Miracle-dz/UniFAR-FTUs huggingface dataset:
Overview of the EVALITA 2016 Part Of Speech on TWitter for ITAlian Task

English. The increasing interest for the extraction of various forms of knowledge from micro-blogs and social media makes crucial the development of resources and tools that can be used for automatically deal with them. PoSTWITA contributes to the advancement of the state-of-the-art for Italian language by: (a) enriching the community with a previously not existing collection of data extracted from Twitter and annotated with grammatical categories, to be used as a benchmark for system evaluation; (b) supporting the adaptation of Part of Speech tagging systems to this particular text domain. Italiano. La crescente rilevanza dell’estrazione di varie forme di conoscenza da testi derivanti da microblog e social media rende cruciale lo sviluppo di strumenti e risorse per il trattamento automatico. PoSTWITA si propone di contribuire all’avanzamento dello stato dell’arte per la lingua italiana in due modi: (a) fornendo alla comunità una collezione di dati estratti da Twitter ed annotati con le categorie grammaticali, risorsa precedentemente non esistente, da utlizzare come banco di prova nella valutazione di sistemi; (b) promuovendo l’adattamento a questo particolare dominio testuale dei sistemi di Part of Speech tagging che partecipano al
Fixed combination therapies in COPD – effect on quality of life

Chronic obstructive pulmonary disease (COPD) represents a major global cause of disability and death. COPD is currently the fourth most common global cause of death and also exerts an enormous toll on patients quality of life. The present database analysis aimed to identify clinical trials using fixed combination therapies that have assessed the impact on the patients quality of life. Within the different studies, questionnaires including the George’s Respiratory Questionnaire (SGRQ) the Chronic Respiratory Disease Questionnaire (CRDQ) and the Clinical COPD Questionnaire (CCQ) were used and differing results in quality of life were obtained when combination therapies such as fluticasone/salmeterol or fluticasone/salmeterol were compared with monotherapies. While there were some differences in favor of combination therapies reported when the combination therapy was compared to inhaled steroid monotherapy there were no consistent differences when combination therapies were compared to bronchodilator monotherapies. Future trials will lead to a proof-of-principle stage concerning the use of combination therapies.
Exploring the Folding Pathways of Proteins through Energy Landscape Sampling: Application to Alzheimer's E-Amyloid Peptide #

The determination of the folding mechanisms of proteins is critical to understand the topological change that can propagate Alzheimer’s and prion diseases. The associated folding time scale generally precludes the use of molecular dynamics simulations. Here we present the details of the activation–relaxation simulations using the generic OPEP energy model. We illustrate the strengths of our approach by studying the folding of a dimer of the Alzheimer's E–amyloid peptide.
Information, volatility and price discovery in oil futures markets

This thesis presents four related empirical essays which investigate the role of information in crude oil futures markets. The first line of investigation examines the impact of futures trading on spot price volatility and finds that the nature of spot price volatility is affected by derivative trading and the improvements in information discovery which such trading brings. Second, the efficiency of futures markets is examined with respect to their ability to provide unbiased estimates of future spot prices. Here it is concluded that while unbiased estimates are generally provided in the long-term, they tend to be largely biased over the short-term. The third area of investigation looks at the relative ability of contemporaneous spot and futures prices to discover information, where it is found that futures generally exhibit price discovery over spot markets but that the relationship can vary considerably over time and in relation to market conditions. In addition, the investigation suggests that previous studies into such relationships have failed to account for all routes through which information passes between spot and futures markets. Finally the thesis probes the question of the relationship within futures markets between volume, volatility and information. The finding is' that futures markets' prices and trading volume exhibit a positive relation and are jointly driven by the rate of information arrival. The results further suggest that the widely held expectation that volume statistics can improve forecasts of future price change does not hold in the case of oil futures. The overall finding of the thesis is that oil futures markets are well-functioning and in general are of benefit to the underlying spot market.
The Prose Storyboard Language: A Tool for Annotating and Directing Movies

The prose storyboard language is a formal language for describing movies shot by shot, where each shot is described with a unique sentence. The language uses a simple syntax and limited vocabulary borrowed from working practices in traditional movie-making, and is intended to be readable both by machines and humans. The language is designed to serve as a high-level user interface for intelligent cinematography and editing systems.
The black hole in the throat - thermodynamics of strongly coupled cascading gauge theories

We numerically construct black hole solutions corresponding to the deconfined, chirally symmetric phase of strongly coupled cascading gauge theories at various temperatures. We compute the free energy as a function of the temperature, and we show that it becomes positive below some critical temperature, indicating the possibility of a first order phase transition at which the theory deconfines and restores the chiral symmetry.
The Ultimate Source of Inflation: A Microfoundation of the Fiscal Theory of the Price Level

The paper explores a fundamental mechanism of inflation by explicitly including a government fs optimization problem into a general equilibrium model assuming a Leviathan government. The result is clear- cut and beautiful: inflation is caused by the difference of the time preference rates between a government and households. This is an inevitable consequence of heterogeneity in time preference rates between a government and households. The model can be seen as a unified model that explains various types of inflation, e.g. hyperinflation, chronic inflation, disinflation and deflation, by this single mechanism. The model shows that inflation has the intrinsic nature of persistence, i.e. inflation rates have a unit root.
A Chi-square testing-based intrusion detection Model

The rapid growth of Internet malicious activities has become a major concern to network forensics and security community. With the increasing use of IT technologies for managing information there is a need for stronger intrusion detection mechanisms. Critical mission systems and applications require mechanisms able to detect any unauthorised activities. An Intrusion Detection System (IDS) acts as a necessary element for monitoring traffic packets on computer networks, performs analysis to suspicious traffic and makes vital decisions. IDSs allow cybercrime forensic specialists to gather useful evidence whenever needed. This paper presents the design and development process of a Network Intrusion Detection System (NIDS) solution, which aims at providing an effective anomaly based detection model using Chi-Square statistics. One of the design objectives in this paper is to minimise the limitations of current statistical network forensics and intrusion detection. Throughout the development process of this statistical detection model several aspects of the process of building an effective detection model are emphasized. These aspects include dataset pre processing and feature selection, network traffic analysis, statistical testing and detection model development. The calculated / output statistical figures of this model are based on certain threshold values which could be used and / or adjusted by a forensic specialist for deciding whether or not a suspicious event took place. The modelling and development process of this proposed anomaly detection has been achieved using various software and development tools. In this paper we focus on modelling dynamic anomaly detection using the Chi-square technique. It investigates a network traffic dataset collected by CAIDA in 2008 that contains signs for denial of service (DoS) attacks called backscatter. The normal dataset patterns are analysed to build a profile for the legitimate network traffic. Any deviations from these normal profiles will be considered anomalous. The dataset was pre processed using Wireshark and T-Shark, the detection model was developed using MATLAB for different variants of denial of services attacks and promising results were achieved.
Best Response Bayesian Reinforcement Learning for Multiagent Systems with State Uncertainty

It is often assumed that agents in multiagent systems with state uncertainty have full knowledge of the model of dynamics and sensors, but in many cases this is not feasible. A more realistic assumption is that agents must learn about the environment and other agents while acting. Bayesian methods for reinforcement learning are promising for this type of learning because they allow model uncertainty to be considered explicitly and offer a principled way of dealing with the exploration/exploitation tradeoff. In this paper, we propose a Bayesian RL framework for best response learning in which an agent has uncertainty over the environment and the policies of the other agents. This is a very general model that can incorporate different assumptions about the form of other policies. We seek to maximize performance and learn the appropriate models while acting in an online fashion by using sample-based planning built from powerful Monte-Carlo tree search methods. We discuss the theoretical properties of this approach and experimental results show that the learning approaches can significantly increase value when compared to initial models and policies.
A study of quasar proximity in O vi absorbers at z= 2–3★

With the goal of investigating the nature of O VI absorbers at high redshifts, we study the effects of proximity to the background quasar. In a sample of 16 quasars at Z QSO between 2.14 and 2.87 observed at high signal-to-noise ratio and 6.6 km s -1 resolution with the Very Large Telescope/Ultraviolet and Visual Echelle Spectrograph (VLT/UVES), we detect 35 O vi absorption-line systems (comprising over 100 individual O vi components) lying within 8000 km s -1 of Z QSO . We present component fits to the O VI absorption and the accompanying Hi, C IV and Nv. The systems can be categorized into nine strong and 26 weak Ovi absorbers. The strong (intrinsic) absorbers are defined by the presence of either broad, fully saturated O vi absorption or partial coverage of the continuum source, and in practice all have log N(O VI) ≥ 15.0; these systems are interpreted as representing either quasar-driven outflows or gas close to the central engine of the active galactic nuclei. The weak (also known as narrow) systems show no partial coverage or saturation, and are characterized by log N(O VI) < 14.5 and a median total velocity width of only 42 km s -1 . The incidence dN/dz of weak O vi systems within 2000 km s -1 of the quasar down to a limiting equivalent width of 8 mA is 42 ± 12. Between 2000 and 8000 km s -1 , dN/dz falls to 14 ± 4, equal to the incidence of intervening O vi absorbers measured in the same spectra. Whereas the accompanying H and C iv column densities are significantly lower (by a mean of ∼1 dex) in the weak Ovi absorbers within 2000 km s -1 of Z QSO than in those at larger velocities, the O VI column densities display no dependence on proximity. Furthermore, significant offsets between the H i and O vi centroids in ≈50 per cent of the weak absorbers imply that (at least in these cases) the H I and O VI lines are not formed in the same phase of gas, preventing us from making reliable metallicity determinations and ruling out single-phase photoionization model solutions. In summary, we find no firm evidence that quasar radiation influences the weak O vi absorbers, suggesting they are collisionally ionized rather than photoionized, possibly in the multiphase haloes of foreground galaxies. Non-equilibrium collisional ionization models are needed to explain the low temperatures in the absorbing gas, which are implied by narrow-line widths (b < 14 km s -1 ) in over half of the observed O vi components.
Desvenlafaxine May Accelerate Neuronal Maturation in the Dentate Gyri of Adult Male Rats

Adult hippocampal neurogenesis has been linked to the effects of anti-depressant drugs on behavior in rodent models of depression. To explore this link further, we tested whether the serotonin-norepinephrine reuptake inhibitor (SNRI) venlafaxine impacted adult hippocampal neurogenesis differently than its primary active SNRI metabolite desvenlafaxine. Adult male Long Evans rats (n = 5–6 per group) were fed vehicle, venlafaxine (0.5 or 5 mg) or desvenlafaxine (0.5 or 5 mg) twice daily for 16 days. Beginning the third day of drug treatment, the rats were given a daily bromodeoxyuridine (BrdU; 50 mg/kg) injection for 5 days to label dividing cells and then perfused 2 weeks after the first BrdU injection to confirm total new hippocampal cell numbers and their phenotypes. The high desvenlafaxine dose increased total new BrdU+ cell number and appeared to accelerate neuronal maturation because fewer BrdU+ cells expressed maturing neuronal phenotypes and more expressed mature neuronal phenotypes in the dentate gyri of these versus vehicle-treated rats. While net neurogenesis was not increased in the dentate gyri of rats treated with the high desvenlafaxine dose, significantly more mature neurons were detected. Our data expand the body of literature showing that antidepressants impact adult neurogenesis by stimulating NPC proliferation and perhaps the survival of neuronal progeny and by showing that a high dose of the SNRI antidepressant desvenlafaxine, but neither a high nor low venlafaxine dose, may also accelerate neuronal maturation in the adult rat hippocampus. These data support the hypothesis that hippocampal neurogenesis may indeed serve as a biomarker of depression and the effects of antidepressant treatment, and may be informative for developing novel fast-acting antidepressant strategies.
Harbour images sequence analysis for control and monitoring

A system devoted to ship traffic control in a harbour environment is proposed, where optic flow approach and monocular image sequences are used. In each frame the scene is segmented, moving and still objects are found, and the movement of each ship is completely tracked. Partial and/or total occlusions are correctly handled by means of suitable grouping algorithms. Quantitative motion estimation is obtained by an iterative procedure that extract precise 3D information from the monocular sequence. The implemented version of the system is able to monitor and control a harbour environment with a substantially low computational effort.
Intrusion Detection of NSM Based DoS Attacks Using Data Mining in Smart Grid

In this paper, we analyze the Network and System Management (NSM) requirements and NSM data objects for the intrusion detection of power systems; NSM is an IEC 62351-7 standard. We analyze a SYN flood attack and a buffer overflow attack to cause the Denial of Service (DoS) attack described in NSM. After mounting the attack in our attack testbed, we collect a data set, which is based on attributes for the attack. We then run several data mining methods with the data set using the Waikato Environment for Knowledge Analysis (WEKA). In the results, we select the decision tree algorithms with high detection rates, and choose key attributes in high level components of the trees. When we run several data mining methods again with the data set of chosen key attributes, the detection rates of most data mining methods are higher than before. We prove that our selected attack attributes, and the proposed detection process, are efficient and suitable for intrusion detection in the smart grid environment.
National Governments and Control of the Internet: A Digital Challenge

In recent years, Internet control has become one of the major indicators to assess the balance between freedom and security in democracies. This book explores and compares why, and to what extent, national governments decide to control the Internet and how this impacts on crucial socio-economic activities and fundamental civil rights. The author provides detailed studies on the US, Germany, Italy and further case studies on Brazil, Canada, India, the Netherlands, South Africa and Switzerland, to address topics such national security, freedom of expression and privacy.
Monetary Policy Under Flexible Exchange Rates: An Introduction to Inflation Targeting

In the past few years, a number of central banks have adopted inflation targeting for monetary policy. The author provides an introduction to inflation targeting, with an emphasis on analytical issues, and the recent experience of middle- and high-income developing countries (which have relatively low inflation to begin with, and reasonably well-functioning financial markets). After presenting a formal analytical framework, the author discusses the basic requirements for inflation targeting, and how such a regime differs from money, and exchange rate targeting regimes. After discussing the operational framework for inflation targeting (including the price index to monitor the time horizon, the forecasting procedures, and the role of asset prices), he examines recent experiences with inflation targets, providing new evidence on the convexity of the Phillips curve for six developing countries. His conclusions: Inflation targeting is a flexible policy framework that allows a country's central bank to exercise some degree of discretion, without putting in jeopardy its main objective of maintaining stable prices. In middle- and high-income developing economies that can refrain from implicit exchange rate targeting, it can improve the design, and performance of monetary policy, compared with other policy approaches that central banks may follow. Not all countries may be able to satisfy the technical requirements (such as adequate price data, adequate understanding of the links between instruments, and targets of monetary policy, and adequate forecasting capabilities), but such requirements should not be overstated. Forecasting capability can never be perfect, and sensible projections always involve qualitative judgment. More important, and often more difficult, is the task of designing, or improving an institutional framework that would allow the central bank to pursue the goal of low, stable inflation, while maintaining the ability to stabilize fluctuations in output.
PASAN : Automatic Patch and Signature Generation for Bu er-Over ow Attacks

Control-hijacking attacks exploit vulnerabilities in programs to take control of the victim applications and eventually their underlying machines. Although much work has been done on detection and prevention of control-hijacking attacks, most of them did not support adequate post-attack response which should include attack signature and patch generation. Ideally, after a control-hijacking attack is detected, the signature generation component should supply the front-end rewall with a ltering rule that could stop the detected attack and its variants from entering the premise, and the patch generation component should create a x that permanently eliminates the vulnerabilities that the detected attack exploits. This paper describes the design, implementation, and evaluation of a security-enhancing compiler PASAN that can instrument the source code of a C program so that it can detect a control-hijacking attack and automatically generate a signature and a software patch for the detected attack. The attack signatures that PASAN generates can capture polymorphic attacks because they contain regular expressions and length constraints. The automatically generated patches are similar to those created manually so that developers can examine and merge them with the original code base with minimal e orts. We have implemented the rst PASAN prototype as a GNU C compiler extension that aims at stack-based bu er over ow attacks but could be easily generalized to accommodate other control-hijacking attacks. Testing this prototype with seven network daemon programs with known vulnerabilities show that the automatically generated attack signatures can indeed stop the detected attacks and that the patches can successfully x the vulnerability. In addition, these patches are similar in their structure to those that programmers generate. The run-time performance overhead of application programs instrumented by PASAN is between 10% and 23%, except two programs, which do not have much internal processing.
PS3-RAM: A Fast Portable and Scalable Statistical STT-RAM Reliability/Energy Analysis Method

The development of emerging spin-transfer torque random access memory (STT-RAM) is facing two major technical challenges-poor write reliability and high write energy, both of which are severely impacted by process variations and thermal fluctuations. The evaluations on STT-RAM design metrics and robustness often require a hybrid simulation flow, i.e., modeling the CMOS and magnetic devices with SPICE and macro-magnetic models, respectively. Very often, such a hybrid simulation flow involves expensive Monte Carlo simulations when the design and behavioral variabilities of STT-RAM are taken into account. In this paper, we propose a fast and scalable semi-analytical method-PS3-RAM, enabling efficient statistical simulations in STT-RAM designs. By eliminating the costly macro-magnetic and SPICE simulations, PS3-RAM achieves more than 100\(000\boldsymbol {\times }\) runtime speedup with excellent agreement with the result of conventional simulation method. PS3-RAM can also accurately estimate the STT-RAM write error rate and write energy distributions at both magnetic tunneling junction switching directions under different temperatures, demonstrating great potential in the analysis of STT-RAM reliability and write energy at the early design stage of memory or micro-architecture.
An intelligent model for predicting the occurrence of skiing injuries

Artificial neural networks offer a unique way to model very complex and innovative systems that can be very effective in anticipating various accident severities. In this article, we propose a neural-network-based model, able to predict the number of severe injuries caused while skiing. The proposed system is intended for use by ski patrol and medical personnel to better prepare themselves in advance for treating ski-injured persons. The ski patrol and any other medical personnel will be able to know the statistics, type and severity of the injuries occurred, and most importantly, will be benefiting from having predictions for each day. Considering that, the number of injured people in a particular place each day was estimated, the results are very promising suggesting that such a system would prove beneficial in accurately predicting skiing injuries.