Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrinsic Organization and Function of Human Brain Networks Supporting the Spontaneous Dynamics of Attention and Pain

The intrinsic dynamics of human brain activity enable us to flexibly shift our attention between the external environment and cognition unrelated to immediate sensation. The interplay of stimulus-dependent and stimulus-independent experience characterizes the nature of our personal interactions with the world and differences among individuals. Brain networks involved in attention, sensory modulation, and stimulus-independent cognition have been described. The present work was designed to disentangle how these networks are intrinsically organized and how fluctuating activity in these networks relates to cognition and a basic perception that is fundamental for survival, pain. The salience/ventral attention network, implicated in stimulusdriven attention, was shown to exhibit right hemisphere-lateralized intrinsic organization, both in structural and functional connectivity. The default mode network (DMN), implicated in attention away from the external environment, was shown to have dynamic fluctuations in the functional organization of its subnetworks that relate to ongoing, freely flowing thoughts (mindiii wandering). In the context of pain, activity in multiple networks was associated with spontaneous attentional fluctuations. Attention toward pain was represented as increased salience/ventral network activation. Attention away from pain occurred during increased DMN activation and enhanced functional connectivity between the DMN and periaqueductal gray (PAG), a key node of the descending pain modulatory system. Furthermore, individuals with higher tendency to mind-wander away from pain exhibited stronger PAG-DMN structural connectivity and more dynamic PAG-DMN spontaneous functional connectivity. These findings underscore the behavioural significance of activity fluctuations within attention and sensory modulation networks. While activity in these networks is constrained by structural and functional properties that underlie inter-individual differences, it is also highly flexible and tracks attentional fluctuations toward and away from the present sensory environment. This work has implications for understanding of pathological states such as chronic pain, where altered activity dynamics in attention/antinociceptive networks may determine disease prognosis and therapeutic outcomes.
Frontier Market Transaction Costs and Diversfication

Frontier markets, sometimes referred to as “emerging emerging markets,” have high transaction costs so investors who rebalance their portfolios monthly do not receive diversification benefits. Rebalancing every three months or longer, however, leads to diversification gains. Diversification benefits are larger in time periods with lower transaction costs and this is linked to risk aversion. Higher risk aversion results in larger transaction costs and larger return correlations between the United States and frontier markets. There is no cross-country relation between diversification benefits and transaction costs or development. Our results are based on comprehensive measures of transaction costs for 19 frontier markets.
An improved method for ECG signal feature point detection based on wavelet transform

The electrocardiogram (ECG) signals not only provide effective diagnosis support information for physicians, but also receive growing attention in areas of human identity recognition as a new kind of biomedical signals. An improved method for ECG feature point detection based on wavelet transform has been proposed in this paper. First, the ECG signal is pre-processed to remove noises and base-line wander. Secondly, QRS complex is located by the improved method based on wavelet transform. Finally, P-wave and T-wave in ECG signal are determined through window search in the predefined range. In comparison, QRS complex is also located through differential threshold method. The proposed method has been tested on the MIT-BIH arrhythmia database. Experimental results show that QRS complex can be detected at the accuracy of up to 99% and the accuracy of the awkward P-wave and T-wave location can be 95% or more. The proposed method is effective and lay foundation for human identification using ECG signal.
Myrmics: A scalable runtime system for global address spaces

iii Πeρίληψη . v Acknowledgements . vii
Mtgr1 Is a Transcriptional Corepressor That Is Required for Maintenance of the Secretory Cell Lineage in the Small Intestine

ABSTRACT Two members of the MTG/ETO family of transcriptional corepressors, MTG8 and MTG16, are disrupted by chromosomal translocations in up to 15% of acute myeloid leukemia cases. The third family member, MTGR1, was identified as a factor that associates with the t(8;21) fusion protein RUNX1-MTG8. We demonstrate that Mtgr1 associates with mSin3A, N-CoR, and histone deacetylase 3 and that when tethered to DNA, Mtgr1 represses transcription, suggesting that Mtgr1 also acts as a transcriptional corepressor. To define the biological function of Mtgr1, we created Mtgr1-null mice. These mice are proportionally smaller than their littermates during embryogenesis and throughout their life span but otherwise develop normally. However, these mice display a progressive reduction in the secretory epithelial cell lineage in the small intestine. This is not due to the loss of small intestinal progenitor cells expressing Gfi1, which is required for the formation of goblet and Paneth cells, implying that loss of Mtgr1 impairs the maturation of secretory cells in the small intestine.
An Approach to Detect Corrupted Schema Mappings in XML P 2 P Databases ( Extended Abstract ) ⋆

In this paper we present an automatic technique for identifying corrupted as well as imprecise links in XML p2p database systems. Our technique, based on static type analysis of XQuery like queries, employs an enhanced version of the μXQ type system [3], that allows for a precise location of errors in queries wrt schema definitions.
How Rigid are Nominal Wages? Evidence and Implications for Germany

Many recent attempts to find evidence on downward nominal wage rigidity in micro data have suffered from problems such as composition bias and the effects of measurement error. In this paper, a model of proportional downward nominal wage rigidity is developed which avoids these problems by taking into account the determinants of wage changes and the measurement process that leads to observable earnings changes. We find a high degree of downward nominal wage rigidity in German micro data. Its real implications for individual expected wage growth, the aggregate wage level and equilibrium unemployment have marked effects for rates of inflation lower than 3 percent.
Hybrid origin of European commercial pigs examined by an in-depth haplotype analysis on chromosome 1

Although all farm animals have an original source of domestication, a large variety of modern breeds exist that are phenotypically highly distinct from the ancestral wild population. This phenomenon can be the result of artificial selection or gene flow from other sources into the domesticated population. The Eurasian wild boar (Sus scrofa) has been domesticated at least twice in two geographically distinct regions during the Neolithic revolution when hunting shifted to farming. Prior to the establishment of the commercial European pig breeds we know today, some 200 years ago Chinese pigs were imported into Europe to improve local European pigs. Commercial European domesticated pigs are genetically more diverse than European wild boars, although historically the latter represents the source population for domestication. In this study we examine the cause of the higher diversity within the genomes of European commercial pigs compared to their wild ancestors by testing two different hypotheses. In the first hypothesis we consider that European commercial pigs are a mix of different European wild populations as a result of movement throughout Europe, hereby acquiring haplotypes from all over the European continent. As an alternative hypothesis, we examine whether the introgression of Asian haplotypes into European breeds during the Industrial Revolution caused the observed increase in diversity. By using re-sequence data for chromosome 1 of 136 pigs and wild boars, we show that an Asian introgression of about 20% into the genome of European commercial pigs explains the majority of the increase in genetic diversity. These findings confirm that the Asian hybridization, that was used to improve production traits of local breeds, left its signature in the genome of the commercial pigs we know today.
Hierarchical Back-Face Culling

We present a sub linear algorithm for computing and culling back facing polygons that yields a signi cant performance improvement in the interactive rendering of large polygonal models The algorithm partitions a polygonal model it into hierarchical clusters based on the normals and positions of the polygons It does not explicitly compute all the back facing polygons but rather decides in expected constant time whether an entire cluster is back facing As a pre processing step the algorithm partitions the space into regions with respect to each cluster During rendering it exploits frame to frame coherence to track the view point The algorithm has been applied to a number of models and its performance is a function of number of clusters the depth of the hierarchies and the characteristics of the graphics system In practice we are able to cull of the polygons in about of the total CPU time per frame on an SGI Indigo Extreme for models composed of tens of thousands of polygons It improves the overall frame rate by as compared to hardware back face culling CR
The intravenous route to blood glucose control.

This article discusses closed-loop blood glucose regulation algorithms that use the intravenous route for insulin delivery to insulin-dependent diabetic patients. Classical control methods and advanced algorithms using implicit knowledge or explicit models (empirical, fundamental, or "gray-box") of the diabetic patient are examined. Current research on characterizing patient variability is presented, in the context of a model predictive controller able to adjust to changes in patient glucose and insulin sensitivity.
Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes

This paper gives an overview of the use of polynomial chaos (PC) expansions to represent stochastic processes in numerical simulations. Several methods are presented for performing arithmetic on, as well as for evaluating polynomial and nonpolynomial functions of variables represented by PC expansions. These methods include {Taylor} series, a newly developed integration method, as well as a sampling-based spectral projection method for nonpolynomial function evaluations. A detailed analysis of the accuracy of the PC representations, and of the different methods for nonpolynomial function evaluations, is performed. It is found that the integration method offers a robust and accurate approach for evaluating nonpolynomial functions, even when very high-order information is present in the PC expansions.
Regional Estimation of Remotely Sensed Evapotranspiration Using the Surface Energy Balance-Advection (SEB-A) Method

Evapotranspiration (ET) is an essential part of the hydrological cycle and accurately estimating it plays a crucial role in water resource management. Surface energy balance (SEB) models are widely used to estimate regional ET with remote sensing. The presence of horizontal advection, however, perturbs the surface energy balance system and contributes to the uncertainty of energy influxes. Thus, it is vital to consider horizontal advection when applying SEB models to estimate ET. This study proposes an innovative and simplified approach, the surface energy balance-advection (SEB-A) method, which is based on the energy balance theory and also takes into account the horizontal advection to determine ET by remote sensing. The SEB-A method considers that the actual ET consists of two parts: the local ET that is regulated by the energy balance system and the exotic ET that arises from horizontal advection. To evaluate the SEB-A method, it was applied to the middle region of the Heihe River in China. Instantaneous ET for three days were acquired and assessed with ET measurements from eddy covariance (EC) systems. The results demonstrated that the ET estimates had a high accuracy, with a correlation coefficient (R2) of 0.713, a mean average error (MAE) of 39.3 W/m2 and a root mean square error (RMSE) of 54.6 W/m2 between the estimates and corresponding measurements. Percent error was calculated to more rigorously assess the accuracy of these estimates, and it ranged from 0% to 35%, with over 80% of the locations within a 20% error. To better understand the SEB-A method, the relationship between the ET estimates and land use types was analyzed, and the results indicated that the ET estimates had spatial distributions that correlated with vegetation patterns and could well demonstrate the ET differences caused by different land use types. The sensitivity analysis suggested that the SEB-A method requested accurate estimation of the available energy, R n − G , but was less constrained with the difference between ground and air temperature, T 0 − T a – l o c .
Automated detection of rotor faults for inverter-fed induction machines under standstill conditions

It is difficult to apply conventional on-line MCSA techniques for diagnosis of rotor faults for closed loop induction motor drives for many applications due to the masking effect of the feedback current controller and/or variable frequency or load operation. Relying solely on traditional off-line inspection techniques during regular maintenance does not allow frequent monitoring of rotor problems and is inconvenient since it requires rotor disassembly and/or manual rotor rotation. In this paper, a new automated technique for testing inverter-fed squirrel cage induction machines at standstill for rotor faults is proposed. The main concept is to use the inverter to excite the machine with a pulsating field at a number of angular positions to observe the variation in the impedance pattern due to broken rotor bars, whenever the motor is stopped. An experimental study on a 7.5hp induction motor verifies that broken bars can be detected with high sensitivity and reliability. It will be shown that the proposed method can provide automated and reliable assessment of rotor condition frequently without motor disassembly, manual rotation, or additional instrumentation. The proposed test can also provide rotor quality assessment independent of variations in motor or load operating conditions since it is a standstill test.
Denial of Service ( DoS ) Attack Detection by Using Fuzzy Logic over Network Flows

Intrusion Detection System (IDS) is the tool that is able to detect occurrences of intrusion at host, network, as well as application. One of the most common network attacks is Denial of Service (DoS) attack. In DoS attack, a single host will send huge number of packets to one machine and thus make the operating of the network and host slow. There are several algorithms that have been proposed to detect DOS attacks and most of these solutions are based on detection mechanisms that have the potential of producing high number of false alarms. In addition, most of the solutions are monitoring and analyzing packets inside the network instead of network flow. In this paper, signature of selected attacks such as Smurf, Mail-Bomb and Ping-of-Death which are based on network flow is considered. The proposed engine monitors the network flows to detect attacks and the results show less false negative error during monitoring. In addition signature based IDS which use fuzzy decision tree for monitoring network flow proves that there are improvements on speed of detection and also performance of system.
A software framework for dynamic self-repair in embedded SoCs exploiting reconfigurable devices

The realization of embedded systems for safety critical applications as Custom embedded SoCs (System on Chip) realized on FPGAs (Field Programmable Gate Array) and hybrid SoCs including both custom ASIC cores, and programmable FPGA is nowadays rapidly increasing. In recent years, fault tolerance solutions for such systems have been developed and today are still addressed by ongoing research. More recently the application of FPGA partial dynamic reconfiguration capabilities to systems fault tolerance has been investigated and demonstrated by many fresh works, but primarily from an hardware perspective. In this paper a software framework for managing dynamic partial reconfiguration is proposed. Such framework is responsible of managing the replacement of faulty hardware peripherals with soft-core versions running on reconfigurable hardware areas allowing the software applications to be unaware of the underlying fault detection and reconfiguration process.
On the Number Density of Sunyaev-Zeldovich Clusters of Galaxies

If the mean properties of clusters of galaxies are well described by the entropy-driven model, the distortion induced by the cluster population on the blackbody spectrum of the cosmic microwave background radiation is proportional to the total amount of intracluster gas, while temperature anisotropies are dominated by the contribution of 1014 M⊙ clusters. This result depends marginally on cluster parameters, and it can be used to estimate the number density of clusters with enough hot gas to produce a detectable Sunyaev-Zeldovich effect. Comparing different cosmological models, the relation depends mainly on the density parameter Ωm. If the number density of clusters could be estimated by a different method, then this dependence could be used to constrain Ωm.
The Repertoire of Heterotrimeric G Proteins and RGS Proteins in Ciona intestinalis

Background Heterotrimeric G proteins and regulators of G protein signaling (RGS) proteins are key downstream interacting partners in the G protein coupled receptor (GPCR) signaling pathway. The highly versatile GPCR transmembrane signaling system is a consequence of the coupling of a diverse set of receptors to downstream partners that include multiple subforms of G proteins and regulatory proteins including RGS proteins, among others. While the GPCR repertoire of Ciona intestinalis, representing the basal chordate is known, the repertoire of the heterotrimeric G proteins and RGS proteins is unknown. Methodology/Principal Findings In the present study, we performed an in-silico genome-wide search of C. intestinalis for its complement of G proteins and RGS proteins. The identification of several one-to-one orthologs of human G proteins at the levels of families, subfamilies and types and of homologs of the human RGS proteins suggests an evolutionarily conserved structure function relationship of the GPCR signaling mechanism in the chordates. Conclusions The C. intestinalis genome encodes a highly conserved, albeit, limited repertoire of the heterotrimeric G protein complexes with the size of subunit types comparable with that in lower eukaryotes.
genome Structured RNAs in the ENCODE selected regions of the human Related Content

Related Content Genome Res. November , 2011 21: 1929-1943 Brian J. Parker, Ida Moltke, Adam Roth, et al. analysis of vertebrate genomes New families of human regulatory RNA structures identified by comparative References http://genome.cshlp. org/content/17/6/852.full. html#related-urls Article cited in: http://genome.cshlp. org/content/17/6/852.full. html#ref-list-1 This article cites 49 articles, 26 of which can be accessed free at:
Co-operation of Biology Related Algorithms for Multi-objective Binary Optimization

A modification of the self-tuning meta-heuristic, called Co-Operation of Biology Related Algorithms, for multiobjective optimization problems with binary variables (COBRA-bm) is introduced. Its basic idea consists of a cooperative work of five well-known bionic algorithms such as the Particle Swarm Optimization, the Wolf Pack Search, the Firefly Algorithm, the Cuckoo Search Algorithm and the Bat Algorithm, namely their versions for solving optimization problems with binary variables, with the use of Pareto optimality theory. The performance of the mentioned algorithms as well as COBRA-bm on the set of benchmark functions is reported. It was established that the proposed approach COBRA-bm performs either comparably or better than its component bionic algorithms and can be used instead any of them.
Tackle three practical classification problems via Ensemble Learning

News Categorization, Intrusion Detection and Spam Detection are three practical problems I in Data Mining and Cybersecurity. Their focus is on string sequences analysis towards application of knowledge discovery techniques for protecting personal computer information by means of detection, prevention, and response to various attacks. These three string sequences analysis problems could be treated as three classification problems. To tackle these three classifications problems, we propose a Ensemble Learning method. The idea of ensemble learning is to employ multiple learners and combine their predictions. These ensemble methods utilize multiple models to obtain better predictive performance than could be obtained from any of the constituent models[13], [14], [16]. In the tasks, we utilize (LDA-,SK-)SVM, (LDA-,SK)GP and (LDA-,SK-)AdaBoost as the weak classifiers, and the experiments shows that ensemble learning method can improve the classification performance significantly.
An Integrated Performance Evaluation Model for the Photovoltaics Industry

Global warming is causing damaging changes to climate around the World. For environmental protection and natural resource scarcity, alternative forms of energy, such as wind energy, fire energy, hydropower energy, geothermal energy, solar energy, biomass energy, ocean power and natural gas, are gaining attention as means of meeting global energy demands. Due to Japan’s nuclear plant disaster in March 2011, people are demanding a good alternative energy resource, which not only produces zero or little air pollutants and greenhouse gases, but also with a high safety level to protect the World. Solar energy, which depends on an infinite resource, the sun, is one of the most promising renewable energy sources from the perspective of environmental sustainability. Currently, the manufacturing cost of solar cells is still very high, and the power conversion efficiency is low. Therefore, photovoltaics (PV) firms must continue to invest in research and development, commit to product differentiation, achieve economies of scale, and consider the possibility of vertical integration, in order to strengthen their competitiveness and to acquire the maximum benefit from the PV market. This research proposes a performance evaluation model by integrating analytic hierarchy process (AHP) and data envelopment analysis (DEA) to assess the current business performance of PV firms. AHP is applied to obtain experts’ opinions on the importance of the factors, and DEA is used to determine which firms are efficient. A case study is performed on the crystalline silicon PV firms in Taiwan. The findings shall help the firms determine their strengths and weaknesses and provide directions for future improvements in business operations.