Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibration of a Macroscopic Traffic Flow Model with Stochastic Saturation Rates

1 It is well known that freeway capacity (saturation rate) is highly uncertain and that a nominal value 2 alone may not suffice for traffic management purposes. To analyze the impact of capacity dis3 ruptions, we consider a macroscopic traffic flow model with stochastic saturation rates, called the 4 stochastic switching cell transmission model (SS-CTM). The SS-CTM enables analysis of traffic 5 delay and throughput drop due to uncertainty of capacity. In this article, we present a method for 6 calibration of parameters of the SS-CTM using real traffic data from the Caltrans Performance 7 Measurement System (PeMS). Nominal parameters except saturation rates are estimated using a 8 linear regression-based method. The stochastic saturation rate model is developed by clustering 9 of observed saturation rates (mode identification) and constructing a Markov chain governing the 10 switches between the identified modes. We also apply the proposed method to a segment of the 11 US Route 101. The results imply that the saturation rate may deviate from the nominal value for a 12 remarkable fraction of time. In addition, correlation between saturation rates at adjacent locations 13 is significant and is thus relevant for delay and throughput analysis. 14 15
A Note on Estimating Market-Based Minimum Capital Risk Requirements: A Multivariate GARCH Approach

Internal risk management models of the kind popularized by J. P. Morgan are now used widely by the world’s most sophisticated financial institutions as a means of measuring risk. Using the returns on three of the most popular futures contracts on the London International Financial Futures Exchange, in this paper we investigate the possibility of using multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models for the calculation of minimum capital risk requirements (MCRRs). We propose a method for the estimation of the value at risk of a portfolio based on a multivariate GARCH model. We find that the consideration of the correlation between the contracts can lead to more accurate, and therefore more appropriate, MCRRs compared with the values obtained from a univariate approach to the problem.
California Winter Precipitation Predictability: Insights From the Anomalous 2015–2016 and 2016–2017 Seasons

The unexpected dry 2015–2016 El Niño winter and extremely wet 2016–2017 La Niña winter in California challenged current seasonal prediction systems. Using the Met Office GloSea5 forecast ensemble, we study the precipitation and circulation differences between these seasons and identify processes relevant to California precipitation predictions. The ensemble mean accurately predicts the midlatitude atmospheric circulation differences between these years, indicating that these differences were predictable responses to the strong oceanic forcing differences. The substantial California precipitation differences were poorly predicted with large uncertainty. Notable differences in high‐latitude circulation anomalies associated with internal variability distinguish the ensemble members that successfully simulate precipitation from those that do not. Specifically, accurate representation of the Arctic Oscillation phase differences improves the accuracy of simulated precipitation differences but these differences were not well predicted in the ensemble mean for these seasons. Improved representation of high‐latitude processes such as the Arctic Oscillation and polar‐midlatitude teleconnections could therefore improve California seasonal predictions.
Providing Occupancy as a Service with Databox

Occupancy modelling for efficient energy management of indoor spaces has gained significant recent attention. Unfortunately, many such models rely on copying sensor data to the cloud for third-party services to process, creating risks of privacy breach. Such matters have become particularly pertinent for companies handling data of EU citizens due to provisions of the General Data Protection Regulation (GDPR). In this paper we present an implementation of "Occupancy-as-a-Service" (OaaS) at the edge, inverting the usual model: rather than ship data to the cloud to be processed, we retain data where it is generated and compute on it locally. This effectively avoids many risks associated with moving personal data to the cloud, and increases the agency of data subjects in managing their personal data. We describe the Databox architecture, its core components, and the OaaS functionality. As well as improving the privacy of the occupants, our approach allows us to offer occupancy data to other applications running on Databox, at a granularity that is not constrained by network usage, storage or processing restrictions imposed by third-party services, but is under data subject control.
The asymptotic manifolds of a perturbed linear system of differential equations

if f(t, x) is sufficiently "small". The present paper investigates this problem further. In particular, our results are motivated by two recent studies. Brauer and Wong [1] obtained quite general results on the asymptotic relationships between the solutions of (1) and (2). In §3 we will weaken significantly the hypotheses of some of their results. Toroselidze [5] considered the problem of perturbing the asymptotic manifold of a nonlinear scalar equation. We will extend these results to the nonlinear system (2) and consider some related questions in §4. The main result of our paper establishes that, under certain conditions, the set of all initial positions of the solutions in a certain asymptotic manifold is an open set. Our techniques are a combination of the well-known comparison principle and the Schauder-Tychonoff fixed point theorem. Fundamental in the application of the comparison principle is a scalar equation which we will denote by
Public-Sector Agricultural Research Priorities for Sustainable Food Security: Perspectives from Plausible Scenarios

There is widespread agreement that our ability to deliver sustainable food security for all will be challenged in three dimensionsâ€”population growth, constrained natural resources, and climate change. Investments in agricultural productivity are essential to dealing with these challenges. This suggests that cooperation across these two sets of institutions (FAO and the CGIAR) to take advantage of their expertise could result in better understanding for all. At the same time, neither of these institutions has some of the expertise badly needed to assess the coming food security challenges, so cooperation should extend to a range of research organizations. The paper highlights three types of joint activitiesâ€”cooperative quantitative modeling, cooperative use of institutional and outside substantive expertise, and sustained cooperation with model intercomparison efforts.
Insight, part of a Special Feature on Science and Governance in a Diverse World: Coproduction and Coproductive Capacities for Environmental Management Seeking our shared wisdom: a framework for understanding knowledge coproduction and coproductive capacities

The widespread disconnect between scientific projections of climate change and the implementation of responsive management actions has escalated calls for knowledge production processes able to exercise a stronger voice in decision making. Recently, the concept of coproduction has been championed as a potential answer. The term 'knowledge coproduction' is used loosely in the literature to describe an inclusive, iterative approach to creating new information; it is distinguished by its focus on facilitating interactions between stakeholders to develop an integrated or transformational understanding of a sustainability problem. Whether a coproduction process is successful in this integration of science and policy depends on a range of capabilities that should be understood as 'coproductive capacities. ' We draw on the literature from sustainability science to propose a conceptual framework that specifies the sequential goals of knowledge coproduction and potential sources of coproductive capacity. We apply this framework to examine our experience facilitating the coproduction of a climate change action plan for Papahānaumokuākea Marine National Monument and World Heritage Site. This framework offers a structure for systematically investigating the capacities, mechanisms, and dynamics of knowledge coproduction and for guiding the design of coproduction processes.
#rescatemineros: global media events in the microblogging age

Contemporary media events are experienced within the complex scenario of convergence media. Broadcasting channels and traditional mass media coexist with online digital news channels and with countless social media services. This paper describes how a global media event (the rescue operation of the 33 Chilean miners trapped underground for 69 days between August and October 2010) can be observed through the communication occurred inside Twitter, the largest microblogging platform. The article proposes a data-driven methodology that, starting from a large data acquisition procedure, is used to investigate: a) what communication patterns emerged within the microblogging sites; and b) how relevant information is propagated through the microblogging network of users.
Measuring preferences for low-carbon energy technologies in South-East England: the case of electricity generation

This paper reports on a mail survey that investigated public preferences in South-East England for the use of low-carbon energy technologies in electricity production, namely on-shore wind, biomass and nuclear power. Using a labelled choice experiment the perceived importance and value to the public of distance and the impacts of these energy options on biodiversity, carbon emissions, land occupation and household cost was explored. Results suggest that the attributes, the name of technology, respondents’ demographic characteristics, general attitudes towards the environment and energy, previous knowledge and experience with the technologies were significant choice determinants. Considering the current debate about the further development of low-carbon technologies and in particular of nuclear power and biomass in the UK and worldwide, this survey aims to provide an insight into the factors that could potentially influence their public acceptability.
How Personality Affects Time Control Over E-Mail Use : The Mediating Effects

It is well established in psychology literature that personality to some degree predicts performance. However, the mediating effect is unique to the context, and usually complex. In the context of e-mail handling, this paper is interested in looking into how personality affects the perceived control of time. Based on data collected from 251 knowledge workers, the results showed that personality predicted the perceived control of time over e-mail handling through both e-mail specific time management behaviors and e-mail self-efficacy. In addition, higher e-mail self-efficacy leads to improve e-mail specific time management behaviors.
Collective coordinate approximation to the scattering of solitons in the (1+1) dimensional NLS model

We present a collective coordinate approximation to model the dynamics of two interacting nonlinear Schrödinger solitons. We discuss the accuracy of this approximation by comparing our results with those of the full numerical simulations and find that the approximation is remarkably accurate when the solitons are some distance apart, and quite reasonable also during their interaction.
Investigating performance of neural networks and gradient boosting models approximating microscopic traffic simulations in traffic optimization tasks

We analyze the accuracy of traffic simulations metamodels based on neural networks and gradient boosting models (LightGBM), applied to traffic optimization as fitness functions of genetic algorithms. Our metamodels approximate outcomes of traffic simulations (the total time of waiting on a red signal) taking as an input different traffic signal settings, in order to efficiently find (sub)optimal settings. Their accuracy was proven to be very good on randomly selected test sets, but it turned out that the accuracy may drop in case of settings expected (according to genetic algorithms) to be close to local optima, which makes the traffic optimization process more difficult. In this work, we investigate 16 different metamodels and 20 settings of genetic algorithms, in order to understand what are the reasons of this phenomenon, what is its scale, how it can be mitigated and what can be potentially done to design better real-time traffic optimization methods.
Improvement of the microwave properties of Y-Ba-Cu-O films with artificial defects

In this paper, the potential of defects for optimizing the microwave properties of YBa/sub 2/Cu/sub 3/O/sub 7/ (YBCO) thin films is demonstrated. On one hand, microscopic Y/sub 2/O/sub 3/ precipitates, which can be created in YBCO thin films by modification of the deposition process, serve as ideal scattering centres for quasiparticles and, thus, lead to a considerable reduction of the microwave surface resistance R/sub s/. The modification R/sub s/(T) can be explained in terms of the two-fluid model. Data for the quasiparticle scattering rate can be obtained from the measurements. On the other hand, the impact of artificial defects, so called antidots, upon the microwave properties is analyzed. R/sub s/ measurements demonstrate that the ion beam etching creates a -20 nm broad damaged area at the edge of the antidots. First measurements of the power handling capability of YBCO thin film resonators indicate that the magnetic contribution to the nonlinear behavior can be reduced by antidots. The implementation of antidots, which have been proven to be an ideal and easy tool to improve active YBCO thin film devices, might be of use for microwave applications as well.
Serum homocysteine levels and diabetic neuropathy in patients with type 2 diabetes mellitus : a systematic review and meta-analysis

Aims/Introduction: Homocysteine (Hcys) is a thiol-containing amino acid formed by a demethylation methionine. Several studies showed a possible association between homocysteine and diabetic neuropathy in patients with type 2 diabetes. We performed a meta-analysis to evaluate the relationship between homocysteine and diabetic neuropathy. Materials and Methods: A systematic review and meta-analysis was performed to comprehensively evaluated the association between serum homocysteine levels and diabetic neuropathy in patients with type 2 diabetes mellitus. Literature updated to October 3, 2015 from Cochrane Library, MEDLINE, Web of Science, China Biology Medicine, and reference lists from relevant articles were identified. Data from eligible studies were pooled using meta-analysis. Results: A total of 784 individuals retrieved from five studies were finally included into the meta-analysis. Meta-analysis showed that there were obviously increased serum homocysteine levels in patients with diabetic neuropathy (WMD = 4.14 μmol/L; 95% CI = 3.32 to 4.95, P < 0.00001). Sensitivity analysis showed that there was no obvious change in the pooled estimated. Conclusion: Homocysteine is involved in the development of diabetic neuropathy in type 2 diabetic patients and hyperhomocysteinemia is very likely to be associated with diabetic neuropathy. Further studies are needed to validate the relationships between hyperhomocysteinemia and diabetic neuropathy.
Futures Basis, Inventory and Commodity Price Volatility: An Empirical Analysis

We employ a large dataset of physical inventory data on 21 different commodities for the period 1993–2011 to empirically analyze the behavior of commodity prices and their volatility as predicted by the theory of storage. We examine two main issues. First, we analyze the relationship between inventory and the shape of the forward curve. Low (high) inventory is associated with forward curves in backwardation (contango), as the theory of storage predicts. Second, we show that price volatility is a decreasing function of inventory for the majority of commodities in our sample. This effect is more pronounced in backwardated markets. Our findings are robust with respect to alternative inventory measures and over the recent commodity price boom.
Study on Body Condition Score and its Relationship with Solid Not Fat and Weekly Fat Corrected Milk Yield in Frieswal Cows

The study was carried out at Military Dairy Farm on Eighty milch Frieswal cows. The cows were divided in five parities and three stages of lactation. The study was done for Body Condition Score and its relation with production parameters namely solid not fat percent and weekly fat corrected milk yield (3.5%) along with the effect of parities and stages of lactation on Body Condition Score, production parameters. The Body Condition Score (BCS) system is a subjective scoring method of evaluating the energy reserve of dairy animal to provide better understanding of biological relationships between body fat, milk production parameters. The overall least square mean of body condition score related to parity as well as stage of lactation was 3.35±0.04. The effect of parity was found significant on body condition score and solid not fat percent and weekly fat corrected milk yield while stage of lactation showed significant effect on body condition score and solid not fat percent. The stage of lactation did not showed significant effect on weekly fat corrected milk yield. The significant correlation and regression of body condition score was not seen with solid not fat and weekly fat corrected milk yield. This can be concluded that parity and stage of lactation have positive and significant impact on milk production parameters.
Quantification of opportunities for early-stage paramedicine students to practice clinical skills during clinical placements compared with an equal dose of simulation-based workshops

Introduction A reported advantage of simulation-based learning environments (SLE) over clinical placements (CPs) is that the former can provide a greater number and breadth of opportunities to practice level-appropriate clinical skills compared with the random patient presentations provided during the latter. Although logical and widely accepted as fact, we find no published evidence to demonstrate the magnitude, nor indeed veracity, of this assumption. We therefore sought to quantify the clinical skills practiced by entry-level paramedicine students attending a well-selected CP compared with an equal dosage of SLE. Methods N=37 first-year paramedicine students completed activity diaries during 3 days of CP and 3 days of SLE. Opportunities to practice clinical skills were quantified and coded as either: level-appropriate, beyond-level or of non-discipline relevance. Results During SLE, the average student was exposed 226 times to 11 level-appropriate clinical procedures. During CP the average student was exposed 48 times to 24 clinical procedures, the majority relevant to paramedicine (63%), but a minority level-appropriate (38%). Students’ opportunities for supervised, ‘hands on’ practice represented only 10% of exposures in either SLE or CP but in terms of raw numbers of level-appropriate opportunities, SLE provided more than CP (n=23 vs 2). Discussion Our results confirm that SLE provides substantially more opportunities than CP for students to practice level-appropriate skills and is therefore more appropriate for repetitive practice. However, CP is likely to remain useful to students for practicing interpersonal skills and contextualisation of knowledge within the broader health system. Educators should therefore carefully articulate learning objectives before choosing between SLE and CP.
ATINER ' s Conference Paper Series IND 2013-0743

The concept of function structures is well-established in early phases of engineering design for clarifying product functions. In addition, it forms the basis for the synthesis of solution principles with morphological boxes. Besides benefits, disadvantages remain with its application. First, function structures typically depend on the background their operator. If several persons with diverse backgrounds create function structures, results may be diverging. Second, function structures are non-reversible. It is easy to conceptualize function structures with given products in mind as well as to turn already existing structures into products. However, with unknown context and main functions of existing products there is no method to elaborate unambiguous function structures reversely from those products. Third, function structures are poorly applicable when mixed levels of product embodiment prevail. To overcome those shortages, an improved approach is proposed in this paper. It relies on function templates that are based on already existing function carriers. This is achieved by conceiving building blocks for functions not only consisting of the function description but also of possible function carriers and all subfunctions. For them the same principle of decomposition applies; down to the stage of elementary functions. The conception of pre-created templates helps generating unambiguous structures. By providing context information due to function carrier inclusion, solutions become traceable. Through the templates’ adaptive nature, mixed levels get manageable. The paper concludes with discussing the approach regarding the development of multi-technology machine tools. To resolve these challenges, function templates are chosen that are based on the technology chain elements of the manufacturing process. ATINER CONFERENCE PAPER SERIES No: IND2013-0743 6
Image reconstruction from incomplete convolution data via total variation regularization

Variational models with Total Variation (TV) regularization have long been known to preserve image edges and produce high quality reconstruction. On the other hand, recent theory on compressive sensing has shown that it is feasible to accurately reconstruct images from a few linear measurements via TV regularization. However, in general TV models are difficult to solve due to the nondifferentiability and the universal coupling of variables. In this paper, we propose the use of alternating direction method for image reconstruction from highly incomplete convolution data, where an image is reconstructed as a minimizer of an energy function that sums a TV term for image regularity and a least squares term for data fitting. Our algorithm, called RecPK, takes advantage of problem structures and has an extremely low per-iteration cost. To demonstrate the efficiency of RecPK, we compare it with TwIST, a state-of-the-art algorithm for minimizing TV models. Moreover, we also demonstrate the usefulness of RecPK in image zooming.
Privacy in spatio-temporal databases : a survey of methods based on k-anonymity and microaggregation

Technologies able to track moving objects such as GPS, GSM, and RFID, have been well-adopted worldwide since the end of the 20th century. As a result, companies and governments manage and control huge spatio-temporal databases, whose publication could lead to previously unknown knowledge such as human behaviour patterns or new road traffic trends (e.g., through Data Mining). Aimed at properly balancing data utility with users’ privacy rights, several microaggregationbased methods for publishing movement data have been proposed. These methods are reviewed in this book chapter. We highlight challenges in the three stages of the microaggregation process namely, clustering, obfuscation, and privacy and utility evaluation. We also address some of these challenges by presenting yet another microaggregation-based method for privacy-preserving publication of spatio-temporal databases.