Miracle-dz/UniFAR-FTUs huggingface dataset:
A Brief Review of recent Artificial Market Simulation ( Agent-Based Model ) Studies for Financial Market Regulations and / or Rules Takanobu Mizuta

It is very difficult to discuss about changing financial market regulations and/or rules by only using results of empirical studies. An artificial market, which is a kind of an agent-based model, can isolate the pure contribution of changing the regulations to the price formation and can treat situations that have never occurred. These are strong points of the artificial market simulation study. Recently, some artificial market studies contributed to discussion what financial regulations and rules should be, for example, price variation limits and short selling regulation whether preventing bubbles and crushes or not, tick size, usage rate of dark pools, rules for investment diversification, speed of order matching systems on financial exchanges, frequent batch auctions, how active funds that trade infrequently make a market more efficient, an interaction between leveraged ETF markets and underlying markets and micro-foundation of price variation model using intelligence of artificial market simulation studies. I will review those studies.
Late Cretaceous‐Cenozoic Multi‐Stage Denudation at the Western Ordos Block: Constraints by the Apatite Fission Track Dating on the Langshan

The apatite fission track dating of samples from the Dabashan (i.e., the Langshan in the northeastern Alxa Block) by the laser ablation method and their thermal history modeling of AFT ages are conducted in this study. The obtained results and lines of geological evidence in the study region indicate that the Langshan has experienced complicated tectonic‐thermal events during the the Late Cretaceous‐Cenozoic. Firstly, it experienced a tectonic‐thermal event in the Late Cretaceous (∼90–70 Ma). The event had little relation with the oblique subduction of the Izanagi Plate along the eastern Eurasian Plate, but was related to the Neo‐Tethys subduction and compression between the Lhasa Block and Qiangtang Block. Secondly, it underwent the dextral slip faulting in the Eocene (∼50–45 Ma). The strike slip fault may develop in the same tectonic setting as sinistral slip faults in southern Mongolia and thrusts in West Qinling to the southwest Ordos Block in the same period, which is the remote far‐field response to the India‐Eurasia collision. Thirdly, the tectonic thermal event existed in the late Cenozoic (since ∼10 Ma), thermal modeling shows that several samples began their denudation from upper region of partial annealing zone (PAZ), and the denudation may have a great relationship with the growth of Qinghai‐Tibetan Plateau to the northeast. In addition, the AFT ages of Langshan indicate that the main body of the Langshan may be an upper part of fossil PAZ of the Late Cretaceous (∼70 Ma). The fossil PAZ were destroyed and deformed by tectonic events repeatedly in the Cenozoic along with the denudation.
on Operational Machine

Sayed Hadi Hashemi, University of Illinois at Urbana-Champaign and National Center for Supercomputing Applications; Paul Rausch; Benjamin Rabe, University of Illinois at UrbanaChampaign and National Center for Supercomputing Applications; Kuan-Yen Chou, University of Illinois at Urbana-Champaign; Simeng Liu, University of Illinois at Urbana-Champaign and National Center for Supercomputing Applications; Volodymyr Kindratenko, National Center for Supercomputing Applications; Roy H Campbell, University of Illinois at Urbana-Champaign
Parameterising and developing models for the use of microalgae as a feedstock for biofuels and valuable chemicals production.

With the development of photobioreactor technology, microalgal culture has been proposed for various purposes in the past decades. However, primarily due to the low productivity, challenge remains to scale up from laboratory studies to mass cultivation. The increasing interest of using microalgae for biodiesel production has put this challenge back on the priority list. In this investigation, fatty acid composition of Nannochloropsis oculata (Droop) (CCAP 849/1) in relation to elemental stoichiometry has been studied and used to inform the development of algal growth models. Through tuning C:N and Chl:C ratios, a model description can be made regulating total fatty acid content and polyunsaturated fatty acid content respectively. This quota-based model also mechanistically describes the dynamics of nutrient (nitrate and phosphate) uptake and depth integrated photosynthesis with growth. When used in a generic descriptive mode, with a bulk description of energy reserve (excess-C), the model was used to evaluate the potential biomass and biofuels production and used to explore the options of optimization of biomass and biofuel productivity from a “typical” microalga under various operational scenarios in a bioreactor. Data from experiments using Nannochloropsis oculata (Droop) (CCAP 849/1) were used to parameterize this bioreactor model. The model fits the data in general terms except for phosphate uptake, probably due to the phosphate precipitation in seawater. While the development of a fully functional model of microalgae growth capable of describing biochemical stoichiometry is still in its infancy. The work described here indicates the potential value and scope of developing the functional model of microalgae growth for biofuels and valuable chemicals production
Learning from Feedback: Evidence from New Ventures

This paper studies how early stage entrepreneurs respond to negative feedback about the quality of their ventures, with a focus on heterogeneity in responsiveness. The data consist of application and judging information linked to outcomes for 4,328 new ventures in 87 competitions. In 34 of the competitions, founders are privately informed of their relative rank. The empirical strategy compares lower and higher ranked losers across competitions in which they did and did not observe their standing. Receiving negative feedback increases average venture abandonment by 13 percent. Among other results, I show that responsiveness is higher when the signal is more precise, the venture is riskier or less mature, and the founder is a woman. The effect appears to reflect feedback regarding the “business” aspects of a startup, rather than the quality of its technology. s s ⇤New York University Stern School of Business. 44 West 4th St KMC 9-93, NY NY 10012; Phone: +1-917-519-8052; Email: sabrina.howell@nyu.edu. This project was funded by the Kauffman Foundation. For especially useful comments, I thank Manuel Adelino, Ashish Arora, Tom Åstebro, Shai Bernstein, Edward Glaeser, Will Gornall, Boyan Jovanovic, Saul Lach, Augustin Landier, Josh Lerner, Song Ma, Ramana Nanda, David Robinson, Rick Townsend, Annette Vissing-Jørgensen, and participants at the NBER Entrepreneurship Working Group, Yale Junior Finance Conference, IDC Herzliya Eagle Labs Conference, Queens University Economics of Entrepreneurship and Innovation Conference, UC BerkeleyStanford Innovation and Finance Conference, Georgia State CEAR Conference, and the Tech Scheller seminar. Finally, I thank Adam Rentschler of Valid Evaluation, and all the others who provided the data, including Lea Lueck, Allison Ernst, and Catherine Cronin. Lucy Gong, Sreyoshi Mukherjee, and Jack Reiss provided excellent research assistance. Electronic copy available at: https://ssrn.com/abstract=3047811
SMU Data

In this paper, we present a method for predicting changes in Bitcoin and Ethereum prices utilizing Twitter data and Google Trends data. Bitcoin and Ethereum, the two largest cryptocurrencies in terms of market capitalization represent over $160 billion dollars in combined value. However, both Bitcoin and Ethereum have experienced signiﬁcant price swings on both daily and long term valuations. Twitter is increasingly used as a news source inﬂuencing purchase decisions by informing users of the currency and its increasing popularity. As a result, quickly understanding the impact of tweets on price direction can provide a purchasing and selling advantage to a cryptocurrency user or a trader. By analyzing tweets, we found that tweet volume, rather than tweet sentiment (which is invariably overall positive regardless of price direction), is a predictor of price direction. By utilizing a linear model that takes as input tweets and Google Trends data, we were able to accurately predict the direction of price changes. By utilizing this model, a person is able to make better informed purchase and selling decisions related to Bitcoin and Ethereum.
International Journal of Application or Innovation in Engineering & Management ( IJAIEM ) Web

Volume 2, Issue 1, January 2013 Page 249 ABSTRACT The first part of the paper an adaptive, data-driven threshold for image denoising via wavelet soft-thresholding is assessed. The threshold is derived in a Bayesian framework and the prior used on the wavelet coefficients is the Generalized Gaussian distribution (GGD) widely used in image processing applications. The threshold is simple and it is adaptive to each sub band because it depends on data-driven estimates of the parameters. Simulation results shown that with this method, called BayesShrink, achieve the state-of-the-art image denoising performance at the low computational complexity. In the second part of the paper an intelligent image compression using neural network is proposed. The objective picture quality measures like Signal to Noise Ratio(SNR), Mean Square Error(MSE), are used to measure the picture quality. It is shown through performance measure that with such denoising and compression the MSE is typically within 5%.
Decision Systems

Business intelligence (BI) is currently a high priority for chief information officers (CIOs) and is a major growth area of business IT. BI systems are the IT-based systems that have the greatest impact on organizational strategy. Unfortunately, BI projects are subject to high failure rates and those that are implemented suffer from a utilization problem where only 10 to 20% of intended users actually use the systems. Most decision support systems (DSS) and BI research and practice treats managers as a homogenous group, but management research reports differences between senior executives and other levels of management, and individual differences between managers. This is a significantly more complex development environment than that assumed by current DSS and BI research. There are no specialist methods for analyzing the decision support requirements of senior executives. This paper discusses the rationale and design of a design-science research project that aims to develop and test such a method. The method will be based on a sound understanding of senior executive information behaviors .
Observed rainfall asymmetry of tropical cyclone in the process of making landfall in Guangdong, south China

This study investigates the rainfall asymmetry of tropical cyclones (TCs) in the process of making landfall in Guangdong (GD), south China, on the basis of satellite‐based and reanalysis precipitation data. We mainly focus on TC rainfall asymmetry, its main influencing factor and its change in the process of landfall, and the difference between different El Niño–Southern Oscillation (ENSO) phases. The results reveal that vertical wind shear (VWS) is the dominant influencing factor of TC rainfall asymmetry in GD. The rainfall maximum is located in downshear left of VWS. The TC rainfall asymmetry has little change in the process of making landfall, though the rain rate decreases. Both the phase and amplitude of TC rainfall asymmetry have no significant change from 24 hr prior to landfall to 12 hr after landfall. The rainfall maximum steadily lies in downshear left. The amplitude of rainfall asymmetry is about 50%, suggesting that the asymmetric rainfall is about half of the axisymmetric rainfall. There is no obvious difference in TC rainfall asymmetry between El Niño, La Niña, and neutral years.
Scheduler-Activated Dynamic Page Migration for Multiprogrammed DSM Multiprocessors 1 2

1Paper to appear in the Journal of Parallel and Distributed Computing, 2002. 2This work was supported by the European Commission under the TMR grant ERBFMGECT950062 and in part by the ESPRIT IV grant No. 21907, the Greek Secretariat of Research and Technology grant No. E.D. -99-566, the Ministry of Education of Spain grants No. TIC-98-511 and TIC97-1445CE, NSF grant EIA-99-75019, a research grant from NSA and a research grant from Intel Corporation. The experiments presented in this paper were conducted with resources provided by the European Center for Parallelism of Barcelona (CEPBA).
What works for whom in a computer mediated communication intervention in community psychiatry ? Moderators of outcome in a cluster randomised trial

1 Department of Health Sciences, Lund university, Lund, Sweden 2 Central Institute for Mental Health, Mannheim, Germany 3 Department for Social and Clinical Psychiatry, Psychiatric University Hospital, Zurich, Switzerland 4 Department of Psychiatry, University of Granada, Granada, Spain 5 The Unit for Social and Community Psychiatry, Queen Mary University of London, London, UK 6 Department of Psychiatry, University of Groningen, Groningen, The Netherlands
2 A Generic Approach to Segmenting Text with Inline Markup

Segmenting documents into discrete, sentence-like units is usually a first step in any natural language processing pipeline. However, current segmentation tools perform poorly on text that contains markup. While stripping markup is a simple solution, we argue for the utility of the extra-linguistic information encoded by markup and present a scheme for normalising markup across disparate formats. We further argue for the need to maintain accountability when preprocessing text, such that a record of modifications to source documents is maintained. Such records are necessary in order to augment documents with information derived from subsequent processing. To facilitate adoption of these principles we present a novel tool for segmenting text that contains inline markup. By converting to plain text and tracking alignment, the tool is capable of state-of-the-art sentence boundary detection using any external segmenter, while producing segments containing normalised markup, with an account of how to recreate the original form.
COEFFICIENT OF CULTURE (Kc) OF Urochloa brizantha cv. MARANDU IN TWO PRODUCTIVE SYSTEM

The use of crop coefficient (Kc) take management irrigation efficient, its represent the water consumption of the plants. However, as for fodder plants, this knowledge is not well-founded. Because of this, this study aims to determine the water consumption of Urochloa brizantha cv. Marandu in single crop and black oats and Italian ryegrass in overseeded cultivation, by means of weighing lysimeters. The experiment was carried out at the ESALQ/USP, both lysimeters were used throughout the experimental period (February to October 2016), one with exclusive culture and the other with overestimation in the month of May. The crop evapotranspiration (ETc) was calculated with the means of the weighting lysimeter variation and the reference evapotranspiration (ETo) determined by PenmanMonteith with data by meteorological stations present at the ESALQ/USP. Were determined Kc values every four days. There was differences between productive system. Water productivity in the fall/winter period was higher in consortium. Kc values varied between 0,30 and 1,24 (single crop in the summer), between 0,24 and 1,14 (single crop in winter) and between 0,33 and 1 (consortium).
A I ] 2 4 M ar 2 01 9 Leveling the Playing Field-Fairness in AI Versus Human Game Benchmarks

From the beginning if the history of AI, there has been interest in games as a platform of research. As the field developed, human-level competence in complex games became a target researchers worked to reach. Only relatively recently has this target been finally met for traditional tabletop games such as Backgammon, Chess and Go. Current research focus has shifted to electronic games, which provide unique challenges. As is often the case with AI research, these results are liable to be exaggerated or misrepresented by either authors or third parties. The extent to which these games benchmark consist of “fair” competition between human and AI is also a matter of debate. In this work, we review the statements made by authors and third parties in the general media and academic circle about these game benchmark results and discuss factors that can impact the perception of fairness in the contest between humans and machines
Just-for-me: an adaptive personalization system for location-aware social music recommendation

In recent years, location-aware music recommendation is increasing in popularity, as more and more users consume music on the move. In this demonstration, we present an intelligent system, called Just-for-Me, to facilitate accurate music recommendation based on where user presents. Our system is developed based on a novel probabilistic generative model, which can effectively integrate the location contexts and global music popularity trends. This approach allows us to gain more comprehensive modeling on user preference and thus significantly enhances the music recommendation performance.
Author ' s personal copy Efficient discovery of risk patterns in medical data

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: Summary Objective: This paper studies a problem of efficiently discovering risk patterns in medical data. Risk patterns are defined by a statistical metric, relative risk, which has been widely used in epidemiological research. Methods: To avoid fruitless search in the complete exploration of risk patterns, we define optimal risk pattern set to exclude superfluous patterns, i.e. complicated patterns with lower relative risk than their corresponding simpler form patterns. We prove that mining optimal risk pattern sets conforms an anti-monotone property that supports an efficient mining algorithm. We propose an efficient algorithm for mining optimal risk pattern sets based on this property. We also propose a hierarchical structure to present discovered patterns for the easy perusal by domain experts. Results: The proposed approach is compared with two well-known rule discovery methods, decision tree and association rule mining approaches on benchmark data sets and applied to a real world application. The proposed method discovers more and better quality risk patterns than a decision tree approach. The decision tree method is not designed for such applications and is inadequate for pattern exploring. The proposed method does not discover a large number of uninteresting superfluous patterns as an association mining approach does. The proposed method is more efficient than an association rule mining method. A real world case study shows that the method reveals some interesting risk patterns to medical practitioners. Conclusion: The proposed method is an efficient approach to explore risk patterns. It quickly identifies cohorts of patients that are vulnerable to a risk outcome from a large data set. The proposed method is useful for exploratory study on large medical data to generate and refine hypotheses. The method is also useful for designing medical surveillance systems.
Collaborative Open Market to Place Objects at your Service

The COMPOSE project aims at enabling new services that can seamlessly integrate real and virtual worlds through the convergence of the Internet of Services with the Internet of Things. COMPOSE will achieve this through the provisioning of an open and scalable marketplace infrastructure, in which smart objects are associated to services that can be combined, managed, and integrated in a standardised way to easily and quickly build innovative applications.The COMPOSE project builds upon existing European research projects and ongoing standardisation activities to provide a comprehensive marketplace framework that will be able to cover the whole service lifecycle by integrating a number of innovative technological enablers in a coherent way. The project will develop novel approaches for virtualising smart objects into services and for managing their interactions. This includes solutions for managing knowledge derivation, for secure and privacy-preserving data aggregation and distribution, and for dynamic service composition advertising and discovering objects' capabilities and service provisioning and monitoring.The COMPOSE project is expected to give birth to a new business ecosystem, building on the convergence of the Internet Grant agreement ID: 317862 Status Closed project COMPOSE
Quantum Scheduling Algorithm

Many scheduling problems are NP− hard. A quantum scheduling algorithm which exploits quantum parallelism and entanglement has the potential to lead to a significant improvement over a classical one. Given a deadline, or a range of deadlines, the algorithm presented in this paper allows us to determine if a solution to an R||Cmax problem with N = 2n jobs and M = 2m machines exists, and if so, it provides the schedule. The algorithm constructs a superposition of all schedules and a superposition of their makespans and then amplifies the one that corresponds to the solution. We perform O( √ 2m2n ) Grover iterations. The time complexity of the quantum scheduling algorithm for an R||Cmax problem is O( √ 2m2n ) while the complexity of a classical algorithm is O(2m2n ).
COMPARISON OF STEREO VISION TECHNIQUES FOR CLOUD-TOP HEIGHT RETRIEVAL

This paper presents an ongoing study for the estimation of the cloud-top height by using only geometrical methods. In agreement with some recent studies showing that it is possible to achieve reliable height estimations not only with the classical methods based on radiative transfer, this article includes a comparison of performances of a selected set of vision algorithms devoted to extract dense disparity maps or motion fields from Infra Red stereo image pairs. This collection includes both area-based techniques and an optical flow-based method and the comparison is accomplished by using a set of cloudy scenes selected from the Along-Track Scanning Radiometer (ATSR2) database. The first group of algorithms yields results generally comparable in terms of the measures used for the inter-comparison, while the maps from the optical flow-based method are slightly better.
Evolution & Development

Funding information Division of Environmental Biology, Grant/Award Numbers: 0640932, 1310874, 1242510 Abstract A key trend in the 210‐million‐year‐old history of modern turtles was the evolution of shell kinesis, that is, shell movement during neck and limb retraction. Kinesis is hypothesized to enhance predator defense in small terrestrial and semiaquatic turtles and has evolved multiple times since the early Cretaceous. This complex phenotype is nonfunctional and far from fully differentiated following embryogenesis. Instead, kinesis develops slowly in juveniles, providing a unique opportunity to illustrate the postembryonic origins of an adaptive trait. To this end, we examined ventral shell (plastral) kinesis in emydine box turtles and found that hatchling plastron shape differs from that of akinetic‐shelled relatives, particularly where the hinge that enables kinesis differentiates. We also demonstrated shape changes relative to plastron size in juveniles, coinciding with a shift in the carapace‐plastron structural connection, rearrangement of ectodermal plates, and bone repatterning. Furthermore, because the shell grows larger relative to the head, complete concealment of the head and extremities is only achieved after relative shell proportions increase. Structural alterations that facilitate the box turtle’s transformation are probably prepatterned in embryos but require function‐ induced changes to differentiate in juveniles. This mode of delayed trait differentiation is essential to phenotypic diversification in turtles and perhaps other tetrapods.