453,212 research outputs found
New Liu Estimators for the Poisson Regression Model: Method and Application
No full textA new shrinkage estimator for the Poisson model is introduced in this paper. This method is a generalization of the Liu (1993) estimator originally developed for the linear regression model and will be generalised here to be used instead of the classical maximum likelihood (ML) method in the presence of multicollinearity since the mean squared error (MSE) of ML becomes inflated in that situation. Furthermore, this paper derives the optimal value of the shrinkage parameter and based on this value some methods of how the shrinkage parameter should be estimated are suggested. Using Monte Carlo simulation where the MSE and mean absolute error (MAE) are calculated it is shown that when the Liu estimator is applied with these proposed estimators of the shrinkage parameter it always outperforms the ML. Finally, an empirical application has been considered to illustrate the usefulness of the new Liu estimators.Estimation; MSE; MAE; Multicollinearity; Poisson; Liu; Simulation
Liqun Liu Interview
No full textEconomicStudies_AnalysisIn the Spring 2022 issue of PERCspectives on Research, Research Scientist Liqun Liu shares his educational and career highlights from Texas A&M University and the Private Enterprise Research Center, as well as his research in public policy analysis, cost-benefit analysis and decision making
Diplomatic visits of commodore M. Perry to Liu Chui island in 1852 and 1853 and its international repercussions
No full textThe article highlights the diplomatic mission of the American Commodore M. Perry to Liu Chui Islands, during the large-scale military expedition of the US Navy Fleet to Japan in 1852 – 1853. The publication of official documents related to the mission of M. Perry, memoirs and travel notes of the members` of that expedition were used as the information source. The author believes that the main purpose of Perry’s visit to Liu Chui Island was dictated by the desire to open the Island to American trade, and in the prospect, to bring the Island into subjection of the US protectorate. Perry has used a range of methods to implement these tasks, from pure diplomacy to direct pressure using the armed assault. As a result, the Commodore succeeded, and the Island, despite the protests of the local authorities in 1853, was opened to foreign trade. This action allowed US to become the second of the most powerful countries in East Asia after the United Kingdom. The process of establishing the contacts between the leader of the expedition and the local authorities of the Island has been examined, as well as the conducted negotiations during the first (1852) and second (1853) Perry`s visits to the capital of the archipelago – the city of Nappa, which resulted in opening this Island to Western trade. For a long time the local governor has been dragging out the negotiations process, but he had to agree to the US’ terms, after the US marines seized his palace up. The United States had an opportunity to trade on Liu Chui Island, purchase the coal for their ships, so as they got freedom of movement across its territory. However, after two decades, the United States abandoned the claims to the Islands. The reasons for this are to be investigated by the author in his following research works
Mecopoda minor Liu & Heller & Wang & Yang & Wu & Liu & Zhang 2020
No full textSpecies group minor Liu Diagnosis (species group “c”). The songs are constituted by numerous discontinuous chirps at the beginning followed by continuous syllables (Figs 3 a–k). Notes. According to the song characteristics (Liu et al., 2019), M. hainanensis He, 2019, should be put into the species group. Included species and subspecies. M. minor sp. n. (East Asia), M. m. yunnana sub sp. n. (China), M. hainanensis He, 2019 (China).Published as part of Liu, Chun-Xiang, Heller, Klaus-Gerhard, Wang, Xue-Song, Yang, Zhen, Wu, Chao, Liu, Fei & Zhang, Tao, 2020, Taxonomy of a katydid genus Mecopoda Serville (Orthoptera: Tettigoniidae, Mecopodinae) from East Asia, pp. 296-310 in Zootaxa 4758 (2) on page 305, DOI: 10.11646/zootaxa.4758.2.5, http://zenodo.org/record/373448
Paikallisten binäärikuvioiden inspiroima tehokas syvä konvoluutioneuroverkko visuaalisten piirteiden oppimiseen
No full textAbstractIn the past decades, deep neural networks (DNNs) have revolutionized the computer vision community with their significant success in a wide range of computer vision tasks. Recent work has focused intensely on accuracy, which has resulted in a large number of huge and complex models designed in the community. However, with the ubiquitous use of edge devices like mobile phones, robots, and embedded systems, efficiency is gradually becoming more and more important for modern computer vision models. In computer vision, the quality of feature representation learning directly determines the quality of the whole machine learning model. The core challenge is to develop feature representation learning algorithms both effectively and efficiently.In this thesis, we put our efforts into the following matters to meet the challenge. On the one hand, we take the merit of traditional local binary pattern (LBP) descriptors of being computationally simple and efficient, and propose improvement in the learnability of LBP to extract more discriminative features. On the other hand, taking advantage of DNNs of high representational capacity, we target building compact DNN modules with less computational cost and model size. These two aspects are either separately developed or combined, and both are considered in this thesis.We start by extending traditional LBP to learnable descriptors, allowing the new descriptors to be learned from the data rather than handcrafted. Based on that, our model obtains a better trade-off than earlier LBP variants including distinctiveness, computational cost, and robustness. Next, we propose two novel types of convolutions that combine LBP and the convolution operation. The new convolutions enjoy the following benefits: capturing higher-order local differential information, being computationally efficient, and being able to be integrated well into existing DNNs. Then, we propose an efficient convolutional neural network (CNN) module that benefits from group convolution and dynamic execution. It shares the efficiency of the standard group convolution without losing representational ability. Finally, we develop a novel binary DNN module for robust point cloud analysis. The proposed point cloud models achieve both running efficiencies through network binarization and rotation invariance at the same time.Original papersOriginal papers are not included in the electronic version of the dissertation.Su, Z., Pietikäinen, M., & Liu, L. (2019). BIRD: Learning binary and illumination robust descriptor for face recognition. In 30th British Machine Visison Conference : BMVC 2019, 1–12.Self-archived versionSu, Z., Fang, L., Kang, W., Hu, D., Pietikäinen, M., & Liu, L. (2020). Dynamic group convolution for accelerating convolutional neural networks. In Computer Vision – ECCV 2020, 138–155. https://doi.org/10.1007/978-3-030-58539-6_9Self-archived versionSu, Z., Liu, W., Yu, Z., Hu, D., Liao, Q., Tian, Q., Pietikainen, M., & Liu, L. (2021). Pixel difference networks for efficient edge detection. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 5097–5107. https://doi.org/10.1109/ICCV48922.2021.00507Self-archived versionSu, Z., Welling, M., Pietikäinen, M., & Liu, L. (2022). SVNet: Where SO(3) equivariance meets binarization on point cloud representation. In 2022 International Conference on 3D Vision (3DV), 547–556. https://doi.org/10.1109/3DV57658.2022.00084Self-archived versionSu, Z., Zhang, J., Wang, L., Zhang, H., Liu, Z., Pietikäinen, M., & Liu, L. (2023). Lightweight pixel difference networks for efficient visual representation learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. Advance online publication. https://doi.org/10.1109/TPAMI.2023.3300513Su, Z., Müller, M., Wofk, D., Pietikäinen, M., & Liu, L. (2023). Spatial and temporal difference network for real-time salient object detection. Manuscript submitted for publication.TiivistelmäViime vuosikymmeninä syvät neuroverkot ovat mullistaneet konenäköä suurella menestyksellä useissa eri tehtävissä. Viimeaikaisin tutkimus on keskittynyt menetelmien tarkkuuteen, mikä on johtanut suuren määrään valtavan kokoisia ja kompleksisia malleja. Kaikella läsnä olevalla tekniikalla, kuten puhelimilla, roboteilla ja sulautetuilla järjestelmillä konenäkö on kasvavassa määrin tärkeämpää ja täten mallien tehokkuus on myös tärkeämpää. Konenäössä piirteiden oppiminen määrittää suoraan koko konenäkö mallin laadun. Keskeinen haaste on kehittää piirteiden oppimisalgoritmeja tehokkaasti.Tässä väitöskirjassa esitetään seuraavia ratkaisuja mallien tehokkuuden ongelmaan. Ensiksi parannetaan laskennallisesti tehokasta ja yksinkertaista paikallista binäärikuva -menetelmää lisäämällä sen piirteiden määrää. Toiseksi hyödynnetään suurta neuroverkkojen piirteiden kapasiteettia kehittämällä laskennallisesti tehokkaampia ja pienempiä moduuleja. Kumpiakin tekniikkoja käytetään erikseen ja yhdessä tässä väitöskirjassa.Perinteisestä paikallisesta binäärikuvio -menetelmästä tehdään oppiva, jolloin uusia piirteitä voidaan oppia datasta, sen sijaan että ne määriteltäisiin algoritmillisesti. Uusi kehitetty oppiva versio on laskennallisesti tehokkaampi, robustimpi ja erottelevaisempi. Seuraavaksi esitellään tekniikka, joka yhdistää paikallisen binäärikuvion ja konvoluution. Kehitetty konvoluutio pystyy irrottamaan korkeamman asteen paikallista informaatiota, se on laskennallisesti tehokas ja se voidaan integroida olemassa oleviin neuroverkkoihin vaivattomasti. Sen jälkeen esitellään konvoluutioneuroverkon moduuli, joka käyttää hyväkseen ryhmäkonvoluutiota ja dynaamista suoritusta. Moduuli pitää normaalin konvoluution piirteidenirrotus kyvyn ollen kuitenkin yhtä tehokas ryhmäkonvoluution kanssa laskennallisesti. Lopuksi esitetään binäärineuroverkko moduuli robustiin pistepilven analyysiin. Kehitetty moduuli parantaa laskennallista tehokkuutta hyödyntämällä sekä binarisointia että pyörimis-invarianttia konvoluutiota.OsajulkaisutOsajulkaisut eivät sisälly väitöskirjan elektroniseen versioon.Su, Z., Pietikäinen, M., & Liu, L. (2019). BIRD: Learning binary and illumination robust descriptor for face recognition. In 30th British Machine Visison Conference : BMVC 2019, 1–12.Rinnakkaistallennettu versioSu, Z., Fang, L., Kang, W., Hu, D., Pietikäinen, M., & Liu, L. (2020). Dynamic group convolution for accelerating convolutional neural networks. In Computer Vision – ECCV 2020, 138–155. https://doi.org/10.1007/978-3-030-58539-6_9Rinnakkaistallennettu versioSu, Z., Liu, W., Yu, Z., Hu, D., Liao, Q., Tian, Q., Pietikainen, M., & Liu, L. (2021). Pixel difference networks for efficient edge detection. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 5097–5107. https://doi.org/10.1109/ICCV48922.2021.00507Rinnakkaistallennettu versioSu, Z., Welling, M., Pietikäinen, M., & Liu, L. (2022). SVNet: Where SO(3) equivariance meets binarization on point cloud representation. In 2022 International Conference on 3D Vision (3DV), 547–556. https://doi.org/10.1109/3DV57658.2022.00084Rinnakkaistallennettu versioSu, Z., Zhang, J., Wang, L., Zhang, H., Liu, Z., Pietikäinen, M., & Liu, L. (2023). Lightweight pixel difference networks for efficient visual representation learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. Advance online publication. https://doi.org/10.1109/TPAMI.2023.3300513Su, Z., Müller, M., Wofk, D., Pietikäinen, M., & Liu, L. (2023). Spatial and temporal difference network for real-time salient object detection. Manuscript submitted for publication.Academic dissertation to be presented with the assent of the Doctoral Programme Committee of Information Technology and Electrical Engineering of the University of Oulu for public defence in the OP auditorium (L10), Linnanmaa, on 16 October 2023, at 12 noonAbstract
In the past decades, deep neural networks (DNNs) have revolutionized the computer vision community with their significant success in a wide range of computer vision tasks. Recent work has focused intensely on accuracy, which has resulted in a large number of huge and complex models designed in the community. However, with the ubiquitous use of edge devices like mobile phones, robots, and embedded systems, efficiency is gradually becoming more and more important for modern computer vision models. In computer vision, the quality of feature representation learning directly determines the quality of the whole machine learning model. The core challenge is to develop feature representation learning algorithms both effectively and efficiently.
In this thesis, we put our efforts into the following matters to meet the challenge. On the one hand, we take the merit of traditional local binary pattern (LBP) descriptors of being computationally simple and efficient, and propose improvement in the learnability of LBP to extract more discriminative features. On the other hand, taking advantage of DNNs of high representational capacity, we target building compact DNN modules with less computational cost and model size. These two aspects are either separately developed or combined, and both are considered in this thesis.
We start by extending traditional LBP to learnable descriptors, allowing the new descriptors to be learned from the data rather than handcrafted. Based on that, our model obtains a better trade-off than earlier LBP variants including distinctiveness, computational cost, and robustness. Next, we propose two novel types of convolutions that combine LBP and the convolution operation. The new convolutions enjoy the following benefits: capturing higher-order local differential information, being computationally efficient, and being able to be integrated well into existing DNNs. Then, we propose an efficient convolutional neural network (CNN) module that benefits from group convolution and dynamic execution. It shares the efficiency of the standard group convolution without losing representational ability. Finally, we develop a novel binary DNN module for robust point cloud analysis. The proposed point cloud models achieve both running efficiencies through network binarization and rotation invariance at the same time.Tiivistelmä
Viime vuosikymmeninä syvät neuroverkot ovat mullistaneet konenäköä suurella menestyksellä useissa eri tehtävissä. Viimeaikaisin tutkimus on keskittynyt menetelmien tarkkuuteen, mikä on johtanut suuren määrään valtavan kokoisia ja kompleksisia malleja. Kaikella läsnä olevalla tekniikalla, kuten puhelimilla, roboteilla ja sulautetuilla järjestelmillä konenäkö on kasvavassa määrin tärkeämpää ja täten mallien tehokkuus on myös tärkeämpää. Konenäössä piirteiden oppiminen määrittää suoraan koko konenäkö mallin laadun. Keskeinen haaste on kehittää piirteiden oppimisalgoritmeja tehokkaasti.
Tässä väitöskirjassa esitetään seuraavia ratkaisuja mallien tehokkuuden ongelmaan. Ensiksi parannetaan laskennallisesti tehokasta ja yksinkertaista paikallista binäärikuva -menetelmää lisäämällä sen piirteiden määrää. Toiseksi hyödynnetään suurta neuroverkkojen piirteiden kapasiteettia kehittämällä laskennallisesti tehokkaampia ja pienempiä moduuleja. Kumpiakin tekniikkoja käytetään erikseen ja yhdessä tässä väitöskirjassa.
Perinteisestä paikallisesta binäärikuvio -menetelmästä tehdään oppiva, jolloin uusia piirteitä voidaan oppia datasta, sen sijaan että ne määriteltäisiin algoritmillisesti. Uusi kehitetty oppiva versio on laskennallisesti tehokkaampi, robustimpi ja erottelevaisempi. Seuraavaksi esitellään tekniikka, joka yhdistää paikallisen binäärikuvion ja konvoluution. Kehitetty konvoluutio pystyy irrottamaan korkeamman asteen paikallista informaatiota, se on laskennallisesti tehokas ja se voidaan integroida olemassa oleviin neuroverkkoihin vaivattomasti. Sen jälkeen esitellään konvoluutioneuroverkon moduuli, joka käyttää hyväkseen ryhmäkonvoluutiota ja dynaamista suoritusta. Moduuli pitää normaalin konvoluution piirteidenirrotus kyvyn ollen kuitenkin yhtä tehokas ryhmäkonvoluution kanssa laskennallisesti. Lopuksi esitetään binäärineuroverkko moduuli robustiin pistepilven analyysiin. Kehitetty moduuli parantaa laskennallista tehokkuutta hyödyntämällä sekä binarisointia että pyörimis-invarianttia konvoluutiota
Test Make Sense?
No full textCorresponding author Changyu Liu should be listed as the first corresponding author.No Full Tex
Study of 1, 2-chlorine migration in (Alpha,alpha-dichlorobenzyl) chlorocarbene generated by laser flash photolysis of 3-chloro-3-(Alpha,alpha-dichlorobenzyl) diazirine
No full textPT: J; CR: BONNEAU R, 1989, J AM CHEM SOC, V111, P5973 GRAHAM WH, 1965, J AM CHEM SOC, V87, P4396 JONES M, 1973, CARBENES, V1 JONES M, 1975, CARBENES, V2 JONES WM, 1980, REARRANGEMENTS GROUN, V1 KIRMSE W, 1971, CARBENE CHEM LAVILLA JA, 1989, J AM CHEM SOC, V111, P6877 LIU MTH, IN PRESS J PHOTOCHEM LIU MTH, IN PRESS J PHYS ORG LIU MTH, 1987, CHEM DIAZIRINES LIU MTH, 1989, J AM CHEM SOC, V111, P6873 LIU MTH, 1990, J AM CHEM SOC, V112, P3915 LIU MTH, 1990, J CHEM SOC CHEM COMM, P1650 MORGAN S, 1991, J AM CHEM SOC, V113, P2782 MOSS RA, 1990, J AM CHEM SOC, V112, P5642 REGITZ M, 1989, METHODEN ORGANISCH E, V19 SCHAEFER HF, 1979, ACCOUNTS CHEM RES, V12, P288; NR: 17; TC: 4; J9: J ORG CHEM; PG: 3; GA: HN858Source type: Electronic(1
Pinnularia molderii fm. spitsbergensis D. M. Williams, Bing Liu & Taxbock 2022, nom. nov.
No full textPinnularia molderii f. spitsbergensis D.M. Williams, Bing Liu & Taxböck nom. nov. ≡ Pinnularia hustedtii f. spitsbergensis Foged 1981: 148, pl. 42, fig. 8 (Foged 1964: 122, “ Pinnularia hustedtii forma”) Type:— Alaska, Spitzbergen, C 466/1963, holotype; ANSP GC 64404, isotype (Mahoney & Reimer 1997: 170).Published as part of Williams, David M., Liu, Bing & Taxböck, Lukas, 2022, Pinnularia hustedtii (Bacillariophyta): Notes on specimens from Wuling Mountains, China and from type material, pp. 294-300 in Phytotaxa 536 (3) on page 299, DOI: 10.11646/phytotaxa.536.3.10, http://zenodo.org/record/633185
Difference based Ridge and Liu type Estimators in Semiparametric Regression Models
No full textWe consider a difference based ridge regression estimator and a Liu type estimator of the regression parameters in the partial linear semiparametric regression model, y = Xβ + f + ε. Both estimators are analysed and compared in the sense of mean-squared error. We consider the case of independent errors with equal variance and give conditions under which the proposed estimators are superior to the unbiased difference based estimation technique. We extend the results to account for heteroscedasticity and autocovariance in the error terms. Finally, we illustrate the performance of these estimators with an application to the determinants of electricity consumption in Germany.Difference based estimator; Differencing estimator, Differencing matrix, Liu estimator, Liu type estimator, Multicollinearity, Ridge regression estimator, Semiparametric model
Neochauliodes punctatolosus Liu & Yang
No full textNeochauliodes punctatolosus Liu & Yang (Fig. 9) Neochauliodes punctatolosus Liu & Yang, 2006b: 193. Type locality: Yunnan (Jingdong). Diagnosis. Head and pronotum entirely orange. Forewing with dense small brownish spots, posterior two RP branches distinctly curved posteriad. Male fused gonocoxites 10 slender suboblong with distal portion broadened in lateral view. See an additional description and other information in Liu & Yang (2006b) and Liu et al. (2010a). Materials examined. 2♀, MYANMAR: N, 21 km E Putao, 550 m, Nan Sa Bon vill., 1-5.V.1998, S. Murzin & V. Siniaev (LDPC); 1♀, MYANMAR: N, 25 km E Putao, 800 m, env. Nan Sa Bon vill., 6-9.V.1998, S. Murzin & V. Siniaev (LDPC). Distribution. China (Yunnan); Laos (Houaphan, Louang Namtha, Vientiane, Xieng Khoang); Myanmar (Kachin); Thailand (Chanthaburi, Chiang Mai); Vietnam (Son La).Published as part of Liu, Xingyue & Dvorak, Libor, 2017, New species and records of Corydalidae (Insecta: Megaloptera) from Myanmar, pp. 428-436 in Zootaxa 4306 (3) on page 433, DOI: 10.11646/zootaxa.4306.3.9, http://zenodo.org/record/84454
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