Search

Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour. © 2019 Howison et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Dyslexia is a type of Specific Learning Disability (SLD) that affects the educational development of a student, as it is the reason of difficulties not only in writing or reading but also in other disciplines like Mathematics. Research has proven that Information and Communication Technologies (ICT) can have a positive effect to the educational development of a dyslexic student. In this paper, twenty 6th-grade primary-school students were observed while using computers during exams - ten with Dyslexia and ten with no SLDs. All students were called to solve a digital test and a paper test that were including some basic mathematic operations. The researcher recorded and examined the differences between the results from the two tests. Descriptive and deductive statistical analysis showed that students had better results when examined via a computer compared to a traditional written examination.

We give an overview of our critiques of "proofs" of security and a guide to our papers on the subject that have appeared over the past decade and a half. We also provide numerous additional examples and a few updates and errata.

Purpose The purpose of this paper is to analyze the reliability of the washing system in a paper plant in a more promising way under vague environment by reducing the accumulating phenomenon of fuzziness and accelerating the computation process using the Tω (weakest t-norm) based fuzzy lambda-tau (TBFLT) technique. Design/methodology/approach This paper presents a unified approach for analyzing the fuzzy reliability of the washing system under vague environment. This approach applies the TBFLT technique which uses triangular fuzzy numbers for incorporating data uncertainty, fault tree and lambda-tau method for finding system failure rate and repair time mathematical expressions while simplified Tω-based arithmetic operations are applied for computing various reliability parameters of the system. The effectiveness of the TBFLT technique has been demonstrated by analyzing fuzzy reliability of the system using five different techniques including TBFLT. Moreover, this paper applies extended Tanaka’s (1983) approach to rank the critical components of the system. Findings The TBFLT technique has the advantage of low computation complexity in comparison to other techniques and effectively reduces the accumulating phenomenon of fuzziness. This main finding verifies the conclusion made by Chen (1994). Originality/value The author has suggested a simple and more applicable technique for analyzing the fuzzy reliability of any complex process industrial system under vague environment. The effectiveness of the technique has been demonstrated by computing various reliability parameters of the washing system of a paper plant.

This discussion provides our reaction to the article by Greven and Scheipl. It contains an overview of their article and a description of the many areas of research that remain open and could benefit from further methodological and computational development.

Peer reviewing is the key ingredient of evaluating the quality of scientific work. Based on the review scores assigned by individual reviewers to papers, program committees of conferences and journal editors decide which papers to accept for publication and which to reject. A similar procedure is part of the selection process of grant applications and, among other fields, in sports. It is well known that the reviewing process suffers from measurement errors due to a lack of agreement among multiple reviewers of the same paper. And if not all papers are reviewed by all reviewers, the naive approach of averaging the scores is biased. Several statistical methods are proposed for aggregating review scores, which all can be realized by standard statistical software. The simplest method uses the well-known fixed-effects two-way classification with identical variances, while a more advanced method assumes different variances. As alternatives a mixed linear model and a generalized linear model are employed. The application of these methods implies an evaluation of the reviewers, which may help to improve reviewing processes. An application example with real conference data shows the potential of these statistical methods.

Association discovery finds closely correlate sets so the presence of some components in an exceedingly frequent set can imply the presence of the remaining components (in identical set). Closed item sets are a solution to the problems described above. These are obtained by partitioning the lattice of frequent item sets into equivalence classes according to the following property: two distinct item sets belong the same class if and only if they occur in the same set of transactions. Closed item sets are the collection of maximal item sets of these equivalence classes. This paper proposes a comprehensive survey of the closed item set mining. The concept of the frequent closed item set mining is also elaborated in detail. The modern methods of frequent closed item set mining are also discussed in brief.

The mission of theoretical cryptography is to define and construct provably secure cryptographic protocols and schemes. Without proofs of security, cryptographic constructs offer no guarantees whatsoever and no basis for evaluation and comparison. As most security proofs necessarily come in the form of a reduction between the security claim and an intractability assumption, such proofs are ultimately only as good as the assumptions they are based on. Thus, the complexity implications of every assumption we utilize should be of significant substance, and serve as the yard stick for the value of our proposals. Lately, the field of cryptography has seen a sharp increase in the number of new assumptions that are often complex to define and difficult to interpret. At times, these assumptions are hard to untangle from the constructions which utilize them. We believe that the lack of standards of what is accepted as a reasonable cryptographic assumption can be harmful to the credibility of our field. Therefore, there is a great need for measures according to which we classify and compare assumptions, as to which are safe and which are not. In this paper, we propose such a classification and review recently suggested assumptions in this light. This follows the footsteps of Naor Crypto 2003. Our governing principle is relying on hardness assumptions that are independent of the cryptographic constructions.

This paper puts across the various approaches and methods that have been proposed in the context of Fuzzy Mathematical Morphology. The underlying principles of Dilation & Erosion, the structuring elements used in various techniques, the unique variations put forth by researchers, new applications in spatial relationships, decision making, segmentation of medical images have been discussed.

This paper provides a critical review of the methods for estimating static discrete games and their relevance for quantitative marketing. We discuss the various modeling approaches, alternative assumptions, and relevant trade-offs involved in taking these empirical methods to data. We consider games of both complete and incomplete information, examine the primary methods for dealing with the coherency problems introduced by multiplicity of equilibria, and provide concrete examples from the literature. We illustrate the mechanics of estimation using a real-world example and provide the computer code and data set with which to replicate our results.

In this paper we present a new semantics, called Local Models Semantics, and use it to provide a foundation to reasoning with contexts. This semantics captures and makes precise the two main intuitions underlying contextual reasoning: (i) reasoning is mainly local and uses only part of what is potentially available (e.g., what is known, the available inference procedures), this part is what we call context (of reasoning); however (ii) there is compatibility among the reasoning performed in different contexts. We validate our semantics by formalizing two important forms of contextual reasoning: reasoning with viewpoints and reasoning about belief.

In a recent paper (Computer Aided Geometric Design 23 (3), 243-260), Yang presented a novel kind of nonlinear and geometry driven subdivision scheme for curve interpolation. The author declared that this scheme was shape preserving when all free parameters were explicitly selected by some specific rules. In this note, we demonstrate that the author (a) ignored the consistency of the definition to straight edges, and also (b) did not give a perfect scheme and proof for the shape preserving subdivision.

In this paper we study a reactive extension of constraint logic programming (CLP). Our primary concerns are search problems in a dynamic environment, where interactions with the user (e.g. in interactive multi-criteria optimization problems) or interactions with the physical world (e.g. in time evolving problems) can be modeled and solved efficiently. Our approach is based on a complete set of query manipulation commands for both the addition and the deletion of constraints and atoms in the query. We define a fully incremental model of execution which, contrary to other proposals, retains as much information as possible from the last derivation preceding a query manipulation command. The completeness of the execution model is proved in a simple framework of transformations for CSLD derivations, and of constraint propagation seen as chaotic iteration of closure operators. A prototype implementation of this execution model is described and evaluated on two applications.

Some cryptographic protocols are vulnerable to replay attacks, a type of weakness that was a focus of attention in the Burroughs-Abadi-Needham logic. Newer, more operational approaches to protocol analysis have not concentrated on this type of attack. This paper fills the gap for the strand space theory. The main technical point is to provide a definition of recency. Our candidate is convenient because we already have a powerful way to prove events recent, namely the incoming and outgoing authentication tests. A secondary purpose of this paper is to illustrate an easily mechanized pattern for using the authentication tests.

This is the third in a short series of papers on measurement theory and practice with particular relevance to research in nursing, midwifery, and healthcare. In this paper I demonstrate how the decisions we make regarding the post hoc treatment of our measurements impact the quality of our data and influence the validity of the inferences we draw from them. I address two variations of this practice, pooling data over response options found on self-report measures, and transforming measurements of continuous variables, such as age, into ranges or ordered categories. The problems inherent in this practice are examined using concepts from information theory. Pooling more precise measurements into less precise ones creates errors of discreteness that introduce unpredictable (positive or negative) bias in our results.

Data collected from a paper mill using a WIC-100 process analyzer was divided into six classes, each representing a different kind of paper grade or quality. Each of the six classes were modeled separately by principal component analysis (PCA). The score values of the calibration data, together with the corresponding confidence limits and the trajectory of the current data, are used to visualize the state of the process. For each of the classes, two collective multivariate control charts have been used to describe the state of the process. The first one of these charts is calculated from the residuals and the second one is based on the Mahalanobis distance of the score values. Both of these charts can be traced back to the original variables. Multivariate control charts and biplots have been applied together with the contribution plots and the feature weights in order to detect any process problems and to isolate the deviating variables. The results have been verified by using parallel coordinates. These methods are useful in detecting and isolating the various types of changes that may occur in the wet end process of a paper machine. The concept of contribution map has also been introduced. In this context, Bonferroni bounds have been used as decision rules for plotting points (warnings) on the contribution map.

Summary This paper discusses visualization methods for discriminant analysis. It does not address numerical methods for classification per se, but rather focuses on graphical methods that can be viewed as pre-processors, aiding the analyst’s understanding of the data and the choice of a final classifier. The methods are adaptations of recent results in dimension reduction for regression, including sliced inverse regression and sliced average variance estimation. A permutation test is suggested as a means of determining dimension, and examples are given throughout the discussion.

The paper by Wang and Triantaphyllou [Ranking irregularities when evaluating alternatives by using some ELECTRE methods. Omega, 2008; 36(1): 45–63] (W & T) pinpoints in an interesting way some limitations of ELECTRE II and III methods. The objective of this note is to draw the attention of the reader to a different philosophy of interpreting the results of ELECTRE methods from the one provided by the authors, particularly the different interpretations of the results reported.

Ahn, Lee, Park and Yoo proved that the best constrained degree reduction of a polynomial f in L"2-norm equals the best weighted Euclidean approximation of the Bernstein-Bezier coefficients of f in a paper, published in the journal, Computer Aided Geometric Design 21 (2) (2004) 181-191. In this note, we point out an error in their paper and give the correct result.

Summary: The Big Five factors structure is currently the benchmark for personality dimensions. In the domain of adjectives, various instruments have been developed to measure the Big Five. In this contribution we propose a methodology to find a simple factorial structure and we apply this methodology to the domain of Big Five as measured by adjectives. Using data collected on a sample of 337 subjects, we propose a five-factor benchmark structure derived from the 50 best marker adjectives selected among the adjectives contained in three instruments specifically developed to measure the Big Five (i.e., Goldberg 's 100 adjectives list, IASR-B5, and SACBIF). We use this common factor structure (or benchmark structure) to investigate the differences and the similarities between the three operationalizations of the Big Five, and to investigate the placements of the full set of adjectives contained in the three instruments. The main features of the proposed methodology and the generalizability of the obtained results are discussed.

Should you always follow instructions? All self-respecting human beings, and therefore all our readers, must answer this question with a resounding NO! In the next paragraph we describe two aspects of our paper-folding, and building, instructions where we do advise rather rigid adherence to our specifications. However, we are very far from recommending that you fold all your regular polygons and construct all your polyhedra exactly as described. What we have done is to give you algorithms for the relevant constructions. Machines follow algorithms with relentless fervor, while human beings look for special ways of doing particular, convenient things. Always feel free to use your ingenuity to avoid an algorithm that is not working for you, or seems to you to be unduly complex. A word to the wise We've done a lot of field-testing of the “hands-on” material in this book. Our instructions seem to be, on the whole, quite comprehensible to most readers. However, there are two basic types of error that people seem prone to make in carrying out our instructions. Material error In doing mathematics, it is absurd to specify the quality of paper on which the mathematics should be done. However, when we describe to you how to make mathematical models, we must insist that the choice of material is not arbitrary. Instructions for making models that are easily constructed using gummed mailing tape are unlikely to be effective if a strip of paper taken from an exercise book is used instead.

Based on fuzzy clustering, a new ensemble method of Bagging F-Bagging is proposed in this paper. Firstly the training data are clustered using fuzzy clustering, and then according to the matrix, dividing the training samples into subset intersect, at last each subset of the data are trained, and proper weighted method is used to base learners. As each subset contains different categories and different training data, thus the members of the classifier are diverse. The number of subsets determines the number of the base learners. Experimental results show that this approach can achieve good results.

Rewriting logic [40] is proposed as a logical framework in which other logics can be represented, and as a semantic framework for the specification of languages and systems. Using concepts from the theory of general logics [39], representations of an object logic L in a framework logic F are understood as mappings L → F that translate one logic into the other in a conservative way. The ease with which such maps can be defined is discussed in detail for the cases of linear logic, logics with quantifiers, and any sequent calculus presentation of a logic for a very general notion of “sequent.” Using the fact that rewriting logic is reflective, it is often possible to reify inside rewriting logic itself a representation map L → RWLogic for the finitely presentable theories of L . Such a reification takes the form of a map between the abstract data types representing the finitary theories of L and of RWLogic.Regarding the different but related use of rewriting logic as a semantic framework, the straightforward way in which very diverse models of concurrency can be expressed and unified within rewriting logic is illustrated with CCS. In addition, the way in which constraint solving fits within the rewriting logic framework is briefly explained.

Secondary 5 students from four schools in Hong Kong were required to classify 18 paper and pencil physics problems in terms of whether the problems contain necessary and sufficient, missing or irrelevant information for their solution. Students' ability to denote missing information correlated rather highly with the solution rates of the problems. In another test, students were asked to classify whether the problems in each of six pairs were similar to or different from each other according to students' self-determined criteria. Students who used a deep structure (i.e. used the underlying physics principles) to classify the problems have significantly higher scores in detecting missing and irrelevant information and in the solution rates than those who used surface structure or features for classification. It is argued that a student who is able to identify what information is sufficient, missing or irrelevant for solving a problem understands the problem structure and so is better able to solve it. Such a student is likely to adopt a deep structure in categorizing physics problems. This latter result corroborates with the findings of the expert-novice research paradigm.

The notion of boosting originated in the Machine Learning literature in the 1980's [VALIANT, L.G. (1984). A theory of the learnable. In Proc. 16th Annual ACM Symposium on Theory of Computing 436-445. ACM Press, New York]. The goal of boosting is to improve the generalization performance of weak (or base) learning algorithms by combining them in a certain way. The first algorithm of this type was discovered by Schapire [SCHAPIRE, R.E. (1990). The strength of weak learnability. Machine Learning 5 197-227] and then the second one by Freund [FREUND, Y. (1995). Boosting a weak learning algorithm by majority. Inform. and Comput. 121 256-285]. Schapire and Freund [FREUND, Y. and Schapire. R.E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. System. Sci. 55 119-139] came up with the idea of a more practical version of boosting and invented the algorithm called AdaBoost that combines simple classification rules into much more powerful and precise classification algorithms. For a fixed number of iterations, AdaBoost runs the weak (or base) learning algorithm on resampled original data sets in a sequential manner and then combines the resulting learning algorithms through a weighted summation at the end of the iteration. Gradually, it became clear that AdaBoost is a special case of a more general statistical methodology of combining simple estimates in classification or regression into more complex and more precise ones. The study of statistical properties of these methods has been conducted in several directions since then in both the machine learning and statistics communities. ¶ The problem of consistency of AdaBoost is posed by Leo Breiman in the first paper in this issue of The Annals of Statistics. Breiman studies one ingredient needed to prove the consistency, the convergence properties of AdaBoost as a numerical method in the population case. This paper has been circulated for a couple of years as a preprint and its results were also covered in the Wald Lectures delivered by Breiman at the IMS Annual Meeting in 2002 in Banff, Canada. The papers by Jiang, Lugosi and Vayatis, and Zhang, published below with discussions, consider various versions of boosting and give answers to the consistency question posed by Breiman.

Space curves can be used to represent elongated objects in 3D images and furthermore to facilitate the computation of shape measures for the represented objects. In our specific application (fibres in 3D images of paper), we want to analyze the fibre network on a voxel level with respect to elongation and bending. For this purpose, it is of interest to estimate the curvature in each voxel along a curve. We present a new discrete curvature estimator fulfilling the multigrid convergence property. Moreover, we present results from a study where curvature together with other shape measures are used to analyze the fibre network.

Publisher Summary This chapter focuses on dynamic interpretation, negation, and VP-anaphora. It discusses the Dynamic Montague Grammar formalism (DMG), and its semantic representation language into the analysis of VP-Ellipsis in Catalan language, following Gardent's version and to adapt this approach to those elliptical constructs that interact with the Negative Polarity Operator (NPO). It is generally accepted that VP-Ellipsis is a kind of anaphora. This means that it is necessary to identify those parameters leading to the retrieval of a previously uttered entity because of which the anaphor is interpretable. As for VP-Ellipsis, the process consists of the suppression of the verb and its complements in the ellipsis, leaving no other evidence in the ellipsis clause than an auxiliary verb—in English—and adverbial modifiers—in Catalan. The antecedent clause (a-clause) will in this way modify the interpretation of the ellipsis clause (e-clause); at the same time, the a-clause interpretation will be possible with the help of a property or relationship that seems to be identical to the property or relationship expressed in the e-clause.

The program for the 2008 Joint Mathematics Meetings in San Diego contains a contributed papers session Cryptology for Undergraduates on the morning of Wednesday, January 9, 2008. The session is bei...

We present two algorithms to construct C1-smooth models of skeletal structures from CT/NMR voxel data. The boundary of the reconstructed models consists of a C1- continuous mesh of triangular algebraic surface patches. One algorithm first constructs C1-continuous piecewise conic contours on each of the CT/NMR data slices and then uses piecewise triangular algebraic surface patches to C1 interpolate the contours on adjacent slices. The other algorithm works directly in voxel space and replaces an initial C0 triangular facet approximation of the model with a highly compressed C1- continuous mesh of triangular algebraic surface patches. Both schemes are adaptive, yielding a higher density of patches in regions of higher curvature.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The section of the Bilbao Crystallographic Server (https://www.cryst.ehu.es/) dedicated to subperiodic groups contains crystallographic and Brillouin‐zone databases for the layer groups. The crystallographic databases include the generators/general positions (GENPOS), Wyckoff positions (WYCKPOS) and maximal subgroups (MAXSUB). The Brillouin‐zone database (LKVEC) offers k‐vector tables and Brillouin‐zone figures of all 80 layer groups which form the background of the classification of their irreducible representations. The symmetry properties of the wavevectors are described applying the so‐called reciprocal‐space‐group approach and this classification scheme is compared with that of Litvin & Wike [(1991), Character Tables and Compatibility Relations of the Eighty Layer Groups and Seventeen Plane Groups. New York: Plenum Press]. The specification of independent parameter ranges of k vectors in the representation domains of the Brillouin zones provides a solution to the problems of uniqueness and completeness of layer‐group representations. The Brillouin‐zone figures and k‐vector tables are described in detail and illustrated by several examples., The set of databases of the Bilbao Crystallographic Server (https://www.cryst.ehu.es/) including crystallographic data on generators, general positions, Wyckoff positions, maximal subgroups and Brillouin‐zone figures and k‐vector tables for all 80 layer groups are discussed in detail and illustrated.

The Constraint Logic Programming (CLP) Scheme was introduced by Jaffar and Lassez. The scheme gave a formal framework, based on constraints, for the basic operational, logical and algebraic semantics of an extended class of logic programs. This paper presents for the first time the semantic foundations of CLP in a self-contained and complete package. The main contributions are threefold. First, we extend the original conference paper by presenting definitions and basic semantic constructs from first principles, giving new and complete proofs for the main lemmas. Importantly, we clarify which theorems depend on conditions such as solution compactness, satisfaction completeness and independence of constraints. Second, we generalize the original results to allow for incompleteness of the constraint solver. This is important since almost all CLP systems use an incomplete solver. Third, we give conditions on the (possibly incomplete) solver which ensure that the operational semantics is confluent, that is, has independence of literal scheduling.

Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside‐the‐box, predictions not found in other state‐of‐the‐art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness., A quick learner: Machine learning based on the innov'SAR algorithm leads to efficient selection of highly robust limonene epoxide hydrolase mutants with enhanced unfolding stability and resistance to aggregation by recognizing epistatic mutational interactions.

A study was undertaken to assess the equivalence of the paper-and-pencil and computer versions of the Self-Directed Search (SDS) for 62 college students. The results showed a significant positive correlation in SDS scores for the two SDS forms; however, users strongly preferred the computer format.

(1974). A Vacuum Table of Use in Paper Conservation. Bulletin of the American Institute for Conservation: Vol. 14, No. 2, pp. 115-122.

In drug development decision-making is often supported through model-based methods, such as physiologically-based pharmacokinetics (PBPK). Global sensitivity analysis (GSA) is gaining use for quality assessment of model-informed inference. However, the inclusion and interpretation of correlated factors in GSA has proven an issue. Here we developed and evaluated a latent variable approach for dealing with correlated factors in GSA. An approach was developed that describes the correlation between two model inputs through the causal relationship of three independent factors: the latent variable and the unique variances of the two correlated parameters. The latent variable approach was applied to a set of algebraic models and a case from PBPK. Then, this method was compared to Sobol’s GSA assuming no correlations, Sobol’s GSA with groups and the Kucherenko approach. For the latent variable approach, GSA was performed with Sobol’s method. By using the latent variable approach, it is possible to devise a unique and easy interpretation of the sensitivity indices while maintaining the correlation between the factors. Compared methods either consider the parameters independent, group the dependent variables into one unique factor or present difficulties in the interpretation of the sensitivity indices. In situations where GSA is called upon to support model-informed decision-making, the latent variable approach offers a practical method, in terms of ease of implementation and interpretability, for applying GSA to models with correlated inputs that does not violate the independence assumption. Prerequisites and limitations of the approach are discussed. Supplementary Information The online version supplementary material available at 10.1007/s10928-021-09764-x.

PURPOSE In rapidly acquired functional MRI (fast fMRI) data, the noise serial correlations (SC) can produce problematically overestimated T-statistics which lead to invalid statistical inferences. This study aims to evaluate and improve the accuracy of high-order autoregressive model (AR(p), where p is the model order) based prewhitening method in the SC correction. METHODS Fast fMRI images were acquired at rest (null data) using a multiband simultaneous multi-slice echo planar imaging pulse sequence with repetition time (TR) = 300 and 500 ms. The SC effect in the fast fMRI data was corrected using the prewhitening method based on two AR(p) models: (1) the conventional model (fixed AR(p)) which preselects a constant p for all the image voxels; (2) an improved model (ARAICc ) that employs the corrected Akaike information criterion voxel-wise to automatically select the model orders for each voxel. To evaluate accuracy of SC correction, false positive characteristics were measured by assuming the presence of block and event-related tasks in the null data without image smoothing. The performance of prewhitening was also examined in smoothed images by adding pseudo task fMRI signals into the null data and comparing the detected to simulated activations (ground truth). RESULTS The measured false positive characteristics agreed well with the theoretical curve when using the ARAICc , and the activation maps in the smoothed data matched the ground truth. The ARAICc showed improved performance than the fixed AR(p) method. CONCLUSION The ARAICc can effectively remove noise SC, and accurate statistical analysis results can be obtained with the ARAICc correction in fast fMRI.

Purpose Multi-phase PCASL has been proposed as a means to achieve accurate perfusion quantification that is robust to imperfect shim in the labelling plane. However, there exists a bias in the estimation process that is a function of noise in the data. In this work, this bias is characterised and then addressed in animal and human data. Methods The proposed algorithm to overcome bias uses the initial biased voxel-wise estimate of phase tracking error to cluster regions with different off-resonance phase shifts, from which a high-SNR estimate of regional phase offset is derived. Simulations were used to predict the bias expected at typical SNR. Multi-phase PCASL in three rat strains (n = 21) at 9.4 T was considered, along with 20 human subjects previously imaged using ASL at 3 T. The algorithm was extended to include estimation of arterial blood flow velocity. Results Based on simulations, a perfusion estimation bias of 6–8% was expected using 8-phase data at typical SNR. This bias was eliminated when a high-precision estimate of phase error was available. In the preclinical data, the bias-corrected measure of perfusion (107±14 mL/100g/min) was lower than the standard analysis (116±14 mL/100g/min), corresponding to a mean observed bias across strains of 8.0%. In the human data, bias correction resulted in a 15% decrease in the estimate of perfusion. Conclusion Using a retrospective algorithmic approach, it was possible to exploit common information found in multiple voxels within a whole region of the brain, offering superior SNR and thus overcoming the bias in perfusion quantification from multi-phase PCASL.

COVID-19 pandemic has affected all aspects of people's lives and disrupted the economy. Forecasting the number of cases infected with this virus can help authorities make accurate decisions on the interventions that must be implemented to control the pandemic. Investigation of the studies on COVID-19 forecasting indicates that various techniques such as statistical, mathematical, and machine and deep learning have been utilized. Although deep learning models have shown promising results in this context, their performance can be improved using auxiliary features. Therefore, in this study, we propose two hybrid deep learning methods that utilize the statistical features as auxiliary inputs and associate them with their main input. Specifically, we design a hybrid method of the multihead attention mechanism and the statistical features (ATT_FE) and a combined method of convolutional neural network and the statistical features (CNN_FE) and apply them to COVID-19 data of 10 countries with the highest number of confirmed cases. The results of experiments indicate that the hybrid models outperform their conventional counterparts in terms of performance measures. The experiments also demonstrate the superiority of the hybrid ATT_FE method over the long short-term memory model.

MotivationRecombinant protein production is a widely used technique in the biotechnology and biomedical industries, yet only a quarter of target proteins are soluble and can therefore be purified.ResultsWe have discovered that global structural flexibility, which can be modeled by normalised B-factors, accurately predicts the solubility of 12,216 recombinant proteins expressed in Escherichia coli. We have optimised B-factors, and derived a new set of values for solubility scoring that further improves prediction accuracy. We call this new predictor the ‘Solubility-Weighted Index’ (SWI). Importantly, SWI outperforms many existing protein solubility prediction tools. Furthermore, we have developed ‘SoDoPE’ (Soluble Domain for Protein Expression), a web interface that allows users to choose a protein region of interest for predicting and maximising both protein expression and solubility.AvailabilityThe SoDoPE web server and source code are freely available at https://tisigner.com/sodope and https://github.com/Gardner-BinfLab/TISIGNER-ReactJS, respectively. The code and data for reproducing our analysis can be found at https://github.com/Gardner-BinfLab/SoDoPE_paper2020.

PURPOSE Intravoxel incoherent motion (IVIM) analysis gives information on tissue diffusion and perfusion and may thus have a potential for e.g. tumor tissue characterization. This work aims to study if clustering based on IVIM parameter maps can identify tumor subregions, and to assess the relevance of obtained subregions by histological analysis. METHODS Fourteen mice with human neuroendocrine tumors were examined with diffusion-weighted imaging to obtain IVIM parameter maps. Gaussian mixture models with IVIM maps from all tumors as input were used to partition voxels into k clusters, where k = 2 was chosen for further analysis based on goodness of fit. Clustering was performed with and without the perfusion-related IVIM parameter D* , and with and without including spatial information. The validity of the clustering was assessed by comparison with corresponding histologically stained tumor sections. A Ki-67-based index quantifying the degree of tumor proliferation was considered appropriate for the comparison based on the obtained cluster characteristics. RESULTS The clustering resulted in one class with low diffusion and high perfusion and another with slightly higher diffusion and low perfusion. Strong agreement was found between tumor subregions identified by clustering and subregions identified by histological analysis, both regarding size and spatial agreement. Neither D* nor spatial information had substantial effects on the clustering results. CONCLUSIONS The results of this study show that IVIM parameter maps can be used to identify tumor subregions using a data-driven framework based on Gaussian mixture models. In the studied tumor model, the obtained subregions showed agreement with proliferative activity.

Publisher Summary This chapter focuses on connection theory of children's language and linguistic theory. It discusses the problem of a child's recognition of nonuniversal aspects of deep structure. It is not the case that deep structure is the same for all languages. Grammatical relations are, by hypothesis, universal, but the order of elements is not. It is not the case that simple, active, declarative sentences always reflect deep structure order directly. It is not the case that the most frequent forms in surface structure reflect deep structure. Traditionally, the determination of base word order depends upon analysis of the transformational component. The most important transformation in German is the verb-second transformation which transfers an element from final position to second position in the creation of declarative sentences. It is the broad applicability of this transformation that makes elegantly simple a description of German that uses a verb-final base structure. The subordinate-clause strategy is a powerful mechanism for the discovery of deep structure.

Background Advanced prediction of the daily incidence of COVID-19 can aid policy making on the prevention of disease spread, which can profoundly affect people's livelihood. In previous studies, predictions were investigated for single or several countries and territories. Objective We aimed to develop models that can be applied for real-time prediction of COVID-19 activity in all individual countries and territories worldwide. Methods Data of the previous daily incidence and infoveillance data (search volume data via Google Trends) from 215 individual countries and territories were collected. A random forest regression algorithm was used to train models to predict the daily new confirmed cases 7 days ahead. Several methods were used to optimize the models, including clustering the countries and territories, selecting features according to the importance scores, performing multiple-step forecasting, and upgrading the models at regular intervals. The performance of the models was assessed using the mean absolute error (MAE), root mean square error (RMSE), Pearson correlation coefficient, and Spearman correlation coefficient. Results Our models can accurately predict the daily new confirmed cases of COVID-19 in most countries and territories. Of the 215 countries and territories under study, 198 (92.1%) had MAEs 0.8. For the 215 countries and territories, the mean MAE was 5.42 (range 0.26-15.32), the mean RMSE was 9.27 (range 1.81-24.40), the mean Pearson correlation coefficient was 0.89 (range 0.08-0.99), and the mean Spearman correlation coefficient was 0.84 (range 0.2-1.00). Conclusions By integrating previous incidence and Google Trends data, our machine learning algorithm was able to predict the incidence of COVID-19 in most individual countries and territories accurately 7 days ahead.

PURPOSE Multi-transmit MRI systems are typically equipped with dedicated hardware to sample the reflected/lost power in the transmit channels. After extensive calibration, the amplitude and phase of the signal at the feed of each array element can be accurately determined. However, determining the phase is more difficult and monitoring errors can lead to a hazardous peak local specific absorption rate (pSAR10g ) underestimation. For this purpose, methods were published for online maximum potential pSAR10g estimation without relying on phase monitoring, but these methods produce considerable overestimation. We present a trigonometric maximization method to determine the actual worst-case pSAR10g without any overestimation. THEORY AND METHOD The proposed method takes advantage of the sinusoidal relation between the SAR10g in each voxel and the phases of input signals, to return the maximum achievable SAR10g in a few iterations. The method is applied to determine the worst-case pSAR10g for three multi-transmit array configurations at 7T: (1) body array with eight fractionated dipoles; (2) head array with eight fractionated dipoles; (3) head array with eight rectangular loops. The obtained worst-case pSAR10g values are compared with the pSAR10g values determined with a commonly used method and with a more efficient method based on reference-phases. RESULTS For each voxel, the maximum achievable SAR10g is determined in less than 0.1 ms. Compared to the reference-phases-based method, the proposed method reduces the mean overestimation of the actual pSAR10g up to 52%, while never underestimating the true pSAR10g . CONCLUSION The proposed method can widely improve the performance of parallel transmission MRI systems without phase monitoring.