Miracle-dz/UniFAR-FTUs huggingface dataset:
Pricing an Emerging Industry: Evidence from Internet Subsidiary Carve-Outs

We examine price behavior in the emerging Internet industry by comparing investor valuation of Internet subsidiary carve-outs with that of the parent. We provide examples of parent firms whose Internet carve-out holdings exceed the market value of the entire parent by a large magnitude and over an extended period of time. We reject alternative tax, liquidity, and agency cost hypotheses previously proposed as explanations of a related phenomenon, the closed-end fund discount. We conclude that investors, or at least an important clientele of investors, value direct Internet asset holdings more richly than indirect holdings via the parent.
Market Imperfections, Discount Factors and Global Dominance: An Empirical Analysis with Oil-Linked Derivatives

Oil-linked derivatives are becoming very important in Modern Investment Theory. Accordingly, the analysis of Pricing Techniques and Portfolio Choice Problems involving these securities is a major topic for both managers and researchers. We focus on both the No-Arbitrage Approach and Stochastic Discount Factor (SDF) based methods in order to study oil-linked derivatives available at The New York Mercantile Exchange, Inc, one of the world's largest markets in energy and precious metals. First, we generalize some theoretical properties of the SDF in order to capture the effects induced by the bid-ask spread when analyzing dominated/efficient portfolios. Secondly, we apply our findings and empirically analyze the existence of dominated assets and portfolios in the oil derivatives market. Our results reveal the systematic presence of dominated prices, which should be taken into account by traders when composing their portfolios. Additionally, the test yields pricing and portfolio choice methods as well as new strategies that may allow brokers to outperform their service for their clients. It is worth to point out that the conclusions of the test have two important characteristics: On the one hand, they are very precise since we draw on perfectly synchronized bid/ask prices, as provided by Reuters. On the other hand, they are robust in the sense that they do not depend on any assumption about the underlying asset price dynamics. Finally, despite the empirical test focuses on oil derivatives, the methodology is general enough to apply to a broad range of markets.
A few questions about nonresponse in the Netherlands

In the recent past the Netherlands has stood out as a country with rather low response rates; not a good starting position in a period when response rates are going down (De Heer 1999; De Leeuw, E. and W. de Heer 2002). This paper seeks to examine a number of assumptions on non-response in the Netherlands. Is the situation really so bad compared to other countries, and are response rates really going down? The answer to these questions will turn out to be negative. This leads to other questions: how were higher response rates achieved in the past few years? Can this level be maintained? And finally: do higher response rates result in a final sample that better represents the target population?
Geometric interpretation of previtrification in hard sphere liquids.

We derive a microscopic criterion for the stability of hard sphere configurations and we show empirically that this criterion is marginally satisfied in the glass. This observation supports a geometric interpretation for the initial rapid rise in viscosity with packing fraction or previtrification. It also implies that barely stable soft modes characterize the glass structure, whose spatial extension is estimated. We show that both the short-term dynamics and activation processes occur mostly along those soft modes and we study some implications of these observations. This article synthesizes new and previous results [C. Brito and M. Wyart, Europhys. Lett. 76, 149 (2006); C. Brito and M. Wyart, J. Stat. Mech. : Theory Exp. 2007, L08003] in a unified view.
Sentiment Analysis using neural architectures

Most sentiment analysis approaches are based on heavy preprocessing of the data which involves carefully choosing the right features based on the nature of the data, intuitive analysis and factors like language. I explore an unconventional approach to sentiment analysis based on recently proposed neural network architectures for NLP that parallel traditional well-performing approaches, both in accuracy and speed, and have the advantage of requiring no manual preprocessing or feature selection.
Sequence reproduction, single trial learning, and mimicry based on a mammalian-like distributed code for time

Animals learn tasks requiring a sequence of actions over time. Waiting a given time before taking an action is a simple example. Mimicry is a complex example, e.g. in humans, humming a brief tune you have just heard. Re-experiencing a sensory pattern mentally must involve reproducing a sequence of neural activities over time. In mammals, neurons in prefrontal cortex have time-dependent firing rates that vary smoothly and slowly in a stereotyped fashion. We show through modeling that a Many are Equal computation can use such slowly-varying activities to identify each timepoint in a sequence by the population pattern of activity at the timepoint. The MAE operation implemented here is facilitated by a common inhibitory conductivity due to a theta rhythm. Sequences of analog values of discrete events, exemplified by a brief tune having notes of different durations and intensities, can be learned in a single trial through STDP. An action sequence can be played back sped up, slowed down, or reversed by modulating the system that generates the slowly changing stereotyped activities. Synaptic adaptation and cellular post-hyperpolarization rebound contribute to robustness. An ability to mimic a sequence only seconds after observing it requires the STDP to be effective within seconds.
Deep Learning and Its Applications to Machine Health Monitoring: A Survey

Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their connection to the Internet. Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. The main purpose of this paper is to review and summarize the emerging research work of deep learning on machine health monitoring. After the brief introduction of deep learning techniques, the applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines (DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Finally, some new trends of DL-based machine health monitoring methods are discussed.
Translation errors from English to Portuguese: an annotated corpus

Analysing the translation errors is a task that can help us finding and describing translation problems in greater detail, but can also suggest where the automatic engines should be improved. Having these aims in mind we have created a corpus composed of 150 sentences, 50 from the TAP magazine, 50 from a TED talk and the other 50 from the from the TREC collection of factoid questions. We have automatically translated these sentences from English into Portuguese using Google Translate and Moses. After we have analysed the errors and created the error annotation taxonomy, the corpus was annotated by a linguist native speaker of Portuguese. Although Google’s overall performance was better in the translation task (we have also calculated the BLUE and NIST scores), there are some error types that Moses was better at coping with, specially discourse level errors.
Epidemiology of syphilis, gonorrhoea and chlamydia trachomatis infection in Finland in the 1990s

In 1995, the incidence of syphilis doubled in Finland to 3.3/100 000 and the main reason for the increased incidence was the import of syphilis by Finnish heterosexual men from Russia. The source partners were mostly sex workers. In 1990 the incidence of gonorrhoea (55/100 000) was markedly higher than in the other Nordic countries. In a prospective study the incidence of endemic gonorrhoea decreased rapidly in 1990–1995 while the amount of imported cases remained at the constant level.
Syntactical Compression of XML Data

One of the most palpable drawbacks of XML can be seen in its excessive storage requirements. In this paper, we address this problem by proposing a syntactical XML compression scheme which makes use of probabilistic modeling of XML structure. Our compression scheme works sequentially and makes on-line processing of the data possible. We describe the current state of development of the prototype compressor and present some preliminary performance evaluation results. The compressor is designed to be extensible, and intended to serve as a platform for further research in the field of syntactical XML data compression.
Strategic Economic Decision-Making: Using Bayesian Belief Networks to Solve Complex Problems

Strategic Economic Decision Making: The Use of Bayesian Belief Networks (BBN) in Solving Complex Problems. - A Literature Review of Bayes' Theorem and Bayesian Belief Networks (BBN). - Statistical Properties of Bayes' Theorem. - Bayes Belief Networks (BBN) Experimental Protocol. - Manufacturing Example. - Political Science Example. - Gambling Example. - Publicly Traded Company Default Example. - Insurance Risk Levels Example. - Acts of Terrorism Example. - Currency Wars Example. - College Entrance Exams Example. - Special Forces Assessment and Selection (SFAS) One-Stage Example. - Special Forces Assessment and Selection (SFAS) Two-Stage Example.
Converging free energy estimates: MM‐PB(GB)SA studies on the protein–protein complex Ras–Raf

Estimating protein–protein interaction energies is a very challenging task for current simulation protocols. Here, absolute binding free energies are reported for the complex H‐Ras/C‐Raf1 using the MM‐PB(GB)SA approach, testing the internal consistency and model dependence of the results. Averaging gas‐phase energies (MM), solvation free energies as determined by Generalized Born models (GB/SA), and entropic contributions calculated by normal mode analysis for snapshots obtained from 10 ns explicit‐solvent molecular dynamics in general results in an overestimation of the binding affinity when a solvent‐accessible surface area‐dependent model is used to estimate the nonpolar solvation contribution. Applying the sum of a cavity solvation free energy and explicitly modeled solute–solvent van der Waals interaction energies instead provides less negative estimates for the nonpolar solvation contribution. When the polar contribution to the solvation free energy is determined by solving the Poisson–Boltzmann equation (PB) instead, the calculated binding affinity strongly depends on the atomic radii set chosen. For three GB models investigated, different absolute deviations from PB energies were found for the unbound proteins and the complex. As an alternative to normal‐mode calculations, quasiharmonic analyses have been performed to estimate entropic contributions due to changes of solute flexibility upon binding. However, such entropy estimates do not converge after 10 ns of simulation time, indicating that sampling issues may limit the applicability of this approach. Finally, binding free energies estimated from snapshots of the unbound proteins extracted from the complex trajectory result in an underestimate of binding affinity. This points to the need to exercise caution in applying the computationally cheaper “one‐trajectory‐alternative” to systems where there may be significant changes in flexibility and structure due to binding. The best estimate for the binding free energy of Ras–Raf obtained in this study of −8.3 kcal mol−1 is in good agreement with the experimental result of −9.6 kcal mol−1, however, further probing the transferability of the applied protocol that led to this result is necessary. © 2003 Wiley Periodicals, Inc. J Comput Chem 2: 238–250, 2003
Agro-biodiversity Management: An Opportunity for Mainstreaming Community-based Adaptation to Climate Change

Climate risks and hazards adversely impact the livelihoods of local communities. Based on a recent participatory study conducted in the western hills of Nepal, this article explores opportunities and strategies for integrating climate change adaptation in poverty reduction projects and programmes in ways that increase the capacity of individuals, households and communities to respond to climate variability and change. The findings of the study suggest that a wide array of agro? -biodiversity management strategies offer options and opportunities for farmers to cope with the adverse impact of climate change. However, this is possible only if adaptation is incorporated into the existing development efforts with sufficient understanding of local livelihood context and strategies instead of separately planning climate change adaptation programmes. Key words: Climate risks and hazards, community-based adaptation, vulnerability, poor, farmers, agriculture Full text is available at the ForestAction website Journal of Forest and Livelihood 8(1) February 2009 pp.111-119
Interpersonal Commitments and the Travel/Activity Scheduling Process

This paper analyzes the influence of commitments to other people on an individual’s activity scheduling process. The data are from the CHASE survey, a computerized activity scheduling survey conducted on 270 households in the Greater Toronto Area. Comparisons between the attributes of activities done alone and those done with other people are made. Relationships between interpersonal commitments to children, household adults, and non-household adults and the planning horizon and rescheduling characteristics of activities are analyzed. The analysis in this paper shows that it is too simplistic to assume that the “skeleton schedule” consists of “mandatory” activities, such as work and school, only.
The Economic Benefits of Surface Water Quality Improvements in Developing Countries: A Case Study of Davao, Philippines

Two nonmarket valuation techniques-the contingent valuation method and travel cost model-are used to estimate the economic value that people in Davao, Philippines, place on improving the water quality of the rivers and sea near their community. The contingent valuation and travel cost estimates are very close to each other and are quite low, both in absolute terms and as a percentage of household income. These findings suggest that water pollution control is simply not a high priority for Davao's residents, and support the argument that households' willingness to pay for environmental amenities such as improved water quality is low.
Learning of Active Object Detection in Mobile Robots

The detection and localization of objects of interest in sensor data is an important step in the analysis of robotic systems environments. Particularly, autonomous visual navigation relies on robust classification under uncertain environment conditions which is illustrated here by the navigation task of a sewer robot [1]. While research on object identification by analysis of a single 2-D pattern undergoes permanent improvement [2], the results always depend on the ambiguity of a particular view, the imprecision of the object model, and the uncertainty in the image formation process [3], [4], [5]. Instead of generating arbitrarily complex classifiers to solve an a priori ill-posed problem, dynamic information is often available to take advantage of the redundancy in subsequent object measurements [6]. Active detection of 3-D objects involves the visual agent in a search for discriminative evidence to disambiguate the initial object hypotheses [5]. Learning a fusion strategy aims then at integrating only evidences from information sources that have already been experienced of providing decisive data w.r. t. the characteristics of a particular task and its environment. Earlier related research is focused on the combined tracking and recognition task on a continual video image stream: Crowley and Berard [7] describe an efficient system to detect and track faces based on control that selects the sequencing of multi-modal perceptual processes. Bregler [8] describes the learning of a probabilistic decomposition of visual dynamics at multiple abstractions integrated into a gesture representation by Hidden Markov Models. Detection is either focused on single frame interpretation [9], working out object recognition on local appearance analysis [10], or constraining dynamic probabilistic recognition on presegmented regions. These systems do not treat strategic decision making on fusion operations w.r. t. a specific task goal, i.e., object detection. The contribution of the presented work is to provide an object detection system for mobile robots that bases its robustness on the integration of multiple evidences from active vision mechanisms. Learning of Active Object Detection in Mobile Robots
ALA Pretreatment Improves Waterlogging Tolerance of Fig Plants

5-aminolevulinic acid (ALA), a natural and environmentally friendly plant growth regulator, can improve plant tolerance to various environmental stresses. However, whether ALA can improve plant waterlogging tolerance is unknown. Here, we investigated the effects of ALA pretreatment on the waterlogging-induced damage of fig (Ficus carica Linn.) plants, which often suffer from waterlogging stress. ALA pretreatment significantly alleviated stress-induced morphological damage, increased leaf relative water content (RWC), and reduced leaf superoxide anion (O2⋅¯) production rate and malonaldehyde (MDA) content in fig leaves, indicating ALA mitigates waterlogging stress of fig plants. We further demonstrated that ALA pretreatment largely promoted leaf chlorophyll content, photosynthetic electron transfer ability, and photosynthetic performance index, indicating ALA significantly improves plant photosynthetic efficiency under waterlogging stress. Moreover, ALA pretreatment significantly increased activities of leaf superoxide dismutase (SOD) and peroxidase (POD), root vigor, and activities of root alcohol dehydrogenase (ADH), and lactate dehydrogenase (LDH), indicating ALA also significantly improves antioxidant ability and root function of fig plants under waterlogging stress. Taken together, ALA pretreatment improves waterlogging tolerance of fig plants significantly, and the promoted root respiration, leaf photosynthesis, and antioxidant ability may contribute greatly to this improvement. Our data firstly shows that ALA can improve plant waterlogging tolerance.
Security of the Improved Fuzzy Vault Scheme in the Presence of Record Multiplicity (Full Version)

Dodis et al. proposed an improved version of the fuzzy vault scheme, one of the most popular primitives used in biometric cryptosystems, requiring less storage and leaking less information. Recently, Blanton and Aliasgari have shown that the relation of two improved fuzzy vault records of the same individual may be determined by solving a system of non-linear equations. However, they conjectured that this is feasible for small parameters only. In this paper, we present a new attack against the improved fuzzy vault scheme based on the extended Euclidean algorithm that determines if two records are related and recovers the elements by which the protected features, e.g., the biometric templates, differ. Our theoretical and empirical analysis demonstrates that the attack is very effective and efficient for practical parameters. Furthermore, we show how this attack can be extended to fully recover both feature sets from related vault records much more efficiently than possible by attacking each record individually. We complement this work by deriving lower bounds for record multiplicity attacks and use these to show that our attack is asymptotically optimal in an information theoretic sense. Finally, we propose remedies to harden the scheme against record multiplicity attacks.
CSE 573 S Protocols for Computer Networks ( Spring 2005 – Final Project )

To Investigate the degree of congestion control synchronization of window-based connections bottlenecked at the same link Abstract : Our project basically investigates the degree of congestion control synchronization of window-based connections bottlenecked at the same link and for this we conducted experiment's in ns2 and in cec labs which is located in Washington University in St. louis. The questions which we expected to answer where " What is the fraction of connections that do not notice congestion before it is resolved by the other connections? " & to identify the conditions for the buffer to always remain full. Some of the considered parameters where number of connections, buffer size, delay of their paths and the bottleneck link capacity.
Relaxin improves TNF-α-induced endothelial dysfunction: the role of glucocorticoid receptor and phosphatidylinositol 3-kinase signalling.

AIMS Human relaxin-2 influences renal and cardiovascular functions. We investigated its effects on experimental endothelial dysfunction. METHODS AND RESULTS Acetylcholine-mediated vasodilation of rat aortic rings, impaired by 48 h tumour necrosis factor-α (TNF-α) treatment, was dose-dependently improved by relaxin co-incubation, an effect sensitive to phosphatidylinositol 3-kinase (PI3K) inhibition and the glucocorticoid receptor (GR) antagonist RU-486. TNF increased endothelial nitric oxide synthase (eNOS) phosphorylation at Thr495 and decreased total eNOS expression and both basal and stimulated eNOS activity. Relaxin co-incubation did not affect eNOS expression but improved its activity via PI3K-dependent Thr495 dephosphorylation and Ser1177 phosphorylation, and additional Ser633 phosphorylation. Via GR, relaxin attenuated the TNF-related stimulation of endothelin-1 expression, superoxide and nitrotyrosine formation, and arginase II expression. Relaxin restored, via GR-CCAAT/enhancer-binding protein-β (c/EBP-β)-mediated promoter stimulation, the compromised expression of superoxide dismutase-1 (SOD1). In rat aortic endothelial cells, relaxin activated protein kinase B (Akt) and repressed TNF-induced nuclear factor-κB and activator protein-1. Finally, the relevance of the different findings to the model used was proved by pharmacological interventions. CONCLUSION Relaxin improved endothelial dysfunction by promoting eNOS activity, suppressing endothelin-1 and arginase-II expression, and up-regulating SOD1 via GR, GR-c/EBP-β, and PI3K-Akt pathways. This corroborates the notion that it functions as an endogenous and potentially therapeutic vasoprotector.