Miracle-dz/UniFAR-FTUs huggingface dataset:
Relational Time-Space Data Structure To Speed Up Conflict Detection Under Heavy Traffic Conditions

In this paper, an innovative technique called Relational Time-Space Data Structures is presented for Conflict Detection (CD) between 4D trajectories, improving the performance of the widely-used pairwise CD algorithms and also overcoming some of the shortages of previous Spatial Data Structures CD algorithm. WP-E project STREAM aims to coordinate the whole European ATM at strategic and tactical levels, which means (particularly at strategic level) having to process big amounts of trajectories under heavy traffic conditions. This new and efficient CD algorithm may contribute to achieve some of the targets of the STREAM project. (Abstract) Keywords-component; ATM, 4D Trajectories, Conflict Detection, MTCD, Spatial Data Structure, linear complexity algorithm
Estimation of stator winding faults in induction motors using an adaptive observer scheme

This paper addresses the subject of inter-turn short circuit estimation in the stator of an induction motor. In the paper an adaptive observer scheme is proposed. The proposed observer is capable of simultaneously estimating the speed of the motor, the amount turns involved in the short circuit and an expression of the current in the short circuit. Moreover the states of the motor are estimated, meaning that the magnetizing currents are made available even though a fault has happened in the motor. To be able to develop this observer, a model particular suitable for the chosen observer design, is also derived. The efficiency of the proposed observer is demonstrated by tests performed on a test setup with a customized designed induction motor. With this motor it is possible to simulate inter-turn short circuit faults.
Serious games combined with wearables in battling pediatric obesity : the medical professionals' perspective

Introduction. The number of people with obesity or overweight in the modern world is increasing, with the number of children aged 5-19 categorized as obese or overweight exceeding 340 million individuals worldwide. Obesity is paired with psychological, physical and environmental issues that might well persist into adulthood. Treatment of obesity is aimed at changing the lifestyle to increase physical activity and increase the consumption of healthy foods. Through the years, these lifestyle changes have been aided by a range of technologies. Currently, wearables and serious games seem to be the next innovation. Serious games are used to track the progress of a participant and provides feedback in a playful learning environment. As research shows that consumers and children experience an overall positive attitude towards serious games and wearables, this research aims to assess the opinion of the medical professional, the physical therapist, tasked with treating children with obesity. Methods. A questionnaire has been set-up based on a literature review exploring the impact of obesity and current developments in the area of serious games and wearables. The questionnaire included questions with an open character to get insight in the current situation, the respondent’s stance on wearables and serious games and introduced a hypothetical game called Healthy Buddy. The gathered data was complemented with information gathered from a casual conversation with 2 graduates (MSc) in the fields of Psychology and Health & Society, and an interview with a pediatrician. Results. A total of 8 physical therapists answered the questionnaire. All of them were enthusiastic about the possibility of implementing serious games combined with a wearable into their treatment sessions. They felt that the combination of serious games and wearables would provide the child with visual cues of its progress and ensure the physical therapist has control over and insight in the lifestyle of the child. However, they felt that the game should be adjusted to the individual goals of the child, should offer variation, challenges and engage peers. Furthermore, the influence of parents, the possibility of the child sabotaging the process, the game being only a hype for a short period of time and an increase of screen time were mentioned as factors that require further consideration. Conclusion. Physical therapists specialized in children were enthusiastic about implementing serious games combined with wearables in their treatment sessions. They felt the technology makes it possible for children to see their progress while therapists (and parents) are able to have insight and control on progress and are able to provide feedback. They felt the technology produced a trendy solution based on the wishes of modern society.
Lyapunov Approach to Consensus Problems

This paper investigates the weighted-averaging dynamic for unconstrained and constrained consensus problems. Through the use of a suitably defined adjoint dynamic, quadratic Lyapunov comparison functions are constructed to analyze the behavior of weighted-averaging dynamic. As a result, new convergence rate results are obtained that capture the graph structure in a novel way. In particular, the exponential convergence rate is established for unconstrained consensus with the exponent of the order of $1-O(1/(m\log_2m))$. Also, the exponential convergence rate is established for constrained consensus, which extends the existing results limited to the use of doubly stochastic weight matrices.
EMC3: Energy-efficient data transfer in mobile crowdsensing under full coverage constraint

This paper proposes a novel mobile crowdsensing (MCS) framework called EMC3, which intends to reduce energy consumption of individual user as well as all participants in data transfer caused by task assignment and data collection of MCS tasks, considering the user privacy issue, minimal number of task assignment requirement and sensing area coverage constraint. Specifically, EMC3 incorporates novel pace control and decision making mechanisms for task assignment, leveraging participants' current call, historical call records as well as predicted future calls and mobility, in order to ensure the expected number of participants to return sensed results and fully cover the target area, with the objective of assigning a minimal number of tasks. Extensive evaluation with a large-scale real-world dataset shows that EMC3 assigns much less sensing tasks compared to baseline approaches, it can save 43%-68% energy in data transfer compared to the traditional 3G-based scheme.
Providing Cognitive and Social Networking Assessment to Virtualized Collaborative Learning in Engineering Courses

In previous research we proposed a new type of learning resource named Collaborative Complex Learning Resource (CC-LR) in support for teaching and learning engineering education by the virtualization of live collaborative sessions, with the aim to leverage the knowledge elicited during the collaboration and produce interactive and attractive resources to be played by engineering learners. We claimed this type of pedagogically and technologically augmented learning resources is able to overcome endemic problems found in collaborative learning of on-line engineering courses, such as lack of authentic interactivity, user empowerment, social identity and challenge, thus having a positive effect in learner engagement. In order to enhance further learning engagement we endowed CC-LR with a multifold cognitive assessment approach that provided effective awareness and constructive feedback to learners from both the live collaborative interaction amongst group members (deferred or retrospective assessment) and the immediate assessment of the learners consuming the CC-LR. In this paper we focus and concentrate in deferred assessment and extend it with innovative assessment indicators from analyzing and representing social network interaction during the live collaboration. These new indicators are integrated into our multi-fold assessment approach to produce a personalized assessment feedback to ultimately enhance and improve the collaborative learning experience with CC-LRs.
Modeling the Driver Load in the Presence of Process Variations

Feature sizes of less than 90 nm and clock frequencies higher than 3 GHz calls for fundamental changes in driver-load models. New driver-load models must consider the process variation impact of the manufacturing procedure, the nonlinear behavior of the drivers, the inductance effects of the loads, and the slew rates of the output waveforms. The present deterministic driver-load models use the conventional deterministic driver-delay model with a single Ceff (one ramp) approach. Neither the statistical property of the driver nor the inductance effects of the interconnect are taken into consideration. Therefore, the accuracy of existing models is questionable. This paper introduces a new driver-load model that predicts the driver-delay changes in the presence of process variations and represents the interconnect load as a distributed resistance, inductance and capacitance (RLC) network. The employed orthogonal polynomial-based probabilistic collocation method (PCM) constructs a driver-delay analytical equation from the circuit's output response. The obtained analytical equation is used to evaluate the driver output delay distribution. In addition, the load is modeled as a two-effective-capacitance in order to capture the nonlinear behavior of the driver. The lossy transmission line approach accounts for the impact of the inductance when modeling the driving-point interconnect load. The new model shows improvements of 9% in the average delay error and 2.2% in the slew rate error over the simulation program with integrated circuit emphasis (SPICE) and the one ramp modeling approaches. Compared with the Monte Carlo method, the proposed model demonstrates a less than 3% error in the expected gate delay value and a less 5% error in the gate delay variance
A 2 . 4-GHz CMOS Receiver for IEEE 802 . 11 Wireless LAN ’ s

This paper describes a radio-frequency receiver targeting spread-spectrum wireless local-area-network applications in the 2.4-GHz band. Based on a direct-conversion architecture, the receiver employs partial channel selection filtering, dc offset removal, and baseband amplification. Fabricated in a 0.6m CMOS technology, the receiver achieves a noise figure of 8.3 dB, IP3 of 9 dBm, IP 2 of +22 dBm, and voltage gain of 34 dB while dissipating 80 mW from a 3-V supply.
The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning

Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. KLI promotes the emergence of instructional principles of high potential for generality, while explicitly identifying constraints of and opportunities for detailed analysis of the knowledge students may acquire in courses. Drawing on research across domains of science, math, and language learning, we illustrate the analyses of knowledge, learning, and instructional events that the KLI framework affords. We present a set of three coordinated taxonomies of knowledge, learning, and instruction. For example, we identify three broad classes of learning events (LEs): (a) memory and fluency processes, (b) induction and refinement processes, and (c) understanding and sense-making processes, and we show how these can lead to different knowledge changes and constraints on optimal instructional choices.
HOODS: finding context-specific neighborhoods of proteins, chemicals and diseases

Clustering algorithms are often used to find groups relevant in a specific context; however, they are not informed about this context. We present a simple algorithm, HOODS, which identifies context-specific neighborhoods of entities from a similarity matrix and a list of entities specifying the context. We illustrate its applicability by finding disease-specific neighborhoods of functionally associated proteins, kinase-specific neighborhoods of structurally similar inhibitors, and physiological-system-specific neighborhoods of interconnected diseases. HOODS can be used via a simple interface at http://hoods.jensenlab. org, from where the source code can also be downloaded.
Direct aerosol forcing: Calculation from observables and sensitivities to inputs

[1] Understanding sources of uncertainty in aerosol direct radiative forcing (DRF), the difference in a given radiative flux component with and without aerosol, is essential to quantifying changes in Earth’s radiation budget. We examine the uncertainty in DRF owing to measurement uncertainty in the quantities on which it depends: aerosol optical depth, single scattering albedo, asymmetry parameter, solar geometry, and surface albedo. Direct radiative forcing at the top of the atmosphere and at the surface is calculated at three locations representing distinct aerosol types and radiative environments. Sensitivities, the changes in DRF in response to unit changes in individual aerosol or surface properties, are also calculated for these conditions. The uncertainty in DRF associated with a given property is computed as the product of the sensitivity and typical measurement uncertainty in the respective property. Sensitivity and uncertainty values permit estimation of total uncertainty in calculated DRF and identification of properties that most limit accuracy in estimating forcing. Absolute total uncertainties in modeled local diurnally averaged forcing range from 0.2 to 3.1 W m � 2 for the ranges of properties examined here. Relative total uncertainties range from � 20 to 80% with larger values at higher latitudes, where fluxes are low. The largest contributor to total uncertainty in DRF is single scattering albedo; however, decreasing measurement uncertainties for any property would increase accuracy in DRF. Comparison of two radiative transfer models suggests the contribution of modeling error is small compared to the total uncertainty although comparable to uncertainty arising from some individual properties.
Motorization and Road Provision in Countries and Cities

Using panel data from 50 countries and 35 urban areas (both covering a wide range of country incomes), this paper summarizes trends in motorization, road provision, and road transport service production (combining vehicles and roads). The growth of both motorization and road provision is related to incomes, land area, population density, urbanization, gasoline prices, and rail transport at the national level, and to a subset of these variables at the urban area level. The ratio of motor vehicles to roads is examined in a production function framework at both the national and urban area level. Regularities are very strong across countries and urban areas and over time. Economic development increases demand for transport, reliance on cars and trucks, and road provision. At the early stage of motorization, commercial vehicles comprise a large share of the fleet; passenger transport by automobile becomes more prominent with income growth. Motorization expands at the same rate as income per capita, but the auto fleet expands more rapidly, and commercial vehicles less rapidly, than income. Both country and urban data show evidence of similar saturation levels for car and motor vehicle ownership. At the national level, road networks expand more slowly than incomes, but paved road networks expand at the same rate as incomes. Road provision appears to be quite responsive to demand nationally, much less so at the urban level. For specific urban areas, per capita road length varies with initial income level but changes little over time, showing that history or urban endowments matter. Annexation of surrounding developed area appears to play a large role in expanding urban road length. Urban areas average roughly 15 times more road length per unit area, and seven times more vehicles per kilometer of road, than countries, and a saturation level exists for urban road length per unit of area. Vehicles per kilometer of road are positively associated with income, with (proxies for) land prices, and with low gasoline prices. * The authors are grateful to Chiaki Yamamoto for research assistance, and to Esra Bennathan, Kenneth Button, Marianne Fay, Tony Gomez-Ibanez, Reuben Gronau, Kenneth Gwilliam, and Lant Pritchett for helpful comments on the earlier draft.
Evaluating the costs of management: a distributed applications management testbed

In today's distributed computing environments, users are making increasing demands on the systems, networks, and applications they use. Users are coming to expect performance, fault tolerance, configuration, security, and accounting services comparable to those on traditional centralized systems. Meeting these expectations implies the need for management. While some successes have been realized in the areas of system and network management, only preliminary investigations have been conducted on management of distributed applications.At this stage, little is known of the costs associated with this type of management. To assess these costs, it is necessary to collect data about the performance and operation of distributed applications and their management systems. Furthermore, it is important to collect this data under varying constraints, configurations, and management assumptions. In order to do this, we have developed a distributed applications management testbed. We present our initial requirements for the testbed, its design, and a prototype implementation. We describe some preliminary results and analysis obtained through its use. Finally, we identify its limits based on our experience to date and outline our plans to evolve the testbed to accommodate further experimentation.
Towards a Theory for Testing Non-terminating Programs

Non-terminating programs are programs that legally perform unbounded computations. Though they are ubiquitous in real-world applications, testing these programs requires new theoretic developments as usual definitions of test data adequacy criteria ignore infinite paths.This paper develops a theory of program-based structural testing based on operational semantics. Reasoning at the program semantics level permits to cope with infinite paths (and non-feasible paths) when defining test data adequacy criteria. As a result, our criteria respect the first Weyuker's property on finite applicability, even for non-terminating programs. We discuss the consequences of this re-interpretation of test data adequacy criteria w.r. t. existing test coverage criteria.
Realizing a Sub-Linear Time String-Matching Algorithm With a Hardware Accelerator Using Bloom Filters

Many network security applications rely on string matching to detect intrusions, viruses, spam, and so on. Since software implementation may not keep pace with the high-speed demand, turning to hardware-based solutions becomes promising. This work presents an innovative architecture to realize string matching in sub-linear time based on algorithmic heuristics, which come from parallel queries to a set of space-efficient Bloom filters. The algorithm allows skipping characters not in a match in the text, and in turn simultaneously inspect multiple characters in effect. The techniques to reduce the impact of certain bad situations on performance are also proposed: the bad-block heuristic, a linear worst-case time method and a non-blocking interface to hand over the verification job to a verification module. This architecture is simulated with both behavior simulation in C and timing simulation in HDL for antivirus applications. The simulation shows that the throughput of scanning Windows executable files for more than 10000 virus signatures can achieve 5.64 Gb/s, while the worst-case performance is 1.2 Gb/s if the signatures are properly specified.
Highly Convergent Particle Swarm Optimization

Particle Swarm Optimization (PSO) algorithm is swarm intelligence based algorithm which is used for solving optimization problem. PSO simulates the intelligent foraging behavior of a flock of birds. This paper presents a modification in PSO and develops an algorithm called Highly Convergent Particle Swarm Optimization (HCPSO) algorithm, in which the velocity of particle is made to be dependent only on the global best position. HCPSO improves the exploitation capability of PSO algorithm. The modification of the algorithm is presented and a set of ten complex benchmark functions is utilized for performing the experiments for checking its validity. Results show that the proposed HCPSO outperforms the PSO algorithm on most functions.
Information Diversity and Complementarities in Trading and Information Acquisition

type="main"> We analyze a model in which different traders are informed of different fundamentals that affect the security value. We identify a source for strategic complementarities in trading and information acquisition: aggressive trading on information about one fundamental reduces uncertainty in trading on information about the other fundamental, encouraging more trading and information acquisition on that fundamental. This tends to amplify the effect of exogenous changes in the underlying information environment. Due to complementarities, greater diversity of information in the economy improves price informativeness. We discuss the relation between our model and recent financial phenomena and derive testable empirical implications.
Important differences between topoisomerase-I and -II targeting agents

Commentary to: Activation of ATM and Histone H2AX Phosphorylation Induced by Mitoxantrone But Not by Topotecan is Prevented by the Antioxidant N-acetyl-L-Cysteine Xuan Huang, Akira Kurose, Toshiki Tanaka, Frank Traganos, Wei Dai and Zbigniew Darzynkiewicz
A new algorithm of SAR target recognition based on advance deep learning neural network

In order to improve the accuracy of synthetic aperture radar images target recognition, we have proposed a new algorithm of SAR target recognition based on advance Deep Learning neural network. The traditional radar recognition algorithm has many disadvantages, In order to improve the accuracy of synthetic aperture radar images target recognition, the author have proposed a new algorithm of SAR target recognition based on advance Deep Learning neural network. In this paper, the author have got the feature of SAR image through the Refine Lee filter and HOG transformation firstly, and then realized the SAR object segmentation and recognition through the multi-layers RBM machine and GRNN neural network. and then realized the SAR object segmentation and recognition through the multi-layers RBM machine and GRNN neural network, and the learning rate parameter of the multilayers RBM machine is optimized through the GA algorithm. The simulation results shows that the object recognition rate of the algorithm proposed in this paper can reach 97%, which can improve the performance of the algorithm obviously.
In praise of subjective truths

‘To determine orientation we occasionally used a PDP‐12 computer to produce a graph of average response vs orientation, generating the slit electronically on a television screen. This method took much longer, and the usual minute‐to‐minute variations in responsiveness of the cell tended to make the curves broader and noisier. We concluded that for both speed and for precision it is hard to beat judgments based on the human ear. Certainly [our curves] could not have been obtained with computer averaging methods before the authors reached the age of mandatory retirement. ’ ( Hubel & Wiesel, 1974 )