Miracle-dz/UniFAR-FTUs huggingface dataset:
Macroeconomic Effects of Public Pension Reforms

The paper explores the macroeconomic effects of three public pension reforms, namely an increase in retirement age, a reduction in benefits and an increase in contribution rates. Using a five-region version of the IMF‘s Global Integrated Monetary and Fiscal model (GIMF), we find that public pension reforms can have a positive effect on growth in both the short run, propelled by rising consumption, and in the long run, due to lower government debt crowding in higher investment. We also find that a reform action undertaken cooperatively by all regions results in larger output effects, reflecting stronger capital accumulation due to higher world savings. An increase in the retirement age reform yields the strongest impact in the short run, due to the demand effects of higher labor income and in the long run because of supply effects.
Rethinking end-to-end congestion control in software-defined networks

TCP is designed to operate in a wide range of networks. Without any knowledge of the underlying network and traffic characteristics, TCP is doomed to continuously increase and decrease its congestion window size to embrace changes in network or traffic. In light of emerging popularity of centrally controlled Software-Defined Networks (SDNs), one might wonder whether we can take advantage of the global network view available at the controller to make faster and more accurate congestion control decisions. In this paper, we identify the need and the underlying requirements for a congestion control adaptation mechanism. To this end, we propose OpenTCP as a TCP adaptation framework that works in SDNs. OpenTCP allows network operators to define rules for tuning TCP as a function of network and traffic conditions. We also present a preliminary implementation of OpenTCP in a ~4000 node data center.
Towards a Serious Game to Help Students Learn Computer Programming

Video games are part of our culture like TV, movies, and books. We believe that this kind of software can be used to increase students' interest in computer science. Video games with other goals than entertainment, serious games, are present, today, in several fields such as education, government, health, defence, industry, civil security, and science. This paper presents a study around a serious game dedicated to strengthening programming skills. Real-Time Strategy, which is a popular game genre, seems to be the most suitable kind of game to support such a serious game. From programming teaching features to video game characteristics, we define a teaching organisation to experiment if a serious game can be adapted to learn programming.
Unified Knowledge Based Economy neural forecasting map

In today's troubled economies, nations are competing in many aspects, including innovation and knowledge progress. Even though there are many composite indicators to measure knowledge and innovation at both micro and macro levels, benefits to decision makers still limited due to numerous progress indicators, without any unified, easy to visualize and evaluate forecasting capabilities. This paper introduces a novel approach to forecasting and finding the aggregated position of many Knowledge-Based Economy (KBE) with a high degree of accuracy. The suggested approach is based on data mining, Neural Networks, Principle Component Analysis (PCA), and Self-Organising Map (SOM). The proposed model has the capability of forecasting and aggregating five major KBE indicators into a unified meaningful map that places any KBE in its league regardless of incomplete missing or little data. The Unified Knowledge Economy Forecast Map (UKFM) reflects the overall position of homogeneous knowledge economies, and it can be used to visualise, identify or evaluate stable, progressing or accelerating KBEs.
Probing synaptic vesicle fusion by altering mechanical properties of the neuronal surface membrane

Because synaptic vesicle exocytosis is a nano-mechanical process, it should be influenced by the mechanical properties of the cell membrane to which the vesicle fuses. By dissolving surfactants at various concentrations in the neuronal membrane, we have perturbed mechanical properties of the membrane and have found that dissolved surfactants lower the probability that a synaptic vesicle will open its fusion pore when the fusion machinery of the vesicle is activated by binding calcium. By using standard theories from the physics and chemistry of surfaces, we can account for this decrease in fusion probability and can infer that a vesicle, when activated, opens its fusion pore ≈3 times out of 4 and that the area of the fusion pore is ≈4 nm2.
The performance of the Haskell containers package

In this paper, we perform a thorough performance analysis of the containers package, the de facto standard Haskell containers library, comparing it to the most of existing alternatives on HackageDB. We then significantly improve its performance, making it comparable to the best implementations available. Additionally, we describe a new persistent data structure based on hashing, which offers the best performance out of available data structures containing Strings and ByteStrings.
The Field of Automatic Entity Relation Extraction Based on Binary Classifier and Reasoning

To solve the difficulty of the field of Automatic Entity Relation Extraction, in this paper, a method that used binary classification thinking, meanwhile combined with reasoning rules to extract the field of entity relation is proposed. considering comprehensively the context information of entity, entity type and their combination of characteristics to construct the feature set, which in order to build the Binary Classifier of entity relation extraction, then taking full advantage of the field characteristics of entity relation, further combine reasoning rules to obtain the type of the field of entity relation. Doing our experiment on the artificial collection of 600 corpuses for tourism field, experimental result shows the method of Binary Classifier combining Reasoning is better than Multiple Classifiers, the F-score is improved 3%.
The Role of Capital Accumulation

This paper provides new evidence for the evolution of regional unemployment rates in Spain over the 1980-2000 period. We argue that interactive dynamic systems of labour demand, wage setting, and labour force equations (i) allow for a richer interpretation of regional disparities, and (ii) can capture the unemployment e ects of growing variables such as capital stock. After classifying the 17 Spanish regions into high and low unemployment groups using kernel and cluster techniques, we estimate a structural labour market model for each group and evaluate the unemployment contributions of investment, benefits, taxes, and the oil price. We find that the main driving force of regional unemployment swings is capital accumulation.
Diagonalizing the genome I: navigation in tree spaces

The orientable cover of the moduli space of real genus zero algebraic curves with marked points is a compact aspherical manifold tiled by associahedra, which resolves the singularities of the space of phylogenetic trees. The resolution maps planar metric trees to their underlying abstract representatives, collapsing and folding an explicit geometric decomposition of the moduli space into cubes. This decomposition endows the resolving space with an interesting canonical pseudometric. The second part of this paper defines a related (stacky) resolution of a space of real quadratic forms, and suggests, perhaps without much justification, that systems of oscillators parametrized by such objects may provide useful models in genomics.
Pilot hopping in massive MIMO systems: Spectral efficiency analysis and achievable rates

In this paper, we analyze the performance of multi-cell least-square (LS) channel estimation using pilot sequence hopping and pilot reuse strategy for pilot decontamination. We obtain achievable rates in uplink synchronous system in L-cell system using LS channel estimation and zero forcing (ZF) detection. Polynomial expansion approximation is performed to obtain a closed form bound of the asymptotic sum-rate. We show that pilot hopping can achieve significant spectral efficiency gain compared to the conventional non-hopping schemes. Moreover, the number of antennas can be also greatly reduced when using the pilot hopping. However, the achieved gains depend on the pilot reuse factor β. It is shown that this gain is not monotonic function of β in some cases and therefore brings to attention that a careful cell planning is required when applying pilot hopping.
Interactive Machine Learning in Diamond

Unindexed search systems, such as Diamond, are more useful than indexed search systems precisely when the cost of indexing cannot be amortized and classifiers are inexpensive to create. This thesis establishes the latter condition for many image classification tasks. To accommodate a wide variety of visual phenomena, a flexible, learned image representation, Semantic Texton Forests, is adapted for use in Diamond. To reduce the amount of interaction required to produce a high-quality classifier, a novel active learning algorithm, Active Learning by Measure Approximation, is theoretically developed. To consolidate all components of the system, a usable interface, Algum, is implemented. The result is an effective workflow realizing the Diamond vision of iterated, interactive hypothesis exploration.
A Practical Indoor Position Estimation by Using a Laptop Computer Equipped With Sensors

The importance of location based service has been recently addressed in many literatures and thus various positioning schemes for outdoors as well as indoors have been introduced. However, there has been a practical issue of implementation over various positioning schemes, although accurate positioning performance can be achieved. In this paper, a practical indoor positioning scheme that effectively locates a pedestrian holding a laptop computer equipped with two commonly used sensors is proposed 1 , and the performance of proposed scheme is evaluated with experiments for a simple path as well as a real route in indoor of general building at university. According to the experiment results, the positioning error was less than 1m in simple path, while the average error was 1.61m for a real route.
Motor dysfunction influence on executive functioning in manifest and premanifest Huntington's disease

Motor disturbances can be present in both manifest and premanifest Huntington's disease (HD). We aimed to investigate the role of motor functioning on executive functioning to better understand the progression of cognitive dysfunction in HD. Forty patients with manifest HD, 21 patients with premanifest HD, and a group of 28 controls were tested twice with a 1‐year interval. For the Symbol Digit Modalities Test and the Figure Fluency Test, extra conditions were designed to measure motor involvement. Subtraction of this motor score from the original test score resulted in isolation of the cognitive component. Groups were compared on motor, cognitive, and original test scores using multilevel regression analysis. Manifest patients had lower baseline scores of 0.53 standard deviations (SD) on the original Symbol Digit Modalities Test (P = 0.03) and 0.71 SD on the motor isolation part (P = 0.006), and they showed a deterioration of 0.47 SD over 1 year of follow‐up on the original Symbol Digit Modalities Test (P = 0.001) compared with controls. Premanifest patients had lower baseline scores of 0.67 SD on the Symbol Digit Modalities motor part (P = 0.008) and deterioration of 0.48 SD on the original (P = 0.001) and cognitive isolation (P = 0.02) parts. Secondary analyses revealed that the premanifest deterioration resulted from the close‐to‐predicted‐onset group. Motor disturbances have a negative influence on performance on the Symbol Digit Modalities Test. Isolation of the cognitive component of this test revealed cognitive deterioration in the premanifest group only, caused by deteriorating scores for patients who were close to their predicted clinical disease onset. The Figure Fluency Test did not prove sensitive to cognitive change. © 2014 International Parkinson and Movement Disorder Society
An Associative Classification Algorithm for Categorizing Text

Constructing predictive, accurate and interpretable classifiers for categorizing texts is a significant task in text mining and knowledge discovery. Associative classification is a data mining research branch that applies association rule to form classification systems (classifiers). This classification approach has shown high accuracy performance in classifying unseen data if compared with other classification techniques. In this paper, we introduce a TC algorithm based on associative classification for the hard problem of TC where documents are classified based on keywords into the right categories. This algorithm employs an efficient intersection method based on DiffSet and a rule filtering procedure by considering more rules during building the classifier. Experimental results on real world textual data set called the Reuter indicated that the proposed model outperforms current rule based TC techniques.
Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer Patients

The nature of clinical data makes it difficult to quickly select, tune and apply machine learning algorithms to clinical prognosis. As a result, a lot of time is spent searching for the most appropriate machine learning algorithms applicable in clinical prognosis that contains either binary-valued or multi-valued attributes. The study set out to identify and evaluate the performance of machine learning classification schemes applied in clinical prognosis of post-operative life expectancy in the lung cancer patients. Multilayer Perceptron, J48, and the Naive Bayes algorithms were used to train and test models on Thoracic Surgery datasets obtained from the University of California Irvine machine learning repository. Stratified 10-fold cross-validation was used to evaluate baseline performance accuracy of the classifiers. The comparative analysis shows that multilayer perceptron performed best with classification accuracy of 82.3%, J48 came out second with classification accuracy of 81.8%, and Naive Bayes came out the worst with classification accuracy of 74.4%. The quality and outcome of the chosen machine learning algorithms depends on the ingenuity of the clinical miner.
Self-aware and self-adaptive autoscaling for cloud based services

Modern Internet services are increasingly leveraging on cloud computing for flexible, elastic and on-demand provision. Typically, Quality of Service (QoS) of cloud-based services can be tuned using different underlying cloud configurations and resources, e.g., number of threads, CPU and memory etc., which are shared, leased and priced as utilities. This benefit is fundamentally grounded by autoscaling: an automatic and elastic process that adapts cloud configurations on-demand according to time-varying workloads. This thesis proposes a holistic cloud autoscaling framework to effectively and seamlessly address existing challenges related to different logical aspects of autoscaling, including architecting autoscaling system, modelling the QoS of cloudbased service, determining the granularity of control and deciding trade-off autoscaling decisions. The framework takes advantages of the principles of self-awareness and the related algorithms to adaptively handle the dynamics, uncertainties, QoS interference and trade-offs on objectives that are exhibited in the cloud. The major benefit is that, by leveraging the framework, cloud autoscaling can be effectively achieved without heavy human analysis and design time knowledge. Through conducting various experiments using RUBiS benchmark and realistic workload on real cloud setting, this thesis evaluates the effectiveness of the framework based on various quality indicators and compared with other state-of-the-art approaches.
Time-Optimal Path Following for Robots With Convex–Concave Constraints Using Sequential Convex Programming

Time-optimal path following considers the problem of moving along a predetermined geometric path in minimum time. In the case of a robotic manipulator with simplified constraints, a convex reformulation of this optimal control problem has been derived previously. However, many applications in robotics feature constraints such as velocity-dependent torque constraints or torque rate constraints that destroy the convexity. The present paper proposes an efficient sequential convex programming (SCP) approach to solve the corresponding nonconvex optimal control problems by writing the nonconvex constraints as a difference of convex (DC) functions, resulting in convex-concave constraints. We consider seven practical applications that fit into the proposed framework even when mutually combined, illustrating the flexibility and practicality of the proposed framework. Furthermore, numerical simulations for some typical applications illustrate the fast convergence of the proposed method in only a few SCP iterations, confirming the efficiency of the proposed framework.
The acute respiratory distress syndrome: fibrosis in the fast lane

The acute respiratory distress syndrome (ARDS) is an acute and severe form of microvascular lung injury which is frequently seen in intensive therapy units. Reductions in mortality have been reported by some centres; however, 40–70% of patients still die from this syndrome.1 2 Treatment at present is largely supportive and, despite our increased understanding of the pathological processes involved, there are no specific treatments of proven benefit. Interstitial and intra-alveolar fibrosis are hallmarks of the more advanced stages of ARDS and are characterised by the abnormal and excessive deposition of extracellular matrix proteins, in particular collagen.3 4 Histologically and biochemically this is similar to the fibrosis seen in other more chronic forms of interstitial lung disease4; however, more is known of the mediators and cellular events that occur in these disorders. The decrease in pulmonary compliance and progressive hypoxia resulting from fibrotic change leads to ventilator dependence. As a result, progressive fibrosis is a direct cause of respiratory death in up to 40% of patients3 5 but is also an indirect cause of death due to nosocomial infection and progressive multi-organ failure in up to 70% of patients who die from ARDS.6 Thus, the fibrotic process is an important determinant of outcome and a potential target for therapeutic intervention. ARDS is traditionally divided into three phases: exudative, proliferative and fibrotic (fig 1). The initial exudative phase involves the leakage of proteinaceous fluid and the migration of cells, in particular neutrophils, from the circulation into the interstitium and alveolar space following diffuse damage to the endothelial and epithelial surfaces. The proliferation of fibroblasts and type II pneumocytes characterises the second phase during which activated fibroblasts secrete a number of extracellular matrix proteins within the interstitium but also migrate into the alveolar space where they form …
Gigascope: a stream database for network applications

We have developed Gigascope, a stream database for network applications including traffic analysis, intrusion detection, router configuration analysis, network research, network monitoring, and performance monitoring and debugging. Gigascope is undergoing installation at many sites within the AT&T network, including at OC48 routers, for detailed monitoring. In this paper we describe our motivation for and constraints in developing Gigascope, the Gigascope architecture and query language, and performance issues. We conclude with a discussion of stream database research problems we have found in our application.
Groupware Support for Software Requirements Inspection

The inspection of software products has proven to be an effective approach to find defects. Inspecting requirements documents offers especially large benefits as it removes defects very early in the development process. On the other hand inspection is also an expensive and sometimes cumbersome process resulting in a large amount of inspection material that has to be sorted, searched, and consolidated. Existing inspection tools whose success has been empirically evaluated are focused on code inspection and fall short for inspection needs of early life cycle documents like requirements specifications. Based (a) on empirical data from our experiments with paperbased inspection of requirements documents and (b) on our experience with groupware support for software requirements negotiation, we have developed a concept for a groupwaresupported requirements inspection process. In this paper we present our concept, discuss potential benefits for software requirements inspection, and propose an approach for empirical evaluation.
A Fuzzy Cognitive Map like recommender system of Learning Resources

In this paper we propose a Fuzzy Cognitive Maps (FCMs) to recommender Learning Resources in a Smart Classroom. We have proposed a Smart Environment for a Classroom in previous works, based on Multi-agent Systems, called SaCI. One of its agents is a recommender system of Learning Resources. In this paper, we define this recommender system using Fuzzy Cognitive Maps. Our recommender system exploits the knowledge, learns, discovers new information, infers preferences, among other thing. For that, it uses five types of knowledge from SaCI: students, learning resources, topics, context and criticism. The performance results of our recommender system based on FCMs are very encouraging.
Failure to Demonstrate Peri-Infarct Depolarizations by Repetitive MR Diffusion Imaging in Acute Human Stroke

Background and Purpose Peri-infarct depolarizations (PIDs) have been demonstrated with diffusion-weighted MRI (DWI) in experimental stroke and are regarded as an important mechanism of ischemic injury. We tested the hypothesis that PIDs are of relevance for the early enlargement of human brain infarcts. Methods Ten stroke patients were investigated by repetitive imaging of the apparent diffusion coefficient (ADC) in the acute phase (7 patients) or subacute phase (3 patients) of developing cortical infarction. In each patient, 20 ADC maps were obtained from serially measured echo-planar DWI (interval of 45 seconds). Data analysis focused on the potential spatial and temporal ADC changes, including structured qualitative analysis, calculation of subtraction images, serial analysis of regions of interest positioned in the infarct core and border, and calculation of hemispheric lesion areas, depending on various ADC thresholds ranging between 0 and 800 &mgr;m2/s. Results Data analysis was unable to disclose any time-dependent changes in ADC that would resemble PID. In ischemic regions, the ADC reduction significantly progressed from the infarct border (555±96 &mgr;m2/s) to the infarct core (431±104 &mgr;m2/s, P <0.01). Conclusions By using an MRI protocol with high temporal resolution and elaborated postprocessing, we were unable to demonstrate a pattern of diffusion changes that would be indicative of PID in human stroke. Experimental infarction and human stroke may differ in the detectability of PID.
CPU and GPU parallel Kramers-Klein calculations

The Kramers-Klein equation is a special Fokker-Planck equation describing the brownian motion in a potential. This equation is used in different areas of biology, chemistry and physics. In this paper we present the comparison between numerical and analytical solutions in case of a linear force and usage of multi-core CPU and GPU for solving Kramers-Klein equation with different boundary conditions. It was shown that the GPU calculations under reflecting boundary conditions are slower as compared with other boundary conditions because in the first case we need to call two kernels sequentially.