Miracle-dz/UniFAR-FTUs huggingface dataset:
A Neuronal Basis for Task-Negative Responses in the Human Brain

Neuroimaging studies have revealed a number of brain regions that show a reduced blood oxygenation level–dependent (BOLD) signal during externally directed tasks compared with a resting baseline. These regions constitute a network whose operation has become known as the default mode. The source of functional magnetic resonance imaging (fMRI) signal reductions in the default mode during task performance has not been resolved, however. It may be attributable to neuronal effects (neuronal firing), physiological effects (e.g., task vs. rest differences in respiration rate), or even increases in neuronal activity with an atypical blood response. To establish the source of signal decreases in the default mode, we used the calibrated fMRI method to quantify changes in the cerebral metabolic rate of oxygen (CMRO2) and cerebral blood flow (CBF) in those regions that typically show reductions in BOLD signal during a demanding cognitive task. CBF:CMRO2 coupling during task-negative responses were linear, with a coupling constant similar to that in task-positive regions, indicating a neuronal source for signal reductions in multiple brain areas. We also identify, for the first time, two modes of neuronal activity in this network; one in which greater deactivation (characterized by metabolic rate reductions) is associated with more effort and one where it is associated with less effort.
Mining unexpected rules by pushing user dynamics

Unexpected rules are interesting because they are either previously unknown or deviate from what prior user knowledge would suggest. In this paper, we study three important issues that have been previously ignored in mining unexpected rules. First, the unexpectedness of a rule depends on how the user prefers to apply the prior knowledge to a given scenario, in addition to the knowledge itself. Second, the prior knowledge should be considered right from the start to focus the search on unexpected rules. Third, the unexpectedness of a rule depends on what other rules the user has seen so far. Thus, only rules that remain unexpected given what the user has seen should be considered interesting. We develop an approach that addresses all three problems above and evaluate it by means of experiments focusing on finding interesting rules.
A signal-space analysis of spatial self-interference isolation for full-duplex wireless

The challenge of full-duplex wireless communication is self-interference received directly from the transmit antennas and backscattered from nearby objects. Spatial isolation of the receive antennas from the transmit antennas can mitigate self-interference, but may cause the spatial resources of the channel to be under-utilized, sacrificing spatial multiplexing performance. We present an analysis of spatial isolation of self-interference for full-duplex base stations, which leverages the antenna-theory based channel model of Poon et. al.We characterize the scattering conditions under which spatial isolation can enable a degrees-of-freedom gain over half-duplex, and show that the gain is inversely proportional to the overlap between the backscattering intervals (set angles of departure/arrival of backscattered self-interference), and the forward scattering intervals (set of angles of departure/arrival to/from the intended users).
Fast restore of checkpointed memory using working set estimation

In order to make save and restore features practical, saved virtual machines (VMs) must be able to quickly restore to normal operation. Unfortunately, fetching a saved memory image from persistent storage can be slow, especially as VMs grow in memory size. One possible solution for reducing this time is to lazily restore memory after the VM starts. However, accesses to unrestored memory after the VM starts can degrade performance, sometimes rendering the VM unusable for even longer. Existing performance metrics do not account for performance degradation after the VM starts, making it difficult to compare lazily restoring memory against other approaches. In this paper, we propose both a better metric for evaluating the performance of different restore techniques and a better scheme for restoring saved VMs. Existing performance metrics do not reflect what is really important to the user -- the time until the VM returns to normal operation. We introduce the time-to-responsiveness metric, which better characterizes user experience while restoring a saved VM by measuring the time until there is no longer a noticeable performance impact on the restoring VM. We propose a new lazy restore technique, called working set restore, that minimizes performance degradation after the VM starts by prefetching the working set. We also introduce a novel working set estimator based on memory tracing that we use to test working set restore, along with an estimator that uses access-bit scanning. We show that working set restore can improve the performance of restoring a saved VM by more than 89% for some workloads.
Hydrated copper oxalate, moolooite, in lichens

Abstract Vivid blue inclusions in white whewellite or weddellite, occurring within the medulla of four lichen species growing on copper-bearing rocks, have been identified as the hydrated copper oxalate, moolooite, CuC2O4. nH2O (n ∼ 0.4–0.7), by infrared spectroscopy and X-ray powder diffraction. The mineral is believed to have been formed by reaction between oxalic acid secreted by the lichen and ground or surface water containing copper.
L1–“Adaptive” Control Always Converges to a Linear PI Control and Does Not Perform Better than the PI

Abstract We show in the paper that the, so–called, “new architecture” of L 1 –adaptive control is, indeed, different from classical model reference adaptive control. Alas, it is not new, since it exactly coincides with a full–state feedback, linear time–invariant proportional plus integral (PI) controller with a decaying additive disturbance. Moreover, it is shown that if the PI controller does not stabilize the plant the L 1 –adaptive controller will not stabilize it either.
Factors that affect the formation of networks for collaborative learning: an empirical study conducted at a Chilean university

Este articulo muestra los resultados de la aplicacion de tecnicas de analisis de redes sociales para explorar los antecedentes en la formacion de redes de aprendizaje colaborativo en Chile. Inmerso en el analisis de aprendizaje, este trabajo senala un conjunto de variables individuales que explican la estructura de las relaciones entre los estudiantes con el proposito de colaborar en el estudio. En particular, el estudio utilizo una regresion multiple basada en el procedimiento de asignacion cuadratica para analizar una red de estudiantes pertenecientes a una universidad chilena. Los resultados apoyan la existencia de tres antecedentes en la formacion de una red para colaborar en el aprendizaje: homofilia, cercania en el rendimiento academico y cercania en el nivel de felicidad.
Exploiting instrumental variables in causal Inference with nonignorable outcome nonresponse using principal stratification

In this paper we consider a specific post-treatment complication that may arise in both randomized and observational studies, namely the problem of nonignorable nonresponse on an outcome variable. This is a typical topic usually known in the econometric literature as endogenous selection; here we tackle this problem specifically within a causal inference framework. By exploiting Principal Stratification, we analyze and propose identification strategies with and without the availability of an instrumental variable for nonresponse. We focus on the different role and meaning of the instrumental variable, also by comparing our framework with a general nonseparable selection model setting. As a motivating example we consider a simplified evaluation study in the field of financial aids to firms, where typically missingness on the outcome variables, such as variables related to firms’performances, can rarely be assumed missing at random.
Pain extent and function in youth with physical disabilities

Background The aim of this study was to increase our understanding of the role that spatial qualities of pain (location and extent) play in functioning, among youths with disabilities and chronic pain. Methods One-hundred and fifteen youths (mean age 14.4 years; SD ±3.3 years) with physical disabilities and chronic pain were interviewed and were asked to provide information about pain locations and their average pain intensity in the past week, and to complete measures of pain interference, psychological function and disability. Most of the participants in this sample were males (56%), Caucasian (68%), and had a cerebral palsy (34%) or muscular dystrophy (25%) problem. Most participants did not report high levels of disability ( X¯=12.7, SD ±9.5, range 0–60) or global pain intensity ( X¯=3.2, SD ±2.4, range 0–10). Results Pain at more than one body site was experienced by 91% of participants. There were positive associations between pain extent with pain interference (r = 0.30) and disability (r = 0.30), and a negative association with psychological function (r = –0.38), over and above average pain intensity. Additionally, pain intensity in the back (as opposed to other locations) was associated with more pain interference (r = 0.29), whereas pain intensity in the shoulders was associated with less psychological function (r = –0.18), and pain intensity in the bottom or hips was associated with more disability (r = 0.29). Conclusion The findings support the need to take into account pain extent in the assessment and treatment of youths with physical disabilities and chronic pain, call our attention about the need to identify potential risk factors of pain extent, and develop and evaluate the benefits of treatments that could reduce pain extent and target pain at specific sites.
Detecting Asymmetric Dark Matter in the Sun with Neutrinos

Dark matter (DM) may have a relic density that is in part determined by a particle/antiparticle asymmetry, much like baryons. If this is the case, it can accumulate in stars like the Sun to sizable number densities and annihilate to Standard Model particles including neutrinos. We show that the combination of neutrino telescope and direct detection data can be used in conjunction to determine or constrain the DM asymmetry from data. Depending on the DM mass, the current neutrino data from Super-Kamiokande and IceCube give powerful constraints on asymmetric DM unless its fractional asymmetry is $\ensuremath{\lesssim}{10}^{\ensuremath{-}2}$. Future neutrino telescopes and detectors like Hyper-K and KM3NeT can search for the resulting signal of high-energy neutrinos from the center of the Sun. The observation of such a flux yields information on both the DM-nucleus cross section and also on the relative abundances of DM and anti-DM.
Norovirus and Rotavirus--two major causative agents of sporadic viral gastroenteritis in hospitalized Polish children.

PURPOSE The aim of the study was to establish the main etiologic agents of acute viral gastroenteritis and to asses the severity of illness associated with the different viral agents of gastroenteritis in children hospitalized during spring/summer season 2008. MATERIAL AND METHODS 181 stool specimens were collected from children under 5 years of age, hospitalized with acute viral diarrhea from April to September 2008. Commercial enzyme immunoassay kits were used to detect noroviruses. The immunochromatographic tests for combined detection of rotaviruses and adenoviruses were performed routinely in our department in all in-patients suspected for viral gastroenteritis. RESULTS A viral etiologic agents were detected in 108 of 181 (59.7%) samples tested. Dual viral pathogens (rotavirus and norovirus) were found in 3 of 181 (1.7%) samples. Rotavirus was the most common viral pathogen found in the study group (86/181; 47.5%), followed by NV (19/181; 10.5%) and adenoviruses (3/181; 1.7%). Approximately, 60% of acute gastroenteritis episodes occurring in children less than 5 years of age were accounted for by infection due to rotavirus and/or norovirus. Norovirus cases were clinically indistinguishable from those of rotavirus origin in children aged less than 2 years whereas they were slightly milder in older group of patients. CONCLUSIONS Rotavirus infections are leading cause of children's hospitalization in spring months whereas Norovirus infections during spring/summer time. There is a great need to apply molecular diagnostic tools to determine the actual and monitoring the changing etiology of acute enteritis in Polish population.
A Novel Localization Algorithm for Wireless Sensor Network Using Two-hop Beacon Nodes

The location estimation of sensor nodes is a fundamental and essential issue for wireless sensor networks (WSNs), because the gathered data is meaningful only when the location information of the sensor nodes is known. In this paper, we proposed a novel localization algorithm. In the proposed scheme, we first estimate the location of sensor nodes using Convex Position Estimation (CPE), and then refine the location of sensor nodes using the location of the two-hop beacon nodes. Simulation shows that compared with convex localization algorithm, the proposed localization algorithm can enhance the localization accuracy efficiently.
Investment effects of land titling and registration: evidence from Nicaragua

This paper analyzes the importance of legal property documents in providing tenure security, enhancing agricultural investment incentives and easing access to credit. While theory predicts that better property rights on land can increase investment through increased security, enhanced trade opportunities and increased collateral value of land, the presence and size of these effects depend crucially on whether those rights are properly enforced. In Nicaragua, a troubled history of land expropriation and invasion has undermined the credibility of the property regime. The effects of a land titling and regularization program are studied to identify the effects of legal documents. In line with legal dispositions, registration is found to increase the probability of carrying out land attached
Detection of cardiac ailments from multilead ECG using diagnostic eigen error features

Accurate detection of life-threatening cardiac ailments is one of the important task in monitoring patient's health. In this paper, a new method for detection and classification of cardiac ailments from multilead electrocardiogram (MECG) is presented. The singular value decomposition (SVD) is used to convert the MECG data matrix into two unitary matrices (eigen matrices) and one diagonal matrix. According to clinical importance, first few atoms from the eigen matrices are selected. The root mean square error (RMSE) between the unitary matrices of both template MECG and analyzed MECG are used as diagnostic eigen error (DEE) features. The combination of singular values of analyzed MECG and DEE features are used as input to the least square support vector machine (LSSVM) classifier. The LSSVM detect the cardiac ailments such as myocardial infarction and hypertrophy. An average accuracy of 95.07% is found using LSSVM classifier with radial basis function (RBF) kernel and 5-fold cross-validation scheme.
Effect of High-power Amplifier on OFDM/TDM

Recently, to reduce a high peak-to-average power ratio (PAPR) of OFDM we proposed OFDM combined with time division multiplexing (OFDM/TDM). Using the frequency-domain equalization (FDE), improved BER performance is achieved. Since the OFDM/TDM cannot completely solve the PAPR problem, some amount of input back-off (IBO) from the saturation point of high-power amplifier (HPA) may be required to reduce the signal distortion caused by the HPA nonlinearity. Moreover, if IBO is not carefuly chosen it may also affect the channel estimation (CE) required for FDE. In this paper, by computer simulation, we study the impact of HPA nonlinearity on OFDM/TDM with pilot-assisted CE over a frequency-selective fading channel. It is shown that the HPA nonlinearity increases the estimation error even when a pilot with constant amplitude in the time-domain is used. However, it is shown in this paper that if IBO is carefully chosen, OFDM/TDM allows lower IBO for the given BER in comparison with OFDM, leading to higher amplifier efficiency.
A Suffix-Based Noun and Verb Classifier for an Inflectional Language

Nouns and verbs pose the major challenge in part-of-speech tagging exercises. In this paper we present a suffix based noun and verb classifier for Assamese, an inflectional, relatively free word order Indic language. We used a tiny dictionary of frequent words to increase the accuracy. We obtained F-score of around 85%.
Estimating the error at given test input points for linear regression

In model selection procedures in supervised learning, a model is usually chosen so that the expected test error over all possible test input points is minimized. On the other hand, when the test input points (without output values) are available in advance, it is more effetive to choose a model so that the test error only at the test input points at hand is minimized. In this paper, we follow this idea and derive an estimator of the test error at the given test input points for linear regression. Our estimator is proved to be an unbiased estimator of the test error at the given test input points under certain conditions. Through the simulations with artificial and standard benchmark data sets, we show that the proposed method is successfully applied in test error estimation and is compared favorably to the standard cross-validation and an empirical Bayesian method in ridge parameter selection.
Credit assignment in adaptive evolutionary algorithms

In this paper, a new method for assigning credit to search operators is presented. Starting with the principle of optimizing search bias, search operators are selected based on an ability to create solutions that are historically linked to future generations. Using a novel framework for defining performance measurements, distributing credit for performance, and the statistical interpretation of this credit, a new adaptive method is developed and shown to outperform a variety of adaptive and non-adaptive competitors.
Semi-Supervised Learning for Multi-Component Data Classification

This paper presents a method for designing a semisupervised classifier for multi-component data such as web pages consisting of text and link information. The proposed method is based on a hybrid of generative and discriminative approaches to take advantage of both approaches. With our hybrid approach, for each component, we consider an individual generative model trained on labeled samples and a model introduced to reduce the effect of the bias that results when there are few labeled samples. Then, we construct a hybrid classifier by combining all the models based on the maximum entropy principle. In our experimental results using three test collections such as web pages and technical papers, we confirmed that our hybrid approach was effective in improving the generalization performance of multi-component data classification.
Automatic child-face age-progression based on heritability factors of familial faces

This paper presents a novel age progression method specifically developed for children that incorporates genetic facial features of siblings and parents to synthetically predict the child's facial appearance up to adult years. The proposed approach is the first age-progression technique that directly incorporates familial data with Active Appearance Models (AAM). This work introduces a novel data set that contains longitudinal face images of children and siblings.
Selectivity and Timing Performance of Funds of Hedge Funds: A Time-Varying Approach*

Abstract This paper empirically examines the time variation of the selectivity and timing performance of funds of hedge funds by employing rolling versions of the performance regression models of Jensen (1968) and Treynor and Mazuy (1966). We propose a cross-sectional regression method similar to those used by Fama and McBeth (1973) for the analysis of the determinants of the performance of funds of hedge funds. Using fund specific style benchmarks, which reflect the performance of the individual strategic asset allocation decision of each fund, and a sample of 1207 funds of hedge funds during January 1994 until December 2006, we find positive selectivity performance has faded away and has become negative in recent years while the timing performance erratically fluctuates around zero. However, both show considerable variation over time, but different magnitudes of cross-sectional dispersion. The cross-sectional regression reveals that the importance of the selectivity and timing seems to rotate over time. Nevertheless selectivity performance seems to be a good discriminating factor for superior funds of hedge funds.
An architecture for driving simulator databases with generic and dynamically changing road networks

For the various demands of the experiments carried out in the IZVW driving simulator a new concept for databases was developed. The road network and the surrounding features of landscape and terrain, flora and buildings are represented in a topological way. Through this the geometrical representation is computed during the simulation in a small area surrounding the driver, including all that is visible for the driver. This generic method offers various possibilities for scenario design. First of all, the extent of the road network is almost unlimited because of the efficient representation. Further on, it can be changed during the simulation when outside the view of the driver. This modification is either carried out interactively by the researcher or automatically by the system itself in supervising conditions freely to be defined for the variables (e.g. physiological measures) available in the simulation.
The Angular Correlations of Galaxies in the COSMOS Field

We present measurements of the two-point galaxy angular correlation function w(θ) in the COSMOS field. Independent determinations of w(θ) as a function of magnitude limit are presented for both the Hubble Space Telescope (HST) ACS catalog and also the ground-based data from Subaru and the Canada-France-Hawaii Telescope. Despite having significantly different masks, these three determinations agree well. At bright magnitudes (IAB < 22), our data generally match very well with existing measurements and with mock catalogs based on the semianalytic galaxy formation calculations of Kitzbichler & White (2007 ) from the Millennium Simulation. The exception is that our result is at the upper end of the expected cosmic variance scatter for θ > 10′, which we attribute to a particularly rich structure known to exist at z ≃ 0.8. For fainter samples, however, the level of clustering is somewhat higher than reported by some previous studies; in all three catalogs we find w(θ = 1′) ≃ 0.014 at a median IAB magnitude of 24. At these very faintest magnitudes, our measurements agree well with the latest determinations from the Canada-France Legacy Survey. This level of clustering is approximately double what is predicted by the semianalytic catalogs (at all angles). The semianalytic results allow an estimate of cosmic variance, which is too small to account for the discrepancy. We therefore conclude that the mean amplitude of clustering at this level is higher than previously estimated.