Miracle-dz/UniFAR-FTUs huggingface dataset:
Concordance between Functional Magnetic Resonance Imaging and Intraoperative Language Mapping

Although the correspondence between functional-magnetic resonance imaging (fMRI) representations of the sensorimotor cortex and intraoperative electrophysiology (including somatosensory evoked potential, SSEP, recordings and direct cortical stimulation) has been reported, a similar correspondence between fMRI and intraoperative localization of the language-sensitive cortex is not as well established. The aim of the present study was to evaluate the concordance between fMRI and intraoperative electrophysiology with respect to the localization of the language-sensitive and sensorimotor cortices. We present the results of 21 patients who underwent language and sensorimotor mapping by fMRI and intraoperative electrophysiology including SSEP recordings (n = 21), direct cortical stimulation of motor cortex (n = 15) and direct cortical stimulation of Broca’s and Wernicke’s area (n = 5). When responses were obtained with both methods, localization of function concurred in all cases. These observations suggest that fMRI represents a reliable preoperative tool for the identification of language-sensitive areas.
A Unified POS Tagging Architecture and its Application to Greek

This paper proposes a flexible and unified tagging architecture that could be incorporated into a number of applications like information extraction, cross-language information retrieval, term extraction, or summarization, while providing an essential component for subsequent syntactic processing or lexicographical work. A feature-based multi-tiered approach (FBT tagger) is introduced to part-of-speech tagging. FBT is a variant of the well-known transformation based learning paradigm aiming at improving the quality of tagging highly inflective languages such as Greek. Additionally, a large experiment concerning the Greek language is conducted and results are presented for a variety of text genres, including financial reports, newswires, press releases and technical manuals. Finally, the adopted evaluation methodology is discussed.
Prognostic role of apoptosis-related gene functional variants in advanced non-small-cell lung cancer patients treated with first-line platinum-based chemotherapy

Background Single-nucleotide polymorphisms in apoptosis-related genes have been shown to play a role in the efficacy of platinum-based chemotherapy and may influence clinical outcomes. Our study aimed to evaluate the correlations of four functional single-nucleotide polymorphisms − FAS −670 A>G, FAS ligand −844 T>C, survivin −31 G>C, and survivin 9386 C>T – with drug response and clinical outcomes in advanced non-small-cell lung cancer patients who received platinum-based chemotherapy. Materials and methods Polymorphisms were evaluated using the polymerase chain reaction-based restriction fragment-length polymorphism technique. Results Patients with the CC genotype of FAS −670 A>G had worse overall survival (OS) than those with the CT or TT genotype (P=0.044), with median OS values of 20.1 months, 22.8 months, and 26.0 months, respectively. Furthermore, progression-free survival was associated with the FAS −670 A>G polymorphism (P=0.032). In addition, patients with the TC and CC genotypes of survivin 9386 C>T experienced improved survival compared with patients with the TT genotype (median OS 31.4 months and 22.8 months, respectively). Conclusion The functional FAS −670 A>G and survivin 9386 C>T polymorphisms are potential independent prognostic factors in advanced non-small-cell lung cancer patients treated with platinum-based chemotherapy.
Poresizes in random line networks

Many natural fibrous networks with fiber diameters much smaller than the average poresize can be described as three-dimensional (3D) random line networks. We consider here a `Mikado' model for such systems, consisting of straight line segments of equal length, distributed homogeneously and isotropically in space. First, we derive analytically the probability density distribution $p(r_{no})$ for the `nearest obstacle distance' $r_{no}$ between a randomly chosen test point within the network pores and its closest neighboring point on a line segment. Second, we show that in the limit where the line segments are much longer than the typical pore size, $p(r_{no})$ becomes a Rayleigh distribution. The single parameter $\sigma$ of this Rayleigh distribution represents the most probable nearest obstacle distance and can be expressed in terms of the total line length per unit volume. Finally, we show by numerical simulations that $\sigma$ differs only by a constant factor from the intuitive notion of average `pore size', defined by finding the maximum sphere that fits into each pore and then averaging over the radii of these spheres.
A New Fuzzy Logic Approach for TCP Congestion Control

Congestion control is a high priority and critical issue in today’s networks. End-to-end congestion control mechanisms such as those in TCP are not enough to prevent congestion control and they must be supplemented by control mechanisms inside the network. In this paper we present a fuzzy logic approach for congestion control in TCP/IP networks. The proposed approach consists of three major parts: the Fuzzy Aggressive RTO Estimator (FARE), the Fuzzy Active Queue Management (Fuzzy AQM) and the Fuzzy Type 2 Scheduler. The FARE proposes an optimization of current RTO (retransmission TimeOut) estimation algorithm. By adding a detection mechanism for bad timeouts and undoing their side effects we are willing to have an aggressive estimator. By using a fuzzy system to adapt the K parameter of RTO estimation formula to network internal state, we try to answer the following questions: how much and when should our estimator act aggressively? On the other hand, the Fuzzy AQM computes the packet drop probability according to preconfigured fuzzy logic using the instantaneous queue length and number of packets dropped in a period of time as input variables. The Fuzzy Type 2 Scheduler uses the benefits of fuzzy type 2 controllers to adjust the service rate of the output buffers in the network routers. Our goal is to improve system performance and resource utilization in congested networks by a simple method. The simulation results with different web-like and FTP traffic show the superiority of TCP using FARE and Fuzzy AQM over a normal TCP. Furthermore it has been shown that the proposed Fuzzy Type 2 Scheduler has better performance than the traditional Weighted Round Robin (WRR) scheduler.
Cross Sections for the Production of Residual Nuclides at Medium-Energies Relevant for Accelerator-Driven Technologies

Integral cross sections for the production of residual nuclides by medium-energy nuclear reactions are of importance for many fields of basic and applied sciences ranging from astrophysics over space and environmental sciences, medicine, accelerator technology, space and aviation technology to accelerator driven transmutation of waste (ADTW) and energy amplification (ADEA). The data needs of those applications cover cross sections for protonand (more importantantly) neutroninduced reactions. The particular problem for accelerator driven technologies is that the data needs are extreme with respect to both, target element coverage and types of reaction data. Since it will be practically impossible to measure all relevant data, one will have to rely to a large degree on theoretical estimates.
Predicting Stock Market Index Trading Signals Using Neural Networks

This study forecasts trading signals of the Australian All Ordinary Index (AORD), one day ahead. These forecasts were based on the current day’s relative return of the Close price of the US S&P 500 Index, the UK FTSE 100 Index, French CAC 40 Index and German DAX Index as well as the AORD. The forecasting techniques examined were feedforward and probabilistic neural networks. Performance of the networks was evaluated by using classification/misclassification rate and trading simulations. For both evaluation criteria, feedforward neural networks performed better. Trading simulations suggested that the predicted trading signals are useful for short term traders.
A Novel DROGI Based Control Algorithm Without PLL for Shunt Compensation Using Four-leg Converter

Four-leg converter has been an attractive solution for addressing power quality problems caused by unbalanced loading condition in three-phase four-wire distributed systems. However, its phase-locked-loop (PLL) based control methods can be affected by the waveform quality of grid voltage and degrade the stability of control system. In this paper, a novel double reduced order generalized integrators (DROGI) based control algorithm without PLL is proposed to improve the performance of four-leg converter. Double ROGIs are employed to decouple compensation mode and rectification mode of the converter. For rectification mode, DC link voltage is regulated by using a grid voltage modulated direct power control (GVM-DPC) to form the non-PLL control system. The effectiveness of the proposed algorithm is validated using both simulation and experimental results for a 1.1 KW system.
Multi-Topic E-mail Authorship Attribution Forensics

In this paper we describe an investigation of forensic authorship identification or categorisation undertaken on multitopic e-mail documents. We use an extended set of e-mail document features such as structural characteristics and linguistic patterns together with a Support Vector Machine learning algorithm. Experiments on a number of e-mail documents generated by different authors on a set of topics gave promising results for both inter- and intra-topic author categorisation.
The Dividends of Good Governance

Building on an extensive literature on corruption, growth, and development, the article shows that corruption is not simply associated with lower levels of socioeconomic development, but that it also has a significant causal effect on a country's level of socioeconomic development. The basic implication of the findings presented in the article is that the reduction in the level of corruption in a given country leads to greater wealth, higher levels of education, longer life expectancy, and lower infant mortality—this is why we speak of the dividends of good governance. For many years, scholars and practitioners have not paid sufficient attention to corruption. Corruption was nothing more than an afterthought. It was considered as a by-product of pre-modern forms of economic organization, as the result of inadequate political development, and as something that could possibly favor development since it could grease the wheels of rigid, incompetent, and excessively bureaucratic public administrations. Scholars pay little attention to the empirical characteristics of corruption, its causes, and its consequences. Development practi- tioners avoided the issue because they considered it as a matter of a country's internal politics, and as such, with no impact whatsoever on development. The empirical research carried out from the mid-1990s onward has made it clear not only that corruption is detrimental for the quality of democracy, that is, a political problem, but also that it has a devastating impact on economic growth and development, that the reduction of corruption may provide a major stimulus to economic growth, and that anti-corruption activities are an essential compo- nent of any successful development strategy. The purpose of the present article is to show, on empirical grounds, how detrimental corruption is for socioeconomic development. In the course of this article, we will show that while there are several ways in which development can be measured, corruption is harmful to development regardless of how it is measured.
Optimizing Prolog for Small Devices: A Case Study

AuthorsM. Carromcarro@fi.upm. esTechnical University of MadridBoadilla del Monte E-28660, Spain.J. Moralesjfran@clip.dia. fi.upm. esTechnical University of MadridBoadilla del Monte E-28660, Spain.Henk L. Mullerhenkm@cs.bris. ac.ukC. S. DepartmentUniversity of BristolG. Pueblamcarro@fi.upm. esTechnical University of MadridBoadilla del Monte E-28660, Spain.M. Hermenegildoherme@fi.upm. es,herme@unm.eduTechnical University of MadridBoadilla del Monte E-28660, Spain, andDepartments of Computer Science and Electrical and ComputerEngineering U. of New Mexico (UNM)KeywordsApplications of (Constraint) Logic Programming and Prolog, Wearable computers, Programanalysis and transformation, Optimizing compilation.Acknowledgementsii
The Impact of the 2018 Trade War on U.S. Prices and Welfare

This paper explores the impacts of the Trump administration’s trade policy on prices and welfare. Over the course of 2018, the U.S. experienced substantial increases in the prices of intermediates and final goods, dramatic changes to its supply-chain network, reductions in availability of imported varieties, and complete passthrough of the tariffs into domestic prices of imported goods. Overall, using standard economic methods, we find that the full incidence of the tariff falls on domestic consumers, with a reduction in U.S. real income of $1.4 billion per month by the end of 2018. We also see similar patterns for foreign countries who have retaliated against the U.S., which indicates that the trade war also reduced real income for other countries.
On Distributed Collaboration for Biomedical Analyses

Cooperation of research groups is nowadays common for the development and execution of biomedical analyses. Multiple partners contribute data in this context, data that is often centralized for processing at some cluster-based or supercomputer-based infrastructure. In contrast, real distributed collaboration that involves processing of data from several partners at different sites is rare. However, such distributed analyses are often very interesting, in particular, for scalability, security and privacy reasons. In this article, we motivate the need for real distributed biomedical analyses in the context of several ongoing projects, including the ICAN project that involves 34 French hospitals and affiliated research groups. We present a set of distributed architectures for such analyses that we have derived from discussions with different medical research groups and a study of related work. These architectures allow for scalability, security/privacy and reproducibility issues to be taken into account. Finally, we illustrate that these architectures can serve as the basis of a development method for biomedical distributed analyses.
Effects of increased dietary inclusion of yellow lupins and enzyme supplementation on performance , ileal digestibility of nutrients and microbial status of large intestine in broiler chickens

Lupin seeds, especially from the “sweet” varieties, such as yellow lupins (Lupinus luteus L.) are important sources of good quality protein for non-ruminant animals, but they contain also relatively large amounts of non-starch polysaccharides (NSP) that are characterized by their anti-nutritional effect. The anti-nutritive activity of NSP is well known. It is recognized that these polysaccharides depress the digestion of starch, protein and lipids in the digestive tract of broiler chickens (WARD and MARQUARDT, 1987; VAN DER KLIS et al., 1993; JAMROZ and PIECH-SCHLEICHER, 1982). The average concentration of NSP varied from 270 to 350 g/kg dry matter (DM) in yellow lupin seed. About 5-10% of them constitutes water-soluble fraction (GATEL, 1994; GDALA and BURACZEWSKA, 1996; EVANS et al., 1993). Lupins contain about 8% in DM of certain oligosaccharides, especially raffinose, stachyose, and verbascose (GATEL, 1994). Oligosaccharides of the raffinose family are not digested due to the lack of alpha-1,6-galactosidase capable of hydrolyzing the alpha-1,6-galactosidic linkages in the intestinal mucosa. As a consequence, these saccharides are not absorbed into the blood stream and are metabolized by the microbes (mainly Clostridia) in the hindgut. This process results in production of large amounts of carbon dioxide and hydrogen, which are contributory factors to the flatulence problem (NOWAK and STEINKRAUS, 1988). Utilization of lupin seeds as a protein source in monogastric animal diets has been limited depending on the content of toxic alkaloids. However, the development of low-alkaloid cultivars of lupins has expanded the potential use of lupin seeds in poultry diets. The suitability of various levels of lupin seeds in broiler diets were evaluated in several studies, but there is no consensus with respect to the maximum level of lupins that can be included in broiler rations. Anti-nutritive effects of high levels of lupins in the diet were described to decrease feed consumption and to reduce growth rate (BRENES et al., 1993; OLVER and JONKER, 1997). Constraints on the level of inclusion of lupins in broiler diets may be due to anti-nutritive activity of NSP and alkaloids as well, but some fructooligosaccharides of legume plants could have prebiotic properties (BAILEY et al., 1991; CHOI et al., 1994). A way to counteract possible anti-nutritive effect is supplementing diets with enzymes (carbohydrases) or application of chemical or physical methods dehulling, autoclaving etc. (RICHTER et al., 2001; FLIS et al., 1996). The purpose of this study was to examine whether addition of commercial carbohydrase enzymes to the wheat-barley rich diets could have a positive effect also when simultaneously different levels of yellow lupins will be used in the diet. The performance, carcass characteristics, ileal digestibility of nutrients and amino acids and content of selected bacteria in the large intestine were determined as the basic parameters.
Performance evaluation of reactive and proactive routing schemes for infrastructure wireless mesh networks

Despite the significative differences between ad hoc networks and Infrastructure Wireless Mesh Networks (iWMNs), the routing schemes designed specifically for the formers have been considered as a potential solution for the routing dilemma in the latter; however, the performance and effects of such approach have not been deeply studied. In this paper we extend some features of popular reactive (AODV) and proactive (DSDV) routing protocols to be used under the hierarchical architecture of iWMNs. Through extensive simulations, we acquire a set of results that evidence the pros and cons of using these mechanisms. The results we get in this work allow us to identify design directions for future iWMNs routing protocols.
Panthera: holistic memory management for big data processing over hybrid memories

Modern data-parallel systems such as Spark rely increasingly on in-memory computing that can significantly improve the efficiency of iterative algorithms. To process real-world datasets, modern data-parallel systems often require extremely large amounts of memory, which are both costly and energy-inefficient. Emerging non-volatile memory (NVM) technologies offers high capacity compared to DRAM and low energy compared to SSDs. Hence, NVMs have the potential to fundamentally change the dichotomy between DRAM and durable storage in Big Data processing. However, most Big Data applications are written in managed languages (e.g., Scala and Java) and executed on top of a managed runtime (e.g., the Java Virtual Machine) that already performs various dimensions of memory management. Supporting hybrid physical memories adds in a new dimension, creating unique challenges in data replacement and migration. This paper proposes Panthera, a semantics-aware, fully automated memory management technique for Big Data processing over hybrid memories. Panthera analyzes user programs on a Big Data system to infer their coarse-grained access patterns, which are then passed down to the Panthera runtime for efficient data placement and migration. For Big Data applications, the coarse-grained data division is accurate enough to guide GC for data layout, which hardly incurs data monitoring and moving overhead. We have implemented Panthera in OpenJDK and Apache Spark. An extensive evaluation with various datasets and applications demonstrates that Panthera reduces energy by 32 – 52% at only a 1 – 9% execution time overhead.
Family Models vs EWMA : Which is the Best Model to Forecast Volatility of the Moroccan Stock Exchange Market ?

Nowadays, modeling and forecasting the volatility of stock markets have become central to the practice of risk management; they have become one of the major topics in financial econometrics and they are principally and continuously used in the pricing of financial assets and the Value at Risk, as well as the pricing of options and derivatives. The aim of this article is to compare the GARCH (Generalised Auto Regressive Conditional Heteroskedasticity) family models –GARCH (1.1), GJR-GARCH, PGARCH, EGARCH, and IGARCH– with the EWMA (Exponentially Weighed Moving Average) model in the hope of finding the best model to forecast the volatility of the Moroccan stock-market index MADEX. We use daily returns covering the period between 01/04/1993 and 30/08/2016. We find that the asymmetric model IGARCH following a normal error distribution yields the best forecasting performance results and therefore, surpasses the EWMA model. Our results could have application in the risk management in Morocco, as well as leading to a better understanding of the Moroccan stock-exchange volatility dynamics, especially with the lack of previous similar studies.
The soluble form of the tumor suppressor Lrig1 potently inhibits in vivo glioma growth irrespective of EGF receptor status

Background Deregulated growth factor signaling is a major driving force in the initiation and progression of glioblastoma. The tumor suppressor and stem cell marker Lrig1 is a negative regulator of the epidermal growth factor receptor (EGFR) family. Here, we addressed the therapeutic potential of the soluble form of Lrig1 (sLrig1) in glioblastoma treatment and the mechanism of sLrig1-induced growth inhibition. Methods With use of encapsulated cells, recombinant sLrig1 was locally delivered in orthotopic glioblastoma xenografts generated from freshly isolated patient tumors. Tumor growth and mouse survival were evaluated. The efficacy of sLrig1 and the affected downstream signaling was studied in vitro and in vivo in glioma cells displaying variable expression of wild-type and/or a constitutively active EGFR mutant (EGFRvIII). Results Continuous interstitial delivery of sLrig1 in genetically diverse patient-derived glioma xenografts led to strong tumor growth inhibition. Glioma cell proliferation in vitro and tumor growth in vivo were potently inhibited by sLrig1, irrespective of EGFR expression levels. Of importance, tumor growth was also suppressed in EGFRvIII-driven glioma. sLrig1 induced cell cycle arrest without changing total receptor level or phosphorylation. Affected downstream effectors included MAP kinase but not AKT signaling. Of importance, local delivery of sLrig1 into established tumors led to a 32% survival advantage in treated mice. Conclusions To our knowledge, this is the first report demonstrating that sLrig1 is a potent inhibitor of glioblastoma growth in clinically relevant experimental glioma models and that this effect is largely independent of EGFR status. The potent anti-tumor effect of sLrig1, in combination with cell encapsulation technology for in situ delivery, holds promise for future treatment of glioblastoma.