Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Stories About Forecasting International Stock Market Returns: Structural Breaks and Theory-Induced Restrictions

We take up the challenge of forecasting out-of-sample monthly returns on stock market indices. Recent contributions show that a wide range of popular predictors poorly forecast the US equity premium when the performance is compared to the average predictor. We revisit these findings focusing on three aspects. First, we report results for four major stock markets: US, UK, Germany and Japan. We find similar patterns of forecastability across countries. Second, this paper focuses on the choice of models and, to a lesser extent, on the choice of predictors. This reflects the changing focus that can be found in recent years in the literature. We explore two structural aspects that have attracted attention: structural changes and theory-induced restrictions. In our samples, models incorporating structural breaks fare better than standard regressions and outperform the average the average predictor at longer horizons. Theory-induced restrictions that were proposed in the literature are shown to be rarely binding and hence the performance is similar to standard regressions. One key finding is that the restrictions produce meaningful results when they are combined with models of structural breaks. These joint models actually do add forecasting power, especially at longer horizons. On the contrary, popular standard models underperform in almost all respects. Finally, we evaluate the forecasting performance in terms of both the size of the forecasting error and the quality of sign predictions. One of the key findings of the paper is that the results look more favorable for forecasting models when it comes to forecasting the direction of market movements.
Political Polarization over Global Warming : Analyzing Twitter Data on Climate Change

The widespread adoption of social media for political communication creates unprecedented opportunities to monitor the opinions of large numbers of politically active individuals in real time. Specifically, several methods have been recently proposed for predicting the political alignment of Twitter users based on the content and structure of their political communication. In this study, we examine political polarization over climate change within the American public by analyzing data containing tweets from 315,862 users which have been talking climate change over the course of time.
Trickling Down: Does Local Job Growth Reduce Poverty?

Was local job growth a significant determinant of poverty reduction between 1990 and 2000? This research takes advantage of newly available data and techniques to explore the job growth on tract-level poverty reduction. Spatial corrections to the model allow for more accurate identification of the significant determinants of poverty reduction across the United States. Results indicate that job growth is a highly significant predictor of poverty reduction, though its effect is modest. While spatial models didn't materially affect the regression coefficients, significant gains in model explanatory power were seen when using a spatial model as compared to OLS.
International Journal of Physical Sciences a Learning Automata-based Algorithm Using Stochastic Minimum Spanning Tree for Improving Life Time in Wireless Sensor Networks

Several algorithms have already been provided for problems of data aggregation in wireless sensor networks, which somehow tried to increase networks lifetimes. In this study, we dealt with this problem using a more efficient method by taking parameters such as the distance between two sensors into account. In this paper, we presented a heuristic algorithm based on distributed learning automata with variable actions set for solving data aggregation problems within stochastic graphs where the weights of edges change with time. To aggregate data, the algorithm, in fact, creates a stochastic minimum spanning tree (SMST) in networks where variable distances of links are considered as edges, and sends data in the form of a single packet to central node after data was processed inside networks. To understand this subject better, we modeled the problem for a stochastic graph having edges with changing weights. Although this assumption that edges weights change with time makes our task difficult, the results of simulations indicate relatively optimal performance of this method.
Applying SMT in symbolic execution of microcode

Microcode is a critical component in modern microprocessors, and substantial effort has been devoted in the past to verify its correctness. A prominent approach, based on symbolic execution, traditionally relies on the use of boolean SAT solvers as a backend engine. In this paper, we investigate the application of Satisfiability Modulo Theories (SMT) to the problem of microcode verification. We integrate MathSAT, an SMT solver for the theory of Bit Vectors, within the flow of microcode verification, and experimentally evaluate the effectiveness of some optimizations. The results demonstrate the potential of SMT technologies over pure boolean SAT.
A Study of an Infrastructure for Research and Development of Many-Core Processors

Many-core processors which have thousands of cores on a chip will be realized. We developed an infrastructure which accelerates the research and development of such many-core processors. This paper describes three main elements provided by our infrastructure. The first element is the definition of simple many-core processor architecture called M-Core. The second is SimMc, a software simulator of M-Core. The third is the software library MClib which helps the development of application programs for M-Core. The simulation speed of SimMc and the parallelization efficiency of M-Core are evaluated using some benchmark programs. We show that our infrastructure accelerates the research and development of many-core processors.
On pricing and hedging basket credit derivatives with dependent structure

In this paper, we study the problem of hedging a basket credit derivatives, in particular, we are interested in basket default swaps. For the pricing of credit derivatives, we consider a factor Copula approach. Single-name credit default swaps will be chosen as the hedging instruments. The hedging mechanism is tested using simulated data with a given measure. Numerical results reveal the efficiency of our proposed hedging method.
Diffusion Tensor Imaging of Motor Connectivity in Selected Subjects with Stroke

Diffusion Tensor Magnetic Resonance Imaging (DTI) is a recently-developed technique that can image in vivo the white matter pathways of the central nervous system. This study used 12-direction diffusion-weighted MRI data from nine stroke patients acquired as part of a three-year stroke rehabilitation study coordinated by the Movement Neuroscience Laboratory at the University of Auckland. DTI was used to investigate corticospinal connectivity. From the FA maps, it is found that in those patients whose motor connectivity has been compromised by the stroke to the extent that no motor evoked potential (MEP) can be elicited from a selected affected muscle group, the asymmetry in mean FA values in the posterior limbs of the internal capsules (PLICs) is correlated with functional recovery as measured by the Fugl-Meyer clinical score. Using probabilistic tractography in the contralesional hemisphere produced CST location and somatotopy results that were consistent with those of previous studies. However, in the ipsilesional hemisphere, connectivity results were highly variable. A measure of change in symmetry of mean connectivity is found to correlate with functional recovery as measured by change in FM score. This supports previous work which has correlated CST integrity and functional improvement and it supports the theory that functional recovery after stroke depends on the extent to which motor CNS symmetry can be regained in the new post-stroke architecture. It also suggests that the movement of the fMRI activations occurs in such a way as to make the most of the preserved white matter connectivity. In a sufficiently complicated problem, actual sampling is better than an examination of all the chains of possibilities. –Stan Ulam, Adventures of a Mathematician
Application of context invariants in airport region of interest detection for multi-spectral satellite imagery

In literature, many target-specific methods are available for target detection on satellite images. Yet for many targets, intra-class variance is high. This situation results in decreased detection performance after generalization. Airfield is one of the targets with high intra-class variance in satellite images. This variance is caused by different compositions observed in airfields. Hence, approaches which aim at detecting airfields in specific regions and compositions are either unsuccessful or inapplicable to images taken from different regions. Context invariants make it possible to generalize target detection algorithms for varying target compositions and regions. In this study, context invariants are proposed for airfield region-of-interest detection and it is observed that context invariance plays an important role in developing robust and reliable algorithm for varying region, climate and compositions.
Beekeeping and sustainable livelihoods.

Bees pollinate flowers and plants and, by so doing, provide the seed for the next harvest. Pollination services are an important source of income in the industrial countries, and the practice is gaining merit wherever commercial farming is encouraged. A colony of bees provides food and income for a family. Honey and wax find ready markets in local communities. Honey is the basis for a range of sweeteners, drinks, medicines and foods, and a well-managed hive can provide 30 kg or more annually. Wax is widely used as an industrial feedstock for candle-making, polish and other products
Environmental Strategies for Electrical and Electronic Equipment Supply Chains: Which to Choose?

Waste electrical and electronic equipment is one of the major world-wide waste streams triggering the emergence of environmental strategies. Environmental regulations, closed-loop supply chain (CLSC) activities and design-for-environment (DfE) practices are environmental friendly strategies being implemented by governments and industry. In this paper, we apply a System Dynamics model to a CLSC of electrical and electronic equipment in Greece. Extensive numerical investigation provides insights regarding the impact of different legislative measures, CLSC activities and DfE practices on the environmental (availability of natural resources and landfills) and economic sustainability.
Dark Matter Substructure and Gamma-Ray Annihilation in the Milky Way Halo

We present initial results from "Via Lactea," the highest resolution simulation to date of Galactic CDM substructure. It follows the formation of a Milky Way-sized halo with Mhalo = 1.8 × 1012 M☉ in a WMAP three-year cosmology, using 234 million particles. Over 10,000 subhalos can be identified at z = 0: their cumulative mass function is well-fit by N(> Msub) = 0.0064 (Msub/Mhalo)-1 down to Msub = 4 × 106 M☉. The total mass fraction in subhalos is 5.3%, while the fraction of surface mass density in substructure within a projected distance of 10 kpc from the halo center is 0.3%. Because of the significant contribution from the smallest resolved subhalos, these fractions have not converged yet. Sub-substructure is apparent in all the larger satellites, and a few dark matter lumps are resolved even in the solar vicinity. The number of dark satellites with peak circular velocities above 10 km s-1 (5 km s-1) is 124 (812): of these, five (26) are found within 0.1rvir, a region that appeared practically smooth in previous simulations. The neutralino self-annihilation γ-ray emission from dark matter clumps is approximately constant per subhalo mass decade. Therefore, while in our run the contribution of substructure to the γ-ray luminosity of the Galactic halo amounts to only 40% of the total spherically averaged smooth signal, we expect this fraction to grow significantly as resolution is increased further. An all-sky map of the expected annihilation γ-ray flux reaching a fiducial observer at 8 kpc from the Galactic center shows that at the current resolution a small number of subhalos start to be bright enough to be visible against the background from the smooth density field surrounding the observer.
Tissue-Specific Expression of Monocarboxylate Transporters during Fasting in Mice

Monocarboxylates such as pyruvate, lactate and ketone bodies are crucial for energy supply of all tissues, especially during energy restriction. The transport of monocarboxylates across the plasma membrane of cells is mediated by monocarboxylate transporters (MCTs). Out of 14 known mammalian MCTs, six isoforms have been functionally characterized to transport monocarboxylates and short chain fatty acids (MCT1-4), thyroid hormones (MCT8, -10) and aromatic amino acids (MCT10). Knowledge on the regulation of the different MCT isoforms is rare. In an attempt to get more insights in regulation of MCT expression upon energy deprivation, we carried out a comprehensive analysis of tissue specific expression of five MCT isoforms upon 48 h of fasting in mice. Due to the crucial role of peroxisome proliferator-activated receptor (PPAR)-α as a central regulator of energy metabolism and as known regulator of MCT1 expression, we included both wildtype (WT) and PPARα knockout (KO) mice in our study. Liver, kidney, heart, small intestine, hypothalamus, pituitary gland and thyroid gland of the mice were analyzed. Here we show that the expression of all examined MCT isoforms was markedly altered by fasting compared to feeding. Expression of MCT1, MCT2 and MCT10 was either increased or decreased by fasting dependent on the analyzed tissue. MCT4 and MCT8 were down-regulated by fasting in all examined tissues. However, PPARα appeared to have a minor impact on MCT isoform regulation. Due to the fundamental role of MCTs in transport of energy providing metabolites and hormones involved in the regulation of energy homeostasis, we assumed that the observed fasting-induced adaptations of MCT expression seem to ensure an adequate energy supply of tissues during the fasting state. Since, MCT isoforms 1–4 are also necessary for the cellular uptake of drugs, the fasting-induced modifications of MCT expression have to be considered in future clinical care algorithms.
Optimal epidemic intervention of HIV spread using the cross-entropy method

The spread of the human immunodeficiency virus (HIV) depends prominently on the migration of people between different regions. An important consequence of this population mobility is that HIV control strategies that are optimal in a regional sense may not be optimal in a national sense. The question is how the local governments can, individually and collectively, make better use of their budgets to reduce the number of new cases of HIV infection taking into account that people travel among regions. We formulate the problem of epidemic intervention as an optimal control problem, finding control sequences (functions) that optimize a given objective function. The mobility of people among regions is modelled via a transition graph as shown in Figure 1
Power allocation over parallel Gaussian multiple access and broadcast channels

In this correspondence, we determine the optimal power allocation, that achieves any specified point on the boundary of the capacity region, for sets of parallel Gaussian multiple-access channels (MACs) and sets of parallel Gaussian broadcast channels (BCs). The power allocation is across the parallel channels and, within each channel, across the users. In both cases, there is a single constraint on the total power used. First, the allocation for the parallel MACs is determined, in a simple form, using the Karush-Kuhn-Tucker (KKT) conditions and a simple Lagrangian argument. Using this result, the allocation for the parallel BCs is derived using recent findings on the duality of the MAC and BC.
HYDROSYS – A mixed reality platform for on-site visualization of environmental data

Modern mobile devices and networks facilitate the development of increasingly graphical mobile GIS applications. We advance the field with the HYDROSYS system, a heterogeneous platform integrating multiple data sources and components. Our key contributions are 1) scalable on-the-fly streaming of environmental sensor data, 2) highly graphical mixed reality representations, 3) access to near real time simulations and other physically-based computations, 4) support for semantically meaningful public participation and 5) integration of the heterogeneous system, balancing the use of standardized formats and optimized mobile transmissions. Our complex system builds on use cases ranging from hydrological professionals and municipal authorities to the environmentally-aware public. We present the challenges, solutions and implementation of the system, providing observations on potential bottlenecks and other issues.
Drawer: An innovative teaching method for blended learning

During the last decade there has been a shift in the way learning process is conducted. One of the main reasons is that technology is changing. Due to this fast movement, concepts like “class”, “workgroup” and “learning process” are changing too. Learning processes are going beyond the boundaries of what was known as “class”. Face-to-face models get mixed with online environments where students are remotely connected through the Internet. This new approach is called blended learning, and it is aimed at improving learning as well as bringing learning where it was impossible or complicated. Nevertheless, one of the main issues is that teachers need innovative tools that support these different learning models. As a consequence, this work is focused on the development of a tool for dealing with the main issues found in blended learning scenarios. It is divided in three phases. First, the blended learning experiences and models of the last decade are reviewed. In a second phase, a tool called Drawer, for supporting the main features of the design and use of blended learning experience is developed. In the last phase, an evaluation is made to assess the outcomes of the new tool.
Calcium quarks.

Elementary subcellular Ca2+ signals arising from the opening of single ion channels may offer the possibility to examine the stochastic behavior and the microscopic chemical reaction rates of these channel proteins in their natural environment. Such an analysis can yield detailed information about the molecular function that cannot be derived from recordings obtained from an ensemble of channels. In this review, we summarize experimental evidence suggesting that Ca2+ sparks, elementary Ca2+ signaling events of cardiac and skeletal muscle excitation contraction coupling, may be comprised of a number of smaller Ca2+ signaling events, the Ca2+ quarks.
Adaptive Activity Spotting Based on Event Rates

To date many activity spotting approaches are static: once the system is trained and deployed it does not change anymore. There are substantial shortcomings of this approach, specifically spotting performance is hampered when patterns or sensor noise level changes. In this work an unsupervised sensitivity adaptation mechanism is proposed for activity event spotting based on expected activity event rates. The expected event rate for activity spotting was derived from the generalisation metric used in information retrieval. To illustrate generalisation effects and depict relations of spotting performance and event rate, different event rates were simulated and their precision-recall spotting performance analysed. Subsequently, the sensitivity adaptation concept is presented and evaluated. For this purpose two large datasets from personal healthcare applications were considered to explore benefits and limitations of this adaptation approach: recognition of drinking motions from inertial sensors and chewing strokes from sound. Results showed up to 28% spotting performance increase for event rate adapted operation, confirming performance benefits for sensitivity adaptation. The approach will be most applicable in situations, where estimated event rate statistics show low variance and long monitoring durations allow effective sensitivity adaptations.
Satisfaction with life and with food-related life in central Chile.

BACKGROUND Eating is one of the most frequent human behaviors, but there are few studies that relate eating and subjective well-being. Typologies of people were distinguished and characterized according to their level of satisfaction with life and food in central Chile. METHOD A survey was applied to a sample of 1,277 people in the main municipalities of this area, distributed proportionally by municipality. The questionnaire included the SWLS scales (Satisfaction with Life Scale), SWFL (Satisfaction with Food-related Life), Health-Related Quality of Life Index (HRQOL), Subjective Happiness Scale (SHS), and respondents' demographic characteristics and eating habits were also ascertained. RESULTS Using hierarchical cluster analysis, three typologies were distinguished with significant differences in the scores on the SWLS, SWFL, SHS, self-perception of health, days with physical or mental problems in the last month, sociodemographic characteristics and frequency with which the family eats together. CONCLUSIONS The results suggest that a higher level of general subjective well-being, and eating is associated with better health, greater family interaction around meals, higher levels of happiness, and with some sociodemographic characteristics.
Spyware Detection: Using Data mining for Windows Portable Executable Files

‫األنعام‬-261 ، 261 (. I ACKNOWLEDGMENTS First and Foremost, I am very grateful to almighty ALLAH whose blessings have always been source of encouragement for me and who gave me the ability to complete this task. This thesis would not exist without the help, advice, support, guidance, and encouragement of many people. In particular, I wish to express my sincere appreciation to my supervisor Dr. Tawfiq S. Barhoom, without his help, guidance, and continuous follow-up; this research would never have been. Also I would like to extend my thanks to the academic staff of the Faculty of Information Technology who taught me different courses and helped me during my Master's study. Special greetings to my family, especially my parents, who have always kept me in their prayers, who have suffered a lot to make me happy. Last but not least, I wish to express my sincere thanks to all those who have one way or another helped me in making this study a success.
A User Friendly Phase Detection Methodology for HPC Systems' Analysis

A wide array of today's high performance computing (HPC) applications exhibits recurring behaviours or execution phases throughout their run-time. Accurate detection of program phases allows reconfiguring the system for a better power/performance trade off, and can reduce the simulation time of programs by identifying regions of code whose performance is critical to the entire program. Program phases are also reflected in different behaviours the system goes through or system phases, which can be used as an alternative means of program phase detection for users lacking expertise. In this paper, we present an execution vector based (EV-based) phase detection, which is an on-line methodology for detecting phases in the behaviour of a HPC system and determining execution points that correspond to these phases. We also present a methodology for defining a small set of EVs representative of the system's behaviour over a fixed period of time and show that EV-based phase detection identifies recurring phases. Our methodology is illustrated with benchmarks and a real life application.