Miracle-dz/UniFAR-FTUs huggingface dataset:
Bootstrapping and validation of metamodels in simulation

Bootstrapping is a resampling technique that requires less computer time than simulation does. Bootstrapping-like simulation-must be defined for each type of application. The paper defines bootstrapping for random simulations with replicated runs. The focus is on linear regression metamodels. The metamodel's parameters are estimated through Generalized Least Squares. Its fit is measured through C.R. Rao's (1959) lack-of-fit F-statistic. The distributions of this statistic is estimated through bootstrapping. The main conclusions are: (i) not the regression residuals should be bootstrapped-instead the deviations that also occur in the standard deviation, should be bootstrapped; (ii) bootstrapping Rao's lack-of-fit statistic is a good alternative to the F-test because it gives virtually identical results when the assumptions of the F-test are known to apply, and somewhat better results otherwise.
Customer Segmentation Architecture Based on Clustering Techniques

Knowledge on consumer habits is essential for companies to keep customers satisfied and to provide them personalised services. We present a data mining architecture based on clustering techniques to help experts to segment customer based on their purchase behaviours. In this architecture, diverse segmentation models are automatically generated and evaluated with multiple quality measures. Some of these models were selected for given quality scores. Finally, the segments are compared. This paper presents experimental results on a real-world data set of 10000 customers over 60 weeks for 6 products. These experiments show that the models identified are useful and that the exploration of these models to discover interesting trends is facilitated by the use of our architecture.
Resistive Switching behaviour in Oxide Ion Conductor Na 0.5 Bi 0.5 Ti 0.98 Mg 0.02 O 3-δ Thin Films

Sol-gel derived perovskite oxide ion conductor thin films with a nominal composition of Na0.5Bi0. 5Ti0.98Mg0. 02O3-δ (NBTM) were fabricated on the In2O3-doped SnO2 (ITO) glass substrates. Bipolar resistive switching requiring no “electroforming” process and exhibiting ultrahigh resistance ON/OFF (RON/OFF) ratio (>104) were observed in the ITO/ NBTM/Au capacitor. A model concerning the oxygen ion migration induced ionic conduction variation was proposed for the observed resistive switching (RS) effect, evidencing by a voltage-polarity dependent rectification switching behaviour, and the I-V behaviour in high purity argon atmosphere. Our model demonstrates that the migration of oxygen ions can reverse the resistance just by modulating the oxygen ionic conduction, which is different from those previously proposed ones relating to oxygenions, e.g. the chemical redox reactions of the oxides or active electrodes, the composition changing of the RS materials, or the Schottky-barrier height modulating of the metal/ oxide interfaces.
Interaction of a human head model with the antenna of mobile terminal

Nowadays, the actual problem of development and usage of mobile communication devices is to solve problems of optimization of structures and characteristics of antennas of mobile terminals. One of the key criteria for evaluating the operation of the mobile terminal, operating near a human, the accepted value is SAR - Specific Absorption Rate. The purpose of this work is to study the dependence of SAR. For calculation and analysis the method of equivalent surface currents and the method of volume polarization currents were chosen, which are implemented using the software package FEKO. The dependences of the SAR on the distance between a model of the human head and the antenna of the mobile terminal with different polarization and its location in space on the example of a planar antenna are obtained. The results allow evaluating the impact of human head to the antenna performance of a mobile terminal in the near field.
ASIP implementation of a low complexity iterative BD precoder for MU-MIMO system

In this paper, we propose a low complexity MU(multi-user)-MIMO precoder and show its ASIP (application specific instruction-set processor) implementation result. To realize DL(downlink)-MU-MIMO transmission, the MU-MIMO precoder is required in AP (access point) side for canceling an IUI (inter-user interference). However, a conventional IBD (iterative block diagonalization) precoder has a high computational complexity problem. Therefore, we propose a LC(low complexity)-IBD precoder whose complexity is reduced by using null vectors from CSI (channel state information) decompression process. The implementation result shows that our precoder can reduce computational complexity and latency.
Throughput Enhancement in Cooperative Wireless Ad Hoc Networks

Next generation wireless networks are expected to provide services which require high performance and reliability. However, channel conditions in wireless networks can affect both throughput and reliability. Cooperative communication has the advantage of offering throughput enhancement and reliability in wireless networks by using several single-antenna nodes to form a virtual antenna array. The performance in cooperative communication depends on the relay selection scheme. Therefore, in this paper, we propose and evaluate a distributed relay-selection scheme based on the channel conditions of candidate relay nodes. Results show that our proposed-relay selection scheme increases the system throughput.
A Statistical Algorithm to Discover Knowledge in Medical Data Sources

Developing intelligent tools to extract information from data collections has long been of critical importance in fields such as knowledge discovery, information retrieval, pattern recognition, and databases. With the advent of electronic medical records and medical data repositories there is new potential to apply these techniques to the analysis of biomedical data sets. Looking for complex patterns within large biomedical data repositories and discovering previously unexpected associations can be of particular interest for understanding the physiology and functionality of the human body as well as tracing the roots of diseases. In the context of a research hospital these analyses may lead to further directed research, better diagnostic capabilities, and improved patient outcomes. This paper describes an implementation of a knowledge discovery algorithm aimed at such data sets.
Skin Effect Modeling in Conductors of Arbitrary Shape Through a Surface Admittance Operator and the Contour Integral Method

An accurate modeling of skin effect inside conductors is of capital importance to solve transmission line and scattering problems. This paper presents a surface-based formulation to model skin effect in conductors of arbitrary cross section, and compute the per-unit-length impedance of a multiconductor transmission line. The proposed formulation is based on the Dirichlet-Neumann operator that relates the longitudinal electric field to the tangential magnetic field on the boundary of a conductor. We demonstrate how the surface operator can be obtained through the contour integral method for the conductors of arbitrary shape. The proposed algorithm is simple to implement, efficient, and can handle arbitrary cross sections, which is the main advantage over the existing approach based on eigenfunctions, which is available only for canonical conductor's shapes. The versatility of the method is illustrated through a diverse set of examples, which includes transmission lines with trapezoidal, curved, and V-shaped conductors. Numerical results demonstrate the accuracy, versatility, and efficiency of the proposed technique.
Overtime Work , Overtime Compensation and the Distribution of Economic Well-Being Evidence for West Germany and Great Britain

Using panel data for West Germany and Great Britain, we show that there are striking differences in overtime work and overtime compensation in the two countries in the 1990s. Our estimates reveal that the observed overtime patterns affect both the evolution of the monthly labour earnings distribution and individual economic well-being differently in West Germany and Great Britain. Besides varying labour market institutions in the two countries a higher incidence of a combination of performance-related pay and unpaid overtime in Great Britain is an important factor in explaining the observed differences. With regards to West Germany, we show that the current policy of transforming paid overtime in “working time accounts”, which is conducted in the spirit of “work-sharing”, is neither beneficial for employed workers in terms of income mobility, nor in terms of overall job satisfaction nor in terms of working time preferences.
A Benders' decomposition approach for optimizing the electric system of offshore wind farms

Optimizing the electric design of an offshore wind farm is a key issue given cost and reliability considerations. The model presented here has been developed to efficiently optimize the electric layout of a wind farm taking into account stochasticity in wind scenarios and component failures. Given the suitable structure of the problem, Benders' decomposition method has been applied to the model successfully finding optimal solutions in a reduced computation time.
Rainbow Generalizations of Ramsey Theory - A Dynamic Survey

In this work, we collect Ramsey-type results concerning rainbow edge colorings of graphs. Revision History • Revision 3: March, 2015. • Revision 2: October, 2014. • Revision 1: July, 2011. • Original: Graphs and Combinatorics, January, 2010. [73] If you have corrections, updates or new results which fit the scope of this work, please contact Colton Magnant at cmagnant@georgiasouthern.edu.
Community evolution prediction in dynamic social networks

Finding patterns of interaction and predicting the future structure of networks has many important applications, such as recommendation systems and customer targeting. Community structure of social networks may undergo different temporal events and transitions. In this paper, we propose a framework to predict the occurrence of different events and transition for communities in dynamic social networks. Our framework incorporates key features related to a community - its structure, history, and influential members, and automatically detects the most predictive features for each event and transition. Our experiments on real world datasets confirms that the evolution of communities can be predicted with a very high accuracy, while we further observe that the most significant features vary for the predictability of each event and transition.
Action-Based Body Maps in the Spinal Cord Emerge from a Transitory Floating Organization

During development primary afferents grow into and establish neuronal connections in the spinal cord, thereby forming the basis for how we perceive sensory information and control our movements. In the somatosensory system, myriads of primary afferents, conveying information from different body locations and sensory modalities, get organized in the dorsal horn of the spinal cord so that spinal multisensory circuits receive topographically ordered information. How this intricate pathfinding is brought about during development is, however, largely unknown. Here we show that a body representation closely related to motor patterns emerges from a transitory floating and plastic organization through profound activity-dependent rewiring, involving both sprouting and elimination of afferent connections, and provide evidence for cross-modality interactions in the alignment of the multisensory input. Thus, far from being inborn and stereotypic, the dorsal horn of the spinal cord now appears to be a highly adaptive brain–body interface.
Multi-resolution volumetric reconstruction using labeled regions

The reconstruction of 3D models from camera views is becoming an element of major importance in many applications that simulate physical interaction with the real world. 3D models are amenable to access through diverse representation modalities that typically imply trade-offs between level of detail, interaction, and computational costs. We present a 3D volumetric reconstruction method that allows users to control selectively the complexity of different surface regions, while requiring only simple 2D image editing operations. An initial reconstruction at coarse resolution is followed by an iterative refining of the surface areas corresponding to the selected regions.
A skyline-based efficient web service selection method supporting frequent requests

With the development of web technology, a sharp increment in the number of services brings in efficiency problem to service selection. Furthermore, in dynamic service selection, services have to be selected as soon as requests arrive. When requests to a service are frequent, the service is prone to failure. This paper proposes a skyline-based web service selection method to deal with efficiency problem as well as solve the frequent requests problem. The method covers a search algorithm and a service scheduling strategy. To improve efficiency of service selection, the search algorithm based on Kd-tree is designed to determine skyline and then narrow search space. To solve the frequent requests problem, lottery scheduling strategy is employed to schedule services. Every service will get a certain amount of tickets in the lottery scheduling strategy, and service with more tickets will be distributed with more requests. Experiments validate the performance of the method.
QUICR-Learning for Multi-Agent Coordination

Coordinating multiple agents that need to perform a sequence of actions to maximize a system level reward requires solving two distinct credit assignment problems. First, credit must be assigned for an action taken at time step t that results in a reward at time step t′ > t. Second, credit must be assigned for the contribution of agent i to the overall system performance. The first credit assignment problem is typically addressed with temporal difference methods such as Q-learning. The second credit assignment problem is typically addressed by creating custom reward functions. To address both credit assignment problems simultaneously, we propose the "Q Updates with Immediate Counterfactual Rewards-learning" (QUICR-learning) designed to improve both the convergence properties and performance of Q-learning in large multi-agent problems. QUICR-learning is based on previous work on single-time-step counterfactual rewards described by the collectives framework. Results on a traffic congestion problem shows that QUICR-learning is significantly better than a Q-learner using collectives-based (single-time-step counterfactual) rewards. In addition QUICR-learning provides significant gains over conventional and local Q-learning. Additional results on a multi-agent grid-world problem show that the improvements due to QUICR-learning are not domain specific and can provide up to a ten fold increase in performance over existing methods.
Robust Classi cation of Blurred Imagery

In this paper we present two novel approaches for the classi cation of blurry images. It is assumed that the blur is linear and space invariant, but that the exact blurring function is unknown. The proposed fusion-based approaches attempt to perform the simultaneous tasks of blind image restoration and classi cation. We call such a problem blind image fusion. The techniques are implemented using the nonnegativity and support constraints recursive inverse ltering (NAS-RIF) algorithm for blind image restoration and the Markov random eld (MRF)-based fusion method for classi cation by Schistad-Solberg et. al . Simulation results on synthetic and real photographic data demonstrate the potential of the approaches. The algorithms are compared with one another and to situations in which blind blur removal is not attempted.
Three Proofs of a Constructive Commuting Quantum Lovász Local Lemma

The recently proven Quantum Lovász Local Lemma generalises the well-known Lovász Local Lemma. It states that, if a collection of subspace constraints are “weakly dependent”, there necessarily exists a state satisfying all constraints. It implies e.g. that certain instances of the quantum k–QSAT satisfiability problem are necessarily satisfiable, or that many-body systems with “not too many” interactions are never frustrated. However, the QLLL only asserts existence; it says nothing about how to find the state. Inspired by Moser’s breakthrough classical results, we present a constructive version of the QLLL in the setting of commuting constraints, proving that a simple quantum algorithm converges efficiently to the required state. In fact, we provide three different proofs, all of which are independent of the original QLLL proof. So these results also provide independent, constructive proofs of the commuting QLLL itself, but strengthen it significantly by giving an efficient algorithm for finding the state whose existence is asserted by the QLLL.
Social networking sites: barriers and facilitators to access for people with aphasia

Background: The use of social networking sites (SNS) such as Facebook and Twitter may help to ameliorate the social isolation prevalent amongst individuals with aphasia (Northcott et al., 2016). For therapists, SNS offer contemporary platforms for communication activities, which encourage generalisation of skills and social uses of language. While some research has explored online, social network content created by people with aphasia (PWA) (Baier et al., 2017), no work to date has directly explored the accessibility of SNS for this group.
A non-volatile memory based physically unclonable function without helper data

Stability across environmental variations such as temperature and voltage, is critically important for Physically Unclonable Functions (PUFs). Nearly all existing PUF systems to date need a mechanism to deal with “bit flips” when exact regeneration of the bitstring is required, e.g., for cryptographic applications. Error correction (ECC) and error avoidance schemes have been proposed but both of these require helper data to be stored for the regeneration process. Unfortunately, helper data adds time and area overhead to the PUF system and provides opportunities for adversaries to reverse engineer the secret bitstring. In this paper, we propose a non-volatile memory-based (NVM) PUF that is able to avoid bit flips without requiring any type of helper data. A voltage-to-digital converter technique is described for digitizing the analog entropy source and a robust median-finding algorithm is proposed as the reprograming strategy. Analysis on published experimental data is presented to demonstrate the practicability of our proposed strategy. We describe the technique in the context of emerging nano-devices, in particular, resistive random access memory (Memristor) cells, but the methodology is applicable to any type of NVM including Flash.
Investigating Health Literacy of Teachers in Asaluyeh

Background: Health literacy is defined as the ability of individuals to read, understand, and act on health-related concepts in order to make healthy decisions. This study was conducted to determine the level of health literacy of teachers in Asaluyeh city, Iran. Methods: This descriptive study was conducted on 200 teachers who were selected using stratified random sampling method in Asaluyeh in the academic year 2015 - 2016. Health Literacy for Iranian Adults questionnaire (HELIA) was used to measure their health literacy. Data were analyzed by SPSS20 and by using descriptive statistics, one way ANOVA, independent- samples T test, and Pearson correlation coefficient. Results: The mean age of participants was 30.56 years (SD = 4.5(, 59% of them were male (n = 118) and 41% were female (n = 82). Teachers' health literacy status was in moderate level for 9.6% of participants (n = 99) regarding access to information, for 44.6% of participants (n = 89) regarding information comprehension in, for 68.4% of participants (n = 136) regarding judgment and assessment, and for 68.4% of participants (n = 136) regarding information use. We found that health literacy had a significant relationship with gender and age. Conclusion: In this study, teachers' health literacy was at moderate level. So, it is necessary to provide effective internet resources and useful sites. We should also conduct health education programs in media such as radio and television to improve health literacy in teachers, especially in more experienced teachers. Keywords: Health Literacy, Teachers, Asaluyeh city
Insulin and GLP-1 infusions demonstrate the onset of adipose-specific insulin resistance in a large fasting mammal: potential glucogenic role for GLP-1

Prolonged food deprivation increases lipid oxidation and utilization, which may contribute to the onset of the insulin resistance associated with fasting. Because insulin resistance promotes the preservation of glucose and oxidation of fat, it has been suggested to be an adaptive response to food deprivation. However, fasting mammals exhibit hypoinsulinemia, suggesting that the insulin resistance‐like conditions they experience may actually result from reduced pancreatic sensitivity to glucose/capacity to secrete insulin. To determine whether fasting results in insulin resistance or in pancreatic dysfunction, we infused early‐ and late‐fasted seals (naturally adapted to prolonged fasting) with insulin (0.065 U/kg), and a separate group of late‐fasted seals with low (10 pmol/L per kg) or high (100 pmol/L per kg) dosages of glucagon‐like peptide‐1 (GLP‐1) immediately following a glucose bolus (0.5 g/kg), and measured the systemic and cellular responses. Because GLP‐1 facilitates glucose‐stimulated insulin secretion, these infusions provide a method to assess pancreatic insulin‐secreting capacity. Insulin infusions increased the phosphorylation of insulin receptor and Akt in adipose and muscle of early‐ and late‐fasted seals; however, the timing of the signaling response was blunted in adipose of late‐fasted seals. Despite the dose‐dependent increases in insulin and increased glucose clearance (high dose), both GLP‐1 dosages produced increases in plasma cortisol and glucagon, which may have contributed to the glucogenic role of GLP‐1. Results suggest that fasting induces adipose‐specific insulin resistance in elephant seal pups, while maintaining skeletal muscle insulin sensitivity, and therefore suggests that the onset of insulin resistance in fasting mammals is an evolved response to cope with prolonged food deprivation.