Miracle-dz/UniFAR-FTUs huggingface dataset:
Is there Causal Association between Exchange Rate and Inflation in Africa ?

While previous studies have widely demonstrated about the positive relationship between exchange rate and inflation, there is a paucity of scientific evidence whether there exists causation between them and in what direction mostly in developing countries such as Africa. The objective of this study is to empirically examine how changes in exchange rate and inflation interacts over time and empirically examines whether there exists causality between them and in what direction, using a panel data across 26 African countries over the time period 2001-2013. The paper adopts the recent advanced econometric panel VAR model within GMM estimation framework to investigate the causal relationship between the two variables. It also utilizes appropriate panel data unit root and co-integration tests in order to avoid spurious regression results. Exploratory analysis reveals that both variables exhibit similar trends over the long run with a strong positive relationship among sampled countries in Africa. The study shows that exchange rate depreciations have been associated with inflationary pressures across countries in the sample. Panel integration test confirms long-run relationship between them and hence examining causality is relevant. A panel granger causality test also confirms substantial positive relationship with bidirectional causation between the variables of interest. The study draws important policy implication that any monetary policy framework in Africa needs to take into account this feedback effects and address both variables simultaneously.
Learning a Maximum Margin Subspace for Image Retrieval

One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap between low-level visual features and high-level semantic concepts. To narrow down this gap, relevance feedback is introduced into image retrieval. With the user-provided information, a classifier can be learned to distinguish between positive and negative examples. However, in real-world applications, the number of user feedbacks is usually too small compared to the dimensionality of the image space. In order to cope with the high dimensionality, we propose a novel semisupervised method for dimensionality reduction called Maximum Margin Projection (MMP). MMP aims at maximizing the margin between positive and negative examples at each local neighborhood. Different from traditional dimensionality reduction algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which effectively see only the global euclidean structure, MMP is designed for discovering the local manifold structure. Therefore, MMP is likely to be more suitable for image retrieval, where nearest neighbor search is usually involved. After projecting the images into a lower dimensional subspace, the relevant images get closer to the query image; thus, the retrieval performance can be enhanced. The experimental results on Corel image database demonstrate the effectiveness of our proposed algorithm.
Choice Shift and Group Polarization

I extend the theoretical domain of sociology into an area of social psychology that heretofore has been the exclusive domain of psychologists. Specifically, I develop a social structural perspective on the choice shifts that individuals make within groups. During interpersonal discussions of issues, choice shifts occur when there is a difference between group members’ mean final opinion and their mean initial opinion. Explanations of choice shifts have emphasized group-level conditions (e.g., a norm, a decision rule, a pool of persuasive arguments, a distribution of initial opinions). I argue that choice shifts are a ubiquitous product of the inequalities of interpersonal influence that emerge during discussions of issues. Hence, I bring choice shifts squarely into the domain of a structural social psychology that attends to the composition of networks of interpersonal influence and into broader sociological perspectives concerned with the formation of status structures.
Incremental Forward Feature Selection with Application to Microarray Gene Expression Data

In this study, the authors propose a new feature selection scheme, the incremental forward feature selection, which is inspired by incremental reduced support vector machines. In their method, a new feature is added into the current selected feature subset if it will bring in the most extra information. This information is measured by using the distance between the new feature vector and the column space spanned by current feature subset. The incremental forward feature selection scheme can exclude highly linear correlated features that provide redundant information and might degrade the efficiency of learning algorithms. The method is compared with the weight score approach and the 1-norm support vector machine on two well-known microarray gene expression data sets, the acute leukemia and colon cancer data sets. These two data sets have a very few observations but huge number of genes. The linear smooth support vector machine was applied to the feature subsets selected by these three schemes respectively and obtained a slightly better classification results in the 1-norm support vector machine and incremental forward feature selection. Finally, the authors claim that the rest of genes still contain some useful information. The previous selected features are iteratively removed from the data sets and the feature selection and classification steps are repeated for four rounds. The results show that there are many distinct feature subsets that can provide enough information for classification tasks in these two microarray gene expression data sets.
Trends in Dietary Intake of Folate, Vitamins B6, and B12 among Japanese Adults in Two Rural Communities from 1974 through 2001

BACKGROUND: The 5th edition of the Japanese food composition table enables us to evaluate intakes of folate and vitamins B6 and B12, which are associated with risk of chronic diseases. METHODS: We investigated long-term trends in dietary intake of those nutrients in two rural communities; Ikawa from 1974 through 2000, and Kyowa from 1982 through 2001. The 24-hour recall method was adopted. Intake of green tea interviewed from 1994 was used to examine food sources for these nutrients in the latest period, but not to evaluate long-term trends. Reduced intakes of nutrients due to cooking were not taken into account. RESULTS: Age-adjusted mean folate intake increased by 30% in Ikawa between the 1970’s and 1980’s, and then leveled off to the latest survey, while that in Kyowa did not change throughout the survey periods. The increased folate intake was primarily due to green/yellow vegetables. Mean vitamin B6 intake did not change except that it increased for Ikawa women in the 1980’s and decreased for Kyowa men in the latest period. No secular trend was found for mean vitamin B12 intake. The largest source for folate intake was total vegetables (38-58% of total intake) and the second largest source was alcohol/beverages including green tea (11-24%). Fish/shellfish was the largest source for vitamins B6 (16-23%) and B12 (77-84%). CONCLUSIONS: Dietary intakes of folate, vitamins B6 and B12 showed no notable long-term trend, except for an increased folate intake between the 1970’s and 1980’s due to an increased intake of green/yellow vegetables.
Real‐time density‐based crowd simulation

Virtual characters in games and simulations often need to plan visually convincing paths through a crowded environment. This paper describes how crowd density information can be used to guide a large number of characters through a crowded environment. Crowd density information helps characters avoid congested routes that could lead to traffic jams. It also encourages characters to use a wide variety of routes to reach their destination. Our technique measures the desirability of a route by combining distance information with crowd density information. We start by building a navigation mesh for the walkable regions in a polygonal two‐dimensional (2‐D) or multilayered three‐dimensional (3‐D) environment. The skeleton of this navigation mesh is the medial axis. Each walkable region in the navigation mesh maintains an up‐to‐date density value. This density value is equal to the area occupied by all the characters inside a given region divided by the total area of this region. These density values are mapped onto the medial axis to form a weighted graph. An A* search on this graph yields a backbone path for each character, and forces are used to guide the characters through the weighted environment. The characters periodically replan their routes as the density values are updated. Our experiments show that we can compute congestion‐avoiding paths for tens of thousands of characters in real‐time. Copyright © 2012 John Wiley & Sons, Ltd.
AN IMPROVED WATERSHED ALGORITHM FOR MEDICAL IMAGE SEGMENTATION

This paper improves watershed segmentation algorithm to overcome its over-segmentation challenges using wavelet transform method at its postpossessing stage. The existing and improved algorithms (watershed algorithm and the improved watershed algorithm) were tested on medical images and comparative analysis in terms of segmentation accuracy, segmentation time and memory consumption was considered. Thirty (30) medical images were used for analysis and the average percentage of segmentation accuracy, segmentation time and memory consumption for existing watershed are 35%, 53% and 53%, respectively. The average percentage of segmentation accuracy, segmentation time and memory consumption of improved watershed for thirty (30) medical images are 65%, 47% and 47%, respectively. Based on the results obtained, the accuracy of the improved watershed was 30% higher than existing watershed while the segmentation time and memory consumption of the existing watershed is lower than the improved watershed by 6% and 6%, respectively.
SARAS: a synthetic aperture radar (SAR) raw signal simulator

An SAR simulator of an extended three-dimensional scene is presented. It is based on a facet model for the scene, asymptotic evaluation of SAR unit response, and a two-dimensional fast Fourier transform code for the data processing. Prescribed statistics of the model account for a realistic speckle of the image. The simulator is implemented in Synthetic Aperture Radar Advance Simulators (SARAS), whose performance is described and illustrated by a number of examples. >
Online Urdu Handwritten Character Recognition: Initial Half Form Single Stroke Characters

Necessity of unfolding the enticing field of handwritten character recognition is revealed with the mushroom growth of portable devices. Effective human machine interaction insists the development of a reliable and efficient online handwritten character recognition system. The quest becomes more challenging when it involves Urdu script based languages especially written in Nastalique font. Urdu, in Nastalique style, is a context sensitive and a highly cursive language. Difficulty arises in this style of writing as the shape of a character depends whether it is written in isolated, initial, medial or terminal position in a word. In this paper, online recognition of Urdu characters in their initial half form have been studied. Data is collected using a pen-tablet and the signal is stored as a binary file containing x & y coordinates and pressure values rather than in an image form to reduce complexity of the recognition problem. Wavelets transform is applied to analyze the character signal. Back propagation neural network classifier for single stroke characters in initial half form is designed with overall accuracy of 91.3%.
Risk-Constrained Optimal Bidding Strategy for Pairing of Wind and Demand Response Resources

Wind power represents a significant percentage of the Spanish generation mix and this trend will increase due to the commitment of the European Union to the full deployment of Directive 2009/28/EC. The increasing penetration of intermittent renewable energy, and the development of advanced information and communication technologies, give rise to questions on how additional flexibility obtained from loads can be used in order to optimize the use of resources and assets. This paper proposes a comprehensive stochastic decision making model for the coordinated operation of wind power producers and demand response (DR) aggregators participating in the day-ahead market. In order to account for the uncertainty around the true outcomes of day-ahead prices and wind power, a minimum conditional value at risk term has been included in the model formulation. Numerical results illustrate how the proposed bidding strategy for wind and demand response (DR) pairing increases the expected benefit of both resources and reduces the related risk.
Gemcitabine plus S-1: a hopeful frontline treatment for Asian patients with unresectable advanced pancreatic cancer.

OBJECTIVE Gemcitabine-based chemotherapy is widely used for unresectable advanced pancreatic cancer which contains locally advanced and metastatic pancreatic cancer. We performed meta-analysis to examine whether gemcitabine plus S-1 could improve treatment efficacy as first-line chemotherapy for those patients when compared with gemcitabine alone. METHODS STATA was used to estimate the summary hazard ratios or odds ratios and their 95% confidence intervals. Heterogeneity among trials was examined by Cochran's χ(2) test. Publication bias was evaluated by Begg's and Egger's tests. Subgroup analysis based on the extent of disease was performed. RESULTS Four randomized controlled trials including 878 Asian patients were analyzed. In total meta-analysis, gemcitabine plus S-1 significantly improved overall survival (hazard ratio, 0.82; 95% confidence interval, 0.70-0.96; P = 0.015), progression-free survival (hazard ratio, 0.64; 95% confidence interval, 0.55-0.74; P < 0.001), overall response rate (odds ratio, 3.00; 95% confidence interval, 2.04-4.41; P < 0.001) and disease control rate (odds ratio, 1.78; 95% confidence interval, 1.32 to 2.39; P < 0.001), and was associated with more but manageable hematologic (leukocytopenia, neutropenia, thrombocytopenia) and non-hematologic (diarrhea, stomatitis, nausea, rash) adverse events. In subgroup analysis, gemcitabine plus S-1, comparing with gemcitabine, significantly improved overall survival in locally advanced patients (hazard ratio, 0.69; 95% confidence interval, 0.48 to 0.99; P = 0.022) but not in metastatic patients (hazard ratio, 0.75; 95% confidence interval, 0.46-1.23; P = 0.256). CONCLUSION This meta-analysis confirmed the survival benefits of gemcitabine plus S-1 as first-line treatment for unresectable advanced pancreatic cancer at least in Asia, while good Eastern Cooperative Oncology group performance status was warranted. Importantly, we highlighted the significant overall survival benefit of gemcitabine plus S-1 in locally advanced patients but not in metastatic patients.
A20 Mutation Is Not a Prognostic Marker for Activated B-Cell-Like Diffuse Large B-Cell Lymphoma

Background Constitutive activation of nuclear factor κB (NF-κB) is a hallmark of activated B-cell-like diffuse large B-cell lymphoma (ABC-DLBCL). Mutations in the A20 gene activate NF-κB, but the prognostic value of A20 mutations in ABC-DLBLC is unclear. Purpose To investigate the prognostic value of A20 mutation in ABC-DLBCL patients. Methods The somatic mutation of A20 was investigated in 68 de novo ABC-DLBCLs by PCR/sequencing. The Kaplan-Meier method was used to estimate median overall survival (OS) and progression-free survival (PFS). Results The A20 mutation rate in ABC-DLBCL patients was 29.4%. Complete remission rates were 35% and 45.8% in patients with and without A20 mutations, respectively (P = 0.410). In patients with and without A20 mutations, the median OS was 24.0 and 30.6 months, respectively (P = 0.58), and the median PFS was 15 and 17.4 months, respectively (P = 0.52). None of the differences between the patient groups were significant. Conclusions Our findings suggested that the A20 mutation is a frequent event in ABC-DLBCLs. However, there was no significant difference in PFS and OS in patients with or without A20 mutations. Further study is required to completely exclude A20 somatic mutation as a prognostic marker in the ABC subtype of DLBLC.
Evaluating the effectiveness of fault tolerance in replicated database management systems

Database management systems (DBMS) achieve high availability and fault tolerance usually by replication. However fault tolerance does not come for free. Therefore, DBMSs serving critical applications with real time requirements must find a trade of between fault tolerance cost and performance. The purpose of this study is two-fold. It evaluates the effectiveness of DBMS fault tolerance in the presence of corruption in database buffer cache, which poses serious threat to the integrity requirement of the DBMSs. The first experiment of this study evaluates the effectiveness of fault tolerance, and the fault impact on database integrity, performance, and availability on a replicated DBMS, ClustRa, in the presence of software faults that corrupt the volatile data buffer cache. The second experiment identifies the weak data structure components in the data buffer cache that give fatal consequences when corrupted, and suggest the need for some forms of guarding them individually or collectively.
N-Cyclohexyl-N-(prop-2-en-1-yl)benzenesulfonamide

The title compound, C15H21NO2S, synthesized by N-alkylation of cyclohexylamine benzenesulfonamide with allyl iodide, is of interest as a precursor to biologically active sulfur-containing heterocyclic compounds. The cyclohexane ring is in a chair form and its mean plane makes a dihedral angle of 53.84 (12)° with the phenyl ring.
Adaptive echo canceller using a modified LMS algorithm

In this paper, an echo canceller is presented, using an adaptive filter with a modified LMS (least mean square) algorithm, where this modification is achieved coding error on conventional LMS algorithm. Simulation results, show a better convergence speed than conventional LMS algorithm, furthermore, coded error algorithm presents less complexity for digital adaptive filters design, due to reduction of floating point operations.
Acoustic echo cancellation using iterative-maximal-length correlation and double-talk detection

The conventional maximal-length-correlation (MLC) algorithm to estimate room impulse response for adaptive echo cancellation (AEC) is disturbed by both far-end and near-end speech. In this paper, a new iterative-maximal-length-correlation (IMLC) algorithm is proposed to reduce the far-end speech interference. To avoid the near-end interference, a new double-talk detection (DTD) method is proposed by tracking the squared coefficients errors of the AEC filter. This DTD method has well-separated detection margins among single-talk (ST), double-talk (DT), and echo path changes. Statistical analysis and computer simulations confirm that our proposed IMLC-DTD algorithm outperforms conventional methods.
Which targets are relevant for therapy of acute ischemic stroke?

BACKGROUND The efficiency of various strategies of neuroprotection is well documented in animal experiments but is thus far disappointing in ischemic stroke, for which only early reperfusion induced by thrombolysis has improved clinical outcome. This discrepancy between expectation from experimental research and clinical reality may be related to differences in the pathogenetic factors contributing to infarction. SUMMARY OF COMMENT Positron emission tomography cerebral blood flow studies within 3 hours of onset were used to identify the various compartments of the infarct outlined on MRI 2 to 3 weeks after a hemispheric stroke in 10 patients. Critical hypoperfusion below the viability threshold accounted for the largest proportion (mean, 70%) of the final infarct, whereas penumbral tissue (18%) and initially sufficiently perfused tissue (12%) were responsible for considerably smaller portions of the final infarct. CONCLUSIONS These results indicate that early critical flow disturbance leading to rapid cell damage is the predominant cause of infarction, while secondary and delayed pathobiochemical processes in borderline or initially sufficiently perfused regions contribute only little to the final infarct. Therefore, emerging therapeutic strategies should be targeted to the initially critically perfused tissue subcompartments. Clinical drug trials might benefit from stratification of patients for target tissue compartments applying functional imaging.
Scalable taint specification inference with big code

We present a new scalable, semi-supervised method for inferring taint analysis specifications by learning from a large dataset of programs. Taint specifications capture the role of library APIs (source, sink, sanitizer) and are a critical ingredient of any taint analyzer that aims to detect security violations based on information flow. The core idea of our method is to formulate the taint specification learning problem as a linear optimization task over a large set of information flow constraints. The resulting constraint system can then be efficiently solved with state-of-the-art solvers. Thanks to its scalability, our method can infer many new and interesting taint specifications by simultaneously learning from a large dataset of programs (e.g., as found on GitHub), while requiring few manual annotations. We implemented our method in an end-to-end system, called Seldon, targeting Python, a language where static specification inference is particularly hard due to lack of typing information. We show that Seldon is practically effective: it learned almost 7,000 API roles from over 210,000 candidate APIs with very little supervision (less than 300 annotations) and with high estimated precision (67%). Further, using the learned specifications, our taint analyzer flagged more than 20,000 violations in open source projects, 97% of which were undetectable without the inferred specifications.
Is there a cliff-edge in object-oriented inheritance?

One open research question in the object-oriented (OO) community is whether a cliff-edge exists in inheritance. In other words, beyond a certain level of OO inheritance depth, are there significant differences between the composition and characteristics of classes at the two levels. Anecdotally, level three has been proposed as that psychological cliff-edge, but no empirical evidence exists to support or refute that theory. In this paper, we analyzed data from nine open-source systems to determine whether significant differences between classes at level three and those at level four existed. We looked at the number of methods, extent of coupling and cohesion in the classes at depth three and compared that with the number of methods at depth four. Results suggested no significant differences in any of the three measures. The data thus refutes the myth that there might be a “cliff-edge” level of inheritance.
A quantitative model for user preferences based on qualitative specifications

With an increasing number of devices having varying capabilities and with different types of network access mechanisms, an ordinary user will consider various tradeoffs for selecting one particular device over the other for execution of his task in an ad hoc environment. We propose a model for specifying user preferences so that the selection of the best device for the user is automated. Users specify their preferences and constraints qualitatively, which are then mapped on to a quantitative utility model. Preferences can be specified both for user's liking and dislike. We analyze the theory of TCP-nets as a formal model for representing and reasoning with qualitative preferences, and show how it can be used to create a quantitative preference model. Using an example scenario, the application of our model is shown and its utility against simple additive models is discussed.