Miracle-dz/UniFAR-FTUs huggingface dataset:
The International Drivers of Domestic Airline Mergers in Twenty Nations: Integrating Industrial Organization and International Business

The domestic airline merger phenomenon of the late 1980s and early 1990s sparked a great deal of Industrial Organization (IO) literature; yet, that literature neglected non-US domestic mergers and potential for international competitive gains. Using an International Business perspective to complement an IO analysis, I argue that factoring international competitive incentives helps explain domestic airline merger activity. A Cournot model of airline competition illustrates that domestic mergers, via enhanced domestic networks and reduced domestic competition, generate international competitive gains. Further, empirical tests-using a structural equations approach on panel data covering interhyphen-national city-pair market segments-support domestic mergers improving international competitiveness. Copyright © 2006 John Wiley & Sons, Ltd.
Factory scheduling: simulation based scheduling using a two-pass approach

Bottleneck-based scheduling is a popular approach in production scheduling, and it has achieved promising results in industry. To incorporate this approach in discrete event simulation tools is difficult since the approach requires multiple passes, forward and backward, to reach a good solution for the scheduling problem. In this paper, we propose a two-pass scheduling approach using discrete-event simulation that takes bottlenecks into consideration. In the first pass, a simulation run is performed and bottlenecks are determined. If significant bottlenecks are identified, a second-pass simulation is performed to reduce the loading on bottlenecks through specific scheduling strategies.
Photochemical production and aging of an urban air mass

Aircraft measurements of O3, NO, NO2, NOy, HCHO, and H2O2 over the Swiss Plateau during 4 days in July 1993 were analyzed. Special emphasis was put on the urban plume of Zurich. An effective photochemical age for an urban plume was introduced, which accounts for a background NOz concentration. Only the effective photochemical age of the urban air mass increased with the time of transport between Zurich and the flight leg, where the aircraft crossed the plume. Ozone and NOz were strongly correlated. Ozone production rates ranged from 2.4 to 6.5 ozone molecules produced per NOx processed. The production of ozone per NOx molecule was lowest when the effective photochemical age was lowest and vice versa. Good correlations between HCHO and NOy have been found in the urban plume of Zurich as well as over the Swiss Plateau. Between H2O2 and NOz, a negative correlation was observed. On the basis of Sillman's [1995] indicator species, the ozone production in the Zurich plume and other air masses over the Swiss Plateau is in the NOx-sensitive range. However, there remains some uncertainty in this approach regarding the influence of biogenic emissions and the initial concentration of indicator species. Taking the low emissions of biogenic hydrocarbons compared to Sillman's model calculation into consideration, the central portion of the Zurich plume falls in the transition or even reactive organic gases (ROG)- limited range of all indicators. Estimates of production rates of HNO3 and peroxides support a ROG-sensitive ozone production in the most polluted portion of the plume.
HIGH-LATITUDE ELECTRIC FIELD STUDIES USING DMSP DATA

Abstract : Analysis using data from the ion drift meter in the SSIES instrument package on the DMSP series of satellites is used to calculate the electrostatic potential distribution in the polar ionosphere. Numerous geophysical parameters such as the location and magnitude of the potential extreme and the classification of the potential distribution as one of the three Heppner-Maynard models can be derived from this data analysis. An earlier version of this analysis procedure (DMSPPOTMOD) is described. This procedure has been expanded into a series of programs under the name DMSPDBASE which generate a database of the ionospheric parameters and results from this analysis. These calculated parameters serve as necessary inputs to the MSFM (Magnetospheric Specification and Forecast Model) used by the Air Weather Service. This report describes each version of the series up to the final version DMSPDBASE4 along with a description of the formats used for the database. New algorithms have been added to the routine in order to improve the selection routine for the endpoints, generalize the Heppner-Maynard model classification algorithm so that it will accommodate any orientation of the satellite track, calculate the true cross polar cap potential drop from the observed potential drop, etc. Analysis results into characterizing the overall potential distribution in both hemispheres during time of southward IMF and analysis of the evolution of the potential distribution during times of northward IMF are presented. Various collaborations with other users incorporating DMSP data are listed along with presentations and publications which have come from this work.
Smart Health Intelligent Healthcare Systems in the Metaverse, Artificial Intelligence, and Data Science Era

In recent decades, healthcare organizations around the world have increasingly appreciated the value of information technologies for a variety of applications. Three of the new technological advancements that are impacting smart health are metaverse, artificial intelligence (AI), and data science. The metaverse is the intersection of three major technologies — AI, augmented reality (AR), and virtual reality (VR). Metaverse provides new possibilities and potential that are still emerging. The increased work efficiency enabled by artificial intelligence and data science in hospitals not only improves patient care but also cuts costs and workload for healthcare providers.The availability of big data enables data scientists to use the data for descriptive, predictive, and prescriptive analytics. This article reviews multiple case studies and the literature on AI and data science applications in hospital administration. The article also presents unresolved research questions and challenges in the applications of the metaverse, AI, and data science in the smart health context.
Polysaccharides from Larch Biomass

There are two species of the genus Larix Mill, Larix sibirica Ledeb. (Figure 1) and Larix gmelinii (Rupr.) Rupr. , considered to be the most abundant trees in the Russian Federation: the total stock of their wood exceeds 26 billion m3. Traditionally, the main economic value of larch wood consists of the manufacture of roundwood (timber), the value of which is connected with the high quality of lumber from this breed of tree. Larch wood can sometimes be used in insignificant quantities in pulp-and-paper manufacture to obtain pulp. About 40% of this valuable breed (as bark, sawdust) enters waste through existing lumber manufacturing processes. Such an irrational approach to the development of larch wood does not allow exploitation of the richest potential of the given renewed source. Meanwhile, biologically active compounds contained in larch biomass can be used for the manufacture of products for medical, food and agricultural purposes with maximal benefit. The development of complex technology for chemical processing of larch biomass and waste timber will considerably raise the economic value of this biological resource.
OpenStream: Expressiveness and data-flow compilation of OpenMP streaming programs

We present OpenStream, a data-flow extension of OpenMP to express dynamic dependent tasks. The language supports nested task creation, modular composition, variable and unbounded sets of producers/consumers, and first-class streams. These features, enabled by our original compilation flow, allow translating high-level parallel programming patterns, like dependences arising from StarSs' array regions, or universal low-level primitives like futures. In particular, these dynamic features can be embedded efficiently and naturally into an unmanaged imperative language, avoiding the complexity and overhead of a concurrent garbage collector. We demonstrate the performance advantages of a data-flow execution model compared to more restricted task and barrier models. We also demonstrate the efficiency of our compilation and runtime algorithms for the support of complex dependence patterns arising from StarSs benchmarks.
Trustworthy Reinforcement Learning for Quadrotor UAV Tracking Control Systems

Simultaneously accurate and reliable tracking control for quadrotors in complex dynamic environments is challenging. As aerodynamics derived from drag forces and moment variations are chaotic and difficult to precisely identify, most current quadrotor tracking systems treat them as simple `disturbances' in conventional control approaches. We propose a novel, interpretable trajectory tracker integrating a Distributional Reinforcement Learning disturbance estimator for unknown aerodynamic effects with a Stochastic Model Predictive Controller (SMPC). The proposed estimator `Constrained Distributional Reinforced disturbance estimator' (ConsDRED) accurately identifies uncertainties between true and estimated values of aerodynamic effects. Simplified Affine Disturbance Feedback is used for control parameterization to guarantee convexity, which we then integrate with a SMPC. We theoretically guarantee that ConsDRED achieves at least an optimal global convergence rate and a certain sublinear rate if constraints are violated with an error decreases as the width and the layer of neural network increase. To demonstrate practicality, we show convergent training in simulation and real-world experiments, and empirically verify that ConsDRED is less sensitive to hyperparameter settings compared with canonical constrained RL approaches. We demonstrate our system improves accumulative tracking errors by at least 70% compared with the recent art. Importantly, the proposed framework, ConsDRED-SMPC, balances the tradeoff between pursuing high performance and obeying conservative constraints for practical implementations
OpenSESAME: simple but extensive structured availability modeling environment

Traditional modeling tools for high availability systems do not combine intuitivity, efficiency, and modeling power under the same umbrella. For example, combinatorial methods like fault trees or reliability block diagrams are quite intuitive, allow for a stepwise refinement of the models and can be analyzed efficiently. However, their modeling power is limited, as traditional methods for analyzing these models rely on the fact that there are no statistical dependencies between the failure and repair behavior of the components. The work presented here is a contribution towards modeling environments which are both user friendly and possess a high modeling power. It shows how the tool OpenSESAME can be used to design models for highly available systems which on the one hand are as intuitive as traditional reliability block diagrams and on the other hand can be enriched with inter-component dependencies. These dependencies can be added and removed at any time without the necessity of changing the hierarchic structure of the model.
The zero resource speech challenge 2015

The Interspeech 2015 Zero Resource Speech Challenge aims at discovering subword and word units from raw speech. The challenge provides the first unified and open source suite of evaluation metrics and data sets to compare and analyse the results of unsupervised linguistic unit discovery algorithms. It consists of two tracks. In the first, a psychophysically inspired evaluation task (minimal pair ABX discrimination) is used to assess how well speech feature representations discriminate between contrastive subword units. In the second, several metrics gauge the quality of discovered word-like patterns. Two data sets are provided, one for English, one for Xitsonga. Both data sets are provided without any annotation except for voice activity and talker identity. This paper introduces the evaluation metrics, presents the results of baseline systems and discusses some of the key issues in unsupervised unit discovery. Index Terms: zero resource speech challenge, feature extraction, unsupervised term discovery, new paradigms
Unemployment flows and vacancies in Spain

En este documento se estudia el problema del desempleo en Espana. Para ello se examinan las fluctuaciones de desempleo, la relacion entre desempleo y vacantes, y la tasa de salida del desempleo Se propone un metodo para corregir la informacion oficial de la administracion publica sobre vacantes. El problema del desempleo parece residir en la probabilidad de dejar el colectivo de desempleo, estimada a traves de la tasa de salida del desempleo. El desempleo hacia fuera en la curva U-V puede explicarse por el aumento en la participacion en el desempleo de largo plazo. Finalmente, se investiga tambien el problema de las externalidades en la busqueda y la probabilidad de equilibrio multiple
On quantum algorithms

Quantum computers use the quantum interference of different computational paths to enhance correct outcomes and suppress erroneous outcomes of computations. In effect, they follow the same logical paradigm as (multi-particle) interferometers. We show how most known quantum algorithms, including quantum algorithms for factorising and counting, may be cast in this manner. Quantum searching is described as inducing a desired relative phase between two eigenvectors to yield constructive interference on the sought elements and destructive interference on the remaining terms.
Neighbor Selection and Recommendations in Social Bookmarking Tools

Web 2.0 applications innovate traditional informative services providing Web users with a set of tools for publishing and sharing information. Social bookmarking systems are an interesting example of this trend where users generate new contents. Unfortunately, the growing amount of available resources makes hard the task of accessing to relevant information in these environments. Recommender systems face this problem filtering relevant resources connected to users' interests and preferences. In particular, collaborative filtering recommender systems produce suggestions using the opinions of similar users, called the neighbors. The task of finding neighbors is difficult in environment such as social bookmarking systems, since bookmarked resources belong to different domains. In this paper we propose a methodology for partitioning users, tags and resources into domains of interest. Filtering tags and resources in accordance to the specific domains we can select a different set of neighbors for each domain, improving the accuracy of recommendations.
Multi-Label Wireless Interference Classification with Convolutional Neural Networks

The steadily growing use of license-free frequency bands require reliable coexistence management and therefore proper wireless interference classification (WIC). In this work, we propose a WIC approach based upon a deep convolutional neural network (CNN) which classifies multiple IEEE 802.15. 1, IEEE 802.11 b/g and IEEE 802.15. 4 interfering signals in the presence of a utilized signal. The generated multi-label dataset contains frequency- and time-limited sensing snapshots with the bandwidth of 10MHz and duration of 12.8 μs, respectively. Each snapshot combines one utilized signal with up to multiple interfering signals. The approach shows promising results for same-technology interference with a classification accuracy of approximately 100% for narrow-band IEEE 802.15. 1 and IEEE 802.15. 4 signals. For cross-technology interference, wide-band IEEE 802.11 b/g signals achieve an accuracy above 90 %.
Intrinsic Plagiarism Detection using N-gram Classes

When it is not possible to compare the suspicious document to the source document(s) plagiarism has been committed from, the evidence of plagiarism has to be looked for intrinsically in the document itself. In this paper, we introduce a novel languageindependent intrinsic plagiarism detection method which is based on a new text representation that we called n-gram classes. The proposed method was evaluated on three publicly available standard corpora. The obtained results are comparable to the ones obtained by the best state-of-the-art methods.
Media Server Markup Language (MSML)

The Media Server Markup Language (MSML) is used to control and invoke many different types of services on IP Media Servers. Clients can use it to define how multimedia sessions interact on a Media Server and to apply services to individuals or groups of users. MSML can be used, for example, to control Media Server conferencing features such as video layout and audio mixing, create sidebar conferences or personal mixes, and set the properties of media streams. As well, clients can use MSML to define media processing dialogs, which may be used as parts of application interactions with users or conferences. Transformation of media streams to and from users or conferences as well as IVR dialogs are examples of such interactions, which are specified using MSML. MSML clients may also invoke dialogs with individual users or with groups of conference participants using VoiceXML.
Investigation of Curve Number Procedure

The curve number procedure of the Soil Conservation Service is investigated to establish a logically consistent, experimentally verifiable system. Several traditional aspects of the system are explored. These include conflicting definitions of the AMC-II condition, physical significance of the maximum potential retention, and the use of the runoff equation to estimate infiltration rate. To develop a consistent system, the runoff equation is taken to be a transform between a rainfall frequency distribution and a runoff frequency distribution. The curve numbers are treated as random variables with the curve numbers associated with antecedent moisture condition II (AMC-II) representing the central tendency. Those associated with AMC-I and AMC-III are shown to be representative of extremes of the distribution.
Approximate Oracles and Synergy in Software Energy Search Spaces

Reducing the energy consumption of software systems through optimisation techniques such as genetic improvement is gaining interest. However, efficient and effective improvement of software systems requires a better understanding of the code-change search space. One important choice practitioners have is whether to preserve the system's original output or permit approximation, with each scenario having its own search space characteristics. When output preservation is a hard constraint, we report that the maximum energy reduction achievable by the modification operators is 2.69 percent (0.76 percent on average). By contrast, this figure increases dramatically to 95.60 percent (33.90 percent on average) when approximation is permitted, indicating the critical importance of approximate output quality assessment for code optimisation. We investigate synergy, a phenomenon that occurs when simultaneously applied source code modifications produce an effect greater than their individual sum. Our results reveal that 12.0 percent of all joint code modifications produced such a synergistic effect, though 38.5 percent produce an antagonistic interaction in which simultaneously applied modifications are less effective than when applied individually. This highlights the need for more advanced search-based techniques.
NeurSF: Neural Shading Field for Image Harmonization

Image harmonization aims at adjusting the appearance of the foreground to make it more compatible with the background. Due to a lack of understanding of the background illumination direction, existing works are incapable of generating a realistic foreground shading. In this paper, we de-compose the image harmonization into two sub-problems: 1) illumination estimation of background images and 2) rendering of foreground objects. Before solving these two sub-problems, we ﬁrst learn a direction-aware illumination descriptor via a neural rendering framework, of which the key is a Shading Module that decomposes the shading ﬁeld into multiple shading components given depth information. Then we design a Background Illumination Estimation Module to extract the direction-aware illumination descriptor from the background. Finally, the illumination descriptor is used in conjunction with the neural rendering framework to generate the harmonized foreground image containing a novel harmonized shading. Moreover, we construct a photo-realistic synthetic image harmonization dataset that contains numerous shading variations by image-based lighting. Extensive experiments on this dataset demonstrate the effectiveness of the proposed method. Our dataset and code will be made publicly available.
"What? So What?" The Next-Generation JHOVE2 Architecture for Format-Aware Characterization

The JHOVE characterization framework is widely used by international digital library programs and preservation repositories. However, its extensive use over the past four years has revealed a number of limitations imposed by idiosyncrasies of design and implementation. With funding from the Library of Congress under its National Digital Information Infrastructure Preservation Program (NDIIPP), the California Digital Library, Portico, and Stanford University are collaborating on a two year project to develop and deploy a next-generation architecture providing enhanced performance, streamlined APIs, and significant new features. The JHOVE2 project generalizes the concept of format characterization to include identification, validation, feature extraction, and policy-based assessment. The target of this characterization is not a simple digital file, but a (potentially) complex digital object that may be instantiated in multiple files.
Using Random Subsets to Build Scalable Network Services

In this paper, we argue that a broad range of large-scale network services would benefit from a scalable mechanism for delivering state about a random subset of global participants. Key to this approach is ensuring that membership in the subset changes periodically and with uniform representation over all participants. Random subsets could help overcome inherent scaling limitations to services that maintain global state and perform global network probing. It could further improve the routing performance of peer-to-peer distributed hash tables by locating topologically-close nodes. This paper presents the design, implementation, and evaluation of RanSub, a scalable protocol for delivering such state. As a first demonstration of the RanSub utility, we construct SARO, a scalable and adaptive application-layer overlay tree. SARO uses RanSub state information to locate appropriate peers for meeting application-specific delay and bandwidth targets and to dynamically adapt to changing network conditions. A large-scale evaluation of 1000 overlay nodes participating in an emulated 20,000- node wide-area network topology demonstrate both the adaptivity and scalability (in terms of per-node state and network overhead) of both RanSub and SARO. Finally, we use an existing streaming media server to distribute content through SARO running on top of the PlanetLab Internet testbed.