Miracle-dz/UniFAR-FTUs huggingface dataset:
Title Holocene sea levels in southeast Asia , Maldives , Indiaand Sri Lanka : the SEAMIS database

We assembled a database of Holocene relative sea-level index points (n1⁄4 213) and marine (n1⁄4 211) and terrestrial (n1⁄4 122) limiting points for the broader South and Southeast Asian region including the Maldives, India and Sri Lanka. The standardized review of published age-elevation information from corals, deltaic, estuarine and mangrove deposits, beachrocks and tidal notches, yielded a new suite of relative sea-level index and limiting points produced according to a standardized protocol. Expected spatial variability in Holocene relative sea-level change due to glacial isostatic adjustment was accounted for, by first subdividing the study area into ten geographic sub-regions from the Central Indian Ocean to the Western Tropical Pacific, and second by comparing sub-regional relative sea-level data to model predictions of glacial isostatic adjustment. Results show that some of the regionally constrained relative sea-level data are characterized by significant inconsistencies that cannot be explained by glacial isostatic adjustment. Such inconsistencies of standardized relative sea-level data become particularly obvious in areas around the Red River Delta in Vietnam, the Gulf of Thailand, the northwest coast of Malaysia and the Spermonde Archipelago in Indonesia. Based on a critical evaluation of the reviewed relative sea-level indicators, we discuss possible sources of local divergence and identify regions where data are currently insufficient to constrain glacial isostatic adjustment predictions. The remaining quality-controlled and consistent relative sea-level data show that glacial isostatic adjustment and syn-/post-formational influences such as tectonic uplift, subsidence and compaction were the dominant local drivers of Holocene relative sea-level change. Collectively, the results of this review suggest that Holocene sea levels in South and Southeast Asia and surrounding regions have been controlled by a variety of global and local drivers and imply that additional index points from the Java Sea in Indonesia would be valuable to better assess the spatial variability, and to calibrate geophysical models of glacial isostatic adjustment. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Maintainability Model with Object-Oriented Software Metrics

Software maintainability is an effective measure for the system quality in the stage of software development. Understandability, modifiability and analyzability are the important factors of the software maintainability that can judge the maintainability of the system. These three factors depend mainly on the 3 size and 8 structural metrics of various UML specifications. We proposed the different models to measure the maintainability and its important three factors in this paper. In the process of developing the models we used exponential nonlinear representation. We utilized regression approach along with two-tailed t-test to find out the significance of the variables. The proposed methods are compared to the other non-linear models also made in this research paper. Index Term-Analyzability Model, Modifiability Model, Maintainability model, Software Quality, t-test. Understandability Model.
No Data in the Void : Values and Distributional Conflicts in Empirical Policy

artificial intelligence (AI) is the hypothetical AI system with the objective of producing as many paperclips as possible. If sufficiently capable, such an AI system might end up annihilating humanity in the pursuit of this objective. Another example is the design of experiments. The majority of experiments in the social and life sciences are designed based on the (implicit) objective of obtaining precise estimates of causal effects. Such experiments randomly assign treatments using fixed probabilities. But if the goal of experiments is instead to inform policy choices, or to help experimental participants, then we want to adaptively learn during the experiment, and use the best performing alternatives more often as the experiment proceeds. These and other examples will be discussed Economics has experienced an empirical turn in the last few decades. We have entered an era of big data, machine learning, and artificial intelligence. Experimental methods have greatly increased in importance in both the social and life sciences. And recent efforts at reforming the publication system promise to improve the replicability and credibility of published findings. One might be tempted to conclude that this increased availability of and reliance on quantitative evidence allows us to dispense with the normative judgements of earlier days. I will argue that the opposite is the case. The choice of objective functions, which define our goals, and of the set of policies to be considered matters ever more in all of these contexts.
Versioned Project Artifacts Provenance Model Storage Property Graph Store ( PROV model ) ( Neo 4 j ) Query Execution Engine Segmentation Operator Summarization Operator Cypher Engine ( Neo 4 j ) Query Facilities Frontend Introspection Monitoring Cypher Versioned

Along with the prosperous data science activities, the importance of provenance during data science project lifecycle is recognized and discussed in recent data science systems research. Increasingly modern data science platforms today have nonintrusive and extensible provenance ingestion mechanisms to collect rich provenance and context information, handle modifications to the same file using distinguishable versions, and use graph data models (e.g., property graphs) and query languages (e.g., Cypher) to represent and manipulate the stored provenance/context information. Due to the schema-later nature of the metadata, multiple versions of the same files, and unfamiliar artifacts introduced by team members, the “provenance graph” is verbose and evolving, and hard to understand; using standard graph query model, it is difficult to compose queries and utilize this valuable information. In this paper, we propose two high-level graph query operators to address the verboseness and evolving nature of such provenance graphs. First, we introduce a graph segmentation operator, which queries the retrospective provenance between a set of source vertices and a set of destination vertices via flexible boundary criteria to help users get insight about the derivation relationships among those vertices. We show the semantics of such a query in terms of a context-free grammar, and develop efficient algorithms that run orders of magnitude faster than state-of-the-art. Second, we propose a graph summarization operator that combines similar segments together to query prospective provenance of the underlying project. The operator allows tuning the summary by ignoring vertex details and characterizing local structures, and ensures the provenance meaning using path constraints. We show the optimal summary problem is PSPACE-complete and develop effective approximation algorithms. The operators are implemented on top of a property graph backend. We evaluate our query methods extensively and show the effectiveness and efficiency of the proposed methods.
DOES CLEARER SPECIFICATION OF HOUSING PREFERENCE OPTIMIZE BUYER SEARCH PROCESS AND RESULTS ? EXPERIMENTAL EVIDENCE

The possibility that a clear specification of housing preference may optimize the buyer search process and results is a long-standing, but controversial, hypothesis in the field of real estate. To date, empirical evidence on this hypothesis has been scarce. We present controlled experimental evidence on the effect of clear specification of buyer’s housing preference on the search process and results in the resale housing market. We compare the data collected from different experimental environment and find that when the buyer specifies their housing preference more clearly, the time-till-purchase is significantly reduced and the number of inspections is significantly decreased, while both the buyer’s savings and the degree of satisfaction are increased slightly (non-significantly).
ON ISLANDS AND SMALL STATES

The paper tests and confirms the hypothesis that trade openness tends to generate GDP growth volatility, and that such growth volatility is mitigated by good economic and political governance. This may explain why some economies do not exhibit a high degree of GDP growth volatility even though they are highly-open. The main implication of these results is that countries that are highly dependent on international trade, including most small states, would be exposed to GDP growth volatility, which has various downsides, as explained in the literature review. However, it does not necessary follow that highly trade-open economies small states in particular are the ones that experience the highest degree of GDP growth volatility, if these countries adopt appropriate policies to attenuate the effect of openness on volatility.
Inflation Puzzles in the New Keynesian Model : The Implications of Anchored Expectations ∗

In this paper we introduce bounded rationality into an otherwise standard New Keynesian Model. Agents are assumed to behave as econometricians, using time-series models to forecast inflation and the output gap similar to that of Stock and Watson (2007). The agent’s perceived optimal forecast rules are defined by the Kalman filter. In a unique equilibrium, the values of the two Kalman gain parameters are pinned down by the observed autocorrelation of inflation and output gap changes. This methodology can be applied directly to U.S. data. We show that if agents perpetually update their estimates of the Kalman gains using a moving window of recent data, the identified Kalman gain for inflation exhibits a downward drift during the so-called “Great Moderation” period. A low Kalman gain implies a low weight on recent inflation in the agent’s forecast rule. This helps anchor inflation near the central bank’s target rate when the output gap falls sharply during the Great Recession. In the longer term, however, the recession leads to a downward revision of agents’inflation forecast, which generates a moderate —but highly persistent —decline in inflation. Thus, the model can help account for both the “missing disinflation” in the immediate wake of the recession as well as the “missing inflation” in recent years. Forecasts with the model suggest that inflation will undershoot the central bank’s target rate for several years after the output gap has fully recovered. Consequently, the model predicts that monetary policy will remain accomodative and contribute to a positive output gap in the future.
Honeybees can spread Colletotrichum acutatum and C. gloeosporioides among citrus plants

M. C. G. Gasparoto, S. A. Lourenc o, F. A. O. Tanaka, M. B. Sp osito, L. C. Marchini, G. J. Silva Junior and L. Amorim* Plant Pathology Department, S~ ao Paulo University, Piracicaba, SP, 13418-900; Crop Science Department, S~ ao Paulo University, Piracicaba, SP 13418-900; Entomology Department, S~ ao Paulo University, Piracicaba, SP 13418-900; and Fundecitrus, Av. Dr. Adhemar Pereira de Barros 201, Araraquara, SP 14807-040, Brazil
Performance evaluation of a downlink packet scheduling algorithm for the long term evolution cellular systems

Long Term Evolution (LTE) is the starting point towards the 4 th generation (4G) of cellular systems to increase the capacity, spectral efficiency and the coverage. One of the most important features of LTE is employing the radio resource management in order to improve the performance of the cellular system. In this study, performance evaluation of a downlink packet scheduling algorithm that depends on the channel quality information in the LTE cellular networks is discussed. Simulation results show that the analyzed downlink packet scheduling algorithm improves the fair resource allocation with a tolerable loss in the throughput.
EXPLORING THE PARAMETER SPACE OF A SEARCH ALGORITHM

This paper addresses the problem of tuning the performance of a metaheuristic search algorithm with respect to its parameters. The operational behavior of typical metaheuristic search algorithms is determined by a set of control parameters, which have to be fine-tuned in order to obtain a best performance for a given problem. The principle challenge here is how to provide meaningful settings for an algorithm, obtained as result of better insight in its behavior. In this context, we discuss the idea of learning a model of an algorithm behavior by data mining analysis of parameter tuning results. The study was conducted using the Differential Ant-Stigmergy Algorithm as an example metaheuristic search algorithm.
The Effects of Age and Central Field Loss on Head Scanning and Detection at Intersections

Purpose Using a driving simulator, we quantified the effects of age and central field loss (CFL) on head scanning when approaching an intersection and investigated the role of inadequate head scanning in detection failures. Methods Participants with CFL (n = 20) and with normal vision (NV; n = 29), middle-aged (36–60 years) or older (67–87 years), drove along city routes with multiple intersections while head movements were recorded. The effects of age and CFL on scanning were analyzed at 32 intersections with stop/yield signs. The relationships between age, CFL, scanning, and detection were examined at four additional intersections with a pedestrian appearing on the far left. Results Older NV participants made fewer total scans than middle-aged NV participants and had smaller maximum scan magnitudes. Head scanning of older CFL and NV participants did not differ, but middle-aged CFL participants made fewer head scans, had higher rates of failing to scan, and made smaller head scans than middle-aged NV participants. For the older NV and both CFL groups, detection failures were high (≥58%); head scan magnitudes were 15° smaller when the pedestrian was not detected than when it was detected. Conclusions Both older NV and CFL participants exhibited head scanning deficits relative to middle-aged NV participants. Unexpectedly, however, it was the middle-aged CFL group that performed least well when scanning, a finding that warrants further investigation. Translational Relevance Failing to head scan sufficiently far at intersections may place older drivers and drivers with vision impairment at a higher risk for causing collisions.
Second Graders' Metacognitive Actions in Problem Solving Revealed through Action Cards.

Despite the important role that metacognition plays in school mathematics, attention has only recently turned to primary grades. The aim of this exploratory qualitative study was to find out to what extent 6 second graders engage in metacognitive behaviors during mathematics problem-solving. The analysis was based on the adaptation of the multi-method interview approach, whose core idea lies upon using action cards consisting of metacognitive cues. The results show that even young children engage in different metacognitive actions. However, the use of action cards revealed some drawbacks with respect to studying young children’s metacognition during mathematics problem-solving.
The Use of Geometric Morphometrics in a Study of Shape Diversity of Ground Beetles ( Coleoptera : Carabidae )

Ground beetles are one of the largest groups in Coleoptera with variety of life styles. The aim of this study is to examine the diversity of head and pronotum shape of ground beetles, and the relationship with body size and phylogenetic relationships using geometric morphometrics. In addition, shape correlation between head and pronotum was examined by partial least square analysis. 300 species of family Carabidae were used in this study (280 for head, 290 for pronotum), covering 15 subspecies. Procrustes superimposition and principal component analysis was applied to head and pronotum independently to extract comparable shape information. Independent contrasts were used for comparison of shape and phylogeny to take into account the phylogenetic relationships. The main shape change associated with PC1 and PC2 (PC1=37.1%, 43.1% and PC2=21.6%, 33.9% for head and pronotum respectively) was in the slender-stout direction for both head and pronotum, and there was a significant correlation between head and pronotum shape change (P<0.0001). Confidence ellipses covering each subfamily showed large overlaps in the shape space constructed by the first two PCs, for both head and pronotum. However, Cicindelinae and Scaritinae were isolated from others due to their discriminative body form and other characteristic shape changes were observed in many clades. Shape variation due to evolutionary allometry, which is the relationship of shape and body size, was significant (P<0.0001 for head, P=0.0137 for pronotum) but small (2.8% for head, 1.3% for pronotum). Significant shape correlation was found associated with head and pronotum either getting slender or getting stout. Furthermore, significant phylogenetic signal was found from mapping the phylogeny onto the shape space and performing permutation test. These results indicate that phylogenetic relationships are considerably influencing the shape variation of ground beetles. Moreover, contribution of diet, flying ability and other behavioral factors should be considered to explain the underlying evolutionary processes of this shape variation. This study was supported by the Institutional Program for Young Researcher Oversea Visits from the Japan Society for the Promotion of Science.
QUANTIFYING THE SEMANTIC VALUE OF WORDS

Hypotheses about the evolution of human language often posit a role for the informativeness of speech acts. However, there has yet to be an accessible method for measuring the semantic value of a word or group of words. This paper outlines a novel test statistic for determining the relative semantic value of words in a language, given a corpus that approximates ecologically valid use of that language. Future work could use this test to disambiguate between social and cognitive factors in linguistic change.
Queensland Storm Surge Forecasting Model Design Using Sensitivity Analysis

: Storm surge is a destructive and life threatening natural hazard associated with tropical cyclone (TC) events with the Queensland (Qld) coastline being particularly vulnerable. In a disaster management context, understanding the full range of possible scenarios that may arise from an impending event is critical for informed evacuation decision making. Tropical cyclones are difficult to forecast in terms of track and intensity, and storm surge magnitude is highly sensitive to the specific direction and location of peak cyclonic wind forcing in interaction with the local bathymetry. Here we investigate the optimal design of the next-generation storm surge forecasting system using a sensitivity analysis approach. The storm surge forecasting system, QSurge consists of a web-based probabilistic hazard mapping interface drawing from a large database of pre-simulated events. The aim of this paper is identify the appropriate increments of various tropical cyclone parameters necessary to capture the full range of possible storm surge outcomes whilst minimising computational run time. Sensitivity modelling experiments were conducted, consisting of simulating a range of TC parameter values within a parametric wind field model and then simulating storm surge using the Coral Sea MIKE21 hydrodynamic model established for this study and implemented on high performance computing. Detailed analysis of the results was undertaken for the study sites of Cairns, Cardwell and Townsville in North Queensland, and used to determine the optimal range of values for the various cyclone parameters. The number of scenarios necessary to complete a full ensemble database for landfall locations between Cooktown and the Whitsundays was initially estimated as 2,165,240. This was reduced to a set of 664,335 scenarios using polynomial relationships between peak storm surge magnitude and TC parameters such as central pressure and radius of maximum winds. Such a simulation set is achievable using high performance computing and given the optimised run time of the numerical model.
Establishing a healthy microbiome with human milk oligosaccharides Supporting immune system development in infancy and beyond Human milk oligosaccharides : the missing link

Human milk oligosaccharides (HMOs) are unique oligosaccharides found in mother’s milk. Collectively, HMOs constitute the third largest solute in human milk, after lactose and fat. The types and levels of HMO vary considerably among women, geographical regions and the stages of lactation. Over 130 different oligosaccharides have been identified in human milk, with 2’-fucosyllactose (2’-FL) being the most abundant (~2 g/l). Recent innovations and regulatory approvals have enabled infant formula companies to incorporate 2’-FL into their products, helping infant formulas to become more like mother’s milk. This article explores the latest research on HMOs and their role in supporting digestive, immune and cognitive development in infants.
A Novel Design of Low-power SRAM cell

SRAM cell is one of the most important part of FPGAs and Caches in digital design domain. This paper presents a new 8T SRAM cell with better performance (power consumption and delay time) than regular 6T and 8T SRAM cells. HSPICE simulations show that this new 8T SRAM cell has at least 43.7% improvement at power consumption in comparison with 6T SRAM cell based on 7nm technology model. Keywords—SRAM cell; power consumption; delay time; standby power; nanotechnology
A Magnified Application of Deficient Data Using Bolzano Classifier

Deficient and Inconsistent knowledge are a pervasive and lasting problem in heavy information sets. However, basic accusation is attractive often used to impute missing data, whereas multiple imputation generates right value to replace. Consequently, variety of machine learning (ML) techniques are developed to reprocess the incomplete information. It is estimated multiple imputation of missing information in large datasets and the performance of MI focuses on several unsupervised ML algorithms like mean, median, standard deviation and Supervised ML techniques for probabilistic algorithm like NBI classifier. Such research is carried out employing a comprehensive range of databases, for which missing cases are first filled in by various sets of plausible values to create multiple completed datasets, then standard completedata operations are applied to each completed dataset, and finally the multiple sets of results combine to generate a single inference. The main aim of this report is to offer general guidelines for selection of suitable data imputation algorithms based on characteristics of the data. Implementing Bolzano Weierstrass theorem in machine learning techniques to assess the functioning of every sequence of rational and irrational number has a monotonic subsequence and specify every sequence always has a finite boundary. For estimate imputation of missing data, the standard machine learning repository dataset has been applied. Tentative effects manifest the proposed approach to have good accuracy and the accuracy measured in terms of percent.
Redesigning roadway infrastructure for mixed autonomous and non-autonomous traffic

Abstract Autonomous vehicles are likely to be one of the major forms of disruptive technology that will be affecting travel behaviour and transport infrastructure development. During the last century, roads have been designed to provide a safe, approachable and efficient environment for the navigation of human drivers in conventional vehicles. The presence of enhanced driving behaviours, such as precise lane guidance and near instantaneous reaction times within autonomous vehicles will transform the planning and design of roadway infrastructure. Acknowledging and leveraging these aspects in coordination with the optimisation of the interaction between conventional and autonomous vehicles will be pivotal for the sustainable adoption of the technology.