Miracle-dz/UniFAR-FTUs huggingface dataset:
Population-Wide Bias of Surround Suppression in Auditory Spatial Receptive Fields of the Owl's Midbrain

The physical arrangement of receptive fields (RFs) within neural structures is important for local computations. Nonuniform distribution of tuning within populations of neurons can influence emergent tuning properties, causing bias in local processing. This issue was studied in the auditory system of barn owls. The owl's external nucleus of the inferior colliculus (ICx) contains a map of auditory space in which the frontal region is overrepresented. We measured spatiotemporal RFs of ICx neurons using spatial white noise. We found a population-wide bias in surround suppression such that suppression from frontal space was stronger. This asymmetry increased with laterality in spatial tuning. The bias could be explained by a model of lateral inhibition based on the overrepresentation of frontal space observed in ICx. The model predicted trends in surround suppression across ICx that matched the data. Thus, the uneven distribution of spatial tuning within the map could explain the topography of time-dependent tuning properties. This mechanism may have significant implications for the analysis of natural scenes by sensory systems.
Image Denoising Based on Fuzzy and Intra-scale Dependency in Wavelet Transform Domain

In this paper, we propose a new wavelet shrinkage algorithm based on fuzzy logic. Fuzzy logic is used for taking neighbor dependency and uncorrelated nature of noise into account in wavelet-based image denoising. For this reason, we use a fuzzy feature for enhancing wavelet coefficients information in the shrinkage step. Then a fuzzy membership function shrinks wavelet coefficients based on the fuzzy feature. We examine our image denoising algorithm in the dual-tree discrete wavelet transform, which is the new shiftable and modified version of discrete wavelet transform. Extensive comparisons with the state-of-the-art image denoising algorithm indicate that our image denoising algorithm has a better performance in noise suppression and edge preservation.
One or Many Knowledge Production Functions? Mapping Innovative Activity Using Microdata

This paper discusses the determinants of three alternative measures of innovative output by looking at firm's own formal RD however, both inputs increase a firm's innovative intensity. Significant evidence is also found that small firms and firms belonging to low-tech sectors rely more on the acquisition of external technologies and on cooperation agreements, while larger firms in high-tech sectors rely more on their own formal R&D.
EXTENDING THE COX-ROSS-RUBINSTEIN ALGORITHM FOR PRICING OPTIONS WITH EXPONENTIAL BOUNDARIES

It is a common belief that the standard binomial algorithm of Cox-Ross-Rubinstein (CRR) cannot be used to deal with barrier options with multiple or time-varying boundaries. We propose an extension of the CRR model to evaluate options with exponential boundaries. The essence of the extended binomial model relies upon the construction of a binomial tree for the underlying asset price dynamics, characterized by sets of nodes that mirror the barriers evolution. As a result, a very easy algorithm is derived that produces accurate prices with respect to the corresponding continuous time values. Moreover, numerical results show that the performance of the extended binomial algorithm is superior to that of the trinomial algorithms usually employed to price these options.
The Cost-Utility of CT Angiography and Conventional Angiography for People Presenting with Intracerebral Hemorrhage

Objective To determine the optimal imaging strategy for ICH incorporating CTA or DSA with and without a NCCT risk stratification algorithm. Methods A Markov model included costs, outcomes, prevalence of a vascular lesion, and the sensitivity and specificity of a risk stratification algorithm from the literature. The four imaging strategies were: (a) CTA screening of the entire cohort; (b) CTA only in those where NCCT suggested a high or indeterminate likelihood of a lesion; (c) DSA screening of the entire cohort and (d) DSA only for those with a high or indeterminate suspicion of a lesion following NCCT. Branch d was the comparator. Results Age of the cohort and the probability of an underlying lesion influenced the choice of optimal imaging strategy. With a low suspicion for a lesion (<12%), branch (a) was the optimal strategy for a willingness-to-pay of $100,000/QALY. Branch (a) remained the optimal strategy in younger people (<35 years) with a risk below 15%. If the probability of a lesion was >15%, branch (b) became preferred strategy. The probabilistic sensitivity analysis showed that branch (b) was the optimal choice 70–72% of the time over varying willingness-to-pay values. Conclusions CTA has a clear role in the evaluation of people presenting with ICH, though the choice of CTA everyone or CTA using risk stratification depends on age and likelihood of finding a lesion.
Snake species richness and shrubland correlate with the short-toed eagle (Circaetus gallicus) distribution in south-eastern Spain

The short-toed eagle (Circaetus gallicus) is a trophic specialist with a diet based almost exclusively on ophidians. In this work, the distribution of this eagle in south-eastern Spain taken from national atlases, is analysed in relation to environmental variables. The results show that the short-toed eagle is distributed primarily in shrublands, probably because there it can easily locate and capture its prey. It also prefers an intermediate cover of forest, because it needs trees for nesting. Lastly, there was a correlation, after statistically controlling for other variables, between snake species richness and eagle presence. It is possible that snake species richness favours the presence of this raptor, as it preys on different species. Alternatively, because this raptor preys preferentially on dominant ophidians in the study area, it is also possible that the presence of this eagle favours snake diversity by a top-down regulation on the ophidian community.
A Dynamic Model of Optimal Capital Structure and Debt Maturity with Stochastic Interest Rates

We thank an anonymous referee, an Associate Editor, Gurdip Bakshi, and Jay Huang for their valuable comments and suggestions. We also thank Henry Cao, Hua He, Hayne Leland, Mark Rubinstein, and Matt Spiegel for their advice on an earlier version where a static model was developed. Abstract This paper develops a model in which an optimal capital structure and an optimal debt maturity are jointly determined in a stochastic interest rate environment. Valuation formulas are derived in closed form and numerical solutions are used to obtain comparative statics. The model yields leverage ratios and debt maturities that are consistent in spirit with empirical observations. It is demonstrated that a dynamic model is crucial to obtain reasonable leverage ratios and that a stochastic interest rate is important to study optimal debt maturity structure. It is also demonstrated that a model of optimal capital structure with a constant interest rate cannot price risky bonds and determine optimal capital and debt structures simultaneously in a satisfactory manner.
What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation

Traditional machine translation systems often require heavy feature engineering and the combination of multiple techniques for solving different subproblems. In recent years, several end-to-end learning architectures based on recurrent neural networks have been proposed. Unlike traditional systems, Neural Machine Translation (NMT) systems learn the parameters of the model and require only minimal preprocessing. Memory and time constraints allow to take only a fixed number of words into account, which leads to the out-of-vocabulary (OOV) problem. In this work, we analyze why the OOV problem arises and why it is considered a serious problem in German. We study the effectiveness of compound word splitters for alleviating the OOV problem, resulting in a 2.5+ BLEU points improvement over a baseline on the WMT’14 German-to-English translation task. For English-to-German translation, we use target-side compound splitting through a special syntax during training that allows the model to merge compound words and gain 0.2 BLEU points.
European integration and changing trade patterns: the case of the Baltic states

The analysis of Baltic regional trade patterns reveals that during the nineties the Baltic states made significant progress to integrate into the Western European division of labour although a significant share of (transit) trade with Russia remained. In view of this development, history seems to matter with respect to the interwar period and the period of Soviet occupation. In addition, a trade entropy analysis and gravity model estimates show that European integration of the Baltic states has a regional centre of gravity located in the Baltic Sea region. The Baltic trade flows increasingly follow the gravitational forces that generally shape trade relations, while regional integration is still much more important than it is normally the case.
Indoor human detection using RGB-D images

Recently, RGB-D sensors such as Kinect and Xtion have received considerable attention since they provide depth image that is robust to light variation in the environment. They are mainly used for human computer interaction, surveillance and so on. In this paper, we concentrate on indoor human detection using RGB-D images. Some RGB image based features such as histogram of oriented gradient (HOG) and local binary pattern (LBP) are first briefly introduced. Then, a new depth feature that describes the self-similarity of an image is proposed. Finally, combination of them is utilized to detect the people. This scheme can efficiently describe the humans in the indoor environment. Extensive experiments demonstrate that the proposed scheme can achieve a respective promising detection accuracy of 99.28%, 95.48% and 99.91% on three different collected RGB-D data sets.
Session reconstruction for HTTP adaptive streaming: Laying the foundation for network-based QoE monitoring

HTTP Adaptive Streaming (HAS) is rapidly becoming a key video delivery technology for fixed and mobile networks. However, today there is no solution that allows network operators or CDN providers to perform network-based QoE monitoring for HAS sessions. We present a HAS QoE monitoring system, based on data collected in the network, without monitoring information from the client. To retrieve the major QoE parameters such as average quality, quality variation, rebuffering events and interactivity delay, we propose a technique called session reconstruction. We define a number of iterative steps and developed algorithms that can be used to perform HAS session reconstruction. Finally, we present the results of a working prototype for the reconstruction and monitoring of Microsoft Smooth Streaming HAS sessions that is capable of dealing with intermediate caching and user interactivity. We describe the main observations when using the platform to analyze more than a hundred HAS sessions.
HEI Working Paper No : 08 / 2004 Modelling Environmental Risk

As environmental issues have become increasingly important in economic research and policy for sustainable development, firms in the private sector have introduced environmental and social issues in conducting their business activities. Such behaviour is tracked by the Dow Jones Sustainable Indexes (DJSI) through financial market indexes that are derived from the Dow Jones Global Indexes. The sustainability activities of firms are assessed using criteria in three areas, namely economic, environmental and social. Risk (or uncertainty) is analysed empirically through the use of conditional volatility models of investment in sustainability-driven firms that are selected through the DJSI. The empirical analysis is based on financial econometric models to determine the underlying conditional volatility, with the estimates showing that there is strong evidence of volatility clustering, short and long run persistence of shocks to the index returns, and asymmetric leverage between positive and negative shocks to returns.
Stemming of French Words Based on Grammatical Categories

Automatic indexing systems use suffix stripping algorithms to cluster various words derived from a common root under the same stem. Currently, removing affixes to either a context-free or context-sensitive operation, where the context refers to the remaining stem. In this article, we propose a suffixing algorithm which uses grammatical categories to enhance the stemming process. This approach supports the use of foreign languages. In our case, the language is French, and a morphological analysis is required for removing inflectional suffixes or morphosyntactic variants of a lemma. After this analysis, we implement a suffix stripping algorithm which uses a dictionary and the grammatical categories to remove derivational suffixes. Our approach always returns a linguistically correct lemma, but not necessarily the “right” one. Based on our tests, this solution is an attractive one, with a mean error rate of 16%. We finish by explaining why we cannot expect significantly better results with this approach.
Writing skills transfer from human to robot using stiffness extracted from sEMG

Studies of human motor behaviors have shown that central neural system (CNS) is able to adapt force and impedance in order to optimally interact with interactive environment. Muscle activities regulated by CNS can be represented by surface electromyography (sEMG) measured by electrodes attached on the skin. Inspired by the idea that muscle impedance adaptation reflects motion skills, sEMG based human-robot skill transfer, in particular, the writing skill transfer has been developed based on impedance adaptation extracted from sEMG. Squaring and low-pass filtering based signal envelop extraction algorithm and as well as re-sampling method are employed to extract incremental smooth stiffness from sEMG signals which is then transferred to robot to mimic human motor behavior. The effect of the proposed sEMG based bio-control is evaluated by writing task in comparison with constant stiffness control. Results show that sEMG based human skill transfer has significant effectiveness for skills transfer between human and robot, and it has a great potential to be used in teleoperation.
Humanistic and economic burden of nausea and vomiting among migraine sufferers

Background While studies have demonstrated the economic burden of migraines in terms of quality of life, health care resource use (HRU), and costs, there exists a notable paucity of data comparing such outcomes among migraineurs with nausea and vomiting (N/V) and those without. The current study aimed to address this gap. Methods This was a retrospective study using data from the 2013 US National Health and Wellness Survey, a cross-sectional, internet-based survey. Respondents self-reported their migraine with or without N/V along with demographics and outcomes including depression (Patient Health Questionnaire total score; PHQ-9), sleep problems (11-item total score of sleep problems), HRU (number of physician visits, emergency room [ER] visits, and hospitalizations) and Work Productivity and Activity Impairment-General Health Scale (WPAI-GH), and associated mean annual costs. Generalized linear models, adjusting for covariates, assessed the burden of N/V on all outcomes. Results Among all migraineurs (N=7,855), 73.4% were female, mean age was 41.82 years old, and 57.6% reported experiencing N/V. Adjusting for covariates, migraineurs with N/V vs without N/V had higher mean PHQ-9 scores (7.91 vs 7.02, p<0.001) and mean sleep problems (3.29 vs 2.64, p<0.001). Mean ER visits were more frequent among migraineurs with N/V than those without N/V (0.48 vs 0.38, p=0.001). This difference translated into a 26.3% increase in estimated mean ER costs (N/V=US$1,499 vs without N/V=US$1,187, p=0.002). Mean percentage activity impairment was higher in migraineurs with N/V than in those without N/V (37.73% vs 35.12%, p=0.002) and migraineurs with N/V had higher work productivity loss costs (N/V=US$10,344 vs without N/V=US$9,218, p=0.016). Conclusion Migraine patients with N/V reported worse depression, sleep problems, and activity impairment, and higher ER visits than those without N/V. Migraine with N/V was also associated with an increase in mean annual ER visit costs and work productivity loss costs. Study findings suggest unmet needs with current treatment options for migraine patients with N/V.
Votes in the UN General Assembly

Appendixes. Preface. Abbreviations and Acronyms. Tables. Figures. Introduction. Part One: The Stage and the Actors. 1: The International Community's Most Representative Forum. A universal organization. The rules of the game. Regarding majorities and minorities. 2: A Half-Century of Debates. An ever-heavier agenda. Consistency and contradiction. The difficult questions. The fundamental issues. The Organization's functioning. Part Two: Voting Patterns. 3: The Resolutions and the Votes. An ocean of resolutions. The vetoes in the Security Council. Triple, double and single rejections.4: Coincidences and Discrepancies. The tools of analysis. General trends, 1946-1996. Some signposts. The five stages. The contours of a new agenda. Epilogue. Appendixes. Index.
Environmental Policy and International Trade

This paper explores the empirical link between environmental policy and international trade. Using an estimated global simulation model, the paper focusses on the extent to which international trade flows are redirected as a result of unilateral versus multilateral taxes on the emission of carbon dioxide. We find that a carbon tax in the United States produces little redirection of trade either in the short run or the long run because electric power generation and local transportation are by far the most carbon intensive activities and both are largely non-traded. We also illustrate the importance of the assumptions about the way in which the revenue from the carbon tax is recycled. We find that the revenue recycling assumption has important macroeconomic implications for saving and investment balances and therefore for the adjustment of trade flows. (This abstract was borrowed from another version of this item.)
The Baseline Approach to Agent Evaluation

Efficient, unbiased estimation of agent performance is essential for drawing statistically significant conclusions in multi-agent domains with high outcome variance. Näıve Monte Carlo estimation is often insufficient, as it can require a prohibitive number of samples, especially when evaluating slow-acting agents. Classical variance reduction techniques typically require careful encoding of domain knowledge or are intrinsically complex. In this work, we introduce the baseline method of creating unbiased estimators for zero-sum, multi-agent high-variance domains. We provide two examples of estimators created using this approach, one that leverages computer agents in self-play, and another that utilizes existing player data. We show empirically that these baseline estimators are competitive with state-ofthe-art techniques for efficient evaluation in variants of computer poker, a zero-sum domain with notably high outcome variance. Additionally, we demonstrate how simple, yet effective, baseline estimators can be created and deployed in domains where efficient evaluation techniques are currently non-existent.
Determinants of Income Inequality and its Effects on Economic Growth

A pharmaceutical composition is disclosed to treat viral infections, particularly HIV and hepatitis, as well as to treat fungal infections of the genus cryptococcus neoformans or curvularai lunata. The composition comprises from about 10 mg to about 6000 mg of a 2-thienyl-imidazolo [4,5]pyridine of the formula:wherein n is from 1 to 4, R is selected from the group consisting of hydrogen, alkyl having from 1 to 7 carbon atoms, chloro, bromo or fluoro, oxychloro, hydroxy, sulfhydryl, alkoxy having the formula -O(CH2)yCH3 wherein y is from 1 to 6, the prodrugs thereof, and the pharmaceutically acceptable salts thereof. The preferred anti-viral compound isor its hydrochloride salt.
Interactive Graph Cut Colorization

Traditional ways to handle monochrome image colorization which always requires considerable user interaction and a lot of time are Segmentation colorization and Colorization using optimization. The former works fast, but always lose the details because of the large segmentation; while the latter looks much more continuous but takes longer time. In this paper we proposed a novel approach: Segmentation colorization based on Graph cut, which is a very fast segmentation technique of global energy optimization. So we can maintain smoothness almost everywhere except for the sharp discontinuity at the boundaries in the image. Firstly, with the few seeds of pixels set manually by the user, we set up a global energy according to the gray value distribution around each pixel, with the conception that similar gray intensity prefers same color. Secondly, we use ‘Graph Cut’ to get the best segmentation, fast and efficiently. As user specifies more colors, the energy minimization will be solved iteratively and much faster. So with few manual specifications, user can colorize a gray image in a very short time and get naturally looking results.
Server-Aided Verification Signatures with Privacy

A server-aided verification signature scheme consists of a digital signature scheme and a server-aided verification protocol. Signatures can be verified by executing the server-aided verification protocol with an untrusted server. In existing server-aided verification signature schemes, the server can learn some information about the message-signature pair to be verified, which is undesirable especially when the message includes some secret information. In this paper, we mainly study the server-aided verification signatures with privacy. Two definitions of privacy for server-aided verification signatures are presented under collusion attacks between the server and the signer. Then based on existing signatures, two concrete server-aided verification signature schemes with privacy are proposed which are both proved secure.
Handbook of empirical economics and finance

Robust Inference with Clustered Data, A. Colin Cameron and Douglas L. Miller Efficient Inference with Poor Instruments: A General Framework, Bertille Antoine and Eric Renault An Information Theoretic Estimator for the Mixed Discrete Choice Model, Amos Golan and William H. Greene Recent Developments in Cross-Section and Panel Count Models, Pravin K. Trivedi and Murat K. Munkin An Introduction to Textual Econometrics, Stephen Fagan and Ramazan Gencay Large Deviations Theory and Econometric Information Recovery, Marian Grendar and George Judge Nonparametric Kernel Methods for Qualitative and Quantitative Data, Jeffrey S. Racine The Unconventional Dynamics of Economic and Financial Aggregates, Karim M. Abadir and Gabriel Talmain Structural Macroeconometric Modeling in a Policy Environment, Martin Fukac and Adrian Pagan Forecasting with Interval and Histogram Data: Some Financial Applications, Javier Arroyo, Gloria Gonzalez-Rivera, and Carlos Mate Predictability of Asset Returns and the Efficient Market Hypothesis, M. Hashem Pesaran A Factor Analysis of Bond Risk Premia, Sydney C. Ludvigson and Serena Ng Dynamic Panel Data Models, Cheng Hsiao A Unified Estimation Approach for Spatial Dynamic Panel Data Models: Stability, Spatial Co-Integration, and Explosive Roots, Lung-fei Lee and Jihai Yu Spatial Panels, Badi H. Baltagi Nonparametric and Semiparametric Panel Econometric Models: Estimation and Testing, Liangjun Su and Aman Ullah Index