Miracle-dz/UniFAR-FTUs huggingface dataset:
Observation of Direct CP Violation in KS,L ! ππ Decays

Author(s): Alavi-Harati, A; Albuquerque, IF; Alexopoulos, T; Arenton, M; Arisaka, K; Averitte, S; Barker, AR; Bellantoni, L; Bellavance, A; Belz, J; Ben David, R; Bergman, DR; Blucher, E; Bock, GJ; Bown, C; Bright, S; Cheu, E; Childress, S; Coleman, R; Corcoran, MD; Corti, G; Cox, B; Crisler, MB; Erwin, AR; Ford, R; Glazov, A; Golossanov, A; Graham, G; Graham, J; Hagan, K; Halkiadakis, E; Hanagaki, K; Hidaka, S; Hsiung, YB; Jejer, V; Jennings, J; Jensen, DA; Kessler, R; Kobrak, HGE; La Due, J; Lath, A; Ledovskoy, A; Mc Bride, PL; Mc Manus, AP; Mikelsons, P; Monnier, E; Nakaya, T; Nauenberg, U; Nelson, KS; Nguyen, H; O’dell, V; Pang, M; Pordes, R; Prasad, V; Qiao, C; Quinn, B; Ramberg, EJ; Ray, RE; Roodman, A; Sadamoto, M; Schnetzer, S; Senyo, K; Shanahan, P; Shawhan, PS; Slater, W; Solomey, N; Somalwar, SV; Stone, RL; Suzuki, I; Swallow, EC; Swanson, RA; Taegar, SA; Tesarek, RJ; Thomson, GB; Toale, PA; Tripathi, A; Tschirhart, R; Wah, YW; Wang, J; White, HB; Whitmore, J; Winstein, B; Winston, R; Wu, JY; Yamanaka, T | Abstract: We have compared the decay rates of KL and KS toΠ+ Π- Π0Π0 final states using a subset of the data from the KTeV experiment (E832) at Fermilab. We find that the direct-CP-violation parameter Re (μ’/μ) is equal to [28.0±3.0(stat)±2.8(syst)]×10-4. This result definitively establishes the existence of CP violation in a decay process. © 1999 The American Physical Society.
Hellenic Natural Zeolite as a Replacement of Sand in Mortar: Mineralogy Monitoring and Evaluation of Its Influence on Mechanical Properties

Abstract: HEU-type zeolite-rich volcaniclastic tuff (Hellenic natural zeolite) is used as a raw material for the production of lighter mortars. The addition of natural zeolite in mortar mixtures of sand and Portland cement leads to a decrease of up to 18.35% unit weight. The increase of the natural zeolite proportions increases the porosity and water absorption of the mortar and, at the same time, decreases the uniaxial compressive strength. These variations in the mortar’s mechanical properties are due to the addition of natural zeolite, which causes incomplete hydration of C 2 S (2CaO.SiO 2 ) and retardation of the mortar’s hardening. Keywords: lightweight mortars; incomplete hydration; retardation of hardening; natural zeolite 1. Introduction Lightweight added materials can be natural (pumice, perlite, diatomites, zeolites, clays, etc. ) or artificial (expanded aggregates), the latter obtained from natural or artificial materials after a processing cycle able to create a cellular or highly porous structure. According to the European Union standard [1],
Combined EEG/MEG Can Outperform Single Modality EEG or MEG Source Reconstruction in Presurgical Epilepsy Diagnosis

We investigated two important means for improving source reconstruction in presurgical epilepsy diagnosis. The first investigation is about the optimal choice of the number of epileptic spikes in averaging to (1) sufficiently reduce the noise bias for an accurate determination of the center of gravity of the epileptic activity and (2) still get an estimation of the extent of the irritative zone. The second study focuses on the differences in single modality EEG (80-electrodes) or MEG (275-gradiometers) and especially on the benefits of combined EEG/MEG (EMEG) source analysis. Both investigations were validated with simultaneous stereo-EEG (sEEG) (167-contacts) and low-density EEG (ldEEG) (21-electrodes). To account for the different sensitivity profiles of EEG and MEG, we constructed a six-compartment finite element head model with anisotropic white matter conductivity, and calibrated the skull conductivity via somatosensory evoked responses. Our results show that, unlike single modality EEG or MEG, combined EMEG uses the complementary information of both modalities and thereby allows accurate source reconstructions also at early instants in time (epileptic spike onset), i.e., time points with low SNR, which are not yet subject to propagation and thus supposed to be closer to the origin of the epileptic activity. EMEG is furthermore able to reveal the propagation pathway at later time points in agreement with sEEG, while EEG or MEG alone reconstructed only parts of it. Subaveraging provides important and accurate information about both the center of gravity and the extent of the epileptogenic tissue that neither single nor grand-averaged spike localizations can supply.
ALBPS: An Adaptive Approach on Reduction Redundancy in Latency-Aware Peer-to-Peer Content Delivery System with Network Coding

In recent research on the Peer-To-Peer (P2P) content delivery system, network coding has been applied as a promising approach to improve the performance of P2P content delivery. Many studies have shown that the live P2P streaming system can take great advantage of network coding. It has also been shown that this technology can also resolves the "last block" problem. Accordingly, P2P seed peers can just simply upload the coded data to the downstream peer without being applied a complicated schedule algorithm. This results in a very high utilization of bandwidth as well as the throughput since the seed peers can contribute their bandwidth as much as they can.However, it has been found that in a network coding delivery procedure for requested segment, the seed peers (serving peers) cannot decide wether they should stop pushing coded block to the downstream peer or not. The difficulty is that the seed peers cannot get the decoded state instantly from the downstream peer. It cause a significant redundancy, named Redundancy caused by Late Stoping Signal (RLSS), especially when the upload bandwidth is high. We propose an approach to reduce such RLSS and preserve the property of high utilization and throughput though. Such approach is named as Adaptive Learning-Based Predictable Stoping (ALBPS). In this approach, seed peers predict their uploading bandwidth allocation for each segment peer by learning from the history and allocate the bandwidth via assigning priority to each segment uploading session. We also demonstrate that the RLSS can be reduced via applying the ALBPS by both simulation and reality.
Essays on the macroeconomic management of foreign aid flows in Africa

The main motivation of this thesis is to contribute to the literature on the macroeconomic effects of foreign aid flows. It consists of four empirical papers, investigating the two main channels through which aid flows impact the recipient economy: (i) the fiscal sector, and (ii) the real exchange rate. The first paper is concerned with the impact of aid on government expenditure, domestic revenues and borrowing. It uses a traditional fiscal response framework with annual data for Ethiopia. The second paper also focuses on the fiscal sector but uses a recently compiled quarterly fiscal dataset and the cointegrated vector autoregression methodology. The main result arising from both papers is the strong correlation between aid inflows and domestic borrowing, possibly as a strategy to smooth unpredictable and volatile aid inflows. Aid is positively correlated with government expenditures, but there is little evidence of tax displacement. There is also evidence of aid heterogeneity, as grants and loans induce different effects. The third paper assesses the impact of foreign aid on the Ethiopian real exchange rate, which is a common measure of external competitiveness. It uses a quarterly macroeconomic dataset and applies two distinct methodologies: (i) single-equation cointegration models, and (ii) an unobserved components model. The results do not provide support for the ‘Dutch disease’ hypothesis. The fourth paper investigates the extent to which foreign aid is ‘absorbed’ and ‘spent’. The empirical analysis uses a panel of 25 African low-income countries and applies recently developed panel cointegration techniques. The findings suggest that aid is fully spent while absorption is higher than previously estimated.
Deregulated Wholesale Electricity Prices in Italy

In this paper we analyze the time series of daily average prices generated in the Italian electricity market, which started to operate as a Pool in April 2004. The objective is to characterize the high degree of autocorrelation and multiple seasonalities in the electricity prices. We use periodic time series models with GARCH disturbances and leptokurtic distributions and compare their performance with more classical ARMA-GARCH processes. The within-year seasonal variation is modelled using the low frequencies components of physical quantities, which are very regular throughout the sample. Results reveal that much of the variability of the price series is explained by deterministic multiple seasonalities which interact with each other. Periodic AR-GARCH models seem to perform quite well in mimicking the features of the stochastic part of the price process.
Fluctuating risk in an aggregated Ss-model

Using U.S. data it is shown that as the stock market goes into a period of high volatility, nondurables consumption is unaffected but durables consumption falls substantially. It is argued that a plausible explanation for this is that consumers face irreversibilities when adjusting their durables stock. They will thus apply Ss-type rules with bandwidths with widths that vary over time as in Hassler (1996). To quantify the aggregate implications of such behavior an aggregated irreversible investment model for consumer durables is estimated on U.S. It is found that a shift to higher risk leads to a simultaneous widening of individual Ss-bands which causes demand to fall substantially. This effect diminishes over time but is substantial also after a year.
Renal and cardiac effects of antihypertensive treatment with ramipril versus metoprolol in autosomal dominant polycystic kidney disease.

BACKGROUND Hypertension is a common complication in autosomal dominant polycystic kidney disease (ADPKD). This prospective randomized double-blind study was performed to compare the renal and cardiac effects of the ACE inhibitor ramipril and the beta-blocker metoprolol as first line therapy in ADPKD patients with hypertension. METHODS Forty-six hypertensive ADPKD patients were randomized to either ramipril (n = 23) or metoprolol (n = 23). Twenty-four hour (24-h) ambulatory blood pressure (BP), glomerular filtration rate (GFR) as calculated by the Cockcroft and Gault formula, urinary albumin excretion (albumin/creatinine ratio), and left ventricular mass index (LVMI) were established at baseline and at yearly intervals. The total follow-up was 3 years. Baseline characteristics were similar in both groups. RESULTS Mean arterial pressure (MAP) decreased significantly in both the ramipril and the metoprolol group (-8 +/- 2 and -6 +/- 2 mmHg; both P < 0.01). There was a significant decline in renal function during follow-up which was similar in patients treated with ramipril or metoprolol (-2.5 +/- 0.7 vs -2.9 +/- 0.8 ml/min/year; P = NS). After the 3 years follow-up, no differences in GFR, LVMI and urinary albumin excretion were observed between the ramipril and the metoprolol group (80.7 +/- 10.7 vs 78.0 +/- 7.6 ml/min, 102.6 +/- 6.8 vs 100.3 +/- 5.4 g/m(2); and 42.6 +/- 12.3 vs 70.3 +/- 32.5 mg/g, respectively; all P = NS). A post-hoc analysis evaluating the effects of BP control, revealed that LVMI increased in patients with standard BP control while it remained stable in patients with rigorous BP control with a significant difference in LVMI between the groups after 3 years of follow-up (110.5 +/- 6.3 vs 90.9 +/- 4.7 g/m(2); P = 0.017). Also, by the end of the study albuminuria was lower in patients with rigorous vs standard BP control (23.5 +/- 6.7 vs 94.8 +/- 35.4 mg/g; P = 0.05). CONCLUSIONS In our study population of hypertensive ADPKD patients, no differences in renal function, urinary albumin excretion and LVMI were detected between those treated with ramipril or metoprolol, respectively, during a 3 years follow-up. Rigorous BP control prevented an increase in LVMI and reduced urinary albumin excretion, suggesting a crucial role of BP control for slowing progression of cardiac and renal organ damage in ADPKD.
Reliable localized on-line learning in non-stationary environments

On-line learning allows to adapt to changing nonstationary environments. But typically with on-line learning a hypothesis of the data relation is adapted based on a stream of single local training examples, continuously changing the global input-output relation. Hence with these single examples the whole hypothesis is revised incrementally, which might be harmful to the overall predictive quality of the learned model. Nevertheless, for a reliable adaptation, the learned model must yield good predictions in every step. Therefor, the IRMA approach to online learning enables an adaptation that reliably incorporates a new example with a stringent local, but minimal global influence on the input-output relation. The main contribution of this paper is twofold. First, it presents an extension of IRMA regarding the setup of the stiffness, i.e. its hyper-parameter. Second, the IRMA approach is investigated for the first time on a non-trivial realworld application in a non-stationary environment. It is compared with state of the art algorithms on predicting future electric loads in a power grid where a continuous adaptation is necessary to adapt to season and weather conditions. The results show that the performance is increased significantly by IRMA.
Overexpression of VrUBC1, a Mung Bean E2 Ubiquitin-Conjugating Enzyme, Enhances Osmotic Stress Tolerance in Arabidopsis

The ubiquitin conjugating enzyme E2 (UBC E2) mediates selective ubiquitination, acting with E1 and E3 enzymes to designate specific proteins for subsequent degradation. In the present study, we characterized the function of the mung bean VrUBC1 gene (Vigna radiata UBC 1). RNA gel-blot analysis showed that VrUBC1 mRNA expression was induced by either dehydration, high salinity or by the exogenous abscisic acid (ABA), but not by low temperature or wounding. Biochemical studies of VrUBC1 recombinant protein and complementation of yeast ubc4/5 by VrUBC1 revealed that VrUBC1 encodes a functional UBC E2. To understand the function of this gene in development and plant responses to osmotic stresses, we overexpressed VrUBC1 in Arabidopsis (Arabidopsis thaliana). The VrUBC1-overexpressing plants displayed highly sensitive responses to ABA and osmotic stress during germination, enhanced ABA- or salt-induced stomatal closing, and increased drought stress tolerance. The expression levels of a number of key ABA signaling genes were increased in VrUBC1-overexpressing plants compared to the wild-type plants. Yeast two-hybrid and bimolecular fluorescence complementation demonstrated that VrUBC1 interacts with AtVBP1 (A. thaliana VrUBC1 Binding Partner 1), a C3HC4-type RING E3 ligase. Overall, these results demonstrate that VrUBC1 plays a positive role in osmotic stress tolerance through transcriptional regulation of ABA-related genes and possibly through interaction with a novel RING E3 ligase.
Alcohol Hits You When It Is Hard: Intoxication, Task Difficulty, and Theta Brain Oscillations

Background Alcohol intoxication is known to impair decision making in a variety of situations. Previous neuroimaging evidence suggests that the neurofunctional system subserving controlled processing is especially vulnerable to alcohol in conflict‐evoking tasks. The present study investigated the effects of moderate alcohol intoxication on the spatiotemporal neural dynamics of event‐related total theta (4 to 7 Hz) power as a function of task difficulty. Methods Two variants of the Simon task manipulated incongruity via simple spatial stimulus‐response mismatch and, in a more difficult version, by combining spatial and semantic interference. Healthy social drinkers participated in both alcohol (0.6 g/kg ethanol for men, 0.55 g/kg for women) and placebo conditions in a counterbalanced design. Whole‐head magnetoencephalography (MEG) signals were acquired and event‐related total theta power was calculated on each trial with Morlet wavelets. MEG sources were estimated using anatomically constrained, noise‐normalized, spectral dynamic statistical parametric mapping. Results Longer reaction times and lower accuracy confirmed the difficulty manipulation. Response conflict (incongruity) increased and alcohol intoxication decreased event‐related theta power overall during both tasks bilaterally in the medial and ventrolateral prefrontal cortices. However, alcohol‐induced theta suppression was selective for conflict only in the more difficult task which engaged the dorsal anterior cingulate (dAC) and anterior inferolateral prefrontal cortices. Theta power correlated negatively with drinking levels and disinhibition, suggesting that cognitive control is susceptible in more impulsive individuals with higher alcohol intake. Conclusions The spatiotemporal theta profile across the 2 tasks supports the concept of a rostrocaudal activity gradient in the medial prefrontal cortex that is modulated by task difficulty, with the dAC as the key node in the network subserving cognitive control. Conflict‐related theta power was selectively reduced by alcohol only under the more difficult task which is indicative of the alcohol‐induced impairment of conflict monitoring and top‐down regulation. Compromised executive control under alcohol may underlie a range of adverse effects including reduced competency in conflict‐inducing or complex situations.
The method of interval multi-attribute decision-making based on cross entropy

This paper presents a new method to solve the problem in the interval multiple attribute decision making based on cross entropy. By using the cross entropy between an alternative (which will be evaluated) and the ideal solution between the alternative and the negative-ideal solution, we define a new relative closeness to the ideal solutions. And the priority is given by the closeness. This method can avoid comparing and ranking interval numbers and effectively keep the information of the interval number. By comparison with other methods of multiple attribute decision making based on distance, we concluded that the proposed method can used to rank when other methods all fail.
A Smartphone-Based Acquisition System for Hips Rotation Fluency Assessment

The present contribution focuses on the estimation of the Cartesian kinematic jerk of the hips’ orientation during a full three-dimensional movement in the context of enabling eHealth applications of advanced mathematical signal analysis. The kinematic jerk index is estimated on the basis of gyroscopic signals acquired offline through a smartphone. A specific free mobile application is used to acquire the gyroscopic signals and to transmit them to a personal computer through a wireless network. The personal computer elaborates the acquired data and returns the kinematic jerk index associated with a motor task. A comparison of the kinematic jerk index value on a number of data sets confirms that such index can be used to evaluate the fluency of hips orientation during motion. The present research confirms that the proposed gyroscopic data acquisition/processing setup constitutes an inexpensive and portable solution to motion fluency analysis. The proposed data-acquisition and data-processing setup may serve as a supporting eHealth technology in clinical bio-mechanics as well as in sports science.
Mathematical Modeling of Crowdsourcing Systems: Incentive Mechanism and Rating System Design

Crowd sourcing systems like Yahoo! Answers, Amazon Mechanical Turk, and Google Helpouts, etc., have seen an increasing prevalence in the past few years. The participation of users, high quality solutions, and a fair rating system are critical to the revenue of a crowd sourcing system. In this paper, we design a class of simple but effective incentive mechanisms to attract users participating, and providing high quality solutions. Our incentive mechanism consists of a task bundling scheme and a rating system, and pay workers according to solution ratings from requesters. We also propose a probabilistic model to capture various human factors like biases in rating, and we quantify its impact on the incentive mechanism, which is shown to be highly robust. We develop a model to characterize the design space of a class of commonly used rating systems - threshold based rating systems. We quantify the impact of such rating systems and the bundling scheme on the incentive mechanism.
A Comparison of Ensemble Methods in Financial Market Prediction

Financial time series prediction is always a focus point of researchers and practitioner for its available data and profitability. As recent studies suggest that the employment of ensemble algorithms may improve the performance of a base learner, a compound experiment for comparison of ensemble methods is designed and implemented to investigated the fact that whether the ensemble methods can be employed to improve the performance of the base learner in financial time series prediction. The empirical results suggest that ensemble algorithms are powerful in improving the performances of base learners in financial time series prediction. When compared with Random Subspace and Stacking, Bagging provides a more stable and better improvement. The iteration of ensemble algorithms should be adjusted according to the situation. Higher value of iteration may not always performs well for over fitting may occur.
An Interactive Procedure to Preserve the Desired Edges during the Image Processing of Noise Reduction

The paper propose a new procedure including four stages in order to preserve the desired edges during the image processing of noise reduction. A denoised image can be obtained from a noisy image at the first stage of the procedure. At the second stage, an edge map can be obtained by the Canny edge detector to find the edges of the object contours. Manual modification of an edge map at the third stage is optional to capture all the desired edges of the object contours. At the final stage, a new method called Edge Preserved Inhomogeneous Diffusion Equation (EPIDE) is used to smooth the noisy images or the previously denoised image at the first stage for achieving the edge preservation. The Optical Character Recognition (OCR) results in the experiments show that the proposed procedure has the best recognition result because of the capability of edge preservation.
Algorithms for Direct 0-1 Loss Optimization in Binary Classification

While convex losses for binary classification are attractive due to the existence of numerous (provably) efficient methods for finding their global optima, they are sensitive to outliers. On the other hand, while the nonconvex 0-1 loss is robust to outliers, it is NP-hard to optimize and thus rarely directly optimized in practice. In this paper, however, we do just that: we explore a variety of practical methods for direct (approximate) optimization of the 0-1 loss based on branch and bound search, combinatorial search, and coordinate descent on smooth, differentiable relaxations of 0-1 loss. Empirically, we compare our proposed algorithms to logistic regression, SVM, and the Bayes point machine showing that the proposed 0-1 loss optimization algorithms perform at least as well and offer a clear advantage in the presence of outliers. To this end, we believe this work reiterates the importance of 0-1 loss and its robustness properties while challenging the notion that it is difficult to directly optimize.