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1
Meng, Li, and Yang Sun. "Research on Automated Forex Trading System Based on BP Neural Network." Advanced Materials Research 753-755 (August 2013): 3080–83. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.3080.
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This paper reports on an expert advisor for forex trading based on Back Propagation Neural Network (BPNN) in MetaTrader4 platform. A single hidden layer feedforward network was established for foreign exchange rate prediction. Trading rules based on the prediction results was designed and realized. Finally, we optimized the parameters according to the profitability performed on EUR/USD, GBP/USD currency pairs separately. The optimized results are able to achieve good results in the training series. In the test series, the strategies are consistently profitable for at least the first twenty days. It is concluded that the BPNN based model do have the ability to make profits from the experimental currency pairs for the period investigated.
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Antonov, Andrii V., and Víctor G. Sychenko. "Resource evaluation of friction pair “contact wire – contact strip”." Archives of Transport 44, no. 4 (November 30, 2017): 7–15. http://dx.doi.org/10.5604/01.3001.0010.6157.
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Purpose. Investigate the impact of current collection system parameters and external factors on the resource of friction pair “contact wire – contact strip”. Relevance. The level of electrified railways reliability depends on the reliability and durability of the individual objects of power supply system and the locomotive facilities, in turn, the current collection quality depends on design of contact network and pantographs, contact wires materials, contact strips and external influencing factors. The resource system “contact wire – contact strip” is an important technical and economic characteristics, and its average actual resource is much less established. It means that the actual resource does not reach the optimum value from an economic point. Increasing velocity of the electric rolling stock exacerbates the problem of improving the current collection quality, becomes an actual task of increasing the resource friction pair “ contact wire – contact strip” for mainline railways. Scientific novelty. This work determined the influence structural parameters of the friction pair, the current collection system parameters and external factors on the wear rate of the friction pair “contact wire – contact strip”. Proposed to create a predictive mathematical model that uses the dependence of the influencing parameters obtained in this work to assess the wear and resource of contact wire and contact strips. Practical importance. Developed the device for wear researching of friction pairs in laboratory conditions allows assessing the wear rate of the system “contact wire – contact strip” and their residual resource at different combinations of the friction pair and external factors. The obtained results allow us to construct a predictive mathematical model to evaluate wear rate of the elements of the friction pair “contact wire – contact strip” and identify their residual resource.
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Way, Samuel F., Allison C. Morgan, Daniel B. Larremore, and Aaron Clauset. "Productivity, prominence, and the effects of academic environment." Proceedings of the National Academy of Sciences 116, no. 22 (April 29, 2019): 10729–33. http://dx.doi.org/10.1073/pnas.1817431116.
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Faculty at prestigious institutions produce more scientific papers, receive more citations and scholarly awards, and are typically trained at more-prestigious institutions than faculty with less prestigious appointments. This imbalance is often attributed to a meritocratic system that sorts individuals into more-prestigious positions according to their reputation, past achievements, and potential for future scholarly impact. Here, we investigate the determinants of scholarly productivity and measure their dependence on past training and current work environments. To distinguish the effects of these environments, we apply a matched-pairs experimental design to career and productivity trajectories of 2,453 early-career faculty at all 205 PhD-granting computer science departments in the United States and Canada, who together account for over 200,000 publications and 7.4 million citations. Our results show that the prestige of faculty’s current work environment, not their training environment, drives their future scientific productivity, while current and past locations drive prominence. Furthermore, the characteristics of a work environment are more predictive of faculty productivity and impact than mechanisms representing preferential selection or retention of more-productive scholars by more-prestigious departments. These results identify an environmental mechanism for cumulative advantage, in which an individual’s past successes are “locked in” via placement into a more prestigious environment, which directly facilitates future success. The scientific productivity of early-career faculty is thus driven by where they work, rather than where they trained for their doctorate, indicating a limited role for doctoral prestige in predicting scientific contributions.
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Henrickson, Phil. "Predicting the costs of war." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 17, no. 3 (March 6, 2019): 285–308. http://dx.doi.org/10.1177/1548512919826375.
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The expected cost of war is a foundational concept in the study of international conflict. However, the field currently lacks a measure of the expected costs of war, and thereby any measure of the bargaining range. In this paper, I develop a proxy for the expected costs of war by focusing on one aspect of war costs – battle deaths. I train a variety of machine learning algorithms on battle deaths for all countries participating in fatal military disputes and interstate wars between 1816 and 2007 in order to maximize out-of-sample predictive performance. The best performing model (random forest) improves performance over that of a null model by 25% and a linear model with all predictors by 9%. I apply the random forest to all interstate dyads in the Correlates of War dataverse from 1816 to 2007 in order to produce an estimate of the expected costs of war for all existing country pairs in the international system. The resulting measure, which I refer to as Dispute Casualty Expectations, can be used to fully explore the implications of the bargaining model of war, as well as allow applied researchers to develop and test new theories in the study of international relations.
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Alvarez, Pedro D., Lucas Delage, Mauricio Valenzuela, and Jorge Zanelli. "Unconventional SUSY and Conventional Physics: A Pedagogical Review." Symmetry 13, no. 4 (April 9, 2021): 628. http://dx.doi.org/10.3390/sym13040628.
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In supersymmetric extensions of the Standard Model, the observed particles come in fermion–boson pairs necessary for the realization of supersymmetry (SUSY). In spite of the expected abundance of super-partners for all the known particles, not a single supersymmetric pair has been reported to date. Although a hypothetical SUSY breaking mechanism, operating at high energy inaccessible to current experiments cannot be ruled out, this reduces SUSY’s predictive power and it is unclear whether SUSY, in its standard form, can help reducing the remaining puzzles of the standard model (SM). Here we argue that SUSY can be realized in a different way, connecting spacetime and internal bosonic symmetries, combining bosonic gauge fields and fermionic matter particles in a single gauge field, a Lie superalgebra-valued connection. In this unconventional representation, states do not come in SUSY pairs, avoiding the doubling of particles and fields and SUSY is not a fully off-shell invariance of the action. The resulting systems are remarkably simple, closely resembling a standard quantum field theory and SUSY still emerges as a contingent symmetry that depends on the features of the vacuum/ground state. We illustrate the general construction with two examples: (i) A 2 + 1 dimensional system based on the osp(2,2|2) superalgebra, including Lorentz and u(1) generators that describe graphene; (ii) a supersymmetric extension of 3 + 1 conformal gravity with an SU(2,2|2) connection that describes a gauge theory with an emergent chiral symmetry breaking, coupled to gravity. The extensions to higher odd and even dimensions, as well as the extensions to accommodate more general internal symmetries are also outlined.
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Pokrovskaya, O. D., and E. S. Rodneva. "Improvement of management systems for the transportation process at the test site of the Northern Railway." Transport Technician: Education and Practice 2, no. 1 (March 27, 2021): 87–96. http://dx.doi.org/10.46684/10.46684/2687-1033.2021.1.87-96.
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The relevance of the study is determined by the fact that today at the test site of the Northern Railway, client-oriented priorities are being actively implemented and measures are being taken to attract new customers to the network, which will increase the share of highly profitable cargo and loading volumes in general, as well as the volume of cargo operations at stations.An attempt was made to propose a change in technology and increase the throughput on the Solombalka – Karpogory section from five to ten pairs of trains per day, based on an assessment of the prospective volumes of work associated with the development of stations in the region and the operating costs associated with their operation.The current state of cargo and commercial operations in the Arkhangelsk region of the Northern Railway was considered, and the prospects for the development of cargo operations were identified.The subject of research is loading stations. The object of the study is the Arkhangelsk territorial administration of the Northern Railway — a branch of JSC Russian Railways.Methods of system analysis, observation method, statistical method, forecasting and predictive modeling are applied.A feasibility study for changing technology and increasing throughput on the Arkhangelsk city – Karpogory section has been carried out. The economic efficiency of the proposal has been confirmed. The research carried out is the result of a two-year interaction with the Northern Railway. The research elements have already been introduced at the Northern Railway test site.
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Bonneville, Russell, Melanie A. Krook, Esko A. Kautto, Jharna Miya, Michele R. Wing, Hui-Zi Chen, Julie W. Reeser, Lianbo Yu, and Sameek Roychowdhury. "Landscape of Microsatellite Instability Across 39 Cancer Types." JCO Precision Oncology, no. 1 (November 2017): 1–15. http://dx.doi.org/10.1200/po.17.00073.
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Purpose Microsatellite instability (MSI) is a pattern of hypermutation that occurs at genomic microsatellites and is caused by defects in the mismatch repair system. Mismatch repair deficiency that leads to MSI has been well described in several types of human cancer, most frequently in colorectal, endometrial, and gastric adenocarcinomas. MSI is known to be both predictive and prognostic, especially in colorectal cancer; however, current clinical guidelines only recommend MSI testing for colorectal and endometrial cancers. Therefore, less is known about the prevalence and extent of MSI among other types of cancer. Methods Using our recently published MSI-calling software, MANTIS, we analyzed whole-exome data from 11,139 tumor-normal pairs from The Cancer Genome Atlas and Therapeutically Applicable Research to Generate Effective Treatments projects and external data sources across 39 cancer types. Within a subset of these cancer types, we assessed mutation burden, mutational signatures, and somatic variants associated with MSI. Results We identified MSI in 3.8% of all cancers assessed—present in 27 of tumor types—most notably adrenocortical carcinoma (ACC), cervical cancer (CESC), and mesothelioma, in which MSI has not yet been well described. In addition, MSI-high ACC and CESC tumors were observed to have a higher average mutational burden than microsatellite-stable ACC and CESC tumors. Conclusion We provide evidence of as-yet-unappreciated MSI in several types of cancer. These findings support an expanded role for clinical MSI testing across multiple cancer types as patients with MSI-positive tumors are predicted to benefit from novel immunotherapies in clinical trials.
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MacGregor, R. J., and G. Tajchman. "Theory of dynamic similarity in neuronal systems." Journal of Neurophysiology 60, no. 2 (August 1, 1988): 751–68. http://dx.doi.org/10.1152/jn.1988.60.2.751.
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1. The techniques of dynamic similarity from the engineering science of fluid mechanics are applied to neuronal systems to suggest how to scale down critical parameters (such as numbers of constituent cells and synapses, synaptic strengths, thresholds, etc.) from naturally occurring systems to computer models. 2. The interconnectivity of a prototypical neuronal junction is defined in terms of the total number of projecting fibers, receiving cells, synapses, and directly connected cell fiber pairs. Critical derivative parameters are defined in terms of these, including: a global convergence factor, alpha ij, which is the ratio of the numbers of projecting fibers to receiving cells; and an interconnectivity completeness parameter or microscopic convergence/divergence parameter, gamma ij, which measures both the percentage of cells to which a given sending fiber projects (and the percentage of fibers from which a given cell receives) and the percentage of cell fiber combinations which are directly connected. 3. Analysis of the differential equations governing neuroelectric activity in constituent neurons suggests the definition of a sensitivity parameter complex, sigma ij (with components eta ij and mu ij) for each ij junction. These numbers represent the ratio of synaptic drive to current leakage in nonactive neurons. 4. A model for quasi-steady firing suggests the definition of a parameter, rho *j, which may be used to characterize the level of activity in a given neuronal population in terms of its synaptic drive and system parameters. It may be considered as the neuronal analog of the Reynolds number in fluid mechanics. 5. The analysis implies that computer models of neuronal systems should be scaled so as to keep the parameters alpha ij, gamma ij, and sigma ij for every junction at the same values as in the corresponding junctions of naturally occurring system being modeled. Equations for a scaling factor, chi, numbers of constituent synapses, thresholds, etc., are provided. The scaling method is illustrated by a computer simulation example and by application to the junction of the perforant path fibers to the granule cells of the hippocampus. 6. The analysis shows that there is a fundamental trade-off in scaled down computer models between verisimilitude at the level of network interconnectivity and verisimilitude at the level of individual neuronal dynamics. 7. The approach of dynamic similarity is discussed with respect to compression of free parameters and predictive comparison of naturally occurring systems.
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Kania, Krzysztof, Przemysław Juszczuk, and Jan Kozak. "Enhanced Symbolic Description in Analyzing Patterns and Volatility on the Forex Market." Vietnam Journal of Computer Science 06, no. 03 (August 2019): 343–62. http://dx.doi.org/10.1142/s2196888819500180.
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In this paper, we propose a novel approach for transforming financial time-series values into symbolic representation based on value changes. Such approach seems to have few advantages over the existing approaches; one of the most obvious is noise reduction in the data and another one is possibility to find patterns which are universal for investigating different currency pairs. To achieve the goal we introduce a preprocessing method that allows initial data transformation. We also define a text-based similarity measure which can be used as an alternative for methods allowing to find the exact patterns in the historical data. To effectively evaluate our method, we present a concept that allows to predict the potential price movement direction and compare it with the actual price direction observed in the historical data. Such a method gives an opportunity not only to indicate the different price patterns based on the symbolic representation but also at the same time evaluate the predictive power of such patterns. The proposed approach is experimentally verified on 10 different currency pairs, each covering approximately a period of 10 years.
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Li, Jin, Yang Huo, Xue Wu, Enze Liu, Zhi Zeng, Zhen Tian, Kunjie Fan, Daniel Stover, Lijun Cheng, and Lang Li. "Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy." Biology 9, no. 9 (September 7, 2020): 278. http://dx.doi.org/10.3390/biology9090278.
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In the prediction of the synergy of drug combinations, systems pharmacology models expand the scope of experiment screening and overcome the limitations of current computational models posed by their lack of mechanical interpretation and integration of gene essentiality. We therefore investigated the synergy of drug combinations for cancer therapies utilizing records in NCI ALMANAC, and we employed logistic regression to test the statistical significance of gene and pathway features in that interaction. We trained our predictive models using 43 NCI-60 cell lines, 165 KEGG pathways, and 114 drug pairs. Scores of drug-combination synergies showed a stronger correlation with pathway than gene features in overall trend analysis and a significant association with both genes and pathways in genome-wide association analyses. However, we observed little overlap of significant gene expressions and essentialities and no significant evidence that associated target and non-target genes and their pathways. We were able to validate four drug-combination pathways between two drug combinations, Nelarabine-Exemestane and Docetaxel-Vermurafenib, and two signaling pathways, PI3K-AKT and AMPK, in 16 cell lines. In conclusion, pathways significantly outperformed genes in predicting drug-combination synergy, and because they have very different mechanisms, gene expression and essentiality should be considered in combination rather than individually to improve this prediction.
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Nedzvedskas, Jonas, and Povilas Aniūnas. "TRANSFORMATIONS IN RISK MANAGEMENT OF CURRENCY EXCHANGE IN LITHUANIAN COMMERCIAL BANKS." Technological and Economic Development of Economy 13, no. 3 (September 30, 2007): 191–97. http://dx.doi.org/10.3846/13928619.2007.9637799.
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After the adoption of International Convergence of Capital Measurement and Capital Standards (widely known as Basel II requirements) in 2004 the risk management in commercial banks has changed dramatically. Lithuanian commercial banks are in transitional period now adapting their risk management systems to Basel II requirements. Market risk is considered one of the key risks in bank risk management structure, so proper management of market risk is essential for a modern bank. Currency exchange risk usually is the main component of market risk. Currency exchange risk management in Lithuanian commercial banks was not good enough; also the Central Bank's regulatory limits were liberal. But after the adoption of Basel II requirements, the entire risk management system is transforming and currency exchange risk management is affected. The objective of this paper is to demonstrate the transformations of currency exchange in Lithuanian commercial banks and propose an effective model for commercial banking. These transformations are performed in the regulatory system imposed by the Central Bank of Lithuania and through transformations of the bank's internal risk management system moving to internal (usually VaR based) models. VaR models are considered as modern methods for risk management. These models proposed by Central bank or other authorities for internal and statutory risk management in commercial banks. In this article, the proposed variation‐covariation VaR model was tested with real data using the back‐testing method. Back‐testing showed that the proposed model is reliable enough, because the number of mismatches was less than 5 % in all tested currency pairs during all testing. In most currency pairs mismatches percentage was lower than 3 %. Back‐testing results confirm that the VaR method is reliable enough for day‐to‐day using by financial institutions and traders.
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Alsaleem, Maan Y. Anad, and Safwan O. Hasoon. "COMPARISON OF DT& GBDT ALGORITHMS FOR PREDICTIVE MODELING OF CURRENCY EXCHANGE RATES." EUREKA: Physics and Engineering 1 (January 31, 2020): 56–61. http://dx.doi.org/10.21303/2461-4262.2020.001132.
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Recently, many uses of artificial intelligence have appeared in the commercial field. Artificial intelligence allows computers to analyze very large amounts of information and data, reach logical conclusions on many important topics, and make difficult decisions, this will help consumers and businesses make better decisions to improve their lives, and it will also help startups and small companies achieve great long-term success. Currency exchange rates are important matters for both governments, companies, banks and consumers. The decision tree is one of the most widely artificial intelligence tools used in data mining. With the development of this field the decision tree and Gradient boosting decision tree are used to predicate through constructed intelligent predictive system based on it. These algorithms have been used in many stock market forecasting systems based on global market data. The Iraqi dinar exchange rates for the US dollar are affected in local markets, depending on the exchange rate of the Central Bank of Iraq and the features of that auction. The proposed system is used to predict the dollar exchange rates in the Iraq markets Depending on the daily auction data of the Central Bank of Iraq (CBI). The decision tree and Gradient boosting decision tree was trained and testing using dataset of three-year issued by the CBI and compare the performance of both algorithms and find the correlation between the data. (Runtime, accuracy and correlation) criteria are adopted to select the best methods. In system, the characteristic of artificial intelligence have been integrated with the characteristic of data mining to solve problems facing organization to use available data for decision making and multi-source data linking, to provide a unified and integrated view of organization data.
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Tamayo, Daniel, Miles Cranmer, Samuel Hadden, Hanno Rein, Peter Battaglia, Alysa Obertas, Philip J. Armitage, et al. "Predicting the long-term stability of compact multiplanet systems." Proceedings of the National Academy of Sciences 117, no. 31 (July 16, 2020): 18194–205. http://dx.doi.org/10.1073/pnas.2001258117.
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We combine analytical understanding of resonant dynamics in two-planet systems with machine-learning techniques to train a model capable of robustly classifying stability in compact multiplanet systems over long timescales of109orbits. Our Stability of Planetary Orbital Configurations Klassifier (SPOCK) predicts stability using physically motivated summary statistics measured in integrations of the first104orbits, thus achieving speed-ups of up to105over full simulations. This computationally opens up the stability-constrained characterization of multiplanet systems. Our model, trained on ∼100,000 three-planet systems sampled at discrete resonances, generalizes both to a sample spanning a continuous period-ratio range, as well as to a large five-planet sample with qualitatively different configurations to our training dataset. Our approach significantly outperforms previous methods based on systems’ angular momentum deficit, chaos indicators, and parametrized fits to numerical integrations. We use SPOCK to constrain the free eccentricities between the inner and outer pairs of planets in the Kepler-431 system of three approximately Earth-sized planets to both be below 0.05. Our stability analysis provides significantly stronger eccentricity constraints than currently achievable through either radial velocity or transit-duration measurements for small planets and within a factor of a few of systems that exhibit transit-timing variations (TTVs). Given that current exoplanet-detection strategies now rarely allow for strong TTV constraints [S. Hadden, T. Barclay, M. J. Payne, M. J. Holman,Astrophys. J.158, 146 (2019)], SPOCK enables a powerful complementary method for precisely characterizing compact multiplanet systems. We publicly release SPOCK for community use.
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Dias, Fabio S., and Gareth W. Peters. "A Non-parametric Test and Predictive Model for Signed Path Dependence." Computational Economics 56, no. 2 (October 22, 2019): 461–98. http://dx.doi.org/10.1007/s10614-019-09934-7.
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Abstract While several tests for serial correlation in financial markets have been proposed and applied successfully in the literature, such tests provide rather limited information to construct predictive econometric models. This manuscript addresses this gap by providing a model-free definition of signed path dependence based on how the sign of cumulative innovations for a given lookback horizon correlates with the future cumulative innovations for a given forecast horizon. Such concept is then theoretically validated on well-known time series model classes and used to build a predictive econometric model for future market returns, which is applied to empirical forecasting by means of a profit-seeking trading strategy. The empirical experiment revealed strong evidence of serial correlation of unknown form in equity markets, being statistically significant and economically significant even in the presence of trading costs. Moreover, in equity markets, given a forecast horizon of one day, the forecasting strategy detected the strongest evidence of signed path dependence; however, even for longer forecast horizons such as 1 week or 1 month the strategy still detected such evidence albeit to a lesser extent. Currency markets also presented statistically significant serial dependence across some pairs, though not economically significant under the trading formulation presented.
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Boukabou, A., and N. Mansouri. "Neural Predictive Control of Unknown Chaotic Systems." Nonlinear Analysis: Modelling and Control 10, no. 2 (April 25, 2005): 95–106. http://dx.doi.org/10.15388/na.2005.10.2.15125.
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In this work, a neural networks is developed for modelling and controlling a chaotic system based on measured input-output data pairs. In the chaos modelling phase, a neural network is trained on the unknown system. Then, a predictive control mechanism has been implemented with the neural networks to reach the close neighborhood of the chosen unstable fixed point embedded in the chaotic systems. Effectiveness of the proposed method for both modelling and prediction-based control on the chaotic logistic equation and Hénon map has been demonstrated.
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Kumar, Satish. "Revisiting the price-volume relationship: a cross-currency evidence." International Journal of Managerial Finance 13, no. 1 (February 6, 2017): 91–104. http://dx.doi.org/10.1108/ijmf-11-2015-0197.
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Purpose The purpose of this paper is to examine the contemporaneous and causal relationship between returns (volatility) and trading volume in the Indian currency futures market for selected currency pairs; USD-INR, EUR-INR, GBP-INR and JPY-INR, from August 2008 to December 2014. Design/methodology/approach The data for all the currency futures series has been taken from National Stock Exchange of India Limited which represents the daily settlement prices along with trading volume. The contemporaneous returns-volume relation is tested using the generalized method of moments, and Granger-causality framework impulse response function is used to test the predictive ability of returns (volatility) and volume for each other. Findings The author reports a positive contemporaneous relationship between futures returns and trading volume which persists even after controlling for heteroskedasticity providing support to mixture of distribution hypothesis. The results show a unidirectional Granger causality from futures returns to volume. However, there is a significant bidirectional Granger causality between returns volatility and volume lending support to sequential arrival of information hypothesis. Next, the results for cross-currencies show significant influence of US dollar on the volume and returns of all other currencies. Overall, the author suggests that the short- to medium-term movements in the currency markets are dominated by market microstructure and not by fundamentals. Practical implications The findings of this paper are very important for the participants in the market and regulators. The participants in the market require alternatives to diversify their risk. The significant relationship between futures returns (volatility) and trading volume implies that the current trading volume help predict the futures prices and should lead to creation of more reliable hedging strategies for investment purposes. Further, it may interest the regulators who need to decide upon the appropriateness of their policies in the currency futures market. Based on returns-volume relation, they need to set forth market restrictions such as daily price movement and position limits. Originality/value To the best of the knowledge, no study has yet investigated the forecast ability of trading volume to price changes and their volatility in the Indian currency futures market. Given that currency futures market is one of the largest markets in the world, and Indian rupee has seen wide fluctuations in the recent years, it seems exciting to explore the price-volume relationship in the Indian currency futures market.
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Gurrib, Ikhlaas. "Performance of the Average Directional Index as a market timing tool for the most actively traded USD based currency pairs." Banks and Bank Systems 13, no. 3 (August 10, 2018): 58–70. http://dx.doi.org/10.21511/bbs.13(3).2018.06.
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The aim of this study is to test a trading system based on the average directional index, which is complemented with the parabolic stop and reverse indicator. The trend-based system is tested onto the most actively traded USD based foreign currency pairs, using both monthly and weekly data set over 2000–2018. Sharpe and Sortino measures are used to track the performance of the currency pairs, based on total risk and downside risk assumptions. Results are robust tested by decomposing the data into pre and post 2008 financial crisis. Using an investment horizon over 18 years, the reliance upon the monthly model produced lower maximum drawdowns and lesser trades than the weekly model. While Swiss Franc had the best (worse) performance in the monthly (weekly) based model, the Chinese Renminbi witnessed the worse (best) performance in the monthly (weekly) based model. Pre and post financial crisis decompositions suggest the weekly-based system is more reliable than the monthly one with relatively more trades and positive performance, where the Chinese Renminbi and Japanese Yen posted the highest Sharpe and Sortino values of 0.996 and 4.452 respectively in the post crisis period. Proportionately high level of negative returns coupled with relatively low positive Sharpe and Sortino values, however, suggest that a trading system relying on the average directional index and parabolic stop and reverse indicator to be further tested and analyzed at higher frequencies.
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Gao, Hong Li, Si Tu Yu, Deng Wan Li, Xi Xi Wu, and Ming Heng Xu. "Study on Screw Pairs Life Prediction Based on Hidden Markov Model and Neural Networks." Advanced Materials Research 139-141 (October 2010): 2527–31. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.2527.
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In order to estimate and predict the screw performance in the process of machining, a screw life monitoring system was built. Current signal was processed and features sensitive to cutting force were selected, a virtual force sensor was constructed to model the relation between cutting force and current by BPNN. Cutting force was indirectly calculated by the model, so the rating life of screw in different condition could be known and residual life also be reckoned by historical database. A three-way vibration sensor was installed on screw pair base; screw condition could be induced by HMM which input was 15 vibration signal features. As machining condition changed, corresponding new HMM would be built by adaptive method. Finally, the residual life of screw could be gotten by multi-HMM and BPNN. The experimental results show the model proposed in the paper is effective and high precision.
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Karaçor, Adil Gürsel, and Turan Erman Erkan. "Exploiting Visual Features in Financial Time Series Prediction." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (April 2020): 61–76. http://dx.doi.org/10.4018/ijcini.2020040104.
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The possibility to enhance prediction accuracy for foreign exchange rates was investigated in two ways: first applying an outside the box approach to modeling price graphs by exploiting their visual properties, and secondly employing the most efficient methods to detect patterns to classify the direction of movement. The approach that exploits the visual properties of price graphs which make use of density regions along with high and low values describing the shape; hence, the authors propose the name ‘Finance Vision.' The data used in the predictive model consists of 1-hour past price values of 4 different currency pairs, between 2003 and 2016. Prediction performances of state-of-the-art methods; Extreme Gradient Boosting, Artificial Neural Network and Support Vector Machines are compared over the same data with the same sets of features. Results show that density based visual features contribute considerably to prediction performance.
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Liu, Wantai, Weicai Xie, Yunong Lv, and Zhan Zhou. "Sensorless Control of Brushless Doubly Fed Induction Generator with Nonlinear Loads for Stand-Alone Power Generation Systems." Mathematical Problems in Engineering 2020 (July 11, 2020): 1–13. http://dx.doi.org/10.1155/2020/6507593.
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This paper presents a sensorless control scheme for the stand-alone brushless doubly fed induction generator (BDFIG) feeding nonlinear loads. The fundamental and harmonic components of the distorted power winding (PW) voltage caused by the nonlinear loads are extracted and controlled separately. A rotor speed observer is employed to estimate the speed based on the PW voltage and control winding (CW) current without the need of any other machine parameters except for the number of pole pairs. Since the d- and q-axis references of the CW current from the PW voltage control loop contain both dc and ac components, which cannot be tracked easily by conventional PI controllers, a CW predictive current controller is designed to regulate the CW current. Finally, the performance of the proposed control scheme is verified by comprehensive experiments on a 35-kVA prototype BDFIG.
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Maysigova, L. A., Sh U. Niyazbekova, K. G. Bunevich, L. P. Moldashbaeva, T. M. Mezentseva, and A. Brodunov. "CURRENT FEATURES OF CURRENCY FORMATION OF CURRENCIES ON THE EXAMPLE OF THE US DOLLAR – EUR." BULLETIN 2, no. 390 (April 15, 2021): 90–97. http://dx.doi.org/10.32014/2021.2518-1467.55.
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The relevance of the research topic is determined by globalization processes, which have a huge impact on our country, as well as on the countries around us and their economies. In modern conditions, it is obvious that the financial difficulties of one country can cause a global crisis. Issues of a qualitative analysis of the monetary system are important for the stability of the economies of countries. The authors of the article emphasize that the monetary system is needed in order to regulate foreign exchange relations. The authors did not choose the EUR/USD pair by chance – it is the most traded currency pair in the Forex market (about 29% of the total daily trading volume). Such popularity is due primarily to the fact that the United States and the European Union are two of the strongest economies in the world. In addition, this pair responds quite predictably to the main economic indicators relating to the United States and the European Union. Based on the analysis, the authors made conclusions, made recommendations on the need to adapt the trading strategy to market volatility. The procedure for forming EUR/USD quotes at various hours, days and months has been studied. EUR/USD is compared with several other currency pairs and their ranges in separate trading sessions. the following conclusions are made: EUR/USD has medium volatility compared to other pairs under consideration, but is clearly inferior to GBPUSD and GBPJPY; the volatility for most couples during the Asian session is low, and in the case of detruding it encourages the use of scalping; during the European and American sessions, volatility is almost doubled.
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Xuan, Zhanwei, Xiang Feng, Jingwen Yu, Pengyao Ping, Haochen Zhao, Xianyou Zhu, and Lei Wang. "A Novel Method for Predicting Disease-Associated LncRNA-MiRNA Pairs Based on the Higher-Order Orthogonal Iteration." Computational and Mathematical Methods in Medicine 2019 (May 2, 2019): 1–13. http://dx.doi.org/10.1155/2019/7614850.
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A lot of research studies have shown that many complex human diseases are associated not only with microRNAs (miRNAs) but also with long noncoding RNAs (lncRNAs). However, most of the current existing studies focus on the prediction of disease-related miRNAs or lncRNAs, and to our knowledge, until now, there are few literature studies reported to pay attention to the study of impact of miRNA-lncRNA pairs on diseases, although more and more studies have shown that both lncRNAs and miRNAs play important roles in cell proliferation and differentiation during the recent years. The identification of disease-related genes provides great insight into the underlying pathogenesis of diseases at a system level. In this study, a novel model called PADLMHOOI was proposed to predict potential associations between diseases and lncRNA-miRNA pairs based on the higher-order orthogonal iteration, and in order to evaluate its prediction performance, the global and local LOOCV were implemented, respectively, and simulation results demonstrated that PADLMHOOI could achieve reliable AUCs of 0.9545 and 0.8874 in global and local LOOCV separately. Moreover, case studies further demonstrated the effectiveness of PADLMHOOI to infer unknown disease-related lncRNA-miRNA pairs.
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Kargas, Nikos, and Nicholas D. Sidiropoulos. "Nonlinear System Identification via Tensor Completion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4420–27. http://dx.doi.org/10.1609/aaai.v34i04.5868.
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Function approximation from input and output data pairs constitutes a fundamental problem in supervised learning. Deep neural networks are currently the most popular method for learning to mimic the input-output relationship of a general nonlinear system, as they have proven to be very effective in approximating complex highly nonlinear functions. In this work, we show that identifying a general nonlinear function y = ƒ(x1,…,xN) from input-output examples can be formulated as a tensor completion problem and under certain conditions provably correct nonlinear system identification is possible. Specifically, we model the interactions between the N input variables and the scalar output of a system by a single N-way tensor, and setup a weighted low-rank tensor completion problem with smoothness regularization which we tackle using a block coordinate descent algorithm. We extend our method to the multi-output setting and the case of partially observed data, which cannot be readily handled by neural networks. Finally, we demonstrate the effectiveness of the approach using several regression tasks including some standard benchmarks and a challenging student grade prediction task.
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Yu, Dan, Peng Liu, Dezhi Qiao, and Xianglong Tang. "A Safety Prediction System for Lunar Orbit Rendezvous and Docking Mission." Algorithms 14, no. 6 (June 21, 2021): 188. http://dx.doi.org/10.3390/a14060188.
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In view of the characteristics of the guidance, navigation and control (GNC) system of the lunar orbit rendezvous and docking (RVD), we design an auxiliary safety prediction system based on the human–machine collaboration framework. The system contains two parts, including the construction of the rendezvous and docking safety rule knowledge base by the use of machine learning methods, and the prediction of safety by the use of the base. First, in the ground semi-physical simulation test environment, feature extraction and matching are performed on the images taken by the navigation surveillance camera. Then, the matched features and the rendezvous and docking deviation are used to form training sample pairs, which are further used to construct the safety rule knowledge base by using the decision tree method. Finally, the safety rule knowledge base is used to predict the safety of the subsequent process of the rendezvous and docking based on the current images taken by the surveillance camera, and the probability of success is obtained. Semi-physical experiments on the ground show that the system can improve the level of intelligence in the flight control process and effectively assist ground flight controllers in data monitoring and mission decision-making.
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Rahman, Afzalur, and Ayub Khan Dawood. "Bitcoin and Future of Cryptocurrency." Ushus - Journal of Business Management 18, no. 1 (January 1, 2019): 61–66. http://dx.doi.org/10.12725/ujbm.46.5.
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There are diverse variables that impact the future usage of bitcoin, from issues related to security to concerns pertaining to garnering acceptance from the market and customers. In this article, few predictive statements have been analysed regarding the future of bitcoin, such as (1) the growth of share of digital purchases, (2) customer acceptance for blockchain innovation in electronic settlements, payment and banking system, (3) the emergence of bitcoin and also various other cryptocurrencies as niche cash, and (4) the implications of bitcoin or some other cryptocurrency as a specific niche cash in nations with specifically weak currency, and (5) the possibilities of regulative dangers of bitcoin usage in these nations.
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Qin, Chongli, and Lucy J. Colwell. "Power law tails in phylogenetic systems." Proceedings of the National Academy of Sciences 115, no. 4 (January 8, 2018): 690–95. http://dx.doi.org/10.1073/pnas.1711913115.
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Covariance analysis of protein sequence alignments uses coevolving pairs of sequence positions to predict features of protein structure and function. However, current methods ignore the phylogenetic relationships between sequences, potentially corrupting the identification of covarying positions. Here, we use random matrix theory to demonstrate the existence of a power law tail that distinguishes the spectrum of covariance caused by phylogeny from that caused by structural interactions. The power law is essentially independent of the phylogenetic tree topology, depending on just two parameters—the sequence length and the average branch length. We demonstrate that these power law tails are ubiquitous in the large protein sequence alignments used to predict contacts in 3D structure, as predicted by our theory. This suggests that to decouple phylogenetic effects from the interactions between sequence distal sites that control biological function, it is necessary to remove or down-weight the eigenvectors of the covariance matrix with largest eigenvalues. We confirm that truncating these eigenvectors improves contact prediction.
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Anwari, S. "Implementation of Neural Predictive Control To Distillation Column." REAKTOR 10, no. 1 (June 12, 2017): 24. http://dx.doi.org/10.14710/reaktor.10.1.24-30.
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This paper presents a neural predictive controller that is applied to distillation column. Distillation columns represent complex multivariable system, with fast and slow dynamics, significant interactions and directionality. A phenomenological model (i.e. a model derived from fundamental equation like mass and energy balance) of a distillation column is very complicated. For this reason, classical linear controller, such as PID (Proportional, Integral and Derivative) controller, will provide robustness only over relatively small range operation because of complexity and operation without lack of robustness. In this work, a neural network is developed for modeling and controlling a distillation column based on measured input-outputdata pairs. In distillation column, a neural network is trained on the unknown parameters of the system. The resulting implementationof the neural predictive controller is able to eliminate the most significant obstacles encountered in conventional predictive control application by facilitating the development of complex multivariable models and providing a rapid, reliable solution to the control algorithm. Controller design and implementation are illustrated for a plant frequently referred to in the literature. Result are given for simulation experiments, which demonstrate the advantage of the neural based predictive controller both at the transient region and at the steady state region to overcome any overshoots.Keywords : neural predictive controller, distillation column, complex multivariable models
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Han, Huirui, Mengxing Huang, Yu Zhang, and Jing Liu. "Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data." Applied Sciences 8, no. 9 (September 9, 2018): 1597. http://dx.doi.org/10.3390/app8091597.
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The clinical decision support system provides an automatic diagnosis of human diseases using machine learning techniques to analyze features of patients and classify patients according to different diseases. An analysis of real-world electronic health record (EHR) data has revealed that a patient could be diagnosed as having more than one disease simultaneously. Therefore, to suggest a list of possible diseases, the task of classifying patients is transferred into a multi-label learning task. For most multi-label learning techniques, the class imbalance that exists in EHR data may bring about performance degradation. Cross-Coupling Aggregation (COCOA) is a typical multi-label learning approach that is aimed at leveraging label correlation and exploring class imbalance. For each label, COCOA aggregates the predictive result of a binary-class imbalance classifier corresponding to this label as well as the predictive results of some multi-class imbalance classifiers corresponding to the pairs of this label and other labels. However, class imbalance may still affect a multi-class imbalance learner when the number of a coupling label is too small. To improve the performance of COCOA, a regularized ensemble approach integrated into a multi-class classification process of COCOA named as COCOA-RE is presented in this paper. To provide disease diagnosis, COCOA-RE learns from the available laboratory test reports and essential information of patients and produces a multi-label predictive model. Experiments were performed to validate the effectiveness of the proposed multi-label learning approach, and the proposed approach was implemented in a developed system prototype.
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Al-Haija, Qasem Abu, and Abdulaziz A. Alsulami. "High Performance Classification Model to Identify Ransomware Payments for Heterogeneous Bitcoin Networks." Electronics 10, no. 17 (August 31, 2021): 2113. http://dx.doi.org/10.3390/electronics10172113.
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The Bitcoin cryptocurrency is a worldwide prevalent virtualized digital currency conceptualized in 2008 as a distributed transactions system. Bitcoin transactions make use of peer-to-peer network nodes without a third-party intermediary, and the transactions can be verified by the node. Although Bitcoin networks have exhibited high efficiency in the financial transaction systems, their payment transactions are vulnerable to several ransomware attacks. For that reason, investigators have been working on developing ransomware payment identification techniques for bitcoin transactions’ networks to prevent such harmful cyberattacks. In this paper, we propose a high performance Bitcoin transaction predictive system that investigates the Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks. Specifically, our system makes use of two supervised machine learning methods to learn the distinguishing patterns in Bitcoin payment transactions, namely, shallow neural networks (SNN) and optimizable decision trees (ODT). To validate the effectiveness of our solution approach, we evaluate our machine learning based predictive models on a recent Bitcoin transactions dataset in terms of classification accuracy as a key performance indicator and other key evaluation metrics such as the confusion matrix, positive predictive value, true positive rate, and the corresponding prediction errors. As a result, our superlative experimental result was registered to the model-based decision trees scoring 99.9% and 99.4% classification detection (two-class classifier) and accuracy (multiclass classifier), respectively. Hence, the obtained model accuracy results are superior as they surpassed many state-of-the-art models developed to identify ransomware payments in bitcoin transactions.
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To, M., P. G. Lovell, T. Troscianko, and D. J. Tolhurst. "Summation of perceptual cues in natural visual scenes." Proceedings of the Royal Society B: Biological Sciences 275, no. 1649 (July 15, 2008): 2299–308. http://dx.doi.org/10.1098/rspb.2008.0692.
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Natural visual scenes are rich in information, and any neural system analysing them must piece together the many messages from large arrays of diverse feature detectors. It is known how threshold detection of compound visual stimuli (sinusoidal gratings) is determined by their components' thresholds. We investigate whether similar combination rules apply to the perception of the complex and suprathreshold visual elements in naturalistic visual images. Observers gave magnitude estimations (ratings) of the perceived differences between pairs of images made from photographs of natural scenes. Images in some pairs differed along one stimulus dimension such as object colour, location, size or blur. But, for other image pairs, there were composite differences along two dimensions (e.g. both colour and object-location might change). We examined whether the ratings for such composite pairs could be predicted from the two ratings for the respective pairs in which only one stimulus dimension had changed. We found a pooling relationship similar to that proposed for simple stimuli: Minkowski summation with exponent 2.84 yielded the best predictive power ( r =0.96), an exponent similar to that generally reported for compound grating detection. This suggests that theories based on detecting simple stimuli can encompass visual processing of complex, suprathreshold stimuli.
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Mower, Justin, Devika Subramanian, and Trevor Cohen. "Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications." Journal of the American Medical Informatics Association 25, no. 10 (July 11, 2018): 1339–50. http://dx.doi.org/10.1093/jamia/ocy077.
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Abstract Objective The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring. Methods Using ≈80 million concept-relationship-concept triples extracted from the literature using the SemRep Natural Language Processing system, distributed vector representations (embeddings) were generated for concepts as functions of their relationships utilizing two unsupervised representational approaches. Embeddings for drugs and side effects of interest from two widely used reference standards were then composed to generate embeddings of drug/side-effect pairs, which were used as input for supervised machine learning. This methodology was developed and evaluated using cross-validation strategies and compared to contemporary approaches. To qualitatively assess generalization, models trained on the Observational Medical Outcomes Partnership (OMOP) drug/side-effect reference set were evaluated against a list of ≈1100 drugs from an online database. Results The employed method improved performance over previous approaches. Cross-validation results advance the state of the art (AUC 0.96; F1 0.90 and AUC 0.95; F1 0.84 across the two sets), outperforming methods utilizing literature and/or spontaneous reporting system data. Examination of predictions for unseen drug/side-effect pairs indicates the ability of these methods to generalize, with over tenfold label support enrichment in the top 100 predictions versus the bottom 100 predictions. Discussion and Conclusion Our methods can assist the pharmacovigilance process using information from the biomedical literature. Unsupervised pretraining generates a rich relationship-based representational foundation for machine learning techniques to classify drugs in the context of a putative side effect, given known examples.
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Chien, Steven I. J., Xiaobo Liu, and Kaan Ozbay. "Predicting Travel Times for the South Jersey Real-Time Motorist Information System." Transportation Research Record: Journal of the Transportation Research Board 1855, no. 1 (January 2003): 32–40. http://dx.doi.org/10.3141/1855-04.
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A dynamic travel-time prediction model was developed for the South Jersey (southern New Jersey) motorist real-time information system. During development and evaluation of the model, the integration of traffic flow theory, measurement and application of collected data, and traffic simulation were considered. Reliable prediction results can be generated with limited historical real-time traffic data. In the study, acoustic sensors were installed at potential congested places to monitor traffic congestion. A developed simulation model was calibrated with the data collected from the sensors, and this was applied to emulate traffic operations and evaluate the proposed prediction model under time-varying traffic conditions. With emulated real–time information (travel times) generated by the simulation model, an algorithm based on Kalman filtering was developed and applied to forecast travel times for specific origin-destination pairs over different periods. Prediction accuracy was evaluated by the simulation model. Results show that the developed travel-time predictive model demonstrates satisfactory performance.
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Schlam, Ilana Miriam, Eithan Orlev-Shitrit, Chelsea Beaton, Ping Wang, Charles Glueck, and Miguel Islas-Ohlmayer. "Adequate Use of the 4T´s Score As a Primary Screening Tool for Heparin Induced Thrombocytopenia." Blood 128, no. 22 (December 2, 2016): 5911. http://dx.doi.org/10.1182/blood.v128.22.5911.5911.
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Abstract Introduction:Thrombocytopenia is a common problem in hospitalized patients, which has a diverse etiology. One of the infrequent causes is heparin-induced thrombocytopenia (HIT), a complex immune disorder in which heparin leads to the production of IgG antibodies, targeting platelet factor 4 (PF4). HIT diagnosis is based on a decrease in the platelet count of more than 50% beginning 5 to 10 days after starting heparin, in association with platelet-activating HIT antibodies (screening test), positive functional tests (confirmatory tests), in patients with no alternative causes for thrombocytopenia, necrosis or thrombosis (Figure 1). Most patients admitted to our institution receive unfractionated or low molecular weight heparin for venous thromboembolism prophylaxis. The risk of HIT depends on the type of heparin the patient receives. Even though HIT is rare, we tend to have a high suspicion and low threshold to order laboratory workup for this condition, occasionally without using the 4T«s score (Table 1). The 4T«s score is a validated system with a very high negative predictive value (NPV) of up to 99%, when the score is ²3. The aim of this study is to determine if the 4T«s score has been used appropriately in our institution. It is a very useful tool with a high negative likelihood ratio and using it systematically may decrease unnecessary laboratory testing, consequently decreasing costs and potentially length of stay. Methods:This is a retrospective descriptive study from a single teaching community hospital. Between January and December of 2015, 57 HIT screening tests (PF4 ELISA) were ordered in our institution. We reviewed all of the patient charts to determine their 4T«s score. The patients were divided into low, moderate and high pretest probability based on their score (low probability if ²3, intermediate 4-5, and high if ³6). The data analysis was completed with SPSS software. Results:57 tests were ordered, 5 patient charts did not have enough information to calculate 4T«s score and were excluded. 52 charts were reviewed, 28 patients were male and 24 were female (53 and 46% respectively), and they were between 25 and 89 years of age. 7 (13.4%) did not receive heparin (during current hospitalization or within 100 days), 11 (21.1%) received therapeutic doses of unfractionated heparin, 11 (21.1%) received prophylactic doses of unfractionated heparin, and 23 (44.2%) received prophylactic low molecular weight heparin (enoxaparin). The 4T«s score was calculated for each patient; 40 were low risk, 10 intermediate risk and 2 high risk (76.9, 19.2, 3.8% respectively). All the patients had HIT screening antibodies ordered; only 10 (19.2%) were positive and from those only 1 (1.9%) was confirmed by the serotonin release assay. This confirmed patient had a 4T«s score of 6. Of the 7 tested patients who did not receive heparin, none had a positive screening test (all had 4T«s score of 1 or 2). 35 of the screening tests were ordered by internal medicine residents, 5 by surgical residents, 12 by attending physicians (67.3, 9.6 and 23% respectively). Previous studies validated that a 4T«s score of ²3 can predict negative results for the confirmatory tests. By using theMcNemar«stest for matched pairs, we attempted to determine a cut off for the 4T«s score that would predict a negativeHIT screeningtest. We found that a 4T«s score ²2 predicted a negativeHIT screeningtest, with aNPVof 75% (p=0.0018). Since our study included a small patient cohort and due to the possibility of laboratory work up not being ordered in patients with a low 4T«s score, we suspect theNPVis actually lower than our current finding. A prospective study with a larger cohort of patients would be necessary to confirm these findings. Conclusions:Only 12% of the patients had a 4T«s score ³4 that would warrant laboratory work up, therefore we concluded that PF4 ELISA screening tests have been over utilized in our institution, increasing false positive results, length of stay and cost of hospitalization in patients without a significant risk for HIT. This could be avoided by calculating the 4T«s score before obtaining any further laboratory tests. The 4T«s scoring system has been validated by multiple studies with large cohorts in the past. We will continue to work with our staff, especially the residents, to improve education on HIT and adequate diagnosis, based on current guidelines. Disclosures No relevant conflicts of interest to declare.
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Debatin, Tobias. "Neuroenergetics and “General Intelligence”: A Systems Biology Perspective." Journal of Intelligence 8, no. 3 (August 26, 2020): 31. http://dx.doi.org/10.3390/jintelligence8030031.
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David C. Geary proposed the efficiency of mitochondrial processes, especially the production of energy, as the most fundamental biological mechanism contributing to individual differences in general intelligence (g). While the efficiency of mitochondrial functioning is undoubtedly an important and highly interesting factor, I outline several reasons why other main factors of neuroenergetics should not be neglected and why a systems biology perspective should be adopted. There are many advantages for research on intelligence to focus on individual differences in the capability of the overall brain metabolism system to produce the energy currency adenosine triphosphate (ATP): higher predictive strength than single mechanisms, diverse possibilities for experimental manipulation, measurement with existing techniques and answers to unresolved questions because of multiple realizability. Many of these aspects are especially important for research on developmental processes and the building and refining of brain networks for adaptation. Focusing too much on single parts of the system, like the efficiency of mitochondrial functioning, carries the danger of missing important information about the role of neuroenergetics in intelligence and valuable research opportunities.
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JIAO, WENHUA, QIYUE WANG, YONGCHAO CHENG, RUI YU, and YUMING ZHANG. "Prediction of Weld Penetration Using Dynamic Weld Pool Arc Images." Welding Journal 99, no. 11 (November 1, 2020): 295s—302s. http://dx.doi.org/10.29391/2020.99.027.
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This work aims to study an improved method to predict weld penetration that is not directly observable during manufacturing but is critical for the integrity of the weld produced. Previous methods used signals acquired at a time, typically a single image or multiple images/signals from the process, to derive the penetration at that given time. Al-though deep learning appears to extract data well, analyses of weld pool physics, previous studies, and skilled weld operation all suggest that the dynamic welding phenomena give a more solid mechanism to assure the adequacy of the needed information. Therefore, this paper proposes to fuse the present weld pool arc image with two previous images, acquired 1⁄6 and 2⁄6 s earlier. The fused single image thus reflects the dynamic welding phenomena. Due to the extraordinary complexity, the weld penetration is correlated to the fused image through a convolutional neural network (CNN). Welding experiments have been conducted in a variety of welding conditions to synchronously collect the needed data pairs to train the CNN. Results show that this method improved the prediction accuracy from 92.7 to 94.2%. Due to the critical role of weld penetration and the negligible impact on system/implementation, this method represents major progress in the important field of weld penetration monitoring and is expected to provide more significant improvements during welding using pulsed current, where the process becomes highly dynamic.
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Mucaj, Roneda, and Alketa Hyso. "ANN in Financial Prediction." International Journal of Business & Technology 2, no. 2 (May 2014): 6–12. http://dx.doi.org/10.33107/ijbte.2014.2.2.02.
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This paper focuses on the treatment of intelligent systems and their application in the financial area. Types of intelligent systems are numerous, but we will focus on those systems, which based on their ability to learn, are able to predict. The concept of inductive reasoning, how these systems learn and reason inductively, the role and their integration in financial services are some of the concepts that will be addressed. The second and the main part focuses on the application developed in the design of an artificial neural network for financial forecasts. Recognizing the need for better predictive models, not just traditional statistical model, we considered with interest the development of an application that will predict currency exchange rates, USD-ALL, given the time series of real data in years 1995-2012. We test some of the learning algorithms in our system and conclude that one of them is most suitable for this problem. This intelligent system reached to create a relational model of data, on the basis of which is able to output satisfactory results forecast. After the presentation of experimental results, the paper closes with a discussion on possible improvements that could be made in the future.
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Störtz, Florian, and Peter Minary. "crisprSQL: a novel database platform for CRISPR/Cas off-target cleavage assays." Nucleic Acids Research 49, no. D1 (October 21, 2020): D855—D861. http://dx.doi.org/10.1093/nar/gkaa885.
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Abstract With ongoing development of the CRISPR/Cas programmable nuclease system, applications in the area of in vivo therapeutic gene editing are increasingly within reach. However, non-negligible off-target effects remain a major concern for clinical applications. Even though a multitude of off-target cleavage datasets have been published, a comprehensive, transparent overview tool has not yet been established. Here, we present crisprSQL (http://www.crisprsql.com), an interactive and bioinformatically enhanced collection of CRISPR/Cas9 off-target cleavage studies aimed at enriching the fields of cleavage profiling, gene editing safety analysis and transcriptomics. The current version of crisprSQL contains cleavage data from 144 guide RNAs on 25,632 guide-target pairs from human and rodent cell lines, with interaction-specific references to epigenetic markers and gene names. The first curated database of this standard, it promises to enhance safety quantification research, inform experiment design and fuel development of computational off-target prediction algorithms.
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Li, Jie, Xuelan Shang, Yan Liang, Wenjie Yang, Zhe Wang, and Hezhao Ji. "Assessment of the Hepatitis C Surveillance System in Henan, China: 2014~2016." BioMed Research International 2018 (September 26, 2018): 1–8. http://dx.doi.org/10.1155/2018/8942152.
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Objectives. To access the Hepatitis C (HepC) surveillance program in Henan, China, 2014~2016. Methods. A total of 8,448 HepC-relevant cases were reviewed and this data was then inquired against the 6,147 archived HepC reports during the same time period. The performance of the HepC surveillance program was evaluated using parameters including Timely Reporting Rate (TRR), pairs of Report Sensitivity (RS)/Underreporting Rate (UR), and False Report Rate (FRR)/Predictive Value Positive (PVP). Longitudinal comparisons of report quality over the three examined years were conducted to determine the temporal trend of HepC surveillance accountability. Results. All HepC reports were submitted within 24 hours post diagnosis, and TRR rates for all examined years remained at 100%. The RS rates significantly improved overtime for clinically diagnosed HepC cases (CDHC) (2014:60.32%, 2015:68.13%, and 2016:82.83%), whereas the RS for confirmed HepC cases (CHC) remained relatively constant (80.77%, 88.64%, and 85.82%). The FRR rates for CDHC and CHC in 2015~2016 were both approximately 30% but at 23.61% and 51.85%, respectively, in 2014. Conclusions. The HepC surveillance system in Henan remains effective and consistent improvement in report accountability was observed over time. However, some issues concerning especially RS and PVP remained to be addressed for ensuring data accountsbility.
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Sun, Jian-Xuan, Qi-Dong Xia, Chen-Qian Liu, Jin-Zhou Xu, Yang Xun, Jun-Lin Lu, Jia Hu, and Shao-Gang Wang. "Construction of a Novel Immune-Related lncRNA Pair Signature with Prognostic Significance for Kidney Clear Cell Renal Cell Carcinoma." Disease Markers 2021 (September 1, 2021): 1–17. http://dx.doi.org/10.1155/2021/8800358.
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Background. Renal cell carcinoma (RCC) is one of the most common aggressive malignant tumors in the urinary system, among which the clear cell renal cell carcinoma (ccRCC) is the most common subtype. The immune-related long noncoding ribonucleic acids (irlncRNAs) which are abundant in immune cells and immune microenvironment (IME) have potential significance in evaluating the prognosis and effects of immunotherapy. The signature based on irlncRNA pairs and independent of the exact expression level seems to have a latent predictive significance for the prognosis of patients with malignant tumors but has not been applied in ccRCC yet. Method. In this article, we retrieved The Cancer Genome Atlas (TCGA) database for the transcriptome profiling data of the ccRCC and performed coexpression analysis between known immune-related genes (ir-genes) and lncRNAs to find differently expressed irlncRNA (DEirlncRNA). Then, we adopted a single-factor test and a modified LASSO regression analysis to screen out ideal DEirlncRNAs and constructed a Cox proportional hazard model. We have sifted 28 DEirlncRNA pairs, 12 of which were included in this model. Next, we compared the area under the curve (AUC), found the cutoff point by using the Akaike information criterion (AIC) value, and distinguished the patients with ccRCC into a high-risk group and a low-risk group using this value. Finally, we tested this model by investigating the relationship between risk score and survival, clinical pathological characteristics, cells in tumor immune microenvironment, chemotherapy, and targeted checkpoint biomarkers. Results. A novel immune-related lncRNA pair signature consisting of 12 DEirlncRNA pairs was successfully constructed and tightly associated with overall survival, clinical pathological characteristics, cells in tumor immune microenvironment, and reactiveness to immunotherapy and chemotherapy in patients with ccRCC. Besides, the efficacy of this signature was verified in some commonly used clinicopathological subgroups and could serve as an independent prognostic factor in patients with ccRCC. Conclusions. This signature was proven to have a potential predictive significance for the prognosis of patients with ccRCC and the efficacy of immunotherapy.
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Fałat, Kamila. "The Differences Between a Standard Costing and Normal Costing Method of Manufacturing Operating Income Calculation Caused by the Implementation of a New Integrated Information System." Folia Oeconomica Stetinensia 20, no. 2 (December 1, 2020): 95–113. http://dx.doi.org/10.2478/foli-2020-0038.
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Abstract Research background: When a company changes a few separated information systems into one integrated information system there can appear the obligation of costing method change. It happens especially when the company is a part of an international manufacturing corporation. Purpose: The main goal of the paper is to compare two methods of manufacturing operating income calculation and data presentation when a company changes a costing method from normal costing to standard costing. Research methodology: In the paper for this research comparative analysis was used between two methods of manufacturing operating income calculation. In the first method manufacturing operating income is the difference between revenues from manufacturing operations and the costs of goods manufactured. In the second one manufacturing operating income is calculated as a sum of production variances, purchase price variances, currency variances and inventory adjustments. Pearson’s correlation coefficients for pairs of variables were calculated in both of the costing methods. A comparative analysis was done on the basis of a case study executed in a big international wholesaler. The company is a member of an international manufacturing corporation. Results: The same manufacturing operating incomes were obtained in both methods. The absolute values of Pearson’s correlation coefficients were similar in normal and standard costing, but they differ in directions. Novelty: In standard costing manufacturing operating income is calculated as a sum of various types of variances. They are calculated as deviations from standard costs. It enables the easier identification of impacting a company’s results factors.
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Barreiro, Enrique, Cristian R. Munteanu, Marcos Gestal, Juan Ramón Rabuñal, Alejandro Pazos, Humberto González-Díaz, and Julián Dorado. "Net-Net AutoML Selection of Artificial Neural Network Topology for Brain Connectome Prediction." Applied Sciences 10, no. 4 (February 14, 2020): 1308. http://dx.doi.org/10.3390/app10041308.
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Brain Connectome Networks (BCNs) are defined by brain cortex regions (nodes) interacting with others by electrophysiological co-activation (edges). The experimental prediction of new interactions in BCNs represents a difficult task due to the large number of edges and the complex connectivity patterns. Fortunately, we can use another special type of networks to achieve this goal—Artificial Neural Networks (ANNs). Thus, ANNs could use node descriptors such as Shannon Entropies (Sh) to predict node connectivity for large datasets including complex systems such as BCN. However, the training of a high number of ANNs for BCNs is a time-consuming task. In this work, we propose the use of a method to automatically determine which ANN topology is more efficient for the BCN prediction. Since a network (ANN) is used to predict the connectivity in another network (BCN), this method was entitled Net-Net AutoML. The algorithm uses Sh descriptors for pairs of nodes in BCNs and for ANN predictors of BCNs. Therefore, it is able to predict the efficiency of new ANN topologies to predict BCNs. The current study used a set of 500,470 examples from 10 different ANNs to predict node connectivity in BCNs and 20 features. After testing five Machine Learning classifiers, the best classification model to predict the ability of an ANN to evaluate node interactions in BCNs was provided by Random Forest (mean test AUROC of 0.9991 ± 0.0001, 10-fold cross-validation). Net-Net AutoML algorithms based on entropy descriptors may become a useful tool in the design of automatic expert systems to select ANN topologies for complex biological systems. The scripts and dataset for this project are available in an open GitHub repository.
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Allen, Richard, Hannah Ryan, Brian W. Davis, Charlotte King, Laurent Frantz, Evan Irving-Pease, Ross Barnett, et al. "A mitochondrial genetic divergence proxy predicts the reproductive compatibility of mammalian hybrids." Proceedings of the Royal Society B: Biological Sciences 287, no. 1928 (June 3, 2020): 20200690. http://dx.doi.org/10.1098/rspb.2020.0690.
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Numerous pairs of evolutionarily divergent mammalian species have been shown to produce hybrid offspring. In some cases, F 1 hybrids are able to produce F 2 s through matings with F 1 s. In other instances, the hybrids are only able to produce offspring themselves through backcrosses with a parent species owing to unisexual sterility (Haldane's Rule). Here, we explicitly tested whether genetic distance, computed from mitochondrial and nuclear genes, can be used as a proxy to predict the relative fertility of the hybrid offspring resulting from matings between species of terrestrial mammals. We assessed the proxy's predictive power using a well-characterized felid hybrid system, and applied it to modern and ancient hominins. Our results revealed a small overlap in mitochondrial genetic distance values that distinguish species pairs whose calculated distances fall within two categories: those whose hybrid offspring follow Haldane's Rule, and those whose hybrid F 1 offspring can produce F 2 s. The strong correlation between genetic distance and hybrid fertility demonstrated here suggests that this proxy can be employed to predict whether the hybrid offspring of two mammalian species will follow Haldane's Rule.
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Chung, Y. Y., and C. K. Sung. "Dynamic Behavior of the Band/Wheel Mechanical System of Industrial Band Saws." Journal of Vibration and Acoustics 120, no. 4 (October 1, 1998): 842–47. http://dx.doi.org/10.1115/1.2893909.
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This paper presents an analytical and experimental investigation on the dynamic behavior of the band/wheel mechanical system of an industrial metal-cutting band saws. In practice, this machine is equipped with two pairs of roller bearings to twist the saw blade perpendicularly to the surface of the workpiece. This results in the existence of the wheel tilt angle. The saw band is modeled as a finite moving beam span that composes three consecutive segments: the middle straight segment, that is, the cutting span, and the neighboring two segments that are considered as twisted beams. The deformation of the band must satisfy the continuity condition at the connections between segments. The equations of motion governing the dynamic behavior of the saw band in axial, torsional and transverse directions are derived using mixed variational principle. The axial motion of the span couples linearly with its torsional motion. The dynamic responses and the natural frequencies of the beam are computed when parameters vary, such as the transport velocity of the saw band, initial tension, wheel tilt angle, and the length of the cutting span. Finally, an experimental study is performed on an industrial band saw for the verification of the mathematical model and the predictive capability proposed in this investigation. Favorable comparisons between the analytical and experimental results are obtained.
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Kang, Sang-Mo, Sajjad Asaf, Abdul Latif Khan, Lubna, Adil Khan, Bong-Gyu Mun, Muhammad Aaqil Khan, Humaira Gul, and In-Jung Lee. "Complete Genome Sequence of Pseudomonas psychrotolerans CS51, a Plant Growth-Promoting Bacterium, Under Heavy Metal Stress Conditions." Microorganisms 8, no. 3 (March 9, 2020): 382. http://dx.doi.org/10.3390/microorganisms8030382.
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In the current study, we aimed to elucidate the plant growth-promoting characteristics of Pseudomonas psychrotolerans CS51 under heavy metal stress conditions (Zn, Cu, and Cd) and determine the genetic makeup of the CS51 genome using the single-molecule real-time (SMRT) sequencing technology of Pacific Biosciences. The results revealed that inoculation with CS51 induced endogenous indole-3-acetic acid (IAA) and gibberellins (GAs), which significantly enhanced cucumber growth (root shoot length) and increased the heavy metal tolerance of cucumber plants. Moreover, genomic analysis revealed that the CS51 genome consisted of a circular chromosome of 5,364,174 base pairs with an average G+C content of 64.71%. There were around 4774 predicted protein-coding sequences (CDSs) in 4859 genes, 15 rRNA genes, and 67 tRNA genes. Around 3950 protein-coding genes with function prediction and 733 genes without function prediction were identified. Furthermore, functional analyses predicted that the CS51 genome could encode genes required for auxin biosynthesis, nitrate and nitrite ammonification, the phosphate-specific transport system, and the sulfate transport system, which are beneficial for plant growth promotion. The heavy metal resistance of CS51 was confirmed by the presence of genes responsible for cobalt-zinc-cadmium resistance, nickel transport, and copper homeostasis in the CS51 genome. The extrapolation of the curve showed that the core genome contained a minimum of 2122 genes (95% confidence interval = 2034.24 to 2080.215). Our findings indicated that the genome sequence of CS51 may be used as an eco-friendly bioresource to promote plant growth in heavy metal-contaminated areas.
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Scagliarini, Tomas, Luca Faes, Daniele Marinazzo, Sebastiano Stramaglia, and Rosario N. Mantegna. "Synergistic Information Transfer in the Global System of Financial Markets." Entropy 22, no. 9 (September 8, 2020): 1000. http://dx.doi.org/10.3390/e22091000.
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Uncovering dynamic information flow between stock market indices has been the topic of several studies which exploited the notion of transfer entropy or Granger causality, its linear version. The output of the transfer entropy approach is a directed weighted graph measuring the information about the future state of each target provided by the knowledge of the state of each driving stock market index. In order to go beyond the pairwise description of the information flow, thus looking at higher order informational circuits, here we apply the partial information decomposition to triplets consisting of a pair of driving markets (belonging to America or Europe) and a target market in Asia. Our analysis, on daily data recorded during the years 2000 to 2019, allows the identification of the synergistic information that a pair of drivers carry about the target. By studying the influence of the closing returns of drivers on the subsequent overnight changes of target indexes, we find that (i) Korea, Tokyo, Hong Kong, and Singapore are, in order, the most influenced Asian markets; (ii) US indices SP500 and Russell are the strongest drivers with respect to the bivariate Granger causality; and (iii) concerning higher order effects, pairs of European and American stock market indices play a major role as the most synergetic three-variables circuits. Our results show that the Synergy, a proxy of higher order predictive information flow rooted in information theory, provides details that are complementary to those obtained from bivariate and global Granger causality, and can thus be used to get a better characterization of the global financial system.
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Martin, Andrea E. "A Compositional Neural Architecture for Language." Journal of Cognitive Neuroscience 32, no. 8 (August 2020): 1407–27. http://dx.doi.org/10.1162/jocn_a_01552.
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Hierarchical structure and compositionality imbue human language with unparalleled expressive power and set it apart from other perception–action systems. However, neither formal nor neurobiological models account for how these defining computational properties might arise in a physiological system. I attempt to reconcile hierarchy and compositionality with principles from cell assembly computation in neuroscience; the result is an emerging theory of how the brain could convert distributed perceptual representations into hierarchical structures across multiple timescales while representing interpretable incremental stages of (de)compositional meaning. The model's architecture—a multidimensional coordinate system based on neurophysiological models of sensory processing—proposes that a manifold of neural trajectories encodes sensory, motor, and abstract linguistic states. Gain modulation, including inhibition, tunes the path in the manifold in accordance with behavior and is how latent structure is inferred. As a consequence, predictive information about upcoming sensory input during production and comprehension is available without a separate operation. The proposed processing mechanism is synthesized from current models of neural entrainment to speech, concepts from systems neuroscience and category theory, and a symbolic-connectionist computational model that uses time and rhythm to structure information. I build on evidence from cognitive neuroscience and computational modeling that suggests a formal and mechanistic alignment between structure building and neural oscillations, and moves toward unifying basic insights from linguistics and psycholinguistics with the currency of neural computation.
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Deist, Timo M., Andrew Patti, Zhaoqi Wang, David Krane, Taylor Sorenson, and David Craft. "Simulation-assisted machine learning." Bioinformatics 35, no. 20 (March 23, 2019): 4072–80. http://dx.doi.org/10.1093/bioinformatics/btz199.
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Abstract Motivation In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel. Results We demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems—three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available. Availability and implementation The Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern. Supplementary information Supplementary data are available at Bioinformatics online.
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Ramachandran, Suchitra, Travis Meyer, and Carl R. Olson. "Prediction suppression and surprise enhancement in monkey inferotemporal cortex." Journal of Neurophysiology 118, no. 1 (July 1, 2017): 374–82. http://dx.doi.org/10.1152/jn.00136.2017.
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Exposing monkeys, over the course of days and weeks, to pairs of images presented in fixed sequence, so that each leading image becomes a predictor for the corresponding trailing image, affects neuronal visual responsiveness in area TE. At the end of the training period, neurons respond relatively weakly to a trailing image when it appears in a trained sequence and, thus, confirms prediction, whereas they respond relatively strongly to the same image when it appears in an untrained sequence and, thus, violates prediction. This effect could arise from prediction suppression (reduced firing in response to the occurrence of a probable event) or surprise enhancement (elevated firing in response to the omission of a probable event). To identify its cause, we compared firing under the prediction-confirming and prediction-violating conditions to firing under a prediction-neutral condition. The results provide strong evidence for prediction suppression and limited evidence for surprise enhancement. NEW & NOTEWORTHY In predictive coding models of the visual system, neurons carry signed prediction error signals. We show here that monkey inferotemporal neurons exhibit prediction-modulated firing, as posited by these models, but that the signal is unsigned. The response to a prediction-confirming image is suppressed, and the response to a prediction-violating image may be enhanced. These results are better explained by a model in which the visual system emphasizes unpredicted events than by a predictive coding model.
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Baker, Nicholas, and Philip J. Kellman. "Constant curvature modeling of abstract shape representation." PLOS ONE 16, no. 8 (August 2, 2021): e0254719. http://dx.doi.org/10.1371/journal.pone.0254719.
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How abstract shape is perceived and represented poses crucial unsolved problems in human perception and cognition. Recent findings suggest that the visual system may encode contours as sets of connected constant curvature segments. Here we describe a model for how the visual system might recode a set of boundary points into a constant curvature representation. The model includes two free parameters that relate to the degree to which the visual system encodes shapes with high fidelity vs. the importance of simplicity in shape representations. We conducted two experiments to estimate these parameters empirically. Experiment 1 tested the limits of observers’ ability to discriminate a contour made up of two constant curvature segments from one made up of a single constant curvature segment. Experiment 2 tested observers’ ability to discriminate contours generated from cubic splines (which, mathematically, have no constant curvature segments) from constant curvature approximations of the contours, generated at various levels of precision. Results indicated a clear transition point at which discrimination becomes possible. The results were used to fix the two parameters in our model. In Experiment 3, we tested whether outputs from our parameterized model were predictive of perceptual performance in a shape recognition task. We generated shape pairs that had matched physical similarity but differed in representational similarity (i.e., the number of segments needed to describe the shapes) as assessed by our model. We found that pairs of shapes that were more representationally dissimilar were also easier to discriminate in a forced choice, same/different task. The results of these studies provide evidence for constant curvature shape representation in human visual perception and provide a testable model for how abstract shape descriptions might be encoded.
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Houston-Ludlam, Alexandra N., Julia D. Grant, Kathleen K. Bucholz, Pamela A. F. Madden, and Andrew C. Heath. "2266 Big data approaches in translational science: The influence of psychiatric and trauma history in predicting smoking during pregnancy in a cohort of female like-sex twin pairs." Journal of Clinical and Translational Science 2, S1 (June 2018): 38. http://dx.doi.org/10.1017/cts.2018.156.
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OBJECTIVES/SPECIFIC AIMS: Smoking during pregnancy (SDP) is associated with negative health outcomes, both proximal (e.g., preterm labor, cardiovascular changes, low birth weight) and distal (e.g., increased child externalizing behaviors and attention deficit/hyperactivity disorder (ADHD) symptoms, increased risk of child smoking). As pregnancy provides a unique, strong incentive to quit smoking, investigating SDP allows analysis of individual predictive factors of recalcitrant smoking behaviors. Utilizing a female twin-pair cohort provides a model system for characterizing genotype×environment interactions using statistical approaches. METHODS/STUDY POPULATION: Using women from the Missouri Adolescent Female Twin Study, parental report of twin ADHD inattentive and hyperactive symptoms at twin median age 15, and twin report of DSM-IV lifetime diagnosis of major depressive disorder, trauma exposure (physical assault and childhood sexual abuse), collected at median age 22, were merged with Missouri birth record data for enrolled twins, leading to 1553 individuals of European ancestry and 163 individuals of African-American ancestry included in final analyses. A SDP propensity score was calculated from sociodemographic variables (maternal age, marital status, educational attainment, first born child) and used as a 6-level ordinal covariate in subsequent logistic regressions. RESULTS/ANTICIPATED RESULTS: For European ancestry individuals, parental report of hyperactive ADHD symptoms and exposure to childhood sexual abuse were predictive of SDP, while a lifetime diagnosis of major depressive disorder, parental report of inattentive ADHD symptoms, and exposure to assaultive trauma were all not significantly predictive of future SDP. For African-American individuals, none of these variables were significant in predicting future SDP. DISCUSSION/SIGNIFICANCE OF IMPACT: Understanding this relationship of risk-mechanisms is important for clinical understanding of early predictors of SDP and tailoring interventions to at risk individuals. Ultimately, the focus of this research is to mitigate risk to pregnant smokers and their children. Additionally, the cohort-ecological approach informs how well research and administrative (vital record) data agree. This allows for evaluation of whether administrative data improve prediction in research cohorts, and conversely if research data improve prediction over standard sociodemographic variables available in administrative data.