Miracle-dz/UniFAR-FTUs huggingface dataset:
A VARIABLE TIMESTEP STRATEGY FOR ACCELERATING THE IMPES SOLUTION ALGORITHM IN RESERVOIR SIMULATION

The IMPES solution algorithm is still popular in reservoir simulation because of their simplicity and low demand of computational resources. In using this algorithm, pressure and saturation equations are partially decoupled in order to solve sequentially each equation for its main variable. However, a severe stability constraint arises from the use of explicit approximations needed for decoupling equations. This is the main disadvantage of the IMPES scheme because extremely small timesteps should be used for overcoming the stability restriction. In order to accelerate the performance of the IMPES algorithm and make it able for solving larger problems, a variable timestep strategy is devised in this work. This strategy consist basically in using larger timesteps for pressure equation than for saturation equation, in order to reduce the number of times that pressure system of equations is solved along a simulation. This is possible because pressure equation is approximated in implicit way, so there is no any stability restriction associated to it. The size of timestep on pressure equation is determined dynamically according to the variation of the total velocity field in a similar way to adaptive timestep schemes used in fully implicit algorithms. By means of a typical problem, we show that the use of this strategy produces large savings in computation time without noticeable increasing of numerical error, compared to conventional IMPES solutions.
IMPACT OF THE ADDITION OF VARIOUS DOSES OF EFFECTIV E MICROORGANISMS TO ARABLE-HUMUS HORIZONS OF MINERAL SOILS ON THEIR PHYSICAL AND WATER PROPERTIES Summary

The paper presents results of investigations which a med to test the effect of varying doses of effect ive microorganisms (EM) on basic physical and water properties of arable-hu mus horizons of mineral soils of different texture composition. The soil material for experiments was collected from two ara ble soils (subtype: proper black earth). Samples of impaired and intact structure intended for investigations were collecte d from pot experiments established under controlled conditions. The following parameters were determined in the examined s oils: texture composition, soil density, solid phas e density, total and drainage porosity, filtration coefficient, soil wat er binding potentials and useful retention. It was found that various EM doses exerted different influence on soils characte ised by different texture. In general, such effect is advantageous for the soil but it always changes nearly all its physical and water properties (primarily: porosity, filtrati on coefficient, binding potential and water availability).
Themes in Undergraduate Students ’ Conceptions of Central Angle and Inscribed Angle

Researchers have investigated students’ multifaceted conceptions of angle and their difficulties with connecting angle measure to arcs or circles. In this study, we investigated three undergraduate students’ thinking about angles in the context of circle geometry, specifically their conceptions of central and inscribed angle. Conceptual analysis of the data revealed that students involved in the tasks and interviews had various conceptions of these angles that either supported or constrained their ability to complete the tasks. Particularly, conceiving the dynamic transformation of both central and inscribed angles, or identifying their common subtended arc was productive, while considering angle as area or ray pair constrained their thinking.
Rule Extraction and Visualization from Data Traces

In this paper we present methods, process and algorithms to decipher the hidden rules or logic of complex traffic patterns – traces of assets, people and equipment -and demonstrate technologies for Automated Rule Learning from Data Traces. We call this system for automated rule learning from data traces TraceLogic. We demonstrate this technology on aircraft carriers. Operations on aircraft carriers are often described as controlled chaos. Complex organizational systems, such as those that govern and effect aircraft movement onboard carriers, do not operate on predetermined low level rules but rather adapt and learn based on high-level guidelines. Critical situations that are often new or unique are best tackled by adaptive and well-trained organizations. We present typical examples of data statistics, visual analytics, and rule and pattern analysis from carrier deck notional data. These techniques help analysts explore the data and improve the operational process. These tools could also support Validation and Verification (V&V) of the rules engine of the simulator. The algorithms and logic developed in TraceLogic are scalable across domains. The TraceLogic methodology, algorithms and tools have a number of potential customers in the Navy community, the broader Department of Defense community and the commercial sector. This technology will scale to manage any asset moving in groups subject to complex spatio-temporal constraints. Some of the possible application domains are depot maintenance, manufacturing shop floor, airport traffic management and even theme parks where people move in groups. ABOUT THE AUTHORS Paul Mario Koola, Ph.D. is a research scientist at Knowledge Based Systems Inc., Texas, USA and a principal investigator managing AI and machine learning projects. Satheesh Ramachandran, Ph.D. is a research scientist at Knowledge Based Systems Inc., Texas, USA and an experienced independent practitioner in the application of machine learning systems across many industries. Kalyan Vadakkeveedu is a researcher at Knowledge Based Systems Inc., Texas, USA and works on machine learning and AI projects.
Competition and Ideological Diversity : Historical Evidence from U . S

Table 1 presents a flexible demand specification in which we test for the effect of household ideology and substitution effects on newspaper demand. The model is estimated at the newspaper-town level. The dependent measure is the log of the newspaper’s circulation. The model includes both newspaper and town fixed effects. The model can therefore be interpreted as a homogenous (IIA) logit model with unobservable newspaper quality and unobservable town-specific outside option quality. All independent measures are interacted with a dummy for whether the newspaper is Republican; coefficients can therefore be interpreted as the effect of each independent measure on the log(circulation) of Republican newspapers relative to Democratic newspapers. The estimated model shows an economically large and statistically significant positive effect of the fraction Republican on the relative demand for Republican newspapers. The estimates also show that additional Republican newspapers reduce the relative demand for other Republican papers, and that additional Democratic newspapers increase the relative demand for Republican papers. (The last effect is not statistically significant.)
onstationary phase estimation using regularized ocal kurtosis maximization

Phase mismatches sometimes occur between final processed seismic sections and zero-phase synthetics based on well logs — despite best efforts for controlled-phase acquisition and processing. Statistical estimation of the phase of a seismic wavelet is feasible using kurtosis maximization by constant-phase rotation, even if the phase is nonstationary. We cast the phase-estimation problem into an optimization framework to improve the stability of an earlier method based on a piecewise-stationarity assumption. After estimation, we achieve space-and-time-varying zero-phasing by phase rotation.
PROCESS MINING FALLS IN BUSINESS INTELLIGENCE

Process mining methods allow for extracting information from event logs. It is a method that establishes a communication between data mining as a business intelligence and business process management. Process mining is to extracting process knowledge from event logs, process models and mining algorithms. The concept of process mining is to analyze, monitor, and enhance real processes. Today many activities occurring in processes are either supported or monitored by information systems. In this paper we show the usage of process minig in different areas and its implementation in real world. Keywords— Process mining, Business intelligence, text mining, web mining, and customer relationship
Mapping German Tweets to Geographic Regions

We present a first attempt at classifying German tweets by region using only the text of the tweets. German Twitter users are largely unwilling to share geolocation data. Here, we introduce a two-step process. First, we identify regionally salient tweets by comparing them to an “average” German tweet based on lexical features. Then, regionally salient tweets are assigned to one of 7 dialectal regions. We achieve an accuracy (on regional tweets) of up to 50% on a balanced corpus, much improved from the baseline. Finally, we show several directions in which this work can be extended and improved.
A Framework for Vietnamese Text Document Retrieval System Based on Phrasal Semantic Analysis

Document retrieval system for Vietnamese language has been recently focused on the semantic aspect. In many research works, the common and general approach of semantic document retrieval systems is based on query expansion. The query expansion is good idea for utilizing existing thesaurus and searching methods to solve the problem of semantic information retrieval. However, this semantic retrieval strategy has to process many expanded queries of an original query. To overcome the problem, a framework of Vietnamese text document retrieval system based on phrasal semantic analysis has been investigated. In the framework, the word expansion task will be performed at indexing process instead of searching process. In addition, the object for indexing is also changed from the keywords to the semantic classes. To evaluation the framework, a text document retrieval system has been build according to the framework for experiment. The retrieval result has F-measure of 84.27% promising some further research for improvements. Keywords-Information retrieval framework; semantic information retrieval; semantic class; Vietnamese query analysis
Integration of paired spiking cerebellar models for voluntary movement adaptation in a closed-loop neuro-robotic experiment. A simulation study

Motor control is a very important feature in the human brain to achieve optimal performance in motor tasks. The biological basis of this feature can be better understood by emulating the cerebellar mechanisms of learning. The cerebellum plays a key role in implementing fine motor control, since it extracts the information about movements from sensory-motor signals, stores it by means of internal models and uses them to adapt to the environment. The hypothesis is that different internal models could work both independently and dependently. So far, there have been a few studies that aimed to prove their dependency; however, this hypothesis has not been widely used in robot control. The purpose of this work is to build paired spiking cerebellar models and to incorporate them into a biologically plausible composite robotic control architecture for movement adaptation. This is achieved by combining feedback error learning and cerebellar internal models theories. Thus the control architecture is composed of cerebellar feed-forward and recurrent loops for torque-based control of a robot. The spiking cerebellar models are able to correct and improve the performance of the two-degrees of freedom robot module Fable by providing both adaptive torque corrections and sensory corrections to the reference generated by the trajectory planner. Simulations are carried out in the Neurorobotics platform of the Human Brain Project. Results show that the contribution provided by cerebellar learning leads to an optimization of the performance with errors being reduced by 30% compared with the case where the cerebellar contribution is not applied.
An Enhanced Round Robin CPU Scheduling Algorithm

CPU scheduling is a process which allows one process to use the CPU while the another process is in waiting state due to unavailability of any resource like I/O etc, thereby making full use of CPU. The aim of CPU scheduling is to make the system efficient, fast and fair. Most CPU scheduling algorithms focus on maximizing CPU utilization, maximum throughput and minimizing waiting time, turnaround time, response time and number of context switches for a set of processes. There are many algorithms available for CPU scheduling and all algorithms have their own advantages and limitations. Round Robin algorithm is designed specifically for time sharing systems. The main aim of this paper is to design a new algorithm that enhances the performance of traditional Round Robin algorithm. The proposed algorithm Enhanced Round Robin (ERR) compared with the traditional Round Robin algorithm and Improved Round Robin algorithm, produces minimal average waiting time (AWT) and average turnaround time (ATT).
Integrating Reading into Writing Instruction in the EFL Programs at Saudi Universities

Several studies have shown that integrating reading into writing instruction improves writing skills (e.g., Alqadi & Alqadi, 2013; Almansour & Alshorman, 2014; Elley & Mangubhai 1983; Hafiz & Tudor, 1989; Iwahori, 2008; Janopoulos, 1986, 2009; Saleem, 2010; Tsang, 1996). Some researchers suggested that reading is the basis for writing (Carson & Leki, 1993) and consider it as an important resource for writing instruction (Hirvela, 2004; Watson, 1982). Theoretically, the reading-writing connection can be viewed throughout three hypotheses, or models: (1) directional hypothesis, (2) non-directional hypothesis, and (3) bidirectional hypothesis. Since reading-writing relationship is mostly discussed in terms of the impact of reading on writing (directional model), this paper attempts to discuss the relationship from 'reading-towrite' perspective and addresses the issue of English as a Foreign Language (EFL) reading-writing connection at the college level in Saudi Arabia. It argues that reading and writing must be taught together and calls for integrating reading into writing instruction as a solution for the problem of the lack of emphasis on reading-writing connection. It also suggests that extensive reading and using models in a second language (L2) enhances L2 writing. Lastly, the paper discusses some pedagogical concerns associated with reading-writing connection and provides some recommendations for successful reading-writing instruction. (Note: in this paper, when I refer to EFL programs at the Saudi universities I mean the EFL programs that are designed for the EFL Saudi college students majoring in English).
Performance-Based Seismic Design : Avant-garde and 1 code-compatible approaches 2

Current force-based codes for the seismic design of structures use design spectra 4 and system-specific behavior factors to satisfy one r two pre-defined structural limit-states. 5 In contrast, performance-based seismic design aims to design a structure to fulfill any number 6 of target performance objectives, defined as user-p r scribed levels of structural response, 7 loss, or casualties to be exceeded at a mean annual frequency less than a given maximum. 8 First, a review of recent advances in probabilistic performance assessment is offered. Second, 9 we discuss the salient characteristics of methodolo gies that have been proposed to solve the 10 inverse problem of design. Finally, an alternative approach is proposed that relies on a new 11 format for visualizing seismic performance, termed Yield Frequency Spectra (YFS). YFS 12 offer a unique view of the entire solution space fo r structural performance of a surrogate 13 single-degree-of-freedom oscillator, incorporating uncertainty and propagating it to the 14 output response to enable rapid determination of a good preliminary design that satisfies any 15 number of performance objectives. 16 CE Database subject headings: Seismic response; Earthquakes; Performance 17 evaluation; Safety; Structural reliability 18 1 Lecturer, School of Civil Engineering, National Te chnical Univ. of Athens, Greece (corresponding auth or). Email: divamva@mail.ntua. gr 2 Research Engineer, J.B. ATE, Herakleion Crete, Gre ece. E-mail: kazantzi@mail.ntua. gr 3 Professor, Dept. of Civil Engineering, Santa Clara Univ., 500 El Camino Real Santa Clara, CA 95053. E-mail: maschheim@scu.edu Author
Functional genomic approaches to improve crop plant heat stress tolerance

Heat stress as a yield limiting issue has become a major threat for food security as global warming progresses. Being sessile, plants cannot avoid heat stress. They respond to heat stress by activating complex molecular networks, such as signal transduction, metabolite production and expressions of heat stress-associated genes. Some plants have developed an intricate signalling network to respond and adapt it. Heat stress tolerance is a polygenic trait, which is regulated by various genes, transcriptional factors, proteins and hormones. Therefore, to improve heat stress tolerance, a sound knowledge of various mechanisms involved in the response to heat stress is required. The classical breeding methods employed to enhance heat stress tolerance has had limited success. In this era of genomics, next generation sequencing techniques, availability of genome sequences and advanced biotechnological tools open several windows of opportunities to improve heat stress tolerance in crop plants. This review discusses the potential of various functional genomic approaches, such as genome wide association studies, microarray, and suppression subtractive hybridization, in the process of discovering novel genes related to heat stress, and their functional validation using both reverse and forward genetic approaches. This review also discusses how these functionally validated genes can be used to improve heat stress tolerance through plant breeding, transgenics and genome editing approaches.
Attacks : Leaking Enclave Secrets via Speculative Execution

This paper presents SGXPECTRE Attacks that exploit the recently disclosed CPU bugs to subvert the confidentiality of SGX enclaves. Particularly, we show that when branch prediction of the enclave code can be influenced by programs outside the enclave, the control flow of the enclave program can be temporarily altered to execute instructions that lead to observable cache-state changes. An adversary observing such changes can learn secrets inside the enclave memory or its internal registers, thus completely defeating the confidentiality guarantee offered by SGX. To demonstrate the practicality of our SGXPECTRE Attacks, we have systematically explored the possible attack vectors of branch target injection, approaches to win the race condition during enclave’s speculative execution, and techniques to automatically search for code patterns required for launching the attacks. Our study suggests that any enclave program could be vulnerable to SGXPECTRE Attacks since the desired code patterns are available in most SGX runtimes (e.g., Intel SGX SDK, Rust-SGX, and Graphene-SGX).
The Hawaiian Ahupua‘a Land Use System: Its Biological Resource Zones and the Challenge for Silvicultural Restoration

The land area of the major Hawaiian Islands was originally divided into districts called moku, and these were further subdivided into ahupua‘a. The latter encompass landscape segments from the ocean to the mountain that served as the traditional human support systems. These life support systems were based on three to five biological resource zones. These were the upland/inland forest zone, or the wao nahele, the agricultural zone, or the wao kanaka, and the coastal zone, or the kaha kai. This latter zone included the strand area, fringing reefs, sea grass beds, lagoons, fish ponds, and estuaries, where present. Actually, estuaries, the muliwai, are mostly on the windward side of the islands and are part of a fourth biological resource zone, the kaha wai or freshwater ecosystems and streams. The ocean (kai), near the shore can be considered the fifth biological resource zone. Thus, the traditional land use was based on the vertical arrangement of a volcanic high island’s natural ecosystems. This vertical arrangement allowed for maximizing the use of biodiversity over short distances and acknowledged the interactive influences of the biological resource and production zones. This interactive influence begins at the top, in the wao nahele. What happens there influences the three other production zones. Therefore, any ahupua‘a restoration that aims at the reintroduction of adaptive and integrative management should start with silvicultural research at an operational scale. Silviculture is concerned with the care of forests. It is based on knowledge gained from research in forest ecology and should be a form of “low input management”. With regard to the ahupua‘a model, silviculture must focus on enhancing the natural processes associated with the function of the forested watershed and stream ecosystem. Silviculture should also aim at restoring a “Hawaiian sense of place” in those ahupua‘a selected for stream restoration. This concept will be explained in some detail in this paper.
A Trusted and Tooled Approach to Design a Network Monitor

Cyber security has been an issue in industrial control systems (ICS) of critical infrastructures. Existing security measures for ordinary enterprise systems are hardly applicable to ICS because they have different requirements. In contrast, whitelisting network monitors attract wide attention as a security measure for ICS that meets the demand for availability during a long lifetime, as well as to exploit the static nature of system configuration. Once defined, the whitelist of allowed packets can detect ever-increasing new attacks without requiring any update. This paper presents a framework for developing reliable and secure whitelisting network monitors for ICS networks such as used in SCADA systems. The proposed approach relies on a model-based development combined with formal verification and proof steps, such that the normal communication model can be verified, the whitelist can be automatically generated from the model and the soundness of the network monitor program can be proven.
14 November 2017 Measuring Market Power in Multi-Sided Markets-Note by Kurt Brekke

This paper by Kurt Brekke was submitted as background material for Item 8 at the 127th meeting of OECD Competition Committee on 21-23 June 2017. This is the final version. The authors were given an opportunity to review and finalise the paper after the discussion. The opinions expressed and arguments employed herein do not necessarily reflect the official views of the Organisation or of the governments of its member countries. More documents related to this discussion can be found at www.oecd. org/daf/competition/rethinking-antitrust-enforcement-tools-in-multi-sided-markets.htm
InferLine : Prediction Pipeline Provisioning and Management for Tight Latency Objectives

Serving prediction pipelines spanning multiple models and hardware accelerators is a key challenge in production machine learning. Optimally configuring these pipelines to meet tight end-to-end latency goals is complicated by the interaction between model batch size, the choice of hardware accelerator, and variation in the query arrival process. In this paper we introduce InferLine, a system which provisions and executes ML prediction pipelines subject to end-to-end latency constraints by proactively optimizing and reactively controlling per-model configurations in a fine-grained fashion. InferLine leverages automated offline profiling and performance characterization to construct a cost-minimizing initial configuration and then introduces a reactive planner to adjust the configuration in response to changes in the query arrival process. We demonstrate that InferLine outperforms existing approaches by up to 7.6x in cost while achieving up to 34.5x lower latency SLO miss rate on realistic workloads and generalizes across state-of-the-art model serving frameworks.