Showing papers by "Istanbul Technical University published in 2017"

Particle-flow reconstruction and global event description with the CMS detector

Abstract: The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic τ decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8\TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions.

Index Modulation Techniques for Next-Generation Wireless Networks

Abstract: What is index modulation (IM)? This is an interesting question that we have started to hear more and more frequently over the past few years. The aim of this paper is to answer this question in a comprehensive manner by covering not only the basic principles and emerging variants of IM, but also reviewing the most recent as well as promising advances in this field toward the application scenarios foreseen in next-generation wireless networks. More specifically, we investigate three forms of IM: spatial modulation, channel modulation and orthogonal frequency division multiplexing (OFDM) with IM, which consider the transmit antennas of a multiple-input multiple-output system, the radio frequency mirrors (parasitic elements) mounted at a transmit antenna and the subcarriers of an OFDM system for IM techniques, respectively. We present the up-to-date advances in these three promising frontiers and discuss possible future research directions for IM-based schemes toward low-complexity, spectrum- and energy-efficient next-generation wireless networks.

The CMS trigger system

Abstract: This paper describes the CMS trigger system and its performance during Run 1 of the LHC. The trigger system consists of two levels designed to select events of potential physics interest from a GHz (MHz) interaction rate of proton-proton (heavy ion) collisions. The first level of the trigger is implemented in hardware, and selects events containing detector signals consistent with an electron, photon, muon, tau lepton, jet, or missing transverse energy. A programmable menu of up to 128 object-based algorithms is used to select events for subsequent processing. The trigger thresholds are adjusted to the LHC instantaneous luminosity during data taking in order to restrict the output rate to 100 kHz, the upper limit imposed by the CMS readout electronics. The second level, implemented in software, further refines the purity of the output stream, selecting an average rate of 400 Hz for offline event storage. The objectives, strategy and performance of the trigger system during the LHC Run 1 are described.

Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV

Abstract: Improved jet energy scale corrections, based on a data sample corresponding to an integrated luminosity of 19.7 fb^(-1) collected by the CMS experiment in proton-proton collisions at a center-of-mass energy of 8 TeV, are presented. The corrections as a function of pseudorapidity η and transverse momentum p_T are extracted from data and simulated events combining several channels and methods. They account successively for the effects of pileup, uniformity of the detector response, and residual data-simulation jet energy scale differences. Further corrections, depending on the jet flavor and distance parameter (jet size) R, are also presented. The jet energy resolution is measured in data and simulated events and is studied as a function of pileup, jet size, and jet flavor. Typical jet energy resolutions at the central rapidities are 15–20% at 30 GeV, about 10% at 100 GeV, and 5% at 1 TeV. The studies exploit events with dijet topology, as well as photon+jet, Z+jet and multijet events. Several new techniques are used to account for the various sources of jet energy scale corrections, and a full set of uncertainties, and their correlations, are provided. The final uncertainties on the jet energy scale are below 3% across the phase space considered by most analyses (p_T > 30 GeV and 0|η| 30 GeV is reached, when excluding the jet flavor uncertainties, which are provided separately for different jet flavors. A new benchmark for jet energy scale determination at hadron colliders is achieved with 0.32% uncertainty for jets with p_T of the order of 165–330 GeV, and |η| < 0.8.

Charged-particle nuclear modification factors in PbPb and pPb collisions at √sNN=5.02 TeV

Abstract: we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS and RFBR (Russia); MESTD (Serbia); SEIDI and CPAN (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.).

Measurements of properties of the Higgs boson decaying into the four-lepton final state in pp collisions at √s=13 TeV

Abstract: Properties of the Higgs boson are measured in the H → ZZ → 4l (l = e, μ) decay channel. A data sample of proton-proton collisions at $ \sqrt{s}=13 $ TeV, collected with the CMS detector at the LHC and corresponding to an integrated luminosity of 35.9 fb$^{−1}$ is used. The signal strength modifier μ, defined as the ratio of the observed Higgs boson rate in the H → ZZ → 4l decay channel to the standard model expectation, is measured to be μ = 1.05$_{− 0.17}^{+ 0.19}$ at m$_{H}$ = 125.09 GeV, the combined ATLAS and CMS measurement of the Higgs boson mass. The signal strength modifiers for the individual Higgs boson production modes are also measured. The cross section in the fiducial phase space defined by the requirements on lepton kinematics and event topology is measured to be 2. 92$_{− 0.44}^{+ 0.48}$ (stat)$_{− 0.24}^{+ 0.28}$ (syst)fb, which is compatible with the standard model prediction of 2.76 ± 0.14 fb. Differential cross sections are reported as a function of the transverse momentum of the Higgs boson, the number of associated jets, and the transverse momentum of the leading associated jet. The Higgs boson mass is measured to be m$_{H}$ = 125.26 ± 0.21 GeV and the width is constrained using the on-shell invariant mass distribution to be Γ$_{H}$ < 1.10 GeV, at 95% confidence level.

CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

Abstract: The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.

Disease detection on the leaves of the tomato plants by using deep learning

Abstract: The aim of this work is to detect diseases that occur on plants in tomato fields or in their greenhouses. For this purpose, deep learning was used to detect the various diseases on the leaves of tomato plants. In the study, it was aimed that the deep learning algorithm should be run in real time on the robot. So the robot will be able to detect the diseases of the plants while wandering manually or autonomously on the field or in the greenhouse. Likewise, diseases can also be detected from close-up photographs taken from plants by sensors built in fabricated greenhouses. The examined diseases in this study cause physical changes in the leaves of the tomato plant. These changes on the leaves can be seen with RGB cameras. In the previous studies, standard feature extraction methods on plant leaf images to detect diseases have been used. In this study, deep learning methods were used to detect diseases. Deep learning architecture selection was the key issue for the implementation. So that, two different deep learning network architectures were tested first AlexNet and then SqueezeNet. For both of these deep learning networks training and validation were done on the Nvidia Jetson TX1. Tomato leaf images from the PlantVillage dataset has been used for the training. Ten different classes including healthy images are used. Trained networks are also tested on the images from the internet.

Multiple-Mode Orthogonal Frequency Division Multiplexing With Index Modulation

Abstract: Orthogonal frequency division multiplexing with index modulation (OFDM-IM) performs transmission by considering two modes over OFDM subcarriers, which are the null and the conventional $M$ -ary signal constellation The spectral efficiency (SE) of the system, however, is limited, since the null mode itself does not carry any information and the number of subcarrier activation patterns increases combinatorially In this paper, a novel IM scheme, called multiple-mode OFDM-IM (MM-OFDM-IM), is proposed for OFDM systems to improve the SE by conveying information through multiple distinguishable modes and their full permutations A practical and efficient mode selection strategy, which is constrained on the phase shift keying/quadrature amplitude modulation constellations, is designed Two efficient detectors that provide different tradeoffs between the error performance and detection complexity are also proposed The principle of MM-OFDM-IM is further extended to the in-phase and quadrature components of OFDM signals, and the method of generating multiple modes from the $M$ -ary pulse amplitude modulation constellation for this modified scheme is also introduced Bit error rate (BER) analyses are provided for the proposed schemes Monte Carlo simulations on BER corroborate the analyses and show that the proposed schemes appear as promising multi-carrier transmission alternatives by outperforming the existing OFDM-IM counterparts

Searches for invisible decays of the Higgs boson in pp collisions at √s = 7, 8, and 13 TeV

Abstract: Searches for invisible decays of the Higgs boson are presented. The data collected with the CMS detector at the LHC correspond to integrated luminosities of 5.1, 19.7, and 2.3 fb−1 at centre-of-mass energies of 7, 8, and 13 TeV, respectively. The search channels target Higgs boson production via gluon fusion, vector boson fusion, and in association with a vector boson. Upper limits are placed on the branching fraction of the Higgs boson decay to invisible particles, as a function of the assumed production cross sections. The combination of all channels, assuming standard model production, yields an observed (expected) upper limit on the invisible branching fraction of 0.24 (0.23) at the 95% confidence level. The results are also interpreted in the context of Higgs-portal dark matter models.

Intuitionistic fuzzy edas method: an application to solid waste disposal site selection

Abstract: Evaluation based on Distance from Average Solution (EDAS) is a new multicriteria decision making (MCDM) method, which is based on the distances of alternatives from the average scores of attributes...

Angiotensin-I-Converting Enzyme (ACE)-Inhibitory Peptides from Plants.

Abstract: Hypertension is an important factor in cardiovascular diseases. Angiotensin-I-converting enzyme (ACE) inhibitors like synthetic drugs are widely used to control hypertension. ACE-inhibitory peptides from food origins could be a good alternative to synthetic drugs. A number of plant-based peptides have been investigated for their potential ACE inhibitor activities by using in vitro and in vivo assays. These plant-based peptides can be obtained by solvent extraction, enzymatic hydrolysis with or without novel food processing methods, and fermentation. ACE-inhibitory activities of peptides can be affected by their structural characteristics such as chain length, composition and sequence. ACE-inhibitory peptides should have gastrointestinal stability and reach the cardiovascular system to show their bioactivity. This paper reviews the current literature on plant-derived ACE-inhibitory peptides including their sources, production and structure, as well as their activity by in vitro and in vivo studies and their bioavailability.

Innovative trend significance test and applications

Abstract: Hydro-climatological time series might embed characteristics of past changes concerning climate variability in terms of shifts, cyclic fluctuations, and more significantly in the form of trends. Identification of such features from the available records is one of the prime tasks of hydrologists, climatologists, applied statisticians, or experts in related topics. Although there are different trend identification and significance tests in the literature, they require restrictive assumptions, which may not be existent in the structure of hydro-climatological time series. In this paper, a method is suggested with statistical significance test for trend identification in an innovative manner. This method has non-parametric basis without any restrictive assumption, and its application is rather simple with the concept of sub-series comparisons that are extracted from the main time series. The method provides privilege for selection of sub-temporal half periods for the comparison and, finally, generates trend on objective and quantitative manners. The necessary statistical equations are derived for innovative trend identification and statistical significance test application. The application of the proposed methodology is suggested for three time series from different parts of the world including Southern New Jersey annual temperature, Danube River annual discharge, and Tigris River Diyarbakir meteorology station annual total rainfall records. Each record has significant trend with increasing type in the New Jersey case, whereas in other two cases, decreasing trends exist.

Enhanced Orthogonal Frequency Division Multiplexing With Index Modulation

Abstract: Index modulation concept has attracted considerable research interest in the past few years As a realization of index modulation in the frequency domain, orthogonal frequency division multiplexing with index modulation (OFDM-IM) has recently been proposed, which conveys information bits through both the subcarrier activation patterns and the amplitude phase modulation constellation points This paper proposes two enhanced OFDM-IM schemes aimed at achieving higher spectral efficiency and diversity gain, respectively The first one, termed OFDM with hybrid in-phase/quadrature index modulation (OFDM-HIQ-IM), explores the I- and Q- dimensions jointly for index modulation, allowing transmission of more index modulation bits in each subcarrier group The second one, termed linear constellation precoded OFDM-IQ-IM (LP-OFDM-IQ-IM), spreads information symbols across two adjacent active subcarriers through linear constellation precoding to harvest additional diversity gain By maximizing the minimum squared Euclidean distance, two different realizations of LP-OFDM-IQ-IM are derived, which leads to a rotated and a diamond-shaped constellation, respectively The proposed OFDM-HIQ-IM and LP-OFDM-IQ-IM, as revealed by both theoretical analyses and computer simulations, enable low-complexity detection and exhibit superior error rate performance over the existing OFDM-IM schemes

Multiword expression processing: A survey

Abstract: Multiword expressions MWEs are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by "MWE processing," distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives.

Observation of Charge-Dependent Azimuthal Correlations in p−Pb Collisions and Its Implication for the Search for the Chiral Magnetic Effect

Abstract: Charge-dependent azimuthal particle correlations with respect to the second-order event plane in pPb and PbPb collisions at a nucleon-nucleon center-of-mass energy of 5.02 TeV have been studied with the CMS experiment at the LHC. The measurement is performed with a three-particle correlation technique, using two particles with the same or opposite charge within the pseudorapidity range abs(eta)<2.4, and a third particle measured in the hadron forward calorimeters (4.4< abs(eta)<5). The observed differences between the same and opposite sign correlations, as functions of multiplicity and eta gap between the two charged particles, are of similar magnitude in pPb and PbPb collisions at the same multiplicities. These results pose a challenge for the interpretation of charge-dependent azimuthal correlations in heavy ion collisions in terms of the chiral magnetic effect.

Search for dark matter produced with an energetic jet or a hadronically decaying W or Z boson at √s = 13 TeV

Abstract: A search for dark matter particles is performed using events with large missing transverse momentum, at least one energetic jet, and no leptons, in proton-proton collisions at root S = 13TeV collected with the CMS detector at the LHC. The data sample corresponds to an integrated luminosity of 12.9 fb(-1). The search includes events with jets from the hadronic decays of a W or Z boson. The data are found to be in agreement with the predicted background contributions from standard model processes. The results are presented in terms of simpli fi ed models in which dark matter particles are produced through interactions involving a vector, axial-vector, scalar, or pseudoscalar mediator. Vector and axial-vector mediator particles with masses up to 1.95TeV, and scalar and pseudoscalar mediator particles with masses up to 100 and 430 GeV respectively, are excluded at 95% con fi dence level. The results are also interpreted in terms of the invisible decays of the Higgs boson, yielding an observed (expected) 95% con fi dence level upper limit of 0.44 (0.56) on the corresponding branching fraction. The results of this search provide the strongest constraints on the dark matter pair production cross section through vector and axial-vector mediators at a particle collider. When compared to the direct detection experiments, the limits obtained from this search provide stronger constraints for dark matter masses less than 5, 9, and 550 GeV, assuming vector, scalar, and axial-vector mediators, respectively. The search yields stronger constraints for dark matter masses less than 200 GeV, assuming a pseudoscalar mediator, when compared to the indirect detection results from Fermi-LAT.

Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology

Abstract: Healthcare management and healthcare industry have been one of the popular and complex topics that many researchers and professionals have focused on. This paper proposes a new multi-expert fuzzy approach integrating intuitionistic fuzzy Data Envelopment Analysis (DEA) and intuitionistic fuzzy Analytic Hierarchy Process (IF-AHP) for solving the performance evaluation problem of healthcare institutions. In this paper, intuitionistic fuzzy sets (IFS) have been preferred since they simultaneously provide information on the membership, non-membership, and hesitancy functions. A real life problem is demonstrated to validate the proposed methodology. A total number of 16 hospitals operating in Istanbul have been analyzed based on a broad set of inputs and outputs. Then, a comparison with crisp DEA has been performed.

Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations

Abstract: We have extracted different types of indicator, price and temporal features.Previous instances and correlation between features are used to design CNN.We predict the hourly direction of 100 Stocks Borsa Istanbul Stock Market.Proposed method outperforms the CNN that uses randomly ordered features.On average we perform 56.3% Macro Average F-Measure rate on 100 stocks. Stock market price data have non-linear, noisy and non-stationary structure, and therefore prediction of the price or its direction are both challenging tasks. In this paper, we propose a Convolutional Neural Network (CNN) architecture with a specifically ordered feature set to predict the intraday direction of Borsa Istanbul 100 stocks. Feature set is extracted using different indicators, price and temporal information. Correlations between instances and features are utilized to order the features before they are presented as inputs to the CNN. The proposed classifier is compared with a CNN trained with randomly ordered features and Logistic Regression. Experimental results show that the proposed classifier outperforms both Logistic Regression and CNN that utilizes randomly ordered features. Feature selection methods are also utilized to reduce training time and model complexity.

Intuitionistic Fuzzy Edas Method: an Application to Solid Waste Disposal Site Selection

Abstract: Evaluation based on Distance from Average Solution (EDAS) is a new multicriteria decision making (MCDM) method, which is based on the distances of alternatives from the average scores of attributes.

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

Abstract: . Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.