Miracle-dz/UniFAR-FTUs huggingface dataset:
Monetary policy shocks and macroeconomic variables: Evidence from fast growing emerging economies

This paper investigates both the effects of domestic monetary policy and external shocks on fundamental macroeconomic variables in six fast growing emerging economies: Brazil, Russia, India, China, South Africa and Turkey - denoted hereafter as BRICS_T. The authors adopt a structural VAR model with a block exogeneity procedure to identify domestic monetary policy shocks and external shocks. Their research reveals that a contractionary monetary policy in most countries appreciates the domestic currency, increases interest rates, effectively controls inflation rates and reduces output. They do not find any evidence of the price, output, exchange rates and trade puzzles that are usually found in VAR studies. Their findings imply that the exchange rate is the main transmission mechanism in BRICS_T economies. The authors also find that that there are inverse J-curves in five of the six fast growing emerging economies and there are deviations from UIP (Uncovered Interest Parity) in response to a contractionary monetary policy in those countries. Moreover, world output shocks are not a dominant source of fluctuations in those economies.
Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition

Humans can easily understand other people’s actions through visual systems, while computers cannot. Therefore, a new bio-inspired computational model is proposed in this paper aiming for automatic action recognition. The model focuses on dynamic properties of neurons and neural networks in the primary visual cortex (V1), and simulates the procedure of information processing in V1, which consists of visual perception, visual attention and representation of human action. In our model, a family of the three-dimensional spatial-temporal correlative Gabor filters is used to model the dynamic properties of the classical receptive field of V1 simple cell tuned to different speeds and orientations in time for detection of spatiotemporal information from video sequences. Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information. Visual attention model based on perceptual grouping is integrated into our model to filter and group different regions. Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons. The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model.
Joint optimization of transmit and relay beamformer for single group multicast transmission

In this paper, single group multicasting is considered for the cooperative relay network. Two-phase transmission is performed and amplify-and-forward relay protocol is used. In the first phase, the base station broadcasts the common signal to the single antenna relays by employing beamforming. Each relay multiplies its received signal by a complex weight and retransmits it to the single antenna users. The aim is to find the beamformer weights at the base station and the relays jointly to minimize the total transmitted power while satisfying signal-to-noise ratio constraint for each user. Nonconvex joint problem is firstly relaxed and then converted to an equivalent biconvex problem by using exact penalty approach. The equivalent problem is solved iteratively using alternating minimization. To the best of our knowledge, this is the first work that considers joint beamforming at the base station and the relays for single group multicasting scenario.
OpenKnowledge FP 6-027253 Comparison of Mapping Techniques

Matching has been recognized as a plausible solution for the semantic heterogeneity problem in many traditional applications, such as schema integration, ontology integration, data warehouses, data integration, and so on. Recently, there have emerged a line of new applications characterized by their dynamics, such as peer-to-peer systems, agents, web-services. In this deliverable we extend the notion of ontology matching, as it has been understood in traditional applications, to dynamic ontology matching. In particular, we examine real-world scenarios and collect the requirements they pose towards a plausible solution. We consider five general matching directions which we believe can appropriately address those requirements. These are: (i) approximate and partial ontology matching, (ii) interactive ontology matching, (iii) continuous ”design-time” ontology matching, (iv) community-driven ontology matching and (v) multi-ontology matching. We give an overview of state of the art matching systems as well as their evaluation principles from the dynamic ontology matching perspective. Finally, the key open issues and challenges towards a plausible dynamic ontology matching solution are discussed, thereby providing a vision for future activities. OpenKnowledge is a 3 year long STReP project financed under the European Commission’s 6th Framework Programme. See, http://www.openk. org/ for details.
A parallel hardware architecture for Scale Invariant Feature Transform (SIFT)

Scale Invariant Feature Transform (SIFT) is an efficient algorithm for extracting distinctive features from images. It is used in many computer vision applications such as object recognition, motion estimation, robot mapping and navigation. Although it has an outstanding performance, its implementation requires extensive computations, and it is very difficult to achieve near real-time feature extraction using software implementation only. Hence, there is a clear advantage in exploring the feasibility of implementing the algorithm using customized hardware with the intent of achieving real-time performance. In this paper, a parallel hardware architecture is proposed to accelerate the SIFT features extraction. The proposed approach is viable and yields promising results in terms of accuracy, speed, and hardware resources.
An Economic Impact Assessment of a Microcredit Program in Malaysia: The Case of Amanah Ikhtiar Malaysia (Aim)

This study uses econometric models to evaluate the economic performance of clients participating in the microcredit program of Amanah Ikhtiar Malaysia (AIM). Several proxies are used for the economic performance variable (dependent variables), such as level of earnings/income, ratio of spending to income and value of assets. The regressors (independent variables) used are education level, age, amount of loan, source of income, and ownership of assets. The study found that the economic performance of AIM participants is significantly determined by the amount of money borrowed from AIM. Other factors found to influence the respondents’ economic performance are education level, age, gender, assets owned before joining AIM, and area of residence. Because level of education is found to contribute significantly to the economic performance of AIM participants, it is suggested that AIM should work to educate its borrowers. In particular, it should provide business training.
ASYMPTOTIC NORMALITY FOR DECONVOLVING KERNEL DENSITY ESTIMATORS

Suppose that we have 11 observations from the convolution model Y = X + £, where X and £ are the independent unobservable random variables, and £ is measurement error with a known distribution. We will discuss the asymptotic normality for deconvolving kernel density estimators of the unknown density f x 0 of X by assuming either the tail of the characteristic function of £ behaves as
Using Ranking – Based Techniques Improvement of Aggregate Recommendation Diversity and Accuracy

Recommendation systems are becoming necessary for individual user and also for providing recommendations at individual level in various types of businesses. Recommender system is a personalized information filtering technique used to identify desired number of items based on interest of user. The system uses data on past user ratings by applying various techniques. This techniques concentrate to improve accuracy in recommendations, with recommendation accuracy it is also necessary improve aggregate diversity of recommendation. In this paper, we proposed number of item ranking techniques and different ratings prediction algorithm to improve recommendation accuracy and aggregate diversity by using realworld rating dataset.
Mapping a Regulatory Network Between Organisms

Identification of gene regulatory networks is useful in understanding gene regulation in any organism. Some regulatory network information has already been determined experimentally for model organisms, but much less has been identified for non-model organisms, and the limited amount of gene expression data available for non-model organisms makes inference of regulatory networks difficult. This paper proposes a method to determine the regulatory links that can be mapped from a model to a non-model organism. Mapping a regulatory network involves mapping the transcription factors and target genes from one genome to another. In the proposed method, Basic Local Alignment Search Tool (BLAST) and InterProScan are used to map the transcription factors, whereas BLAST along with the transcription factor binding site motifs are used to map the target genes. Experiments are performed to map the regulatory network data of S. cerevisiae to A. thaliana and analyze the results. Since limited information is available about gene regulatory network links, gene expression data is used to analyze results. A set of rules are defined on the gene expression experiments to identify the predicted regulatory links that are well supported. More than two-thirds of the predicted regulatory links that were analyzed using gene expression data have been verified as correctly mapped regulatory links in the target genome.
The Impact of Competition on the Price of Wireless Communications Services

Dynamic models of industrial organization predict that more competition would increase price if the magnitude of the dynamic efficiencies outweighed the static one. In this paper, I test whether this prediction holds in the wireless markets where the dynamic efficiencies might be significant. The empirical test relies on the change in the intensity of competition, which is introduced by the entry of a fourth wireless operator in France, and the merger between the third and the fourth wireless operators in Austria. Using a hedonic price model and a double-difference matching estimation, I find that the entry into the French market has raised the unit price of mobile data services; contrary to the merger in the Austrian market which lowers the unit price of mobile data. These results suggest that the magnitude of the dynamic efficiencies actually outweigh the static one in the wireless industry. Therefore, a merger from four to three wireless operators may raise both firms profit and consumer surplus.
Globular Cluster Systems in Elliptical Galaxies of Coma

Globular cluster systems of 17 elliptical galaxies have been studied in the Coma Cluster of galaxies. Surface-brightness fluctuations have been used to determine total populations of globular clusters, and specific frequency (SN) has been evaluated for each individual galaxy. Enormous differences in SN between similar galaxies are found. In particular, SN results vary by an order of magnitude from galaxy to galaxy. Extreme cases are the following: (1) at the lower end of the range, NGC 4673 has SN = 1.0 ± 0.4, a surprising value for an elliptical galaxy, but typical for spiral and irregular galaxies; (2) at the upper extreme, MCG +5-31-063 has SN = 13.0 ± 4.2 and IC 4051 has SN = 12.7 ± 3.2, and they are more likely to belong to supergiant cD galaxies than to "normal" elliptical galaxies. Furthermore, NGC 4874, the central supergiant cD galaxy of the Coma Cluster, also exhibits a relatively high specific frequency (SN = 9.0 ± 2.2). The other galaxies studied have SN in the range [2, 7], the mean value being SN = 5.1. No single scenario seems to account for the observed specific frequencies, so the history of each galaxy must be deduced individually by suitably combining the different models (in situ, mergers, and accretions). The possibility that Coma is formed by several subgroups is also considered. If only the galaxies of the main subgroup defined by Gurzadyan & Mazure are used, a trend in SN arises in the sense of SN being bigger in higher-density regions. This result needs further confirmation.
Voluntary activation of the knee extensors measured using transcranial magnetic stimulation

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Drum Sound Analysis for the Manipulation of Rhythm in Drum Loops

This paper addresses the issue of drum sound classification in the context of automatic rhythm modification of drum loops. The proposed method segments the signal using an onset detection algorithm, characterises segmented sounds using a spectral feature set, and classifies them using k-means clustering. We propose a simple taxonomy for the grouping of different instrumental sounds under a few utilitarian labels. Results demonstrate the adequacy of our proposed taxonomy while showing that our classification approach outperforms commonly-used supervised learning techniques
The IVF Outcome Counseling Based on the Model Combining DHEAS and Age in Patients with Low AMH Prior to the First Cycle of GnRH Antagonist Protocol of Ovarian Stimulation

Objective. To investigate the endocrine and/or clinical characteristics of women with low anti-Müllerian hormone (AMH) that could improve the accuracy of IVF outcome prediction based on the female age alone prior to the first GnRH antagonist IVF cycle. Methods. Medical records of 129 patients with low AMH level (<6.5 pmol/L) who underwent their first GnRH antagonist ovarian stimulation protocol for IVF/ICSI were retrospectively analyzed. The main outcome measure was the area under the ROC curve (AUC-ROC) for the models combining age and other potential predictive factors for the clinical pregnancy. Results. Clinical pregnancy rate (CPR) per initiated cycles was 11.6%. For the prediction of clinical pregnancy, DHEAS and age showed AUC-ROC of 0.726 (95%CI 0.641–0.801) and 0.662 (95%CI 0.573–0.743), respectively (P = 0.522). The predictive accuracy of the model combining age and DHEAS (AUC-ROC 0.796; 95%CI 0.716–0.862) was significantly higher compared to that of age alone (P = 0.013). In patients <37.5 years with DHEAS >5.7 pmol/L, 60% (9/15) of all pregnancies were achieved with CPR of 37.5%. Conclusions. DHEAS appears to be predictive for clinical pregnancy in younger women (<37.5 years) with low AMH after the first GnRH antagonist IVF cycle. Therefore, DHEAS-age model could refine the pretreatment counseling on pregnancy prospects following IVF.
Identification of Membrane-bound Variant of Metalloendopeptidase Neurolysin (EC 3.4.24.16) as the Non-angiotensin Type 1 (Non-AT1), Non-AT2 Angiotensin Binding Site*

Background: Angiotensin II, the renin-angiotensin system effector peptide, interacts with a recently discovered binding site that is distinctly different from its classic receptors. Results: A radioiodinated angiotensin II photoprobe bound to a ∼75-kDa membrane protein, enabling its isolation and identification. Conclusion: Membrane-bound metalloendopeptidase neurolysin (EC 3.4.24.16) is the novel angiotensin-binding protein. Significance: This metalloendopeptidase may be a crucial component of the renin-angiotensin system. Recently, we discovered a novel non-angiotensin type 1 (non-AT1), non-AT2 angiotensin binding site in rodent and human brain membranes, which is distinctly different from angiotensin receptors and key proteases processing angiotensins. It is hypothesized to be a new member of the renin-angiotensin system. This study was designed to isolate and identify this novel angiotensin binding site. An angiotensin analog, photoaffinity probe 125I-SBpa-Ang II, was used to specifically label the non-AT1, non-AT2 angiotensin binding site in mouse forebrain membranes, followed by a two-step purification procedure based on the molecular size and isoelectric point of the photoradiolabeled binding protein. Purified samples were subjected to two-dimensional gel electrophoresis followed by mass spectrometry identification of proteins in the two-dimensional gel sections containing radioactivity. LC-MS/MS analysis revealed eight protein candidates, of which the four most abundant were immunoprecipitated after photoradiolabeling. Immunoprecipitation studies indicated that the angiotensin binding site might be the membrane-bound variant of metalloendopeptidase neurolysin (EC 3.4.24.16). To verify these observations, radioligand binding and photoradiolabeling experiments were conducted in membrane preparations of HEK293 cells overexpressing mouse neurolysin or thimet oligopeptidase (EC 3.4.24.15), a closely related metalloendopeptidase of the same family. These experiments also identified neurolysin as the non-AT1, non-AT2 angiotensin binding site. Finally, brain membranes of mice lacking neurolysin were nearly devoid of the non-AT1, non-AT2 angiotensin binding site, further establishing membrane-bound neurolysin as the binding site. Future studies will focus on the functional significance of this highly specific, high affinity interaction between neurolysin and angiotensins.
Predicting subject performance level from EEG signal complexity when engaged in BCI paradigm

The ability to monitor or even to predict the performance level of a subject when engaged in a cognitive task can be useful in various real-life scenarios. In this article we focus on a popular EEG-based Brain Computer Interface (BCI) paradigm and report on the complexity of the EEG signals in relation to the subject's performance level. We estimate signal complexity with a multivariate, multiscale version of Sample Entropy (MMSE) to account for multiple temporal scales as well as within and cross-channel dependencies. Furthermore, we apply Multivariate Empirical Mode Decomposition (MEMD) to render the temporal scales data driven instead of predefined. Our pilot study shows that the multivariate entropy of EEG signals changes during the course of the experiment and that it can be used for predicting the subject's performance level (accuracy).
Dynamic linking and personalization on web

Web browsing is a complex activity and users need to be supported with additional guidance. In our research, we developed a novel Semantic Web browser, which is called SemWeB, in order to assist Web browsing using linked data and Adaptive Hypermedia (AH). SemWeB is an extension to the Mozilla Firefox Web browser. SemWeB adds a semantic layer to Web documents: it annotates Web pages using a linked data domain (i.e. DBpedia) and creates context-based hyperlinks on Web pages to guide users to relevant pages. In addition, the information presented to the user is personalized based on a novel behavior based user model. We evaluated our approach on DBpedia, DBLP and ECS (University of Southampton) linked data domains. Our study showed that SemWeB provides a new way of supporting dynamic linking and personalization on Web documents using different linked data domains.
Repairing Programs with Semantic Code Search Yalin

Automated program repair can potentially reduce debugging costs and improve software quality but recent studies have drawn attention to shortcomings in the quality of automatically generated repairs. We propose a new kind of repair that uses the large body of existing open-source code to find potential fixes. The key challenges lie in efficiently finding code semantically similar (but not identical) to defective code and then appropriately integrating that code into a buggy program. We present SearchRepair, a repair technique that addresses these challenges by (1) encoding a large database of human-written code fragments as SMT constraints on input-output behavior, (2) localizing a given defect to likely buggy program fragments and deriving the desired input-output behavior for code to replace those fragments, (3) using state-of-the-art constraint solvers to search the database for fragments that satisfy that desired behavior and replacing the likely buggy code with these potential patches, and (4) validating that the patches repair the bug against program test suites. We find that SearchRepair repairs 150 (19%) of 778 benchmark C defects written by novice students, 20 of which are not repaired by GenProg, TrpAutoRepair, and AE. We compare the quality of the patches generated by the four techniques by measuring how many independent, not-used-during-repair tests they pass, and find that SearchRepair-repaired programs pass 97.3% of the tests, on average, whereas GenProg-, TrpAutoRepair-, and AE-repaired programs pass 68.7%, 72.1%, and 64.2% of the tests, respectively. We conclude that SearchRepair produces higher-quality repairs than GenProg, TrpAutoRepair, and AE, and repairs some defects those tools cannot.
Multi-objective immune algorithm with dynamic memetic Cauchy mutation

In this paper, a novel immune algorithm with dynamic memetic Cauchy mutation (DMCMIA) for multi-objective optimization is proposed. The idea of memetics is incorporated into the mutation process and a dynamic memetic Cauchy mutation (DMCM) operator is developed. The DMCM operator combines global exploration and local refinement efficiently, which adopts a generation-dependent parameter to guarantee a good balance between global search and local search. Comparison is made to another multi-objective optimization algorithm, nondominated neighbor immune algorithm, termed as NNIA, in solving five ZDT and five DTLZ standard test problems. Simulation results based on coverage of two set, convergence metric and spacing show that DMCMIA performs better than NNIA in generating approximations to the true Pareto front. In addition, the effectiveness of the novel dynamic memetic Cauchy mutation is verified by comparison to polynomial mutation and Gaussian mutation, the experimental results reinforce the advantage of the DMCM operator.