Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Relations Asserting the Etiology of Genetic Diseases

Considerable research is being directed at extracting molecular biology information from text. Particularly challenging in this regard is to identify relations between entities, such as protein-protein interactions or molecular pathways. In this paper we present a natural language processing method for extracting causal relations between genetic phenomena and diseases. After presenting the results of preliminary evaluation, we suggest the use of a graphical display application for viewing the semantic predications produced by the system.
Reinforcement-Driven Dimensionality Reduction - A Model for Information Processing in the Basal Ganglia

Although anatomical studies of the basal ganglia show the existence of extensive convergence and lateral inhibitory connections, physiological studies failed to show correlated neural activity or lateral interaction in these nuclei. These seemingly contradictory results could be explained with a model in which the basal ganglia reduce the dimensionality of cortical information using optimal extraction methods. Simulations of this model predict a transient change in the efficacy of the feed-forward and lateral synapses following changes in reinforcement signal, causing an increase in correlated firing rates. This process ultimately restores the steady-state situation with diminished efficacy of lateral inhibition and no correlation of firing. Our experimental results confirm the model's predictions: rate correlations show a drastic decrease between the input stage (cortex) and output stage (pallidum). Moreover, preliminary analysis revealed that pallidal correlations show a transient increase following discrepancies between the animal's predictions and reality. We therefore propose that by using a reinforcement-driven dimensionality reduction process the basal ganglia achieve efficient extraction of cortical salient information that may then be used by the frontal cortex for execution and planning of forthcoming actions.
Oil palm phenolics confer neuroprotective effects involving cognitive and motor functions in mice

Abstract Objectives Phenolics are important phytochemicals which have positive effects on chronic diseases, including neurodegenerative ailments. The oil palm (Elaeis guineensis) is a rich source of water-soluble phenolics. This study was carried out to discover the effects of administering oil palm phenolics (OPP) to mice, with the aim of identifying whether these compounds possess significant neuroprotective properties. Methods OPP was given to BALB/c mice on a normal diet as fluids for 6 weeks while the controls were given distilled water. These animals were tested in a water maze and on a rotarod weekly to assess the effects of OPP on cognitive and motor functions, respectively. Using Illumina microarrays, we further explored the brain gene expression changes caused by OPP in order to determine the molecular mechanisms involved. Real-time quantitative reverse transcription-polymerase chain reaction experiments were then carried out to validate the microarray data. Results We found that mice given OPP showed better cognitive function and spatial learning when tested in a water maze, and their performance also improved when tested on a rotarod, possibly due to better motor function and balance. Microarray gene expression analysis showed that these compounds up-regulated genes involved in brain development and activity, such as those under the regulation of the brain-derived neurotrophic factor. OPP also down-regulated genes involved in inflammation. Discussion These results suggest that the improvement of mouse cognitive and motor functions by OPP is caused by the neuroprotective and anti-inflammatory effects of the extract.
Non-Negative Matrix Factorization for Stock Market Pricing

In this paper, we use Non-negative matrix factorization (NMF) to analyze the data from stock market. By using the multiplicative update rules algorithm, we decompose the data matrix V of the daily closing prices of the 40 stocks, of which the Shenzhen component index is made up, into two matrices W and H, in which the columns of W correlate to the underlying trends. In addition, the Euclidean distance between the 40 stocks and the underlying forces is constructed. By means of the Kmeans routine in MATLAB, the 40 stocks are classified into different clusters with the center of the underlying forces, which can be finished automatically by MATLAB. Finally, the properties of these clusters are studied.
A Dynamic Minimum Variance Hedge

The study presents an operational dynamic minimum variance hedge ratio (DMV) that allows for updates of both cash and futures positions. It is shown that DMV is more general than other operational models in the hedging literature, including the traditional static minimum-variance hedge ratio (SMV). Estimation of DMV is illustrated with a corn storage problem. The example reveals relatively noticeable differences among the magnitudes of DMV and alternative operational hedge ratios. However, gains in hedging effectiveness from using DMV instead of the simpler SMV are negligible.
Moving Path Following for Unmanned Aerial Vehicles With Applications to Single and Multiple Target Tracking Problems

This paper introduces the moving path following (MPF) problem, in which a vehicle is required to converge to and follow a desired geometric moving path, without a specific temporal specification, thus generalizing the classical path following that only applies to stationary paths. Possible tasks that can be formulated as an MPF problem include tracking terrain/air vehicles and gas clouds monitoring, where the velocity of the target vehicle or cloud specifies the motion of the desired path. We derive an error space for MPF for the general case of time-varying paths in a two-dimensional space and subsequently an application is described for the problem of tracking single and multiple targets on the ground using an unmanned aerial vehicle (UAV) flying at constant altitude. To this end, a Lyapunov-based MPF control law and a path-generation algorithm are proposed together with convergence and performance metric results. Real-world flight tests results that took place in Ota Air Base, Portugal, with the ANTEX-X02 UAV demonstrate the effectiveness of the proposed method.
Against method-ism: Exploring the limits of method

Provides a critique of method‐ism ‐ the view that methodology is necessary and sufficient for information systems’ development success; method‐ism presupposes also that systems developers understand the value of methodology and will prefer to work with it rather than without it. Argues, against method‐ism, that method flows from understanding, and not the reverse. Hence method cannot be a substitute for understanding. Discusses the way in which humans tend to interact with the world by means of ready‐to‐hand tools, using the ideas of Heidegger and Ihde. Shows that tools are used only if available (ready‐to‐hand) in the world of doing. If a methodology is not ready‐to‐hand, it will break down and be ignored in the pragmatics of getting the job done. Presents a number of arguments why methodologies by design will tend to break down (not be ready‐to‐hand) and hence be discarded.
Contemporary Art and Cybernetics: Waves of Cybernetic Discourse within Conceptual, Video and New Media Art

ABSTRACT This paper aims to highlight the interplay of technology and cybernetics within conceptual art. Just as Lucy Lippard has illustrated the influence of information theory within 1960s conceptual art, this paper traces the technological discourses within conceptual art through to contemporary digital art—specifically, establishing a correlation between Katherine Hayles's mapping of first-, second- and third-wave cybernetic narratives and, respectively, 1960s–1970s conceptual art, 1970s–1990s video art and new media art. Technology is shown to have a major influence on conceptual art, but one often based on historical, social and cybernetic narratives. This paper echoes Krzystof Ziarek's call for a Heideggerian poiesis and Adorno/Blanchotnian “nonpower” within conceptual art and advocates Ziarek's notion of “powerfree” artistic practices within new media and transgenic art.
Dynamic Data-Driven Estimation of Non-Parametric Choice Models

We study non-parametric estimation of choice models, which were introduced to alleviate unreasonable assumptions in traditional parametric models, and are prevalent in several application areas. Existing literature focuses only on the static observational setting where all of the observations are given upfront, they are not equipped with explicit convergence rate guarantees, and consequently they cannot provide an a priori analysis for the model accuracy vs sparsity trade-off on the actual estimated model returned by their algorithms. As opposed to this, we focus on estimating a non-parametric choice model from observational data in a dynamic setting, where observations are obtained over time. We show that choice model estimation can be cast as a convex-concave saddle-point (SP) joint estimation and optimization (JEO) problem, and we provide a primal-dual framework for deriving algorithms to solve this based on online convex optimization. By tailoring our framework carefully to the choice model estimation problem, we obtain tractable algorithms with provable convergence guarantees and explicit bounds on the sparsity of the estimated model. Our numerical experiments confirm the effectiveness of the algorithms derived from our framework.
Virtual Password : a New Way of Protecting Users from Password Theft

People enjoy the convenience of on-line services, but online environments may bring many risks. In this paper, we discuss how to prevent users’ passwords from being stolen by adversaries. We propose a virtual password concept involving a small amount of human computing to secure users’ passwords in on-line environments. We propose differentiated security mechanisms in which a user has the freedom to choose a virtual password scheme ranging from weak security to strong security. The tradeoff is that the stronger the scheme, the more complex the scheme may be. Among the schemes, we have a default method (i.e., traditional password scheme), system recommended function, user-specified function, userspecified program, etc. A function/program is used to implement the virtual password concept with a trade off of security for complexity requiring a small amount of human computing. We further propose several functions to serve as system recommended functions and provide a security analysis. We analyze how the proposed schemes defend against phishing, key logger, shoulder-surfing attacks, and multiple attacks. For user-specified functions, we adopt secret little functions, in which security is enhanced by hiding secret functions/algorithms.
WORKING PAPER SERIES INFLATION TARGETING , PRICE-PATH TARGETING AND OUTPUT

The dramatic improvement in macroeconomic outcomes during the 1990s stable, low inflation and high, stable growth can be at least partly ascribed to improved monetary policy. Central banks became more independent and many of them adopted inflation targeting. This paper examines the potential for further improvements by refining the concept of inflation targeting. We construct a general model that encompasses a broad array of possible target regimes, and apply it to the data. Our results suggest that the vast majority of countries could benefit from moving to pricepath targeting, where the central bank makes up for periods of above (below) target inflation with later periods of below (above) target inflation. Stephen G. Cecchetti Junhan Kim Department of Economics Department of Economics Ohio State University Ohio State University Columbus, Ohio 43210 Columbus, Ohio 43210 and NBER kim.1052@osu.edu cecchetti.1@osu.edu
Generalized Performance Model for Flexible Approximate String Matching on a Distributed System

This paper proposes a generalized and practical parallel algorithm for flexible approximate string matching which is executed for several kinds of clusters such as homogeneous cluster and heterogeneous cluster. This parallel algorithm is based on the master worker paradigm and it implements different partitioning schemes such as static and dynamic load balancing cooperating with different data allocation techniques such as the allocation of texts and allocation of text pointers. Furthermore, the parallel algorithm is analyzed experimentally using the Message Passing Interface (MPI) library for different strategies of load balancing and data allocation onto two kinds of clusters. Further, we propose a general performance model that can be used to predict the performance of the parallel flexible approximate string matching algorithm for both types of clusters. The theoretical performance model has been validated against experimental results and it is shown that the model is able to predict the parallel performance accurately.
Estimation of the Conditional Variance in Paired Experiments

In paired randomized experiments units are grouped in pairs, often based on covariate information, with random assignment within the pairs. Average treatment effects are then estimated by averaging the within-pair differences in outcomes. Typically the variance of the average treatment effect estimator is estimated using the sample variance of the within-pair differences. However, conditional on the covariates the variance of the average treatment effect estimator may be substantially smaller. Here we propose a simple way of estimating the conditional variance of the average treatment effect estimator by forming pairs-of-pairs with similar covariate values and estimating the variances within these pairs-of-pairs. Even though these within-pairs-of-pairs variance estimators are not consistent, their average is consistent for the conditional variance of the average treatment effect estimator and leads to asymptotically valid confidence intervals.
Essays on decentralisation, public services and well-being in Indonesia

Decentralisation has been viewed as one means for promoting citizen well-being by bringing public goods and services closer to local needs. Yet empirical evidence across developing countries shows mixed results. This study aims to examine the association between decentralisation, public services and well-being in decentralised Indonesia. It argues that decentralisation enhances citizen well-being through improved local government capacity better able to deliver public goods and services. With lack of local government capacity and accountability, decentralisation decreases well-being. This study contributes to existing research on decentralisation and well-being in three ways. First, while most studies discuss decentralisation and well-being in a cross-country context, this examines the relationship in a cross-local government context, and specifically within a developing country. Second, while most decentralisation studies focus on objective measures of well-being, this study uses both a subjective measure (i.e. happiness and citizen satisfaction with public services) and an objective measure (i.e. child health and healthcare demand). Third, while most studies use either aggregate or individual analyses to examine the effect of decentralisation on well-being, this study uses multilevel analysis to examine the effect of local government determinants on individual well-being.This study uses unique datasets which combine individual and household level data and local government data. Individual and household level data is taken from the Governance and Decentralisation Survey (GDS) 2006, the Indonesian Family Life Survey (IFLS) 2007, and the Indonesia socio-economic survey (Susenas) 2009. Local government level data comes from the national village census (Podes) 2006-2008, the national election database 2004-2007, the national health database 2009, local development budget and expenditure information 2004-2008, and the consumer prices indices 2004-2009. The main findings show that well-being among Indonesians varies across local governments, and that disparities in both public services and well-being appear between more developed and less developed regions. Well-being is not only associated with individual and household determinants, but also with local government determinants. The results are consistent, namely, that variation in well-being is associated with the capacity of local governments to deliver public goods and services. Citizens report being happier and more satisfied when local governments are able to provide better public goods and services for them (i.e. able to spend more of their budget on providing public services). In contrast, well-being decreases in the face of local corruption and of weak capacity to govern. These findings suggest that improving local government capacity to provide effective policies and good public services is vital to improve citizen well-being in decentralised Indonesia.
Enhanced cardiac PI 3 K a signalling mitigates arrhythmogenic electrical remodelling in pathological hypertrophy and heart failure

Enhanced cardiac PI3Ka signalling mitigates arrhythmogenic electrical remodelling in pathological hypertrophy and heart failure
The African Growth and Opportunity Act : a preliminary assessment

The African Growth and Opportunity Act (AGOA) was enacted into law as part of the Trade and Development Act of 2000. It is based on the general philosophy of "trade, not aid" as the chief tool for promoting economic development. This report offers an early assessment of the utility of the AGOA tariff preferences as an instrument of special and differential treatment for the beneficiary countries. It's objective is to reach quantifiable conclusions regarding how useful AGOA has been to exporting countries in Sub-Saharan Africa.
Identification and Estimation of Firms' Marginal Cost Functions with Incomplete Knowledge of Strategic Behavior

In this paper I develop a new approach for identification and estimation of the parameters of an oligopoly model, without relying on a potentially unverifiable equilibrium assumption. Rather, I consider inference on model parameters when the researcher does not know precisely what decision rule firms use, but is willing to consider a set of possibilities. In contrast to traditional approaches in the literature, the proposed methodology allows firm behavior to vary flexibly across observations, in a manner consistent with many Nash Equilibria. I derive identification results for both homogeneous product and differentiated product markets. Due to the flexibility afforded to firm behavior, the arameters of firms' marginal cost functions may only be set identified rather than point identified. The restrictions of the model are, however, still informative. I find that the size of the identified set for marginal cost parameters depends on the elasticity of market demand, the set of decision rules considered, and the functional form assumptions imposed. I formulate how to compute consistent set estimates for marginal cost parameters and demonstrate the proposed methodology with price and quantity data on the Joint Executive Committee, a 19th century railway cartel. To perform statistical inference implement the methodology of Rosen (2005) to construct asymptotically valid confidence regions for the partially identified marginal cost parameters. The application illustrates how the precision of estimated marginal costs depends on the elasticity of market demand as well as the extent to which firm behavior is allowed to vary.
A Formal Approach to the Verification of Networks on Chip

The current technology allows the integration on a single die of complex systems-on-chip (SoCs) that are composed of manufactured blocks (IPs), interconnected through specialized networks on chip (NoCs). IPs have usually been validated by diverse techniques (simulation, test, formal verification) and the key problem remains the validation of the communication infrastructure. This paper addresses the formal verification of NoCs by means of a mechanized proof tool, the ACL2 theorem prover. A metamodel for NoCs has been developed and implemented in ACL2. This metamodel satisfies a generic correctness statement. Its verification for a particular NoC instance is reduced to discharging a set of proof obligations for each one of the NoC constituents. The methodology is demonstrated on a realistic and state-of-the-art design, the Spidergon network from STMicroelectronics.
The Degree of Leukocytosis and Urine GATA-3 mRNA Levels Are Risk Factors for Severe Acute Kidney Injury in Puumala Virus Nephropathia Epidemica

Puumala hantavirus (PUUV) infection, also known as nephropathia epidemica, is the most common cause of hemorrhagic fever with renal syndrome (HFRS) in Europe. The pathogenesis of PUUV nephropathia epidemica is complex and multifactorial, and the risk factors for severe acute kidney injury (AKI) during acute PUUV infection are not well defined. We conducted a prospective study of hospitalized patients with PUUV infection in Tampere, Finland to identify acute illness risk factors for HFRS severity. Serial daily blood and urine samples were collected throughout acute illness and at 2 week and 6 month convalescent visits. By univariate analyses, the maximum white blood cell count during acute illness was a risk factor for severe AKI. There were no significant associations between PUUV-induced AKI severity and platelet counts, C-reactive protein, or alanine aminotransferase levels. Maximum plasma interleukin (IL)-6, urine IL-6, and urine IL-8 concentrations were positively associated with PUUV-induced AKI. Finally, the maximum urinary sediment GATA-3 mRNA level was positively correlated with the peak fold-change in serum creatinine, regardless of AKI severity classification. By multivariate analyses, we found that the maximum levels of leukocytes and urinary sediment GATA-3 mRNA during acute illness were independent risk factors for severe PUUV-induced AKI. We have identified novel acute illness risk factors for severe PUUV-induced AKI.