Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Text Complexity for Italian as a Second Language Learning Purposes

The selection of texts for second language learning purposes typically relies on teachers’ and test developers’ individual judgment of the observable qualitative properties of a text. Little or no consideration is generally given to the quantitative dimension within an evidence-based framework of reproducibility. This study aims to fill the gap by evaluating the effectiveness of an automatic tool trained to assess text complexity in the context of Italian as a second language learning. A dataset of texts labeled by expert test developers was used to evaluate the performance of three classifier models (decision tree, random forest, and support vector machine), which were trained using linguistic features measured quantitatively and extracted from the texts. The experimental analysis provided satisfactory results, also in relation to which kind of linguistic trait contributed the most to the final outcome.
ASYMPTOTIC OF INNER PRODUCT KERNEL SPECTRAL CLUSTERING

We study in this article the asymptotic performance of spectral clustering with inner product kernel for Gaussian mixture models of high dimension with numerous samples. As is now classical in large dimensional spectral analysis, we establish a phase transition phenomenon by which a minimum distance between the class means and covariances is required for clustering to be possible from the dominant eigenvectors. Beyond this phase transition, we evaluate the asymptotic content of the dominant eigenvectors thus allowing for a full characterization of clustering performance. However, a surprising finding is that in some particular scenarios, the phase transition does not occur and clustering can be achieved irrespective of the class means and covariances. This is evidenced here in the case of the mixture of two Gaussian datasets having the same means and arbitrary difference between covariances.
The oil-slick trade : An analysis on embodied crude oil in China ’ s trade and consumption during 1995-2011

China has become the world’s second largest crude oil importer and consumer. Meanwhile, China has also become the world workshop and the world’s largest exporter of commodities. The globalization of production and consumption has important impact on China’s oil consumption, which can’t be adequately assessed only through checking China’s direct imports and exports of crude oil. Based on a global multi-regional input-output model, this article examining both China’s direct trade of crude oil and embodied crude oil in China’ exports and imports of goods and services during 1995-2011. The results showed that about one fifth to one third of imported oil is used to producing exports in China. Therefore, China is playing the role as a transit hub of crude oil from extraction places to global final consumers, particularly those in advanced countries. Foreign consumers actually benefit from China’s global hunt for natural resources, a fact usually overlooked by critics of China’s oil thirst. China was net exporter of embodied oil. Net exports of embodied oil increased after China’s entry into the World Trade of Organization in 2001, but decreased quickly after the financial crisis starting in 2008 due to weak foreign demand and aggressive economic stimulus in China. In addition, China’s oil demand from production was greater than that from consumption, whereas the gap has shrunk quickly in recent years. We also found that China’s oil import dependence assessed from consumption end was higher than import dependence assessed from production end, a traditional indicator often discussed in the context of energy security. We discussed various policy implications of these results in context of China’s recent reforms in economic and environmental governance.
Catechol-O-methyl transferase SNP rs 4680 influence risk of mood disorder : a meta-analysis

Background: The association between Val158Met (rs4680) polymorphism in the catechol-O-methyl transferase (COMT) gene and mood disorder (MD) risk remains controversial and inconclusive. Therefore, a meta-analysis involving latest relevant studies was performed to assess the quality reevaluation of their association. Methods: Six major public databases (Medline, Embase, Cochrane Library, China National Knowledge Infrastructure, Wangfang and CBM) were searched for eligible studies. Pooled odds ratios (OR) with 95% confidence intervals were calculated to estimate the association between COMT Val158Met mutation and mood disorder risk. Results: Our metaanalysis results indicate that the COMT rs4680 Met carrier was associated with increased risks of bipolar disorder (BPD) and major depressive disorder (MDD). Similarly, a subgroup analysis based on ethnicity suggests that COMT rs4680 polymorphism only influenced BPD risk in the Asian subgroup, and increased MDD risk in both the Asian and Caucasian subgroups. However, the influence of COMT rs4680 gene polymorphism on BPD and MDD risks varied in other subgroups. Limitations: Our results should be treated with caution due to the lack of data to perform gene-gene and gene-environment interactions. In addition, some subgroups lack a more in-depth meta-analysis. Conclusions: This meta-analysis study indicates that COMT rs4680 polymorphism may play a role in BPD and MDD development.
The workload Web Clients Trade Action Trade Servlets Trade Services DB 2 Database Trade Server Pages WebSphere Application Server

There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. A virtualized server environment allows computing resources to be shared among multiple performance-isolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain the desired quality-of-service (QoS) while achieving higher server utilization and energy efficiency. We implement and validate a dynamic resource provisioning framework for virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme. The proposed approach accounts for the switching costs incurred while provisioning virtual machines and explicitly encodes the corresponding risk in the optimization problem. Experiments using the Trade6 enterprise application show that a server cluster managed by the controller conserves, on average, 26% of the power required by a system without dynamic control while still maintaining QoS goals.
SENSORY EVALUATION TECHNIQUES DO’S & DON’TS

Sensory evaluation done to differentiate the fragrance character between two samples is different from knowing which sample is preferred over the other. Defining the test objective is critical and is the primary criteria in any sensory evaluation as it informs exactly what needs are to be determined, the type of testing that is to be carried out, the panellist to be selected, the methodology to be followed, and the level of statistical risk factors that is to be used for interpreting the observations and arrive at the result to provide recommendation.
Performance Analysis of Brain Control Interface in Drone Applications

The main objective of this study is to find efficient methods to utilize brain control interface in conjunction with aerial drones. The study investigates how effective the EPOC+ is by challenging users of diverse genders and ages to complete tasks using mental commands or facial expressions to control a Parrot AR-Drone 2.0. After a mental and facial commands calibration phase, the designed experiments were conducted using randomly selected participants (n=20). Preliminary analysis of the collected data indicated that there was no significant difference between the rating of difficulty before and after, between the mental and facial commands. Furthermore, this study showed that from group of participants more individuals had greater difficulty controlling the mental and facial commands than they originally expected.
1 Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks

Online social networks, World Wide Web, media and technological networks, and other types of so-called information networks are ubiquitous nowadays. These information networks are inherently heterogeneous and dynamic. They are heterogeneous as they consist of multi-typed objects and relations, and they are dynamic as they are constantly evolving over time. One of the challenging issues in such heterogeneous and dynamic environments is to forecast those relationships in the network that will appear in the future. In this paper, we try to solve the problem of continuous-time relationship prediction in dynamic and heterogeneous information networks. This implies predicting the time it takes for a relationship to appear in the future, given its features that have been extracted by considering both heterogeneity and temporal dynamics of the underlying network. To this end, we first introduce a feature extraction framework that combines the power of meta-path-based modeling and recurrent neural networks to effectively extract features suitable for relationship prediction regarding heterogeneity and dynamicity of the networks. Next, we propose a supervised non-parametric approach, called Non-Parametric Generalized Linear Model (Np-Glm), which infers the hidden underlying probability distribution of the relationship building time given its features. We then present a learning algorithm to train Np-Glm and an inference method to answer time-related queries. Extensive experiments conducted on synthetic data and three real-world datasets, namely Delicious, MovieLens, and DBLP, demonstrate the effectiveness of Np-Glm in solving continuous-time relationship prediction problem vis-à-vis competitive baselines1.
Annual Review of Fluid Mechanics Elastocapillarity : When Surface Tension Deforms Elastic Solids

Although negligible at large scales, capillary forces may become dominant for submillimetric objects. Surface tension is usually associated with the spherical shape of small droplets and bubbles, wetting phenomena, imbibition, or the motion of insects at the surface of water. However, beyond liquid interfaces, capillary forces can also deform solid bodies in their bulk, as observed in recent experiments with very soft gels. Capillary interactions, which are responsible for the cohesion of sandcastles, can also bend slender structures and induce the bundling of arrays of fibers. Thin sheets can spontaneously wrap liquid droplets within the limit of the constraints dictated by differential geometry. This review aims to describe the different scaling parameters and characteristic lengths involved in elastocapillarity. We focus on three main configurations, each characterized by a specific dimension: three-dimensional (3D), deformations induced in bulk solids; 1D, bending and bundling of rod-like structures; and 2D, bending and stretching of thin sheets. Although each configuration deserves a detailed review, we hope our broad description provides a general view of elastocapillarity. 629 Click here to view this article's online features: • Download figures as PPT slides • Navigate linked references • Download citations • Explore related articles • Search keywords ANNUAL REVIEWS Further A nn u. R ev . F lu id M ec h. 2 01 8. 50 :6 29 -6 59 . D ow nl oa de d fr om w w w . a nn ua lr ev ie w s. or g A cc es s pr ov id ed b y U ni ve rs ite P ie rr e et M ar ie C ur ie ( U PM C ) on 0 1/ 11 /1 8. F or p er so na l u se o nl y. FL50CH24_Bico ARI 11 November 2017 10:4 Capillary length: c = √ γ ρg
In vitro fermentation of [ 15 N-] ryegrass and ruminal digesta of sheep grazing a ryegrass-based pasture in the morning or in the afternoon

Ruminal digesta contents of eight fistulated sheep grazing morningor afternoon-allocated herbage were used to examine in vitro fermentation patterns, nitrogen (N) partitioning and microbial N use using (N-) perennial ryegrass (Lolium perenne L.) as a substrate. Samples of digesta contents were collected two hours after a new allocation of a ryegrass-based pasture. Atom percent excess (APE) of vegetative plant material in the substrate was 4.5% (44.9 mg N/g plant N). An in vitro batch culture system was used to examine fermentation characteristics at predetermined times after ingestion (0, 2, 6, 12, and 24 h). Despite similar concentrations of total volatile fatty acids, concentrations of acetate and yields of ammonia were greater for ruminal digesta contents of afternoon-allocated herbage. In vitro true dry matter disappearance was similar among digesta treatments, but microbial N (MN; 50.4 vs. 44.0 mg) and microbial efficiency (MN/available N; 42.1 vs. 33.3%) were greater for digesta contents of afternoonallocated herbage. Under the current conditions, enhancing water soluble carbohydrate in afternoon herbage was not at the expense of N concentration, and promoted a lipogenic type of fermentation. Increased microbial efficiency, rather than increased ammonia uptake, suggested improvements in N utilisation in digesta contents collected from sheep grazing afternoon herbage.
Developing intelligent agents for training systems that learn their strategies from expert players

Developing Intelligent Agents for Training Systems That Learn Their Strategies from Expert Players. (August 2005). Jonathan Hunt Whetzel, B.S., Texas A&M University Chair of Advisory Committee: Dr. Richard A. Volz Computer-based training systems have become a mainstay in military and private institutions for training people how to perform certain complex tasks. As these tasks expand in difficulty, intelligent agents will appear as virtual teammates or tutors assisting a trainee in performing and learning the task. For developing these agents, we must obtain the strategies from expert players and emulate their behavior within the agent. Past researchers have shown the challenges in acquiring this information from expert human players and translating it into the agent. A solution for this problem involves using computer systems that assist in the human expert knowledge elicitation process. In this thesis, we present an approach for developing an agent for the game Revised Space Fortress, a game representative of the complex tasks found in training systems. Using machine learning techniques, the agent learns the strategy for the game by observing how a human expert plays. We highlight the challenges encountered while designing and training the agent in this real-time game environment, and our solutions toward handling these problems. Afterward, we discuss our experiment that examines whether trainees experience a difference in performance when training with a human or virtual partner, and how expert agents that express distinctive behaviors affect the learning of a human trainee. We show from our results that a partner agent that learns its strategy from an expert player serves the same benefit as a training partner compared to a programmed expert-level agent and a human partner of equal intelligence to the trainee.
Centre for Practical Quantitative Finance CPQF Working Paper Series No . 23 Unifying Exotic Option Closed Formulas

This paper aims to unify exotic option closed formulas by generalizing a large class of existing formulas and by setting a framework that allows for further generalizations. The formula presented covers options from the plain vanilla to most, if not all, mountain range exotic options and is developed in a multi-asset, multi-currency BlackScholes model with time dependent parameters. The general formula not only covers existing cases but also enables the combination of diverse features from different types of exotic options. It also creates implicitly a language to describe payoffs that can be used in industrial applications to decouple the functions of payoff definition from ∗Corresponding author. †The author wishes to thank Millennium bcp investimento, S.A. for the financial support being provided during his PhD. studies.
Dyr Bul Shchyl. Proxying Sound Symbolism With Word Embeddings

This paper explores modern word embeddings in the context of sound symbolism. Using basic properties of the representations space one can construct semantic axes. A method is proposed to measure if the presence of individual sounds in a given word shifts its semantics of that word along a specific axis. It is shown that, in accordance with several experimental and statistical results, word embeddings capture symbolism for certain sounds.
A Master/Slave Approach to Power Flow and Overvoltage Control in Low-Voltage Microgrids

This paper proposes a technique to control distributed energy resources in low-voltage microgrids aiming at (i) allowing power flow control at the point of connection with the upstream grid, (ii) keeping voltage profiles within the operational limits. The first feature is crucial in smart low-voltage power systems. In fact, it enables both demand-responses, which is extremely valuable from the point of view of distribution system operators and for energy trading, and the autonomous operation of the microgrid. The latter can be achieved by regulating to zero the power exchanged with the main grid. The second feature allows to limit voltage increases due to active power injection by distributed energy resources and, thus, to limit stresses on the electrical infrastructure and the served loads, which is a concrete issue as renewables become widely deployed in the low-voltage scenario. The proposed approach is firstly described in detail, then a systematic analysis of its local and global properties is reported. All the obtained results are verified considering the IEEE 37 test feeder in realistic operating conditions.
Improve User Retention with Causal Learning

User retention is a key focus for consumer based internet companies and promotions are an effective lever to improve retention. However, companies rely either on non-causal churn prediction to capture heterogeneity or on regular A/B testing to capture average treatment effect. In this paper, we propose a heterogeneous treatment effect optimization framework to capture both heterogeneity and causal effect. We propose two algorithms to maximize heterogeneous treatment effect: 1) Ranking based on point estimates of heterogeneous treatment effects obtained using existing estimation methods with training labels adjusted based on Lagrangian Subgradient method. 2) A novel ranking algorithm which combines estimation and optimization in one stage and directly optimizes for the aggregated targeted treatment effect. We also develop an evaluation metric that captures the real-world business value of different methods and use this to evaluate various approaches on our large-scale experiment data set both offline and online. Our algorithm (approach 2) performs significantly better than random explore benchmark and existing estimators (approach 1) in both offline and online tests. This method has been deployed to production and is currently live in multiple cities all over the world.
Financial Crises in Latin America

Latin American countries face the financial crisis of 2007-2009 under exceptional conditions with respect to the historical patterns of crises in the region. With few exceptions, such as the case of Jamaica and Ecuador, there were not balance of payments problems and contagion in financial markets was attenuated. Unlike Europe and the U.S., most Latin American countries recovered growth within a year from the most critical moments of the crisis in 2009. What made this time different? Three hypotheses have been given by the literature. The first, the decoupling hypothesis, proposes that countries in this region suffered less contagion because their business cycles have been decoupled from the business cycles of the U.S. and Europe. The second, the “learning” hypothesis, attributes the phenomenon to the improved management of economic policy during the last decades. The third hypothesis highlights the role of external conditions, in particular, the boom of commodity prices that the global economy experienced in the years before and following the crisis. Testing the three hypotheses for the crisis experiences of eight Latin American countries between 1900 and 2010, I find that it is not possible to attribute the phenomenon to the decoupling hypothesis. In addition, my results show that a combination of factors, policy and external factors may explain the phenomenon. However, several weaknesses remain in the region today, such as the high dependence on a few export commodities and low productivity rates. JEL Classification Numbers: C33; E52; F44; F47
USING 3 D DATA FOR MONTE CARLO LOCALIZATION IN COMPLEX INDOOR ENVIRONMENTS

With this paper we present the integration of a 3D laser range sensor into a Monte Carlo Localization (MCL) system. Having the detailed environment perception of the 3D sensor, robust localization in difficult indoor scenes is possible. The presented localization system is able to handle moving people and other unmapped obstacles. At the same time it is possible to use a simplified worldmodel that consists of 2D walls only. This kind of map is easy to generate and independent of displaced furniture. Following the description of the localization system, realworld experiments on position tracking and global localization will demonstrate the functionality of our approach.
2 Notation for Univariate High-frequency Time Series

6 Using a reduced rank regression framework as well as information criteria we investigate 7 the presence of commonalities in the intraday periodicity, a dominant feature in the return 8 volatility of most intraday financial time series. We find that the test has little size distortion 9 and reasonable power even in presence of jumps. We also show that only three factors are 10 needed to describe the intraday periodicity of thirty US asset returns sampled at the 5-minute 11 frequency. Interestingly, we find that for most series the models imposing these commonalities 12 deliver better forecasts of the conditional variance than those where the intraday periodicity is 13 estimated equation by equation. 14 JEL: C10, C32. 15
Synthesis and Characterization of Some Mixed Ligand Complexes of Quinaldic Acid and α-Picoline with Some Metal Salts

Some metal ions (Mn +2 , Co +2 , Ni +2 , Cu +2 , Zn +2 , Cd +2 and Hg +2 ) complexes of quinaldic acid (QuinH) and α-picoline (α-Pic) have been synthesized and characterized on the basis of their , FTIR, (U.V-Vis) spectroscopy, conductivity measurements, magnetic susceptibility and atomic absorption. From the results obtained the following general formula has suggested for the prepared complexes [M(Quin)2( α-Pic)2]. XH2O where M +2 = (Mn, Co, Ni, Cu, Zn, Cd and Hg), X = 2, X = zero for (Co +2 and Hg +2 ) complexes, (Quin ) = quinaldate ion, (α-Pic) = αpicoline. The results showed that the deprotonated ligand (QuinH) by using (KOH) coordinated to metal ions as bidentate ligand through the oxygen atom of the carboxylate group (-COO ) and the nitrogen atom of the pyridine group. The αpicoline coordinates as amonodentate through the nitrogen of the pyridine group.