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1.Orthogonal Learning Particle Swarm Optimization

Author:Zhan, ZH;Zhang, J;Li, Y;Shi, YH

Source:IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,2011,Vol.15

Abstract:Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood's best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when searching in complex problem spaces. Hence, designing learning strategies that can utilize previous search information (experience) more efficiently has become one of the most salient and active PSO research topics. In this paper, we proposes an orthogonal learning (OL) strategy for PSO to discover more useful information that lies in the above two experiences via orthogonal experimental design. We name this PSO as orthogonal learning particle swarm optimization (OLPSO). The OL strategy can guide particles to fly in better directions by constructing a much promising and efficient exemplar. The OL strategy can be applied to PSO with any topological structure. In this paper, it is applied to both global and local versions of PSO, yielding the OLPSO-G and OLPSOL algorithms, respectively. This new learning strategy and the new algorithms are tested on a set of 16 benchmark functions, and are compared with other PSO algorithms and some state of the art evolutionary algorithms. The experimental results illustrate the effectiveness and efficiency of the proposed learning strategy and algorithms. The comparisons show that OLPSO significantly improves the performance of PSO, offering faster global convergence, higher solution quality, and stronger robustness.

2.The role of big data in smart city

Author:Hashem, IAT;Chang, V;Anuar, NB;Adewole, K;Yaqoob, I;Gani, A;Ahmed, E;Chiroma, H

Source:INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT,2016,Vol.36

Abstract:The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the state-of-the-art communication technologies and smart based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model of big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data. (C) 2016 Elsevier Ltd. All rights reserved.

3.Robust Text Detection in Natural Scene Images

Author:Yin, XC;Yin, XW;Huang, KZ;Hao, HW

Source:IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2014,Vol.36

Abstract:Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations. Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and clustering threshold are learned automatically by a novel self-training distance metric learning algorithm. The posterior probabilities of text candidates corresponding to non-text are estimated with a character classifier; text candidates with high non-text probabilities are eliminated and texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition database; the f-measure is over 76%%, much better than the state-of-the-art performance of 71%%. Experiments on multilingual, street view, multi-orientation and even born-digital databases also demonstrate the effectiveness of the proposed method. Finally, an online demo of our proposed scene text detection system has been set up at http://prir.ustb.edu.cn/TexStar/scene-text-detection/.

4.TRY plant trait database - enhanced coverage and open access

Author:Kattge, J;Bonisch, G;Diaz, S;Lavorel, S;Prentice, IC;Leadley, P;Tautenhahn, S;Werner, GDA;Aakala, T;Abedi, M;Acosta, ATR;Adamidis, GC;Adamson, K;Aiba, M;Albert, CH;Alcantara, JM;Alcazar, CC;Aleixo, I;Ali, H;Amiaud, B;Ammer, C;Amoroso, MM;Anand, M;Anderson, C;Anten, N;Antos, J;Apgaua, DMG;Ashman, TL;Asmara, DH;Asner, GP;Aspinwall, M;Atkin, O;Aubin, I;Baastrup-Spohr, L;Bahalkeh, K;Bahn, M;Baker, T;Baker, WJ;Bakker, JP;Baldocchi, D;Baltzer, J;Banerjee, A;Baranger, A;Barlow, J;Barneche, DR;Baruch, Z;Bastianelli, D;Battles, J;Bauerle, W;Bauters, M;Bazzato, E;Beckmann, M;Beeckman, H;Beierkuhnlein, C;Bekker, R;Belfry, G;Belluau, M;Beloiu, M;Benavides, R;Benomar, L;Berdugo-Lattke, ML;Berenguer, E;Bergamin, R;Bergmann, J;Carlucci, MB;Berner, L;Bernhardt-Romermann, M;Bigler, C;Bjorkman, AD;Blackman, C;Blanco, C;Blonder, B;Blumenthal, D;Bocanegra-Gonzalez, KT;Boeckx, P;Bohlman, S;Bohning-Gaese, K;Boisvert-Marsh, L;Bond, W;Bond-Lamberty, B;Boom, A;Boonman, CCF;Bordin, K;Boughton, EH;Boukili, V;Bowman, DMJS;Bravo, S;Brendel, MR;Broadley, MR;Brown, KA;Bruelheide, H;Brumnich, F;Bruun, HH;Bruy, D;Buchanan, SW;Bucher, SF;Buchmann, N;Buitenwerf, R;Bunker, DE;Burger, J;Burrascano, S;Burslem, DFRP;Butterfield, BJ;Byun, C;Marques, M;Scalon, MC;Caccianiga, M;Cadotte, M;Cailleret, M;Camac, J;Camarero, JJ;Campany, C;Campetella, G;Campos, JA;Cano-Arboleda, L;Canullo, R;Carbognani, M;Carvalho, F;Casanoves, F;Castagneyrol, B;Catford, JA;Cavender-Bares, J;Cerabolini, BEL;Cervellini, M;Chacon-Madrigal, E;Chapin, K;Chapin, FS;Chelli, S;Chen, SC;Chen, AP;Cherubini, P;Chianucci, F;Choat, B;Chung, KS;Chytry, M;Ciccarelli, D;Coll, L;Collins, CG;Conti, L;Coomes, D;Cornelissen, JHC;Cornwell, WK;Corona, P;Coyea, M;Craine, J;Craven, D;Cromsigt, JPGM;Csecserits, A;Cufar, K;Cuntz, M;da Silva, AC;Dahlin, KM;Dainese, M;Dalke, I;Dalle Fratte, M;Anh, TDL;Danihelka, J;Dannoura, M;Dawson, S;de Beer, AJ;De Frutos, A;De Long, JR;Dechant, B;Delagrange, S;Delpierre, N;Derroire, G;Dias, AS;Diaz-Toribio, MH;Dimitrakopoulos, PG;Dobrowolski, M;Doktor, D;Drevojan, P;Dong, N;Dransfield, J;Dressler, S;Duarte, L;Ducouret, E;Dullinger, S;Durka, W;Duursma, R;Dymova, O;E-Vojtko, A;Eckstein, RL;Ejtehadi, H;Elser, J;Emilio, T;Engemann, K;Erfanian, MB;Erfmeier, A;Esquivel-Muelbert, A;Esser, G;Estiarte, M;Domingues, TF;Fagan, WF;Fagundez, J;Falster, DS;Fan, Y;Fang, JY;Farris, E;Fazlioglu, F;Feng, YH;Fernandez-Mendez, F;Ferrara, C;Ferreira, J;Fidelis, A;Finegan, B;Firn, J;Flowers, TJ;Flynn, DFB;Fontana, V;Forey, E;Forgiarini, C;Francois, L;Frangipani, M;Frank, D;Frenette-Dussault, C;Freschet, GT;Fry, EL;Fyllas, NM;Mazzochini, GG;Gachet, S;Gallagher, R;Ganade, G;Ganga, F;Garcia-Palacios, P;Gargaglione, V;Garnier, E;Garrido, JL;de Gasper, AL;Gea-Izquierdo, G;Gibson, D;Gillison, AN;Giroldo, A;Glasenhardt, MC;Gleason, S;Gliesch, M;Goldberg, E;Goldel, B;Gonzalez-Akre, E;Gonzalez-Andujar, JL;Gonzalez-Melo, A;Gonzalez-Robles, A;Graae, BJ;Granda, E;Graves, S;Green, WA;Gregor, T;Gross, N;Guerin, GR;Gunther, A;Gutierrez, AG;Haddock, L;Haines, A;Hall, J;Hambuckers, A;Han, WX;Harrison, SP;Hattingh, W;Hawes, JE;He, TH;He, PC;Heberling, JM;Helm, A;Hempel, S;Hentschel, J;Herault, B;Heres, AM;Herz, K;Heuertz, M;Hickler, T;Hietz, P;Higuchi, P;Hipp, AL;Hirons, A;Hock, M;Hogan, JA;Holl, K;Honnay, O;Hornstein, D;Hou, EQ;Hough-Snee, N;Hovstad, KA;Ichie, T;Igic, B;Illa, E;Isaac, M;Ishihara, M;Ivanov, L;Ivanova, L;Iversen, CM;Izquierdo, J;Jackson, RB;Jackson, B;Jactel, H;Jagodzinski, AM;Jandt, U;Jansen, S;Jenkins, T;Jentsch, A;Jespersen, JRP;Jiang, GF;Johansen, JL;Johnson, D;Jokela, EJ;Joly, CA;Jordan, GJ;Joseph, GS;Junaedi, D;Junker, RR;Justes, E;Kabzems, R;Kane, J;Kaplan, Z;Kattenborn, T;Kavelenova, L;Kearsley, E;Kempel, A;Kenzo, T;Kerkhoff, A;Khalil, MI;Kinlock, NL;Kissling, WD;Kitajima, K;Kitzberger, T;Kjoller, R;Klein, T;Kleyer, M;Klimesova, J;Klipel, J;Kloeppel, B;Klotz, S;Knops, JMH;Kohyama, T;Koike, F;Kollmann, J;Komac, B;Komatsu, K;Konig, C;Kraft, NJB;Kramer, K;Kreft, H;Kuhn, I;Kumarathunge, D;Kuppler, J;Kurokawa, H;Kurosawa, Y;Kuyah, S;Laclau, JP;Lafleur, B;Lallai, E;Lamb, E;Lamprecht, A;Larkin, DJ;Laughlin, D;Le Bagousse-Pinguet, Y;le Maire, G;le Roux, PC;le Roux, E;Lee, T;Lens, F;Lewis, SL;Lhotsky, B;Li, YZ;Li, XE;Lichstein, JW;Liebergesell, M;Lim, JY;Lin, YS;Linares, JC;Liu, CJ;Liu, DJ;Liu, U;Livingstone, S;Llusia, J;Lohbeck, M;Lopez-Garcia, A;Lopez-Gonzalez, G;Lososova, Z;Louault, F;Lukacs, BA;Lukes, P;Luo, YJ;Lussu, M;Ma, SY;Pereira, CMR;Mack, M;Maire, V;Makela, A;Makinen, H;Malhado, ACM;Mallik, A;Manning, P;Manzoni, S;Marchetti, Z;Marchino, L;Marcilio-Silva, V;Marcon, E;Marignani, M;Markesteijn, L;Martin, A;Martinez-Garza, C;Martinez-Vilalta, J;Maskova, T;Mason, K;Mason, N;Massad, TJ;Masse, J;Mayrose, I;McCarthy, J;McCormack, ML;McCulloh, K;McFadden, IR;McGill, BJ;McPartland, MY;Medeiros, JS;Medlyn, B;Meerts, P;Mehrabi, Z;Meir, P;Melo, FPL;Mencuccini, M;Meredieu, C;Messier, J;Meszaros, I;Metsaranta, J;Michaletz, ST;Michelaki, C;Migalina, S;Milla, R;Miller, JED;Minden, V;Ming, R;Mokany, K;Moles, AT;Molnar, VA;Molofsky, J;Molz, M;Montgomery, RA;Monty, A;Moravcova, L;Moreno-Martinez, A;Moretti, M;Mori, AS;Mori, S;Morris, D;Morrison, J;Mucina, L;Mueller, S;Muir, CD;Muller, SC;Munoz, F;Myers-Smith, IH;Myster, RW;Nagano, M;Naidu, S;Narayanan, A;Natesan, B;Negoita, L;Nelson, AS;Neuschulz, EL;Ni, J;Niedrist, G;Nieto, J;Niinemets, U;Nolan, R;Nottebrock, H;Nouvellon, Y;Novakovskiy, A;Nystuen, KO;O'Grady, A;O'Hara, K;O'Reilly-Nugent, A;Oakley, S;Oberhuber, W;Ohtsuka, T;Oliveira, R;Ollerer, K;Olson, ME;Onipchenko, V;Onoda, Y;Onstein, RE;Ordonez, JC;Osada, N;Ostonen, I;Ottaviani, G;Otto, S;Overbeck, GE;Ozinga, WA;Pahl, AT;Paine, CET;Pakeman, RJ;Papageorgiou, AC;Parfionova, E;Partel, M;Patacca, M;Paula, S;Paule, J;Pauli, H;Pausas, JG;Peco, B;Penuelas, J;Perea, A;Peri, PL;Petisco-Souza, AC;Petraglia, A;Petritan, AM;Phillips, OL;Pierce, S;Pillar, VD;Pisek, J;Pomogaybin, A;Poorter, H;Portsmuth, A;Poschlod, P;Potvin, C;Pounds, D;Powell, AS;Power, SA;Prinzing, A;Puglielli, G;Pysek, P;Raevel, V;Rammig, A;Ransijn, J;Ray, CA;Reich, PB;Reichstein, M;Reid, DEB;Rejou-Mechain, M;de Dios, VR;Ribeiro, S;Richardson, S;Riibak, K;Rillig, MC;Riviera, F;Robert, EMR;Roberts, S;Robroek, B;Roddy, A;Rodrigues, AV;Rogers, A;Rollinson, E;Rolo, V;Romermann, C;Ronzhina, D;Roscher, C;Rosell, JA;Rosenfield, MF;Rossi, C;Roy, DB;Royer-Tardif, S;Ruger, N;Ruiz-Peinado, R;Rumpf, SB;Rusch, GM;Ryo, M;Sack, L;Saldana, A;Salgado-Negret, B;Salguero-Gomez, R;Santa-Regina, I;Santacruz-Garcia, AC;Santos, J;Sardans, J;Schamp, B;Scherer-Lorenzen, M;Schleuning, M;Schmid, B;Schmidt, M;Schmitt, S;Schneider, JV;Schowanek, SD;Schrader, J;Schrodt, F;Schuldt, B;Schurr, F;Garvizu, GS;Semchenko, M;Seymour, C;Sfair, JC;Sharpe, JM;Sheppard, CS;Sheremetiev, S;Shiodera, S;Shipley, B;Shovon, TA;Siebenkas, A;Sierra, C;Silva, V;Silva, M;Sitzia, T;Sjoman, H;Slot, M;Smith, NG;Sodhi, D;Soltis, P;Soltis, D;Somers, B;Sonnier, G;Sorensen, MV;Sosinski, EE;Soudzilovskaia, NA;Souza, AF;Spasojevic, M;Sperandii, MG;Stan, AB;Stegen, J;Steinbauer, K;Stephan, JG;Sterck, F;Stojanovic, DB;Strydom, T;Suarez, ML;Svenning, JC;Svitkova, I;Svitok, M;Svoboda, M;Swaine, E;Swenson, N;Tabarelli, M;Takagi, K;Tappeiner, U;Tarifa, R;Tauugourdeau, S;Tavsanoglu, C;te Beest, M;Tedersoo, L;Thiffault, N;Thom, D;Thomas, E;Thompson, K;Thornton, PE;Thuiller, W;Tichy, L;Tissue, D;Tjoelker, MG;Tng, DYP;Tobias, J;Torok, P;Tarin, T;Torres-Ruiz, JM;Tothmeresz, B;Treurnicht, M;Trivellone, V;Trolliet, F;Trotsiuk, V;Tsakalos, JL;Tsiripidis, I;Tysklind, N;Umehara, T;Usoltsev, V;Vadeboncoeur, M;Vaezi, J;Valladares, F;Vamosi, J;van Bodegom, PM;van Breugel, M;Van Cleemput, E;van de Weg, M;van der Merwe, S;van der Plas, F;van der Sande, MT;van Kleunen, M;Van Meerbeek, K;Vanderwel, M;Vanselow, KA;Varhammar, A;Varone, L;Valderrama, MY;Vassilev, K;Vellend, M;Veneklaas, EJ;Verbeeck, H;Verheyen, K;Vibrans, A;Vieira, I;Villacis, J;Violle, C;Vivek, P;Wagner, K;Waldram, M;Waldron, A;Walker, AP;Waller, M;Walther, G;Wang, H;Wang, F;Wang, WQ;Watkins, H;Watkins, J;Weber, U;Weedon, JT;Wei, LP;Weigelt, P;Weiher, E;Wells, AW;Wellstein, C;Wenk, E;Westoby, M;Westwood, A;White, PJ;Whitten, M;Williams, M;Winkler, DE;Winter, K;Womack, C;Wright, IJ;Wright, SJ;Wright, J;Pinho, BX;Ximenes, F;Yamada, T;Yamaji, K;Yanai, R;Yankov, N;Yguel, B;Zanini, KJ;Zanne, AE;Zeleny, D;Zhao, YP;Zheng, JM;Zheng, J;Zieminska, K;Zirbel, CR;Zizka, G;Zo-Bi, IC;Zotz, G;Wirth, C

Source:GLOBAL CHANGE BIOLOGY,2020,Vol.26

Abstract:Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

5.Particle Swarm Optimization with an Aging Leader and Challengers

Author:Chen, WN;Zhang, J;Lin, Y;Chen, N;Zhan, ZH;Chung, HSH;Li, Y;Shi, YH

Source:IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,2013,Vol.17

Abstract:In nature, almost every organism ages and has a limited lifespan. Aging has been explored by biologists to be an important mechanism for maintaining diversity. In a social animal colony, aging makes the old leader of the colony become weak, providing opportunities for the other individuals to challenge the leadership position. Inspired by this natural phenomenon, this paper transplants the aging mechanism to particle swarm optimization (PSO) and proposes a PSO with an aging leader and challengers (ALC-PSO). ALC-PSO is designed to overcome the problem of premature convergence without significantly impairing the fast-converging feature of PSO. It is characterized by assigning the leader of the swarm with a growing age and a lifespan, and allowing the other individuals to challenge the leadership when the leader becomes aged. The lifespan of the leader is adaptively tuned according to the leader's leading power. If a leader shows strong leading power, it lives longer to attract the swarm toward better positions. Otherwise, if a leader fails to improve the swarm and gets old, new particles emerge to challenge and claim the leadership, which brings in diversity. In this way, the concept "aging" in ALC-PSO actually serves as a challenging mechanism for promoting a suitable leader to lead the swarm. The algorithm is experimentally validated on 17 benchmark functions. Its high performance is confirmed by comparing with eight popular PSO variants.

6.Coexistence of Wi-Fi and Heterogeneous Small Cell Networks Sharing Unlicensed Spectrum

Author:Zhang, HJ;Chu, XL;Guo, WS;Wang, SY

Source:IEEE COMMUNICATIONS MAGAZINE,2015,Vol.53

Abstract:As two major players in terrestrial wireless communications, Wi-Fi systems and cellular networks have different origins and have largely evolved separately. Motivated by the exponentially increasing wireless data demand, cellular networks are evolving towards a heterogeneous and small cell network architecture, wherein small cells are expected to provide very high capacity. However, due to the limited licensed spectrum for cellular networks, any effort to achieve capacity growth through network densification will face the challenge of severe inter-cell interference. In view of this, recent standardization developments have started to consider the opportunities for cellular networks to use the unlicensed spectrum bands, including the 2.4 GHz and 5 GHz bands that are currently used by Wi-Fi, Zigbee and some other communication systems. In this article, we look into the coexistence of Wi-Fi and 4G cellular networks sharing the unlicensed spectrum. We introduce a network architecture where small cells use the same unlicensed spectrum that Wi-Fi systems operate in without affecting the performance of Wi-Fi systems. We present an almost blank sub-frame (ABS) scheme without priority to mitigate the co-channel interference from small cells to Wi-Fi systems, and propose an interference avoidance scheme based on small cells estimating the density of nearby Wi-Fi access points to facilitate their coexistence while sharing the same unlicensed spectrum. Simulation results show that the proposed network architecture and interference avoidance schemes can significantly increase the capacity of 4G heterogeneous cellular networks while maintaining the service quality of Wi-Fi systems.

7.Brain Storm Optimization Algorithm

Author:Shi, YH

Source:ADVANCES IN SWARM INTELLIGENCE, PT I,2011,Vol.6728

Abstract:Human being is the most intelligent animal in this world. Intuitively, optimization algorithm inspired by human being creative problem solving process should be superior to the optimization algorithms inspired by collective behavior of insects like ants, bee, etc. In this paper, we introduce a novel brain storm optimization algorithm, which was inspired by the human brainstorming process. Two benchmark functions were tested to validate the effectiveness and usefulness of the proposed algorithm.

8.Towards fog-driven IoT eHealth: Promises and challenges of loT in medicine and healthcare

Author:Farahani, B;Firouzi, F;Chang, V;Badaroglu, M;Constant, N;Mankodiya, K

Source:FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,2018,Vol.78

Abstract:Internet of Things (loT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of loT in healthcare and medicine by presenting a holistic architecture of loT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: (1) device, (2) fog computing and (3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven loT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device-network-human interfaces, security, and privacy. (C) 2017 Elsevier B.V. All rights reserved.

9.One-class kernel subspace ensemble for medical image classification

Author:Zhang, YG;Zhang, BL;Coenen, F;Xiao, JM;Lu, WJ

Source:EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING,2014,Vol.2014

Abstract:Classification of medical images is an important issue in computer-assisted diagnosis. In this paper, a classification scheme based on a one-class kernel principle component analysis (KPCA) model ensemble has been proposed for the classification of medical images. The ensemble consists of one-class KPCA models trained using different image features from each image class, and a proposed product combining rule was used for combining the KPCA models to produce classification confidence scores for assigning an image to each class. The effectiveness of the proposed classification scheme was verified using a breast cancer biopsy image dataset and a 3D optical coherence tomography (OCT) retinal image set. The combination of different image features exploits the complementary strengths of these different feature extractors. The proposed classification scheme obtained promising results on the two medical image sets. The proposed method was also evaluated on the UCI breast cancer dataset (diagnostic), and a competitive result was obtained.

10.A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems

Author:Chen, WN;Zhang, J;Chung, HSH;Zhong, WL;Wu, WG;Shi, YH

Source:IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,2010,Vol.14

Abstract:Particle swarm optimization (PSO) is predominately used to find solutions for continuous optimization problems. As the operators of PSO are originally designed in an n-dimensional continuous space, the advancement of using PSO to find solutions in a discrete space is at a slow pace. In this paper, a novel setbased PSO (S-PSO) method for the solutions of some combinatorial optimization problems (COPs) in discrete space is presented. The proposed S-PSO features the following characteristics. First, it is based on using a set-based representation scheme that enables S-PSO to characterize the discrete search space of COPs. Second, the candidate solution and velocity are defined as a crisp set, and a set with possibilities, respectively. All arithmetic operators in the velocity and position updating rules used in the original PSO are replaced by the operators and procedures defined on crisp sets, and sets with possibilities in S-PSO. The S-PSO method can thus follow a similar structure to the original PSO for searching in a discrete space. Based on the proposed S-PSO method, most of the existing PSO variants, such as the global version PSO, the local version PSO with different topologies, and the comprehensive learning PSO (CLPSO), can be extended to their corresponding discrete versions. These discrete PSO versions based on S-PSO are tested on two famous COPs: the traveling salesman problem and the multidimensional knapsack problem. Experimental results show that the discrete version of the CLPSO algorithm based on S-PSO is promising.

11.Canonical genetic signatures of the adult human brain

Author:Hawrylycz, M;Miller, JA;Menon, V;Feng, D;Dolbeare, T;Guillozet-Bongaarts, AL;Jegga, AG;Aronow, BJ;Lee, CK;Bernard, A;Glasser, MF;Dierker, DL;Menche, J;Szafer, A;Collman, F;Grange, P;Berman, KA;Mihalas, S;Yao, ZZ;Stewart, L;Barabasi, AL;Schulkin, J;Phillips, J;Ng, L;Dang, C;Haynor, DR;Jones, A;Van Essen, DC;Koch, C;Lein, E

Source:NATURE NEUROSCIENCE,2015,Vol.18

Abstract:The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.

12.The genomes of two key bumblebee species with primitive eusocial organization

Author:Sadd, BM;Barribeau, SM;Bloch, G;de Graaf, DC;Dearden, P;Elsik, CG;Gadau, J;Grimmelikhuijzen, CJP;Hasselmann, M;Lozier, JD;Robertson, HM;Smagghe, G;Stolle, E;Van Vaerenbergh, M;Waterhouse, RM;Bornberg-Bauer, E;Klasberg, S;Bennett, AK;Caamara, F;Guigo, R;Hoff, K;Mariotti, M;Munoz-Torres, M;Murphy, T;Santesmasses, D;Amdam, GV;Beckers, M;Beye, M;Biewer, M;Bitondi, MMG;Blaxter, ML;Bourke, AFG;Brown, MJF;Buechel, SD;Cameron, R;Cappelle, K;Carolan, JC;Christiaens, O;Ciborowski, KL;Clarke, DF;Colgan, TJ;Collins, DH;Cridge, AG;Dalmay, T;Dreier, S;du Plessis, L;Duncan, E;Erler, S;Evans, J;Falcon, T;Flores, K;Freitas, FCP;Fuchikawa, T;Gempe, T;Hartfelder, K;Hauser, F;Helbing, S;Humann, FC;Irvine, F;Jermiin, LS;Johnson, CE;Johnson, RM;Jones, AK;Kadowaki, T;Kidner, JH;Koch, V;Kohler, A;Kraus, FB;Lattorff, HMG;Leask, M;Lockett, GA;Mallon, EB;Antonio, DSM;Marxer, M;Meeus, I;Moritz, RFA;Nair, A;Napflin, K;Nissen, I;Niu, J;Nunes, FMF;Oakeshott, JG;Osborne, A;Otte, M;Pinheiro, DG;Rossie, N;Rueppell, O;Santos, CG;Schmid-Hempel, R;Schmitt, BD;Schulte, C;Simoes, ZLP;Soares, MPM;Swevers, L;Winnebeck, EC;Wolschin, F;Yu, N;Zdobnov, EM;Aqrawi, PK;Blankenburg, KP;Coyle, M;Francisco, L;Hernandez, AG;Holder, M;Hudson, ME;Jackson, L;Jayaseelan, J;Joshi, V;Kovar, C;Lee, SL;Mata, R;Mathew, T;Newsham, IF;Ngo, R;Okwuonu, G;Pham, C;Pu, LL;Saada, N;Santibanez, J;Simmons, D;Thornton, R;Venkat, A;Walden, KKO;Wu, YQ;Debyser, G;Devreese, B;Asher, C;Blommaert, J;Chipman, AD;Chittka, L;Fouks, B;Liu, J;O'Neill, MP;Sumner, S;Puiu, D;Qu, J;Salzberg, SL;Scherer, SE;Muzny, DM;Richards, S;Robinson, GE;Gibbs, RA;Schmid-Hempel, P;Worley, KC

Source:GENOME BIOLOGY,2015,Vol.16

Abstract:Background: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats. Results: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits. Conclusions: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation.

13.A global synthesis reveals biodiversity-mediated benefits for crop production

Author:Dainese, M;Martin, EA;Aizen, MA;Albrecht, M;Bartomeus, I;Bommarco, R;Carvalheiro, LG;Chaplin-Kramer, R;Gagic, V;Garibaldi, LA;Ghazoul, J;Grab, H;Jonsson, M;Karp, DS;Kennedy, CM;Kleijn, D;Kremen, C;Landis, DA;Letourneau, DK;Marini, L;Poveda, K;Rader, R;Smith, HG;Tscharntke, T;Andersson, GKS;Badenhausser, I;Baensch, S;Bezerra, ADM;Bianchi, FJJA;Boreux, V;Bretagnolle, V;Caballero-Lopez, B;Cavigliasso, P;Cetkovic, A;Chacoff, NP;Classen, A;Cusser, S;Silva, FDDE;de Groot, GA;Dudenhoffer, JH;Ekroos, J;Fijen, T;Franck, P;Freitas, BM;Garratt, MPD;Gratton, C;Hipolito, J;Holzschuh, A;Hunt, L;Iverson, AL;Jha, S;Keasar, T;Kim, TN;Kishinevsky, M;Klatt, BK;Klein, AM;Krewenka, KM;Krishnan, S;Larsen, AE;Lavigne, C;Liere, H;Maas, B;Mallinger, RE;Pachon, EM;Martinez-Salinas, A;Meehan, TD;Mitchell, MGE;Molina, GAR;Nesper, M;Nilsson, L;O'Rourke, ME;Peters, MK;Plecas, M;Potts, SG;Ramos, DD;Rosenheim, JA;Rundlof, M;Rusch, A;Saez, A;Scheper, J;Schleuning, M;Schmack, JM;Sciligo, AR;Seymour, C;Stanley, DA;Stewart, R;Stout, JC;Sutter, L;Takada, MB;Taki, H;Tamburini, G;Tschumi, M;Viana, BF;Westphal, C;Willcox, BK;Wratten, SD;Yoshioka, A;Zaragoza-Trello, C;Zhang, W;Zou, Y;Steffan-Dewenter, I

Source:SCIENCE ADVANCES,2019,Vol.5

Abstract:Human land use threatens global biodiversity and compromises multiple ecosystem functions critical to food production. Whether crop yield-related ecosystem services can be maintained by a few dominant species or rely on high richness remains unclear. Using a global database from 89 studies (with 1475 locations), we partition the relative importance of species richness, abundance, and dominance for pollination; biological pest control; and final yields in the context of ongoing land-use change. Pollinator and enemy richness directly supported ecosystem services in addition to and independent of abundance and dominance. Up to 50%% of the negative effects of landscape simplification on ecosystem services was due to richness losses of service-providing organisms, with negative consequences for crop yields. Maintaining the biodiversity of ecosystem service providers is therefore vital to sustain the flow of key agroecosystem benefits to society.

14.Genetic Learning Particle Swarm Optimization

Author:Gong, YJ;Li, JJ;Zhou, YC;Li, Y;Chung, HSH;Shi, YH;Zhang, J

Source:IEEE TRANSACTIONS ON CYBERNETICS,2016,Vol.46

Abstract:Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for "learning." This leads to a generalized "learning PSO" paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.

15.Crop pests and predators exhibit inconsistent responses to surrounding landscape composition

Author:Karp, DS;Chaplin-Kramer, R;Meehan, TD;Martin, EA;DeClerck, F;Grab, H;Gratton, C;Hunt, L;Larsen, AE;Martinez-Salinas, A;O'Rourke, ME;Rusch, A;Poveda, K;Jonsson, M;Rosenheim, JA;Schellhorn, NA;Tscharntke, T;Wratten, SD;Zhang, W;Iverson, AL;Adler, LS;Albrecht, M;Alignier, A;Angelella, GM;Anjum, MZ;Avelino, J;Batary, P;Baveco, JM;Bianchi, FJJA;Birkhofer, K;Bohnenblust, EW;Bommarco, R;Brewer, MJ;Caballero-Lopez, B;Carriere, Y;Carvalheiro, LG;Cayuela, L;Centrella, M;Cetkovic, A;Henri, DC;Chabert, A;Costamagna, AC;De la Mora, A;de Kraker, J;Desneux, N;Diehl, E;Diekotter, T;Dormann, CF;Eckberg, JO;Entling, MH;Fiedler, D;Franck, P;van Veen, FJF;Frank, T;Gagic, V;Garratt, MPD;Getachew, A;Gonthier, DJ;Goodell, PB;Graziosi, I;Groves, RL;Gurr, GM;Hajian-Forooshani, Z;Heimpel, GE;Herrmann, JD;Huseth, AS;Inclan, DJ;Ingrao, AJ;Iv, P;Jacot, K;Johnson, GA;Jones, L;Kaiser, M;Kaser, JM;Keasar, T;Kim, TN;Kishinevsky, M;Landis, DA;Lavandero, B;Lavigne, C;Le Ralec, A;Lemessa, D;Letourneau, DK;Liere, H;Lu, YH;Lubin, Y;Luttermoser, T;Maas, B;Mace, K;Madeira, F;Mader, V;Cortesero, AM;Marini, L;Martinez, E;Martinson, HM;Menozzi, P;Mitchell, MGE;Miyashita, T;Molina, GAR;Molina-Montenegro, MA;O'Neal, ME;Opatovsky, I;Ortiz-Martinez, S;Nash, M;Ostman, O;Ouin, A;Pak, D;Paredes, D;Parsa, S;Parry, H;Perez-Alvarez, R;Perovic, DJ;Peterson, JA;Petit, S;Philpott, SM;Plantegenest, M;Plecas, M;Pluess, T;Pons, X;Potts, SG;Pywell, RF;Ragsdale, DW;Rand, TA;Raymond, L;Ricci, B;Sargent, C;Sarthou, JP;Saulais, J;Schackermann, J;Schmidt, NP;Schneider, G;Schuepp, C;Sivakoff, FS;Smith, HG;Whitney, KS;Stutz, S;Szendrei, Z;Takada, MB;Taki, H;Tamburini, G;Thomson, LJ;Tricault, Y;Tsafack, N;Tschumi, M;Valantin-Morison, M;Trinh, MV;van der Werf, W;Vierling, KT;Werling, BP;Wickens, JB;Wickens, VJ;Woodcock, BA;Wyckhuys, K;Xiao, HJ;Yasuda, M;Yoshioka, A;Zou, Y

Source:PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA,2018,Vol.115

Abstract:The idea that noncrop habitat enhances pest control and represents a win-win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win-win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies.

16.Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems

Author:Zhan, ZH;Li, JJ;Cao, JN;Zhang, J;Chung, HSH;Shi, YH

Source:IEEE TRANSACTIONS ON CYBERNETICS,2013,Vol.43

Abstract:Traditional multiobjective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multiobjective optimization problems (MOPs). However, this consideration may cause difficulty to assign fitness to individuals because different objectives often conflict with each other. In order to avoid this difficulty, this paper proposes a novel coevolutionary technique named multiple populations for multiple objectives (MPMO) when developing MOEAs. The novelty of MPMO is that it provides a simple and straightforward way to solve MOPs by letting each population correspond with only one objective. This way, the fitness assignment problem can be addressed because the individuals' fitness in each population can be assigned by the corresponding objective. MPMO is a general technique that each population can use existing optimization algorithms. In this paper, particle swarm optimization (PSO) is adopted for each population, and coevolutionary multiswarm PSO (CMPSO) is developed based on the MPMO technique. Furthermore, CMPSO is novel and effective by using an external shared archive for different populations to exchange search information and by using two novel designs to enhance the performance. One design is to modify the velocity update equation to use the search information found by different populations to approximate the whole Pareto front (PF) fast. The other design is to use an elitist learning strategy for the archive update to bring in diversity to avoid local PFs. CMPSO is comprehensively tested on different sets of benchmark problems with different characteristics and is compared with some state-of-the-art algorithms. The results show that CMPSO has superior performance in solving these different sets of MOPs.

17.A Wrinkled PEDOT:PSS Film Based Stretchable and Transparent Triboelectric Nanogenerator for Wearable Energy Harvesters and Active Motion Sensors

Author:Wen, Z;Yang, YQ;Sun, N;Li, GF;Liu, YN;Chen, C;Shi, JH;Xie, LJ;Jiang, HX;Bao, DQ;Zhuo, QQ;Sun, XH

Source:ADVANCED FUNCTIONAL MATERIALS,2018,Vol.28

Abstract:The functionalized conductive polymer is a promising choice for flexible triboelectric nanogenerators (TENGs) for harvesting human motion energy still poses challenges. In this work, a transparent and stretchable wrinkled poly(3,4-ethylenedioxythiophene):poly(4-styrenesulfonate) (PEDOT:PSS) electrode based TENG (WP-TENG) is fabricated. The optimum conductivity and transparency of PEDOT:PSS electrode can reach 0.14 k Omega square(-1) and 90%%, respectively, with maximum strain of approximate to 100%%. Operating in single-electrode mode at 2.5 Hz, the WP-TENG with an area of 6 x 3 cm(2) produces an open-circuit voltage of 180 V, short-circuit current of 22.6 mu A, and average power density of 4.06 mW m(-2). It can be worn on the wrist to harvest hand tapping energy and charge the capacitor to 2 V in approximate to 3.5 min, and then drive an electronic watch. Furthermore, the WP-TENG as the human motion monitoring sensor could inspect the bending angle of the elbow and joint by analyzing the peak value of voltage and monitor the motion frequency by counting the peak number. The triboelectric mechanism also enables the WP-TENG to realize high-performance active tactile sensing. The assembled 3 pixel x 3 pixel tactile sensor array is fabricated for mapping the touch location or recording the shape of object contacted with the sensor array.

18.Corticosteroid treatment of patients with coronavirus disease 2019 (COVID-19)

Author:Zha, L;Li, SR;Pan, LL;Tefsen, B;Li, YS;French, N;Chen, LY;Yang, G;Villanueva, EV

Source:MEDICAL JOURNAL OF AUSTRALIA,2020,Vol.212

Abstract:Objectives To assess the efficacy of corticosteroid treatment of patients with coronavirus disease 2019 (COVID-19). Design, setting Observational study in the two COVID-19-designated hospitals in Wuhu, Anhui province, China, 24 January - 24 February 2020. Participants Thirty-one patients infected with the severe acute respiratory coronavirus 2 (SARS-CoV-2) treated at the two designated hospitals. Main outcome measures Virus clearance time, length of hospital stay, and duration of symptoms, by treatment type (including or not including corticosteroid therapy). Results Eleven of 31 patients with COVID-19 received corticosteroid treatment. Cox proportional hazards regression analysis indicated no association between corticosteroid treatment and virus clearance time (hazard ratio [HR], 1.26; 95%% CI, 0.58-2.74), hospital length of stay (HR, 0.77; 95%% CI, 0.33-1.78), or duration of symptoms (HR, 0.86; 95%% CI, 0.40-1.83). Univariate analysis indicated that virus clearance was slower in two patients with chronic hepatitis B infections (mean difference, 10.6 days; 95%% CI, 6.2-15.1 days). Conclusions Corticosteroids are widely used when treating patients with COVID-19, but we found no association between therapy and outcomes in patients without acute respiratory distress syndrome. An existing HBV infection may delay SARS-CoV-2 clearance, and this association should be further investigated.

19.Antibiotic resistance genes in manure-amended soil and vegetables at harvest

Author:Wang, FH;Qiao, M;Chen, Z;Su, JQ;Zhu, YG

Source:JOURNAL OF HAZARDOUS MATERIALS,2015,Vol.299

Abstract:Lettuce and endive, which can be eaten raw, were planted on the manure-amended soil in order to explore the influence of plants on the abundance of antibiotic resistance genes (ARGs) in bulk soil and rhizosphere soil, and the occurrence of ARGs on harvested vegetables. Twelve ARGs and one integrase gene Until) were detected in all soil samples. Five ARGs (sulI, tetG, tetC, tetA, and tetM) showed lower abundance in the soil with plants than those without. ARGs and intI1 gene were also detected on harvested vegetables grown in manure-amended soil, including endophytes and phyllosphere microorganisms. The results demonstrated that planting had an effect on the distribution of ARGs in manure-amended soil, and ARGs were detected on harvested vegetables after growing in manure-amended soil, which had potential threat to human health. (C) 2015 Elsevier B.V. All rights reserved.

20.A Methodology for Optimization of Power Systems Demand Due to Electric Vehicle Charging Load

Author:Zhang, P;Qian, KJ;Zhou, CK;Stewart, BG;Hepburn, DM

Source:IEEE TRANSACTIONS ON POWER SYSTEMS,2012,Vol.27

Abstract:This paper presents a methodology of optimizing power systems demand due to electric vehicle (EV) charging load. Following a brief introduction to the charging characteristics of EV batteries, a statistical model is presented for predicting the EV charging load. The optimization problem is then described, and the solution is provided based on the model. An example study is carried out with error and sensitivity analysis to validate the proposed method. Four scenarios of various combinations of EV penetration levels and charging modes are considered in the study. A series of numerical solutions to the optimization problem in these scenarios are obtained by serial quadratic programming. The results show that EV charging load has significant potential to improve the daily load profile of power systems if the charging loads are optimally distributed. It is demonstrated that flattened load profiles may be achieved at all EV penetration levels if the EVs are charged through a fast charging mode. In addition, the implementation of the proposed optimization is discussed with analyses on the impact of travel pattern and the willingness of customers.
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