyright &y& Elsevier]

Abstract: For univariate forecasting, there are various statistical models and computational algorithms available. In real-world exercises, too many choices can create difficulties in selecting the most appropriate technique, especially for users lacking sufficient knowledge of forecasting. This study focuses on rule induction for forecasting method selection by understanding the nature of historical forecasting data. A novel approach for selecting a forecasting method for univariate time series based on measurable data characteristics is presented that combines elements of data mining, meta-learning, clustering, classification and statistical measurement. We conducted a large-scale empirical study of over 300 time series using four of the most popular forecasting methods. To provide a rich portrait of the global characteristics of univariate time series, we extracted measures from a comprehensive set of features such as trend, seasonality, periodicity, serial correlation, skewness, kurtosis, nonlinearity, self-similarity, and chaos. Both supervised and unsupervised learning methods are used to learn the relationship between the characteristics of the time series and the forecasting method suitability, providing both recommendation rules, as well as visualizations in the feature space. A derived weighting schema based on the rule induction is also used to improve forecasting accuracy based on combined forecasting models. [Copyright &y& Elsevier]

The algorithm selection problem [Rice 1976] seeks to answer the question: Which algorithm is likely to perform best for my problem? Recognizing the problem as a learning task in the early 1990's, the machine learning community has developed the field of meta-learning, focused on learning about learning algorithm performance on classification problems. But there has been only limited generalization of these ideas beyond classification, and many related attempts have been made in other disciplines (such as AI and operations research) to tackle the algorithm selection problem in different ways, introducing different terminology, and overlooking the similarities of approaches. In this sense, there is much to be gained from a greater awareness of developments in meta-learning, and how these ideas can be generalized to learn about the behaviors of other (nonlearning) algorithms. In this article we present a unified framework for considering the algorithm selection problem as a learning problem, and use this framework to tie together the crossdisciplinary developments in tackling the algorithm selection problem. We discuss the generalization of meta-learning concepts to algorithms focused on tasks including sorting, forecasting, constraint satisfaction, and optimization, and the extension of these ideas to bioinformatics, cryptography, and other fields. [ABSTRACT FROM AUTHOR]

Spinal cord hemisection (SCH) results in atrophy of skeletal muscle and altered contractile properties. In this study our purpose was to assess staircase and posttetanic potentiation in the rat gastrocnemius muscle in situ, 1 week after SCH. Tetanic force was reduced by SCH, but twitch amplitude was not. The time course and magnitude of staircase during stimulation at 5 H Z (for 21 s) was similar in the control, sham-operated, and SCH groups. However, posttetanic potentiation observed after 100-, 500-, and 1000-ms tetanic (200 H Z) contractions was absent or drastically reduced after SCH. Twitch force increased 44 ± 8.7%, 47 ± 7.4%, and 15 ± 2.8% for the control, sham, and SCH groups, respectively, after the 1000-ms tetanic contraction. After the 1000-ms tetanic contraction, twitch active force decreased in all groups and was significantly reduced at 5 min relative to pretetanic twitch. In the control and sham groups, but not SCH, the active force recovered to the pretetanic level by 15 min. Resting regulatory light chain (RLC) phosphorylation was 15.4 ± 2.5% and 10.97 ± 3.3% for the control and SCH groups, respectively. After the 1-s tetanic contraction, values were 41.6 ± 2.8% and 9.3 ± 2.9%, respectively. The potentiation observed in the SCH animals with 1000-ms contraction apparently occurred without increases in RLC phosphorylation. One week after SCH there were clear changes in the contractile properties typically associated with prior activation. It is concluded that activity-dependent potentiation can occur by a mechanism that is independent of RLC phosphorylation, and accelerated fatigue can mask the potentiating effects of prior activity. Muscle Nerve, 2008 [ABSTRACT FROM AUTHOR]

The key challenge in kernel based learning algorithms is the choice of an appropriate kernel and its optimal parameters. Selecting the optimal degree of a polynomial kernel is critical to ensure good generalisation of the resulting support vector machine model. In this paper we propose Bayesian and Laplace approximation methods to estimate the polynomial degree. A rule based meta-learning approach is then proposed for automatic polynomial kernel and its optimal degree selection. The new approach is constructed and tested on different sizes of 112 datasets with binary class as well as multi class classification problems. An extensive computational evaluation of these methods is conducted, and rules are generated to determine when these approximation methods are appropriate. [ABSTRACT FROM AUTHOR]

Abstract: Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods such as support vector machine (SVM). Automatic kernel selection is a key issue given the number of kernels available, and the current trial-and-error nature of selecting the best kernel for a given problem. This paper introduces a new method for automatic kernel selection, with empirical results based on classification. The empirical study has been conducted among five kernels with 112 different classification problems, using the popular kernel based statistical learning algorithm SVM. We evaluate the kernels’ performance in terms of accuracy measures. We then focus on answering the question: which kernel is best suited to which type of classification problem? Our meta-learning methodology involves measuring the problem characteristics using classical, distance and distribution-based statistical information. We then combine these measures with the empirical results to present a rule-based method to select the most appropriate kernel for a classification problem. The rules are generated by the decision tree algorithm C5.0 and are evaluated with 10 fold cross validation. All generated rules offer high accuracy ratings. [Copyright &y& Elsevier]

Subsurface exploration of ice-covered planets and moons presents communications challenges because of the need to communicate through kilometers of ice. The objective of this task is to develop the capability to wirelessly communicate through kilometers of ice and thus complement the potentially failure-prone tethers deployed behind an ice-penetrating probe on Ocean Worlds. In this paper, the preliminary work on the development of wireless deep-ice communication is presented and discussed. The communication test and acoustic attenuation measurements in ice have been made by embedding acoustic transceivers in glacial ice at the Matanuska Glacier, Anchorage, Alaska. Field test results show that acoustic communication is viable through ice, demonstrating the transmission of data and image files in the 13–18 kHz band over 100 m. The results suggest that communication over many kilometers of ice thickness could be feasible by employing reduced transmitting frequencies around 1 kHz, though future work is needed to better constrain the likely acoustic attenuation properties through a refrozen borehole. [ABSTRACT FROM AUTHOR]

The inherent difficulty of solving a continuous, static, bound-constrained and single-objective black-box optimization problem depends on the characteristics of the problem's fitness landscape and the algorithm being used. Exploratory landscape analysis (ELA) uses numerical features generated via a sampling process of the search space to describe such characteristics. Despite their success in a number of applications, these features have limitations related with the computational costs associated with generating accurate results. Consequently, only approximations are available in practice which may be unreliable, leading to systemic errors. The overarching aim of this paper is to evaluate the reliability of five well-known ELA feature sets across multiple dimensions and sample sizes. For this purpose, we propose a comprehensive experimental methodology combining exploratory and statistical validation stages, which uses resampling techniques to minimize the sampling cost, and statistical significance tests to identify strengths and weaknesses of individual features. The data resulting from the methodology is collected and made available in the LEarning and OPtimization Archive of Research Data v1.0. The results show that instances of the same function can have feature values that are significantly different; hence, non-generalizable across instances, due to the effects produced by the boundary constraints. In addition, some landscape features under evaluation are highly volatile, and strongly susceptible to changes in sample size. Finally, the results show evidence of a curse of modality, meaning that the sample size should increase with the number of local optima. [ABSTRACT FROM AUTHOR]

This paper considers the Rotating Workforce Scheduling Problem, and shows how the strengths and weaknesses of various solution methods can be understood by the in-depth evaluation offered by a recently developed methodology known as Instance Space Analysis. We first present a set of features aiming to describe hardness of test instances. We create a new, more diverse set of instances based on an initial instance space analysis that reveals gaps in the instance space, and offers the opportunity to generate additional instances to add diversity to the test suite. The results of three algorithms on our extended instance set reveal insights based on this visual methodology. We observe different regions of strength and weakness in the instance space for each algorithm, as well as a phase transition from feasible to infeasible instances with more challenging instances at the phase transition boundary. This rigorous and insightful approach to analyzing algorithm performance highlights the critical role played by the choice of test instances, and the importance of ensuring diversity and unbiasedness of test instances to support valid conclusions. [ABSTRACT FROM AUTHOR]

The HDoutliers algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. In this article, we propose an algorithm that addresses these limitations. We define an anomaly as an observation where its k-nearest neighbor distance with the maximum gap is significantly different from what we would expect if the distribution of k-nearest neighbors with the maximum gap is in the maximum domain of attraction of the Gumbel distribution. An approach based on extreme value theory is used for the anomalous threshold calculation. Using various synthetic and real datasets, we demonstrate the wide applicability and usefulness of our algorithm, which we call the stray algorithm. We also demonstrate how this algorithm can assist in detecting anomalies present in other data structures using feature engineering. We show the situations where the stray algorithm outperforms the HDoutliers algorithm both in accuracy and computational time. This framework is implemented in the open source R package stray. for this article are available online. [ABSTRACT FROM AUTHOR]

This paper investigates event extraction and early event classification in contiguous spatio-temporal data streams, where events need to be classified using partial information, i.e. while the event is ongoing. The framework incorporates an event extraction algorithm and an early event classification algorithm. We apply this framework to synthetic and real problems and demonstrate its reliability and broad applicability. The algorithms and data are available in the R package eventstream, and other code in the supplementary material. [ABSTRACT FROM AUTHOR]

In this paper we present a novel method for finding unknown parameters for an unknown morphogen. We postulate the existence of an unknown morphogen in a given three-dimensional domain due to the spontaneous arrangement of a downstream species on the domain boundary for which data is known. Assuming a modified Helmholtz model for the morphogen and that it is produced from a single source in the domain, our method accurately estimates the source location and other model parameters. Notably, our method does not require the forward solution of the model to be computed which can often be a challenge for three-dimensional PDE model parameter fitting. Instead, an extension is made from the problem domain to an infinite domain and the analytic nature of the fundamental solution is exploited. We explore in this manuscript strategies for best conditioning the problem and rigorously explore the accuracy of the method on two test problems. Our tests focus on the effect of source location on accuracy but also the robustness of the algorithm to experimental noise. [ABSTRACT FROM AUTHOR]

This article proposes a framework that provides early detection of anomalous series within a large collection of nonstationary streaming time-series data. We define an anomaly as an observation, that is, very unlikely given the recent distribution of a given system. The proposed framework first calculates a boundary for the system's typical behavior using extreme value theory. Then a sliding window is used to test for anomalous series within a newly arrived collection of series. The model uses time series features as inputs, and a density-based comparison to detect any significant changes in the distribution of the features. Using various synthetic and real world datasets, we demonstrate the wide applicability and usefulness of our proposed framework. We show that the proposed algorithm can work well in the presence of noisy nonstationarity data within multiple classes of time series. This framework is implemented in the open source R package oddstream. R code and data are available in the online . [ABSTRACT FROM AUTHOR]