ELASTICITIES OF DEMAND FOR IMPORTED MEATS IN RUSSIA

Elasticities of demand for meat imports in Russia are estimated using an AIDS model. The model differentiates among sources of imports as well as kinds of meat, but since the number of observations on Russian imports is limited, an improved block-substitutability restriction is introduced to conserve degress of freedom. The estimates of expenditure elasticities are positive for beef, pork, and chicken imported from western countries, and for beef and chicken, are larger than one. The expenditure elasticities are negative for beef and pork imported from former Soviet trade block countries. (Chicken is not imported from these countries.) Consistent with logic, the (compensated) cross-price elasticities indicate that products imported from different sources are substitutes. These estimates are perhaps the first available for the Russian economy, and not surprisingly, they indicate that declining real incomes in Russia mean decreasing meat imports from western countries.Demand and Price Analysis,

Spin-orbit torques for current parallel and perpendicular to a domain wall

We report field- and current-induced domain wall (DW) depinning experiments in Ta/Co20Fe60B20/MgO nanowires through a Hall cross geometry. While purely field-induced depinning shows no angular dependence on in-plane fields, the effect of the current depends crucially on the internal DW structure, which we manipulate by an external magnetic in-plane field. We show for the first time depinning measurements for a current sent parallel to the DW and compare its depinning efficiency with the conventional case of current flowing perpendicularly to the DW. We find that the maximum efficiency is similar for both current directions within the error bars, which is in line with a dominating damping-like spin-orbit torque (SOT) and indicates that no large additional torques arise for currents parallel to the DW. Finally, we find a varying dependence of the maximum depinning efficiency angle for different DWs and pinning levels. This emphasizes the importance of our full angular scans compared to previously used measurements for just two field directions (parallel and perpendicular to the DW) and shows the sensitivity of the spin-orbit torque to the precise DW structure and pinning sites.Comment: 11 pages, 3 figure

Preceding rule induction with instance reduction methods

A new prepruning technique for rule induction is presented which applies instance reduction before rule induction. An empirical evaluation records the predictive accuracy and size of rule-sets generated from 24 datasets from the UCI Machine Learning Repository. Three instance reduction algorithms (Edited Nearest Neighbour, AllKnn and DROP5) are compared. Each one is used to reduce the size of the training set, prior to inducing a set of rules using Clark and Boswell's modification of CN2. A hybrid instance reduction algorithm (comprised of AllKnn and DROP5) is also tested. For most of the datasets, pruning the training set using ENN, AllKnn or the hybrid significantly reduces the number of rules generated by CN2, without adversely affecting the predictive performance. The hybrid achieves the highest average predictive accuracy

Empirical Evaluation of the Difficulty of Finding a Good Value of k for the Nearest Neighbor

As an analysis of the classification accuracy bound for the Nearest Neighbor technique, in this work we have studied if it is possible to find a good value of the parmeter k for each example according to their attribute values. Or at least, if there is a pattern for the parameter k in the original search space. We have carried out different approaches based onthe Nearest Neighbor technique and calculated the prediction accuracy for a group of databases from the UCI repository. Based on the experimental results of our study, we can state that, in general, it is not possible to know a priori a specific value of k to correctly classify an unseen example

Exploring the performance of resampling strategies for the class imbalance problem

The present paper studies the influence of two distinct factors on the performance of some resampling strategies for handling imbalanced data sets. In particular, we focus on the nature of the classifier used, along with the ratio between minority and majority classes. Experiments using eight different classifiers show that the most significant differences are for data sets with low or moderate imbalance: over-sampling clearly appears as better than under-sampling for local classifiers, whereas some under-sampling strategies outperform over-sampling when employing classifiers with global learning

On Optimizing Locally Linear Nearest Neighbour Reconstructions Using Prototype Reduction Schemes

This paper concerns the use of Prototype Reduction Schemes (PRS) to optimize the computations involved in typical k-Nearest Neighbor (k-NN) rules. These rules have been successfully used for decades in statistical Pattern Recognition (PR) applications, and have numerous applications because of their known error bounds. For a given data point of unknown identity, the k-NN possesses the phenomenon that it combines the information about the samples from a priori target classes (values) of selected neighbors to, for example, predict the target class of the tested sample. Recently, an implementation of the k-NN, named as the Locally Linear Reconstruction (LLR) [11], has been proposed. The salient feature of the latter is that by invoking a quadratic optimization process, it is capable of systematically setting model parameters, such as the number of neighbors (specified by the parameter, k) and the weights. However, the LLR takes more time than other conventional methods when it has to be applied to classification tasks. To overcome this problem, we propose a strategy of using a PRS to efficiently compute the optimization problem. In this paper, we demonstrate, first of all, that by completely discarding the points not included by the PRS, we can obtain a reduced set of sample points, using which, in turn, the quadratic optimization problem can be computed far more expediently. The values of the corresponding indices are comparable to those obtained with the original training set (i.e., the one which considers all the data points) even though the computations required to obtain the prototypes and the corresponding classification accuracies are noticeably less. The proposed method has been tested on artificial and real-life data sets, and the results obtained are very promising, and has potential in PR applications

OWA-FRPS: A Prototype Selection method based on Ordered Weighted Average Fuzzy Rough Set Theory

The Nearest Neighbor (NN) algorithm is a well-known and effective classification algorithm. Prototype Selection (PS), which provides NN with a good training set to pick its neighbors from, is an important topic as NN is highly susceptible to noisy data. Accurate state-of-the-art PS methods are generally slow, which motivates us to propose a new PS method, called OWA-FRPS. Based on the Ordered Weighted Average (OWA) fuzzy rough set model, we express the quality of instances, and use a wrapper approach to decide which instances to select. An experimental evaluation shows that OWA-FRPS is significantly more accurate than state-of-the-art PS methods without requiring a high computational cost.Spanish Government TIN2011-2848

Human Lifespan: To Live and Outlive 100 Years?

Starenje populacije je dominantno demografsko obilježje razvijenih zemalja. Stogodišnjaci su selekcionirana skupina i samo jedna od 7.000 do 10.000 osoba dosegne tu dob. Čimbenici dugovječnosti vjerojatno su brojni i uključuju gensko predodređenje (lokus na 4. kromosomu), zdrav okoliš i zdrave životne navike (prehrana s malo kalorija), redovita tjelesna i psihička aktivnost, kao i dostupnost te učinkovitost zdravstvene zaštite s primjenom geroprofi lakse. Stogodišnjaci se adaptiraju na novi život i na gubitak tjelesnih funkcija koji bivaju postupno sve izraženiji kako se dob povisuje. Granice ljudskog života produžuju se - do sada najstarija poznata osoba doživjela je 128 godina. Pojedina zemljopisna područja bilježe izrazito veći broj stogodišnjaka. Navedene su i neke dugovječne osobe s više od 100 godina u svijetu i na području Republike Hrvatske i nekih susjednih zemalja. Iako se uglavnom smatra da se granica trajanja života čovjeka ne može produžiti iznad 120 godina, za sada je ipak teško predvidjeti gdje su njezine granice.Aged population dominates in developed countries. Centenarians are a select group, and only one in 7,000 to 10,000 reach that age. Factors of longevity are numerous and include genetic predisposition (a locus on chromosome 4), environment, healthy lifestyle (hypocaloric diet, regular physical and mental exercise), accessible health services, and effi cient health protection at old age. Centenarians are well adapted to the new life and compensate for the loss of functions with age. The limits of human life are extended, so that nowadays the oldest person has reached the age of 128. Some geographic areas are characterised by higher numbers of centenarians. This article mentions a few individuals who outlived 100 years in the world, Croatia, and neighbouring countries. Although some argue that the limits of human life cannot be extended over the age of 120 years, for now we cannot predict the actual limits of human life

Improving imbalanced classification by anomaly detection

Although the anomaly detection problem can be considered as an extreme case of class imbalance problem, very few studies consider improving class imbalance classification with anomaly detection ideas. Most data-level approaches in the imbalanced learning domain aim to introduce more information to the original dataset by generating synthetic samples. However, in this paper, we gain additional information in another way, by introducing additional attributes. We propose to introduce the outlier score and four types of samples (safe, borderline, rare, outlier) as additional attributes in order to gain more information on the data characteristics and improve the classification performance. According to our experimental results, introducing additional attributes can improve the imbalanced classification performance in most cases (6 out of 7 datasets). Further study shows that this performance improvement is mainly contributed by a more accurate classification in the overlapping region of the two classes (majority and minority classes). The proposed idea of introducing additional attributes is simple to implement and can be combined with resampling techniques and other algorithmic-level approaches in the imbalanced learning domain.Horizon 2020(H2020)Algorithms and the Foundations of Software technolog

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures