Miracle-dz/UniFAR-FTUs huggingface dataset:
Accounting for the learnability of saltation in phonological theory: A maximum entropy model with a P-map bias

Abstract: Saltatory alternations occur when two sounds alternate with each other, excluding a third sound that is phonetically intermediate between the two alternating sounds (e.g. [p] alternates with [β], with nonalternating, phonetically intermediate [b]). Such alternations are attested in natural language, so they must be learnable; however, experimental work suggests that they are dispreferred by language learners. This article presents a computationally implemented phonological framework that can account for both the existence and the dispreferred status of saltatory alternations. The framework is implemented in a maximum entropy learning model (Goldwater & Johnson 2003) with two significant components. The first is a set of constraints penalizing correspondence between specific segments, formalized as *Map constraints (Zuraw 2007, 2013), which enables the model to learn saltatory alternations at all. The second is a substantive bias based on the P-map (Steriade 2009 [2001]), implemented via the model’s prior probability distribution, which favors alternations between perceptually similar sounds. Comparing the model’s predictions to results from artificial language experiments, the substantively biased model outperforms control models that do not have a substantive bias, providing support for the role of substantive bias in phonological learning.
THREE ELEMENT CONTROL OF BOILER DRUM USING PARTICLE SWARM OPTIMIZATION TECHNIQUE

Boilers are the important instrument in any production plant which is used to store large amount of water (demineralized) for the purpose of production of steam. The generated steam is used to drive the prime mover (turbine) which rotates the shaft in the generator for the production of power. Water level control in boiler drum is very important. Low level of water in the drum may cause explosion of boiler whereas higher level in the boiler reduces steam production as well as plant efficiency. Three element control is the main criteria involving in the boiler water level control. Now a days many plants doing the three element control using PI controller with the help of trained persons. This project provides a new way for very fast auto tuning, shorter calculation time and stable convergence PI controller for three element control in boiler. Steam flow, feed water flow and water level in drum are the three elements that has to be controlled. In this project, feed water flow is taken as the primary variable and other two elements are cascaded. Boiler specification and data are taken from the Madras Fertilizers Limited.
Time-Dependent Viscoelastic Stress Transfer and Earthquake Triggering

We summarize key features of time-dependent viscoelastic stress transfer following the 1992 MW=7.3 Landers and MW=7.1 Hector Mine earthquakes in southern California, for which substantial seismological, geodetic, and geological constraints are available. The 1999 Hector Mine quake occurred only 20 km from but 7 years after the 1992 Landers event, indicating the possibility of time-dependent postseismic stress triggering. We developed 3-D viscoelastic models to simulate stress transfer from the ductile lower crust and/or upper mantle to the brittle upper crust in the 7 years following the 1992 Landers quake. We further derived viscoelastic parameters that can reproduce the observed post-Landers horizontal surface deformation. Our calculations revealed that post-Landers viscous flow in either lower crust or upper mantle can cause postseismic stress increases of up to 1-2 bars at the Hector Mine hypocenter, potentially advancing the occurrence of the 1999 Hector Mine earthquake. Furthermore, the Landers-Hector Mine earthquake sequence has caused significant stress changes on parts of the southern San Andreas and other faults in southern California. For example, the combined coseismic and postseismic stress increase is 2.3–3.5 bars on the San
Solvatochromic Study of Organic Dyes : A Qualitative Approach using Semiempirical ( ZINDO-IEFPCM ) Method

The present investigation accounts for determining computationally the solvatochromic behavior of Coumarin dyes (C478, C519 and C523) in series of alcohols. Experimentally observed positive and negative solvatochromism are accounted for and their behavior is well explained by computational results. The semi empirical ) 30 ( T E scale has been adopted to define the polarity dependent solvent-solute interaction. The ZINDO/PCM method follows the observed experimental trends for all the Coumarin dyes. The small discrepancies with experimental values are discussed in detail. Computational results suggest that ZINDO/PCM values lie in closer proximity to the experimental values depending on the polarity of Coumarin molecules. Switching between positive and negative solvatochromism in polarity scale referred as polarity switch is discussed.
The p85α Regulatory Subunit of Class IA Phosphoinositide 3-Kinase Regulates β-Selection in Thymocyte Development1

We examined the role of class IA PI3K in pre-TCR controlled β-selection and TCR-controlled positive/negative selection in thymic development. Using mice deficient for p85α, a major regulatory subunit of the class IA PI3K family, the role of class IA PI3K in β-selection was examined by injection of anti-CD3ε mAb into p85α−/−Rag-2−/− mice, which mimics pre-TCR signals. Transition of CD4−CD8− double-negative (DN) to CD4+CD8+ double-positive (DP) thymocytes triggered by anti-CD3ε mAb was significantly impaired in p85α−/−Rag-2−/− compared with p85α+/−Rag-2−/− mice. Furthermore, DP cell numbers were lower in p85α−/−DO11.10/Rag-2−/− TCR-transgenic mice than in DO11.10/Rag-2−/− mice. In addition, inhibition by IC87114 of the major class IA PI3K catalytic subunit expressed in lymphocytes, p110δ, blocked transition of DN to DP cells in embryonic day 14.5 fetal thymic organ culture without affecting cell viability. In the absence of phosphatase and tensin homolog deleted on chromosome 10, where class IA PI3K signals would be amplified, the DN to DP transition was accelerated. In contrast, neither positive nor negative selection in Rag-2−/−TCR-transgenic mice was perturbed by the lack of p85α. These findings establish an important function of class IA PI3K in the pre-TCR-controlled developmental transition of DN to DP thymocytes.
A Virtual Teams Model for Supporting Maritime Technology Management

SUMMARY Computer and networking technology on board ships is increasing in complexity as the levels of automation and monitoring at sea evolve. Ships are acknowledged to be a System of systems, including both IT (Information Technology) and OT (Operational technology). A virtual team that includes members of the various on-board departments, shore based technical support staff and vendors perform management of technical equipment, largely in isolation. It is possible to conclude that the technology installation is one of the only operational aspects of a vessel, where no one has the full picture and could drive a coordinated response to a major technology issue. This paper will propose a best practice framework for governance of technology on board ships. This framework will serve as an input to the process of including cyber security in the ISM Safety Management Manual for the 2021 deadline.
New Approaches Codon Model Using Kronecker Product

Models of codon evolution have attracted particular interest because of their unique capabilities to detect selection forces and their high fit when applied to sequence evolution. We described here a novel approach for modeling codon evolution, which is based on Kronecker product of matrices. The 61 61 codon substitution rate matrix is created using Kronecker product of three 4 4 nucleotide substitution matrices, the equilibrium frequency of codons, and the selection rate parameter. The entities of the nucleotide substitution matrices and selection rate are considered as parameters of the model, which are optimized by maximum likelihood. Our fully mechanistic model allows the instantaneous substitution matrix between codons to be fully estimated with only 19 parameters instead of 3,721, by using the biological interdependence existing between positions within codons. We illustrate the properties of our models using computer simulations and assessed its relevance by comparing the AICc measures of our model and other models of codon evolution on simulations and a large range of empirical data sets. We show that our model fits most biological data better compared with the current codon models. Furthermore, the parameters in our model can be interpreted in a similar way as the exchangeability rates found in empirical codon models.
PRODUCTIVITY GROWTH AND COMPETITION IN SPANISH MANUFACTURING FIRMS: WHAT HAS HAPPENED IN RECENT YEARS?

This paper addresses the issue of the relationship between productivity and market competition. In comparison to the economies of other European countries, the Spanish economy has been growing, while productivity growth has stagnated. Here we provide empirical evidence about the relationship between productivity and market competition from Spanish manufacturing firms at firm level between 1994 and 2004. Correcting for selection bias, our study pays special attention to the patterns of productivity growth between openness and non-openness firms. When market competition increases the effect on firms operating in domestic markets is positive but when the level of competition is high incentives to invest in innovation and productivity gains disappear. The empirical relationship between competition and productivity is an inverted U-shape, where productivity growth is highest at intermediate levels of competition. The productivity growth of firms operating in international markets is higher than that of non-openness firms, but when market competition rises they moderate their productivity growth. Our empirical results suggest that the correct competition policy in the Spanish economy should remove the barriers to competition in internal markets in order to increase the incentives for manufacturing firms to invest in innovation and productivity growth.
Effect of Mental Fatigue on Error Monitoring

Performance monitoring tasks generate response-locked event-related potentials (ERPs), generated from anterior cingulate cortex (ACC) region of the medial prefrontal cortex. However, the quantitative relationship between mental fatigue and error monitoring has not been closely investigated, which is the purpose of this study. Twenty-one participants were asked to perform a 100-minute 2-back work memory task for inducing mental fatigue and assessing error monitoring. The results showed that the participants were less accurate and slower during mental fatigue. The error-related negativity (ERN) amplitudes were statistically reduced after carrying out the 2-back task. The current pattern of results provides evidence that sustained attention plays an important role in maintaining error monitoring. Introduction Human error is considered one of the primary contributors to accidents in complex work domains such as trucking, nuclear power plants, aviation and aerospace [1-3]. Many factors, including task characteristics, physical environment, and human system interaction, result in human error. An especially important factor is performance monitoring, which is vital for adapting one's own behavior and enabling effective cognitive control. Error monitoring and feedback processing are two important facets of performance monitoring [4, 5]. As a critical function of the human cognitive system, error monitoring is the ability to monitor and evaluate the outcomes and consequences of behavior in order to optimize and adapt subsequent behavior to realize goals such as driving vehicles, the rendezvous and docking (RAD) in human spaceflight, and so on. Error processing and monitoring usually accompanies a negative deflection appearing in the event-related potentials (ERPs) after the time an error is committed, called the error-related negativity (ERN) [6-9]. The ERN is a negative potential that has a peak amplitude as high as about10 μV, and a peak latency of approximately 50-150 ms after the onset of electromyography activity associated with an erroneous response, which is located at the fronto-central scalp [10-12]. The ERN reflects an error monitoring system in the brain, including error detection and error evaluation. Error monitoring is known to involve the anterior cingulate cortex (ACC) region of the medial prefrontal cortex (PFC) [13]. 276 This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Copyright © 2017, the Authors. Published by Atlantis Press. 2nd International Conference on Biomedical and Biological Engineering 2017 (BBE 2017) Advances in Biological Sciences Research (ABSR), volume 4
Meso-Level of Economy: Theoretical Approaches and Math Modeling

В статье представлен ретроспективный анализ развития мезоэкономической идеи от первого упоминания этого термина в научной литературе (Wittfogel, 1962) до опубликованных в настоящее время исследовательских работ. Выделено три основных направления понимания мезоуровня в современной англоязычной неортодоксальной экономической литературе: неошумпетерианская интерпретация, эволюционный подход и неоинституциональная трактовка. Нами рассмотрены все три направления, показаны взаимосвязи, сходства и различия между ними. Выявлено основное различие между англоязычными и русскоязычными традициями мезоэкономических исследований. Для англоязычных авторов характерной особенностью является внимание к задаче преодоления принципов методологического индивидуализма в экономических исследованиях, что необходимо для понимания процессов институционализации правил взаимодействия между экономическими индивидами. Напротив, рассмотрение российскими или, точнее, постсоветскими экономистами мезоэкономических тем было обусловлено историческими реалиями перехода от советской плановой экономики к рыночной экономике постсоветской России, характеризовавшимися эффектом распада связей между экономическими субъектами.
Zero-shot Learning for Speech Recognition with Universal Phonetic Model

There are more than 7,000 languages in the world, but due to the lack of training sets, only a small number of them have speech recognition systems. Multilingual speech recognition provides a solution if at least some audio training data is available. Often, however, phoneme inventories differ between the training languages and the target language, making this approach infeasible. In this work, we address the problem of building an acoustic model for languages with zero audio resources. Our model is able to recognize unseen phonemes in the target language, if only a small text corpus is available. We adopt the idea of zero-shot learning, and decompose phonemes into corresponding phonetic attributes such as vowel and consonant. Instead of predicting phonemes directly, we first predict distributions over phonetic attributes, and then compute phoneme distributions with a customized acoustic model. We extensively evaluate our English-trained model on 20 unseen languages, and find that on average, it achieves 9.9% better phone error rate over a traditional CTC based acoustic model trained on English.
Technical Note - Time Inconsistency of Optimal Policies of Distributionally Robust Inventory Models

The authors extend previous studies of time inconsistency to risk averse (distributionally robust) inventory models and show that time inconsistency is not unique to robust multistage decision making, but may happen for a large class of risk averse/distributionally robust settings. In particular, they demonstrate that if the respective risk measures are not strictly monotone, then there may exist infinitely many optimal policies which are not base-stock and not time consistent. This is in a sharp contrast with the risk neutral formulation of the inventory model where all optimal policies are base-stock and time consistent.
Eunomos , a legal document management system based on legislative XML and ontologies ( position paper )

In this position paper, we illustrate the ongoing work and future developments on the Eunomos software, an advanced legal document management system to classify norms, based on legislative XML representation of laws which are retrieved automatically from institutional legislative portals, which complements the tool for building legal ontologies called Legal Taxonomy Syllabus.
Nephroprotective role of alcoholic extract of Pterocarpus marsupium heartwood against experimentally induced diabetic nephropathy

Context: Heartwood of Pterocarpus marsupium has been widely reported for its effect on diabetes clinical or preclinically. However, role in diabetic complications is yet to be revealed. Aims: To investigate the effect of alcoholic extract of the heartwood of Pterocarpus marsupiumin experimentally induced diabetic nephropathy in rats. Methods: The streptozotocin (STZ) 55 mg/kg, i.p., once daily induced diabetes in Sprague-Dawley rats. These animals were treated orally with alcoholic extract of P. marsupium (100, 200 and 400 mg/kg) or glimepiride (10 mg/kg) for 60 days. Body weight, blood glucose, glycosylated hemoglobin (HBA1c), biochemical markers of renal function were estimated on day 30 and at the end of study (day 60). Kidney weight measurement, oxidative stress markers such as lipid peroxidation (TBARS), catalase, superoxide dismutase, reduced glutathione were estimated in kidney tissues, and histopathological evaluation was carried out at the end of study period. Results: The administration of an alcoholic extract of P. marsupium showed a decrease in blood glucose, HBA1c, kidney weight, serum creatinine, blood urea nitrogen, serum uric acid, urea, urine volume, urine albumin and the level of TBARS. While the increase in urine creatinine, the activity of superoxide dismutase and glutathione were observed when compared to the diabetic control group. This effect was observed significantly at the highest dose of the plant extract. The histopathological study also confirmed that alcoholic extract prevented structural kidney damage. Conclusions: These results suggest that the alcoholic extract of P. marsupium has renoprotective effects against STZ induced diabetic nephropathy.
Location and Image-Based Plant Recognition and Recording System

The earth supports a rich diversity of plants. However, people seldom know their names. One method of identifying plant species is by referring to illustrated plant handbooks. However, this method is ineﬀective for people unfamiliar with plant features. To help people identify plants, this study develops an application that automatically iden-tiﬁes leaves by utilizing content-based image retrieval and location information. Back-ground subtraction, the ﬁrst step in our method, was performed using Otsu, color slicing, and GrabCut methods. The most favorable method was determined by evaluating the image segmentation rate. Subsequently, the plant features were extracted using one-and two-dimensional Fourier descriptors. Twenty images each of 21 plant species were collected indoors. The GrabCut method and one-dimensional Fourier descriptor demonstrated the optimal performance. Plant species were automatically identiﬁed through leaf recognition using mobile device. The mobile application records plant information through crowdsourcing. To test the system, we collected 10 outdoor images each of 50 plant species. Location-based search engine assisted plant recognition. The experimental results were 78% accurate, that is, the correct species was the ﬁrst-ranked suggestion in 78% of the cases.
Privacy Protection in E-commerce : Identity-based Anonymous Privacy Agent

An identity-based anonymous agent privacy protection scheme is proposed to deal with the problems of privacy information disclosure. In this paper, some analysis about the status quo of the privacy protection research in China and some partitioning of privacy data in ecommerce are given. We further illuminate the whole process of privacy protection by an ecommerce privacy protection scheme based on the identity of the anonymous agent. At last, the simulation results are analyzed. Experimental results show that the novel scheme can ensure the anonymous reliability, integrity and efficiency to realize privacy protection in ecommerce process.
First normal stress difference and crystallization in a dense sheared granular fluid

The first normal stress difference (N1) and the microstructure in a dense sheared granular fluid of smooth inelastic hard-disks are probed using event-driven simulations. While the anisotropy in the second moment of fluctuation velocity, which is a Burnett-order effect, is known to be the progenitor of normal stress differences in dilute granular fluids, we show here that the collisional anisotropies are responsible for the normal stress behavior in the dense limit. As in the elastic hard-sphere fluids, N1 remains positive (if the stress is defined in the compressive sense) for dilute and moderately dense flows, but becomes negative above a critical density, depending on the restitution coefficient. This sign-reversal of N1 occurs due to the microstructural reorganization of the particles, which can be correlated with a preferred value of the average collision angle θav=π/4±π/2 in the direction opposing the shear. We also report on the shear-induced crystal-formation, signaling the onset of fluid–solid co.
Artificial Intelligence Neural Networks Applications in Forecasting Financial Markets and Stock Prices

The interest in using artificial neural networks (ANN’s) for forecasting has led to a tremendous surge in research activities over time. Artificial Neural Networks are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. Forecasting problems arise in so many different disciplines and the literature on forecasting using ANN’s is scattered in so many diverse fields that it is hard for a researcher to be aware of all the work done to date in the area. There is an extensive literature in financial applications of ANN’s. Naturally forecasting stock price or financial markets has attracted considerable interest and it has been one of the biggest challenges. This paper reviews the history of the application of artificial neural networks for forecasting future stock prices. From the introduction of the back-propagation algorithm in 1980’s for training an MLP neural network by Werbos, who used this technique to train a neural network and claimed that neural networks are better than regression methods and Box-Jenkins model in prediction problems through the application of such technics to financial markets forecasting by pioneers in the field like White, Kimoto and Kamijo to the more recent studies of stocks prices in not only the biggest capital markets but also in some emerging and illiquid markets, we will look at the progress made in the past more than twenty five years of