Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic implications of patenting for crop genetic resources

In a climate of rapid technological change, it is important to evaluate policies on the innovation incentives that result from the introduction of intellectual property rights as they relate to agricultural genetic resources. In this paper, we use a stylized model of cumulative innovation to explore the dynamics of introducing patent protection with licensing agreements, and then we contrast those results with the comparative-statics viewpoint. We also investigate the dynamic effects of claims on behalf of farmers on the profits of private crop breeders whose output is newly protected by patents. We show that the choices about patent life and licensing share that optimize worldwide dynamic social welfare can be quite different from the values that maximize steady-state social welfare. Further, recognition of farmers' rights entails a dynamic welfare loss to producers and consumers that is not revealed in a comparative-statics analysis.
Optimizing Data Analysis with a Semi-structured Time Series Database

Most modern systems generate abundant and diverse log data. With dwindling storage costs, there are fewer reasons to summarize or discard data. However, the lack of tools to efficiently store and cross-correlate heterogeneous datasets makes it tedious to mine the data for analytic insights. In this paper, we present Splunk, a semi-structured time series database that can be used to index, search and analyze massive heterogeneous datasets. We share observations, lessons and case studies from real world datasets, and demonstrate Splunk's power and flexibility for enabling insightful data mining searches.
Social Determinants of Tooth Loss among a Group of Iranian Female Heads of Household

Background and aims. Tooth loss may lead to mastication disability, which in turn has important impact on individual’s quality of life. Social and psychological factors have been shown to be associated with tooth loss. This study aimed to investigate the social determinants and prevalence of tooth loss, and presence of functional dentition among female heads of household under support of Welfare Organization in Mashhad, Iran. Materials and methods. In current study 556 participants aged 16-76 years were recruited. Sociodemographic characteristics (age, level of education, family size, and monthly income) were collected using interviewer-led questionnaire. Data about number of teeth and functional dentition were obtained by oral examination. The data were analyzed using Chi-square, Kruskal-Wallis, T-test and binary logistic regression analysis. Results. Four percent of participants were edentulous. Tooth loss was significantly associated with level of education, age and family size (P < 0.001). There was no significant association between level of income and tooth loss (P = 0.88). Only 37.5% of dentate subjects had functional dentition (anterior and premolar teeth). Women older than 40 years were 0.63 times less likely to have functional dentition than those younger than 40 years. Females with at least a high-school diploma were six times more likely to have functional dentition than their illiterate counterparts. Conclusion. Social determinants of functional dentition should be taken into account when planning oral health promoting programs for female heads of household. For reducing oral health inequalities access to dental services should be facilitated.
Socioeconomic status and the expectation of disability in old age: estimates for England

OBJECTIVES The longer life expectancy in old age of more privileged socioeconomic groups is well established, but less clear is whether the net effect of additional years of life is a lengthened, stable or reduced duration of disability. Estimates of healthy and disabled life expectancy (using definitions including dependency in activities of daily living and cognitive impairment) were made, contrasting occupational classes I and II (professional and managerial) with the rest. DESIGN Disability prevalence was estimated from the Medical Research Council Cognitive Function and Ageing study. Sullivan's method was used to calculate health expectancy. SUBJECTS 10 377 people aged 65 years or over in Cambridgeshire, Newcastle, Nottingham and Oxford. Subjects were classified as disabled if they had evidence of dementia (using the Automated Geriatric Examination Computer Assisted Taxonomy) or scored 11 or more on the modified Townsend Disability scale, at baseline screen. RESULTS The prevalence of disability overall and need for “constant care” was lower in both men and women in social classes I and II compared with the rest. Men aged 65 to 69 in classes I and II can expect nearly 14 years of life free of disability compared with 11.5 years for those in classes III to V: for women the equivalent expectations are 15.5 and 13.8 years. Men aged 65 to 69 in classes I and II can also expect a shorter duration of disability: 1 year compared with 1.6 years for classes III to V. In women expectation of disability is higher overall, but shows little difference by occupational class. CONCLUSIONS Relatively privileged socioeconomic groups in England, especially men, can expect fewer years of disability despite longer overall life expectancy. These findings lend weight to optimistic scenarios for the future numbers of older people with disability.
MicroRNA-9 Regulates Neurogenesis in Mouse Telencephalon by Targeting Multiple Transcription Factors

microRNA-9-2 and microRNA-9-3 double-mutant mice demonstrate that microRNA-9 (miR-9) controls neural progenitor proliferation and differentiation in the developing telencephalon by regulating the expression of multiple transcription factors. As suggested by our previous study, the Foxg1 expression was elevated, and the production of Cajal-Retzius cells and early-born neurons was suppressed in the miR-9-2/3 double-mutant pallium. At embryonic day 16.5 (E16.5), however, the Foxg1 expression was no longer elevated. The expression of an AU-rich RNA-binding protein Elavl2 increased at E16.5, Elav2 associated with Foxg1 3′ untranslated region (UTR), and it countered the Foxg1 suppression by miR-9. Later, progenitor proliferation was reduced in the miR-9-2/3 double-mutant pallium with the decrease in Nr2e1 and Pax6 expression and the increase in Meis2 expression. The analyses suggest that microRNA-9 indirectly inhibits Pax6 expression by suppressing Meis2 expression. In contrast, together with Elavl1 and Msi1, microRNA-9 targets Nr2e1 mRNA 3′ UTR to enhance the expression. Concomitantly, cortical layers were reduced, each cortical projection was malformed, and the tangential migration of interneurons into the pallium was impaired in the miR-9-2/3 double mutants. miR-9 also targets Gsh2 3′ UTR, and Gsh2, as well as Foxg1, expression was elevated in the miR-9-2/3 double-mutant subpallium. The subpallium progenitor proliferation was enhanced, and the development of basal ganglia including striatum and globus pallidus was suppressed. Pallial/subpallial boundary shifted dorsally, and the ventral pallium was lost. Corridor was malformed, and thalamocortical and corticofugal axons were misrouted in the miR-9-2/3 double mutants.
Presynaptic actions of transcranial and local direct current stimulation in the red nucleus

Transcranial direct current stimulation (tDCS) may affect both postsynaptic neurons and presynaptic axons providing input to these neurons. Here, we show that presynaptic subcortical actions of tDCS outlast the duration of its application by up to 1 h and may contribute to long‐lasting facilitation of activation of neurons in the red nucleus in experiments on deeply anaesthetized animals. This is demonstrated by increased axonal excitability of the interpositorubral neurons that, in turn, activate rubrospinal neurons. We also show that effects of electric fields induced by tDCS may be reproduced by local cathodal polarization, by applying <1 μA direct current within the red nucleus. Such polarization replicates effects of anodal tDCS in the cat as well as cathodal tDCS in the rat and evokes similar long‐lasting facilitation.
Inventories and quantity constrained equilibria in regulated markets: the US Petroleum Industry, 1947-1972

Reliable knowledge of how domestic energy supplies respond to changing prices is needed to assess the impact of an oil disruption and to estimate the value of ameliorative policies such as import tariffs and the Strategic Petroleum Reserves. Over the longer run, supply response has a bearing on how rapidly alternatives to imports will be developed. Further, price responsiveness is a central consideration in debates over the wisdom of price controls. Historical data on prices and supplies must be used in deriving empirical estimates of this response. Unfortunately, these data represent observations taken under strikingly different regimes, especially with regard to import restrictions and market demand prorationing in the case of oil. Are these data, distorted as they are, useful in projecting the response of domestic producers to future import disruptions or government policy shifts. This report addresses these issues by applying the theory of rational expectations to develop a model of the oil industry under prorationing restrictions. The model is computationally tractable, and the supply functions derived from it can be contrasted to supply relationships in the absence of regulation. The contrast is sharp: the two relationships predict systematically different responses to price. Based on these differences, the authorsmore » argue that much of the observed low responsiveness to price during the period of prorationing can be attributed to the controls and may convey a seriously misleading picture of how suppliers will react to price changes now that the controls no longer act to stabilize prices. While the model is based on a highly stylized description of the structure and operation of the petroleum industry, the methodology developed in the paper points a way toward more accurate estimation of how producers will respond to changing prices and regulatory environments. « less
A Bayesian periodogram finds evidence for three planets in HD 11964

A Bayesian multiplanet Kepler periodogram has been developed for the analysis of precision radial velocity data. The periodogram employs a parallel tempering Markov chain Monte Carlo algorithm. The HD 11964 data have been re-analysed using 1, 2, 3 and 4 planet models. Assuming that all the models are equally probable a priori, the three planet model is found to be ≥600 times more probable than the next most probable model which is a two planet model. The most probable model exhibits three periods of 38.02 +0,06 -0,05. 360 +4 -4( and 1924 +44 -43 d, and eccentricities of 0.22 +0.11 -0.22 , 0.63 +0.11 -0.17 ( and 0.05 +0.03 -0.05 , respectively. Assuming the three signals (each one consistent with a Keplerian orbit) are caused by planets, the corresponding limits on planetary mass (M sin i) and semimajor axis are (0.090 +0.15 -0.14Mj, 0.253 +0.009 -0.009 au), (0.21 +0.06 -0.07 Mj, 1.13 +0.04 -0.04 au) and (0.77 +0.08 -0.08 Mj, 3.46 +0.13 -0.13 au), respectively. The small difference (1.3σ) between the 360-d period and one year suggests that it might be worth investigating the barycentric correction for the HD 11964 data.
Performance analysis of ideal binary masks in speech enhancement

Binary masks are essential elements to be used in monaural speech segregation and hearing aids. The performances of the ideal binary masks in terms of signal to noise ratio were evaluated in this article, and a method to predict it before application was proposed. Within the framework of the computational auditory scene analysis (CASA), Ideal binary mask (IBM) has the optimum performance in time-frequency (T-F) units. It can be used as an object goal in global level, which was confirmed by the experiments on a speech mixture database. Furthermore, energy distribution of the target and interfere signals were used together to estimate the performance of IBM in mixture separation.
Natural variations in expression of regulatory and detoxification related genes under limiting phosphate and arsenate stress in Arabidopsis thaliana

Abiotic stress including nutrient deficiency and heavy metal toxicity severely affects plant growth, development, and productivity. Genetic variations within and in between species are one of the important factors in establishing interactions and responses of plants with the environment. In the recent past, natural variations in Arabidopsis thaliana have been used to understand plant development and response toward different stresses at genetic level. Phosphorus deficiency negatively affects plant growth and metabolism and modulates expression of the genes involved in Pi homeostasis. Arsenate, As(V), a chemical analog of Pi, is taken up by the plants via phosphate transport system. Studies suggest that during Pi deficiency, enhanced As(V) uptake leads to increased toxicity in plants. Here, the natural variations in Arabidopsis have been utilized to study the As(V) stress response under limiting Pi condition. The primary root length was compared to identify differential response of three Arabidopsis accessions (Col-0, Sij-1, and Slavi-1) under limiting Pi and As(V) stress. To study the molecular mechanisms responsible for the differential response, comprehensive expression profiling of the genes involved in uptake, detoxification, and regulatory mechanisms was carried out. Analysis suggests genetic variation-dependent regulatory mechanisms may affect differential response of Arabidopsis natural variants toward As(V) stress under limiting Pi condition. Therefore, it is hypothesized that detailed analysis of the natural variations under multiple stress conditions might help in the better understanding of the biological processes involved in stress tolerance and adaptation.
A Logic-based Approach to Generatively Defined Discriminative Modeling

Conditional random fields (CRFs) are usually specified by graphical models but in this paper we propose to use probabilistic logic programs and specify them generatively. Our intension is first to provide a unified approach to CRFs for complex modeling through the use of a Turing complete language and second to offer a convenient way of realizing generative-discriminative pairs in machine learning to compare generative and discriminative models and choose the best model. We implemented our approach as the D-PRISM language by modifying PRISM, a logic-based probabilistic modeling language for generative modeling, while exploiting its dynamic programming mechanism for efficient probability computation. We tested D-PRISM with logistic regression, a linear-chain CRF and a CRF-CFG and empirically confirmed their excellent discriminative performance compared to their generative counterparts, i.e.\ naive Bayes, an HMM and a PCFG. We also introduced new CRF models, CRF-BNCs and CRF-LCGs. They are CRF versions of Bayesian network classifiers and probabilistic left-corner grammars respectively and easily implementable in D-PRISM. We empirically showed that they outperform their generative counterparts as expected.
A motor imagery BCI experiment using wavelet analysis and spatial patterns feature extraction

A brain computer interface (BCI) is a system that aims to control devices by analyzing brain signals patterns. In this work, a convenient time-frequency representation (TFR) for visualizing ERD/ERS phenomenon (Event related synchronization and desynchronization) based on Hilbert transform and spatial patterns is addressed, and a wavelet based feature extraction method for motor imagery tasks is presented. The feature vectors are constructed with four statistical and energy parameters obtained from wavelet decomposition, based on the sub-band coding algorithm. Experimentation with three classification methods for comparison purposes was carried out using Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Support Vector Machine (SVM). In each case, ten-fold validation is used to obtain average misclassification rates.
Adapting LSI for Fine-Grained and Multi-Level Document Comparison

In recent years, Latent Semantic Indexing (LSI) has been recognized as an effective tool for Information Retrieval in text documents. The level of “granularity” in LSI (i.e. whether LSI is performed on documents, paragraphs, sentences, phrases, etc.) is somewhat of a limiting factor, in that LSI comparisons can only be made at the level of granularity chosen. Here we argue that, as long as a record of the document structure is maintained, the level of granularity may be arbitrarily fine while still allowing for comparison at any coarser granularity. It is shown that the reduced-dimension vector for any particular section of a document is a function of the vectors of its constituent subsections. Using this information, we illustrate how LSI can be used to compare documents at multiple structural levels. One possible application (automated plagiarism detection) is discussed as an example of how this method of multilevel comparison may be used to improve query time in fine-granularity LSI applications.
Regularization of the Chapman-Enskog Expansion and Its Description of Shock Structure

In the continuum transition ow regime, we propose to truncate the Chapman-Enskog expansion of the Boltzmann equation to the Navier-Stokes order only without going to the Burnett or super Burnett orders. However, the local particle collision time has to be generalized to depend not only on the local macroscopic ow variables, but also their gradients in the rare ed gas regime. Based on the gas-kinetic BGK model, the relation between the conventional collision time and the general one is obtained. More speci cially, a generalized constitutive relation for stress and heat ux is proposed. This new model is applied to the study of argon gas shock structure. There is good agreement between the predicted shock structure and experimental results for a wide range of Mach numbers.
Algebraic chase decoding of Reed-Solomon codes using module minimisation

This paper proposes a low-complexity high performance soft-in hard-out decoding algorithm for Reed-Solomon (RS) codes. The Guruswami-Sudan (GS) algebraic list decoding algorithm can correct errors beyond half the distance bound by performing a curve-fitting decoding process. However, its extra error-correction capability is exchanged with a high computational cost which is dominated by the interpolation. In this paper, an algebraic Chase decoding (ACD) approach that utilises the module minimisation (MM) technique is introduced, namely the ACD-MM algorithm. The MM technique solves the interpolation problem with less computational cost than the conventional Koetter's interpolation. The proposal exploits the soft received information to formulate the interpolation test-vectors. The re-encoding transform is further employed to reduce the size of the entries of the module, benefiting a simpler MM process. Our analyses show that the ACD-MM algorithm can outperform the legacy Koetter-Vardy (KV) soft-decision list decoding algorithm with a much lower complexity. Moreover, the proposal allows parallel execution of each Chase decoding trial, resulting in a low decoding latency for practical interest.
A Multimodal Virtual Reality Interface for VTK

The object oriented Visualization Toolkit (VTK) is widely used for scientific visualization. VTK is a visualization library that provides functions for presenting 3D data. Interaction with the visualized data is done by mouse and keyboard. Support for three-dimensional and multimodal input is non-existent. This paper describes VR-VTK: a multimodal interface to VTK on a desktop virtual environment. Six degree of freedom input devices are used for spatial 3D interaction. Head tracking is used for camera control. Pedals for clutching. Speech input is used for application commands and system control. To address several problems specific for spatial 3D interaction, a number additional features, such as more complex interaction methods and enhanced depth perception are discussed. Furthermore, the need for multimodal input to support interaction with the visualization is shown. An existing VTK application is ported to VR-VTK. Informal user experiences are presented.
Robust Adaptive Control of Feedback Linearizable MIMO Nonlinear Systems With Prescribed Performance

A novel robust adaptive controller for multi-input multi-output (MIMO) feedback linearizable nonlinear systems possessing unknown nonlinearities, capable of guaranteeing a prescribed performance, is developed in this paper. By prescribed performance we mean that the tracking error should converge to an arbitrarily small residual set, with convergence rate no less than a prespecified value, exhibiting a maximum overshoot less than a sufficiently small prespecified constant. Visualizing the prescribed performance characteristics as tracking error constraints, the key idea is to transform the ldquoconstrainedrdquo system into an equivalent ldquounconstrainedrdquo one, via an appropriately defined output error transformation. It is shown that stabilization of the ldquounconstrainedrdquo system is sufficient to solve the stated problem. Besides guaranteeing a uniform ultimate boundedness property for the transformed output error and the uniform boundedness for all other signals in the closed loop, the proposed robust adaptive controller is smooth with easily selected parameter values and successfully bypasses the loss of controllability issue. Simulation results on a two-link robot, clarify and verify the approach.
Speaker Verification Using A Novel Set of Dynamic Features

Dynamic cepstral features such as delta and delta-delta cepstra have been shown to play an essential role in capturing the transitional characteristics of the speech signal. In this paper, a set of new dynamic features for speaker verification system are introduced. These new features, known as delta cepstral energy (DCE) and delta-delta cepstral energy (DDCE), can compactly represent the information in the delta and delta-delta cepstra. Furthermore, it is shown theoretically that DCE carries the same information as the delta cepstrum using an entropy criterion. Experimental speaker verification results on the TIMIT database support the theoretical result, showing a significant improvement in terms of equal error rate compared with conventional feature extraction methods using delta and delta-delta cepstra
A Model-driven Approach for Price/Performance Tradeoffs in Cloud-based MapReduce Application Deployment.

This paper describes preliminary work in developing a modeldriven approach to conducting price/performance tradeo s for Cloudbased MapReduce application deployment. The need for this work stems from the signi cant variability in both the MapReduce application characteristics and price/performance characteristics of the underlying cloud platform. Our approach involves a model-based machine learning capability that trains itself from executing a variety of MapReduce applications on di erent cloud service providers, and in turn providing useful price/performance tradeo information to MapReduce application users. Additionally, the model-based platform serves to automate the deployment of a MapReduce application to the cloud by incorporating the tradeo choices.