Intrinsic Complexity Research Articles

State and superstate sampling in hybridization-expansion continuous-time quantum Monte Carlo

Due to the intrinsic complexity of the quantum many-body problem, quantum Monte Carlo algorithms and their corresponding Monte Carlo configurations can be defined in various ways. Configurations corresponding to few Feynman diagrams often lead to severe sign problems. On the other hand, computing the configuration weight becomes numerically expensive in the opposite limit in which many diagrams are grouped together. Here we show that for continuous-time quantum Monte Carlo in the hybridization expansion the efficiency can be substantially improved by dividing the local impurity trace into fragments, which are then sampled individually. For this technique, which also turns out to preserve the fermionic sign, a modified update strategy is introduced in order to ensure ergodicity. Our (super)state sampling is particularly beneficial to calculations with many $d$-orbitals and general local interactions, such as full Coulomb interaction. For illustration, we reconsider the simple albeit well-known case of a degenerate three-orbital model at low temperatures. This allows us to quantify the coherence properties of the "spin-freezing" crossover, even close to the Mott transition.

Oracle or false prophet? Can we predict AAV efficacy based on preexisting antibody titers?

Oracle or false prophet? Can we predict AAV efficacy based on preexisting antibody titers?

Electrostatic Control of Macrocyclization Reactions within Nanospaces.

The intrinsic structural complexity of proteins makes it hard to identify the contributions of each noncovalent interaction behind the remarkable rate accelerations of enzymes. Coulombic forces are evidently primary, but despite developments in artificial nanoreactor design, a picture of the extent to which these can contribute has not been forthcoming. Here we report on two supramolecular capsules that possess structurally identical inner-spaces that differ in the electrostatic potential (EP) field that envelops them: one positive and one negative. This architecture means that only changes in the EP field influence the chemical properties of encapsulated species. We quantify these influences via acidity and rates of cyclization measurements for encapsulated guests, and we confirm the primary role of Coulombic forces with a simple mathematical model approximating the capsules as Born spheres within a continuum dielectric. These results reveal the reaction rate accelerations possible under Coulombic control and highlight important design criteria for nanoreactors.

Exploiting block co-occurrence to control block sizes for entity resolution

The problem of identifying duplicated entities in a dataset has gained increasing importance during the last decades. Due to the large size of the datasets, this problem can be very costly to be solved due to its intrinsic quadratic complexity. Both researchers and practitioners have developed a variety of techniques aiming to speed up a solution to this problem. One of these techniques is called blocking, an indexing technique that splits the dataset into a set of blocks, such that each block contains entities that share a common property evaluated by a blocking key function. In order to improve the efficacy of the blocking technique, multiple blocking keys may be used, and thus, a set of blocking results is generated. In this paper, we investigate how to control the size of the blocks generated by the use of multiple blocking keys and maintain reasonable quality results, which is measured by the quality of the produced blocks. By controlling the size of the blocks, we can reduce the overall cost of solving an entity resolution problem and facilitate the execution of a variety of tasks (e.g., real-time and privacy-preserving entity resolution). For doing so, we propose many heuristics which exploit the co-occurrence of entities among the generated blocks for pruning, splitting and merging blocks. The experimental results we carry out using four datasets confirm the adequacy of the proposed heuristics for generating block sizes within a predefined range threshold as well as maintaining reasonable blocking quality results.

Hybrid Adsorption-Compression Systems for Air Conditioning in Efficient Buildings: Design through Validated Dynamic Models

Hybrid sorption-compression systems are gaining interest for heating/cooling/ refrigeration purposes in different applications, since they allow exploiting the benefits of both technologies and a better utilization of renewable sources. However, design of such components is still difficult, due to the intrinsic complexity of the systems and the lack of reliable models. In particular, the combination of adsorption-compression cascade unit has not been widely explored yet and there are no simulations or sizing tools reported in the literature. In this context, the present paper describes a model of a hybrid adsorption-compression system, realised in Modelica language using the commercial software Dymola. The models of the main components of the sorption and vapour compression unit are described in details and their validation presented. In addition, the integrated model is used for proving the feasibility of the system under dynamic realistic conditions and an example of the technical sizing that the model is able to accomplish is given.

The Role of Microglia in Bacterial Meningitis: Inflammatory Response, Experimental Models and New Neuroprotective Therapeutic Strategies.

Microglia have a pivotal role in the pathophysiology of bacterial meningitis. The goal of this review is to provide an overview on how microglia respond to bacterial pathogens targeting the brain, how the interplay between microglia and bacteria can be studied experimentally, and possible ways to use gained knowledge to identify novel preventive and therapeutic strategies. We discuss the dual role of microglia in disease development, the beneficial functions crucial for bacterial clearing, and the destructive properties through triggering neuroinflammation, characterized by cytokine and chemokine release which leads to leukocyte trafficking through the brain vascular endothelium and breakdown of the blood-brain barrier integrity. Due to intrinsic complexity of microglia and up until recently lack of specific markers, the study of microglial response to bacterial pathogens is challenging. New experimental models and techniques open up possibilities to accelerate progress in the field. We review existing models and discuss possibilities and limitations. Finally, we summarize recent findings where bacterial virulence factors are identified to be important for the microglial response, and how manipulation of evoked responses could be used for therapeutic or preventive purposes. Among promising approaches are: modulations of microglia phenotype switching toward anti-inflammatory and phagocytic functions, the use of non-bacterolytic antimicrobials, preventing release of bacterial components into the neural milieu and consequential amplification of immune activation, and protection of the blood-brain barrier integrity.

NHL Pathological Image Classification Based on Hierarchical Local Information and GoogLeNet-Based Representations.

Background Accurate classification for different non-Hodgkin lymphomas (NHL) is one of the main challenges in clinical pathological diagnosis due to its intrinsic complexity. Therefore, this paper proposes an effective classification model for three types of NHL pathological images, including mantle cell lymphoma (MCL), follicular lymphoma (FL), and chronic lymphocytic leukemia (CLL). Methods There are three main parts with respect to our model. First, NHL pathological images stained by hematoxylin and eosin (H&E) are transferred into blue ratio (BR) and Lab spaces, respectively. Then specific patch-level textural and statistical features are extracted from BR images and color features are obtained from Lab images both using a hierarchical way, yielding a set of hand-crafted representations corresponding to different image spaces. A random forest classifier is subsequently trained for patch-level classification. Second, H&E images are cropped and fed into a pretrained google inception net (GoogLeNet) for learning high-level representations and a softmax classifier is used for patch-level classification. Finally, three image-level classification strategies based on patch-level results are discussed including a novel method for calculating the weighted sum of patch results. Different classification results are fused at both feature 1 and image levels to obtain a more satisfactory result. Results The proposed model is evaluated on a public IICBU Malignant Lymphoma Dataset and achieves an improved overall accuracy of 0.991 and area under the receiver operating characteristic curve of 0.998. Conclusion The experimentations demonstrate the significantly increased classification performance of the proposed model, indicating that it is a suitable classification approach for NHL pathological images.

Automated Glycan Assembly: A Perspective.

The intrinsic complexity of carbohydrate structures has hampered access to pure glycans and hence impeded progress in the glycosciences. Automated Glycan Assembly (AGA) has facilitated the procurement of synthetic glycans, to be used in diagnostics, vaccine development, enzyme characterization and structure–function relationship studies. A general approach for obtaining complex glycans from mammalian, bacterial, fungal and plant classes provides molecular tools for glycobiology research. Recent advances in AGA technology pave the way for the production of novel carbohydrate materials. This perspective describes the state-of-the art of AGA and aspects of the technology where additional improvements are needed.

Social frame and tax compliance modulate electrophysiological and autonomic responses following tax-related decisions

Given the intrinsic complexity of cognitive and affective processes affecting how people reason about taxes and their decisions to be compliant with such social duty, we aimed at exploring those latent processes by combining the analysis of their central and peripheral physiological correlates. We asked participants to make realistic economic decisions concerning tax-payment and manipulated the social vs. individual decisional frame. In addition, we took into account the potential role of tax-compliance trait. Thirty self-employed professionals took part in the study and completed a public good game while their autonomic (skin conductance – SC – and heart rate – HR) and neural brain (electroencephalography – EEG) activities were recorded. The analysis of physiological responses during the feedback phase – where participants could be presented or not with a fiscal audit – highlighted: (i) increased tonic SC levels and theta activity in the social condition than in the individual one; (ii) increased HR values when a fiscal audit did not take place, especially in participants who presented an enforced tax-compliance trait. Present findings support the idea that classic economic theories of tax behaviour developed under the assumption that taxpayers act as rational and individualist agents do not provide a comprehensive account for the decision-making process. They add to available evidence highlighting the contribution of psychological and social-affective variables to individuals’ decision-making processes to pay or evade taxes and to their appraisal of the consequences of such choice, as suggested by the ‘slippery slope’ framework.

Molecular and antigenic characterization of Trypanosoma cruzi TolT proteins.

BackgroundTolT was originally described as a Trypanosoma cruzi molecule that accumulated on the trypomastigote flagellum bearing similarity to bacterial TolA colicins receptors. Preliminary biochemical studies indicated that TolT resolved in SDS-PAGE as ~3–5 different bands with sizes between 34 and 45 kDa, and that this heterogeneity could be ascribed to differences in polypeptide glycosylation. However, the recurrent identification of TolT-deduced peptides, and variations thereof, in trypomastigote proteomic surveys suggested an intrinsic TolT complexity, and prompted us to undertake a thorough reassessment of this antigen.Methods/Principle findingsGenome mining exercises showed that TolT constitutes a larger-than-expected family of genes, with at least 12 polymorphic members in the T. cruzi CL Brener reference strain and homologs in different trypanosomes. According to structural features, TolT deduced proteins could be split into three robust groups, termed TolT-A, TolT-B, and TolT-C, all of them showing marginal sequence similarity to bacterial TolA proteins and canonical signatures of surface localization/membrane association, most of which were herein experimentally validated. Further biochemical and microscopy-based characterizations indicated that this grouping may have a functional correlate, as TolT-A, TolT-B and TolT-C molecules showed differences in their expression profile, sub-cellular distribution, post-translational modification(s) and antigenic structure. We finally used a recently developed fluorescence magnetic beads immunoassay to validate a recombinant protein spanning the central and mature region of a TolT-B deduced molecule for Chagas disease serodiagnosis.Conclusion/SignificanceThis study unveiled an unexpected genetic and biochemical complexity within the TolT family, which could be exploited for the development of novel T. cruzi biomarkers with diagnostic/therapeutic applications.

The influence of transformational leadership on employees’ creative process engagement

PurposeUsing a multi-level perspective, the purpose of this paper is to investigate impact of transformational leadership on employees’ creative process engagement and mediating roles of intrinsic motivation, task complexity and innovation support in the process of influence.Design/methodology/approachThis study follows a quantitative method. Using a multi-item survey instrument, a total of 400 questionnaires were distributed among employees of small and medium enterprises registered with the Chittagong Chamber of Commerce and Industry in Bangladesh. Collected data were analyzed using structural equation model as well as factor analysis and path analysis to test the hypotheses and to assess the moderating and mediating effects of the variables.FindingsThe findings reveal that transformational leadership has a significant impact on employees’ creative process engagement. The study further shows that task complexity and support for innovation moderate the relationship between transformational leadership and employees’ creative process engagement.Research limitations/implicationsBased on the premises of interactionist perspectives on creativity, this study integrates multi-level variables to investigate leaders’ influences on followers’ creative process engagement. This study contributes to the existing literature by providing empirical evidence on influence of transformational leadership on employees’ creative process engagement as well as the impact of both individual- and organizational-level variables.Originality/valueThe study adopts a distinct model comprising five different variables to investigate creative process engagement from a multi-level perspective, i.e., creative process engagement and intrinsic motivation at the individual level, task complexity at the unit level, and support for innovation and leadership at the organizational level. This integrated model of using predictors from multiple levels supports the theoretical assumptions that creative process engagement results from the interaction of individual-, group- and organizational-level factors.

Numerical Modelling as a Support Tool for River Habitat Studies: An Italian Case Study

Numerical modelling is becoming a major tool for supporting environmental studies at different scales, thanks to the ability of up-to-date codes to reproduce the complex mechanisms of the natural environment in quite a reliable manner. In evaluating the habitat diversity of anthropized rivers, however, many issues are rising because of the intrinsic complexity of the physical processes involved and the limitations associated with numerical models. Using a reach of the Po River in Italy as a case study, the present works aims to provide a qualitative description of the changes of the Eco-Environmental Diversity index as a response to different constant flow discharges typically observed along this reach. The goals are achieved by means of two solvers of the freeware iRIC suite, applied in cascade to first simulate the 2D fluvial hydrodynamics and subsequently provide a qualitative estimate of the habitat conditions. Despite the several simplifications intrinsically present in the modelling cascade and the ones introduced for practical purposes, the results show that an extremely strong and long-lasting reduction of the flow discharge, like the one very recently observed, can ultimately threaten the overall biological status of the river. Because of the modelling uncertainties, these preliminary outcomes are only qualitative and show the need for more research, both in terms of data acquisition and numerical schematization, to adequately and quantitatively evaluate the effects of transient hydrology on the river ecosystems. Moreover, additional field surveys are necessary to calibrate and validate the used biological parameters, aiming to obtain sufficiently reliable estimates.

Switched projected gradient descent algorithms for secure state estimation under sparse sensor attacks

This paper investigates the secure state estimation problem of cyber–physical systems (CPSs) under sparse sensor attacks. First, a novel algorithm, which uses a switched gradient descent technique to harness the intrinsic combinatorial complexity of the secure state estimation problem, is proposed to estimate the state. The computational complexity is reduced through improving the convergence rate, reducing the number of candidates to be searched, reducing the search times, and reducing the computing resources consumed by each incorrect candidate selection simultaneously. Second, based on the proposed switched gradient descent algorithm, an observer-based algorithm is proposed to efficiently update the state estimation while new measurements are available. Compared with the existing methods, the computational complexity is reduced greatly without introducing any constraint except the basic observability assumption by adopting the proposed algorithms.

Performance Bounds For Co-/Sparse Box Constrained Signal Recovery

Abstract The recovery of structured signals from a few linear measurements is a central point in both compressed sensing (CS) and discrete tomography. In CS the signal structure is described by means of a low complexity model e.g. co-/sparsity. The CS theory shows that any signal/image can be undersampled at a rate dependent on its intrinsic complexity. Moreover, in such undersampling regimes, the signal can be recovered by sparsity promoting convex regularization like ℓ 1- or total variation (TV-) minimization. Precise relations between many low complexity measures and the sufficient number of random measurements are known for many sparsity promoting norms. However, a precise estimate of the undersampling rate for the TV seminorm is still lacking. We address this issue by: a) providing dual certificates testing uniqueness of a given cosparse signal with bounded signal values, b) approximating the undersampling rates via the statistical dimension of the TV descent cone and c) showing empirically that the provided rates also hold for tomographic measurements.

A Signal-Level Maximum Likelihood Detection Based on Partial Candidates for MIMO FBMC-QAM System With Two Prototype Filters

In this paper, we consider a signal-level maximum likelihood detection for multiple-input multiple-output filter-bank multicarrier-quadrature amplitude modulation (MIMO FBMC-QAM) system with two prototype filters. Under high-frequency selective channels, the conventional symbol-level maximum likelihood detection suffers from degraded bit error rate performances because it cannot utilize the oversampling characteristics of the signal-level. However, due to intrinsic interference and high computational complexity, a maximum likelihood detection cannot be applied directly to the signal-level. To solve this problem, we propose a signal-level maximum likelihood detection based on partial candidates. Utilizing the diagonal dominant characteristics of effective channels, the proposed signal-level maximum likelihood detection can reduce the effect of intrinsic interference with practical computational complexity. Moreover, a region ordering algorithm based on Euclidean distance can improve the efficiency of the proposed signal-level maximum likelihood detection. Simulation results show that the proposed signal-level maximum likelihood detection can outperform the bit error rate performance of the conventional symbol-level maximum likelihood detection.

Solving k-center clustering (with outliers) in MapReduce and streaming, almost as accurately as sequentially

Center-based clustering is a fundamental primitive for data analysis and becomes very challenging for large datasets. In this paper, we focus on the popular k -center variant which, given a set S of points from some metric space and a parameter k < | S |, requires to identify a subset of k centers in S minimizing the maximum distance of any point of S from its closest center. A more general formulation, introduced to deal with noisy datasets, features a further parameter z and allows up to z points of S (outliers) to be disregarded when computing the maximum distance from the centers. We present coreset-based 2-round MapReduce algorithms for the above two formulations of the problem, and a 1-pass Streaming algorithm for the case with outliers. For any fixed ϵ > 0, the algorithms yield solutions whose approximation ratios are a mere additive term ϵ away from those achievable by the best known polynomial-time sequential algorithms, a result that substantially improves upon the state of the art. Our algorithms are rather simple and adapt to the intrinsic complexity of the dataset, captured by the doubling dimension D of the metric space. Specifically, our analysis shows that the algorithms become very space-efficient for the important case of small (constant) D . These theoretical results are complemented with a set of experiments on real-world and synthetic datasets of up to over a billion points, which show that our algorithms yield better quality solutions over the state of the art while featuring excellent scalability, and that they also lend themselves to sequential implementations much faster than existing ones.

Empirical mode decomposition based hybrid ensemble model for electrical energy consumption forecasting of the cement grinding process

Forecasting the electrical energy consumption of the cement grinding process remains a difficult task due to the intrinsic complexity and irregularity of its time series. To solve this difficulty and improve the prediction accuracy, a novel hybrid model is proposed based on the “decomposition-prediction-integration” methodology. The hybrid model integrates empirical mode decomposition (EMD), moving average filter (MAF), least squares support vector regression (LSSVR), and quadratic exponential smoothing (QES). And it is suitable for non-stationary time series. The proposed model is tested using hourly electrical energy consumption data of one cement grinding process in China. EMD is first applied to decompose the original data series into a limited number of independent intrinsic mode functions (IMFs) and a trend component. Then, MAF is used to reduce the high frequency noises in the IMFs. Next LSSVR is adopted to predict different IMFs, and QES is utilized to predict the trend component. At last, the predicted IMFs and trend component are summed to formulate an ensemble forecast for the original series. The performance of the EMD-LSSVR&QES hybrid model is compared with five other forecasting models. Experiment results indicate that the proposed hybrid ensemble model can give full play to the advantages of each algorithm and outperform other forecasting models.

Breakthrough Potential in Near-Infrared Spectroscopy: Spectra Simulation. A Review of Recent Developments.

Near-infrared (12,500–4,000 cm−1; 800–2,500 nm) spectroscopy is the hallmark for one of the most rapidly advancing analytical techniques over the last few decades. Although it is mainly recognized as an analytical tool, near-infrared spectroscopy has also contributed significantly to physical chemistry, e.g., by delivering invaluable data on the anharmonic nature of molecular vibrations or peculiarities of intermolecular interactions. In all these contexts, a major barrier in the form of an intrinsic complexity of near-infrared spectra has been encountered. A large number of overlapping vibrational contributions influenced by anharmonic effects create complex patterns of spectral dependencies, in many cases hindering our comprehension of near-infrared spectra. Quantum mechanical calculations commonly serve as a major support to infrared and Raman studies; conversely, near-infrared spectroscopy has long been hindered in this regard due to practical limitations. Advances in anharmonic theories in hyphenation with ever-growing computer technology have enabled feasible theoretical near-infrared spectroscopy in recent times. Accordingly, a growing number of quantum mechanical investigations aimed at near-infrared region has been witnessed. The present review article summarizes these most recent accomplishments in the emerging field. Applications of generalized approaches, such as vibrational self-consistent field and vibrational second order perturbation theories as well as their derivatives, and dense grid-based studies of vibrational potential, are overviewed. Basic and applied studies are discussed, with special attention paid to the ones which aim at improving analytical spectroscopy. A remarkable potential arises from the growing applicability of anharmonic computations to solving the problems which arise in both basic and analytical near-infrared spectroscopy. This review highlights an increased value of quantum mechanical calculations to near-infrared spectroscopy in relation to other kinds of vibrational spectroscopy.

Systems Approach to Human Hair Fibers: Interdependence Between Physical, Mechanical, Biochemical and Geometric Properties of Natural Healthy Hair.

Contextual interpretation of hair fiber data is often blind to the effects of the dynamic complexity between different fiber properties. This intrinsic complexity requires systems thinking to decipher hair fiber accurately. Hair research, studied by various disciplines, follows a reductionist research approach, where elements of interest are studied from a local context with a certain amount of detachment from other elements or contexts. Following a systems approach, the authors are currently developing a cross-disciplinary taxonomy to provide a holistic view of fiber constituents and their interactions within large-scale dynamics. Based on the development process, this paper presents a review that explores the associated features, interrelationships and interactive complexities between physical, mechanical, biochemical and geometric features of natural, healthy hair fibers. Through the review, the importance of an appropriate taxonomy for interpreting hair fiber data across different disciplines is revealed. The review also demonstrates how seemingly unrelated fiber constituents are indeed interdependent and that these interdependencies may affect the behavior of the fiber. Finally, the review highlights how a non-integrative approach may have a negative impact on the reliability of hair data interpretation.

Household Expenditure on Leisure: a Comparative Study of Italian Households with Children from Y- and Z-Generation

The intrinsic complexity of post-materialist society makes it challenging to investigate the connection between social changes and generations. However, the study of consumption might help in the analysis of such a connection. In this paper, we analyse empirical data of consumption on leisure of Italian households, and focus on families at a very precise stage of family life-cycle, that is, couples with teenager children. We look at consumption of households at different points in time, 2001, 2007, and 2012, in order to investigate the impact of both social change and generation of children–Y-generation in 2001 and 2007, and Z-generation in 2012–on the leisure expenditure patterns of families. Specifically, we consider secondary data of yearly expenditure on a wide range of different leisure activities, and use hierarchical clustering and logistic regression to highlight specificities in family consumption patterns on leisure, depending on both the generation of offspring and the inter-time between sampled cohorts, 2001–2012 and 2007–2012. Our analysis indicates the presence of differences between the consumption patterns on leisure of families with Y-generation children and families with Z-generation children. However, our results also point out that such differences cannot be explained by solely invoking the different generation of offspring, and that social changes should also be taken into account.