Ritsumeikan University

Geant4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Its functionality and modeling capabilities continue to be extended, while its performance is enhanced. An overview of recent developments in diverse areas of the toolkit is presented. These include performance optimization for complex setups; improvements for the propagation in fields; new options for event biasing; and additions and improvements in geometry, physics processes and interactive capabilities

AUTORES: Daniel J Klionsky1745,1749*, Kotb Abdelmohsen840, Akihisa Abe1237, Md Joynal Abedin1762, Hagai Abeliovich425,
\nAbraham Acevedo Arozena789, Hiroaki Adachi1800, Christopher M Adams1669, Peter D Adams57, Khosrow Adeli1981,
\nPeter J Adhihetty1625, Sharon G Adler700, Galila Agam67, Rajesh Agarwal1587, Manish K Aghi1537, Maria Agnello1826,
\nPatrizia Agostinis664, Patricia V Aguilar1960, Julio Aguirre-Ghiso784,786, Edoardo M Airoldi89,422, Slimane Ait-Si-Ali1376,
\nTakahiko Akematsu2010, Emmanuel T Akporiaye1097, Mohamed Al-Rubeai1394, Guillermo M Albaiceta1294,
\nChris Albanese363, Diego Albani561, Matthew L Albert517, Jesus Aldudo128, Hana Alg€ul1164, Mehrdad Alirezaei1198,
\nIraide Alloza642,888, Alexandru Almasan206, Maylin Almonte-Beceril524, Emad S Alnemri1212, Covadonga Alonso544,
\nNihal Altan-Bonnet848, Dario C Altieri1205, Silvia Alvarez1497, Lydia Alvarez-Erviti1395, Sandro Alves107,
\nGiuseppina Amadoro860, Atsuo Amano930, Consuelo Amantini1554, Santiago Ambrosio1458, Ivano Amelio756,
\nAmal O Amer918, Mohamed Amessou2089, Angelika Amon726, Zhenyi An1538, Frank A Anania291, Stig U Andersen6,
\nUsha P Andley2079, Catherine K Andreadi1690, Nathalie Andrieu-Abadie502, Alberto Anel2027, David K Ann58,
\nShailendra Anoopkumar-Dukie388, Manuela Antonioli832,858, Hiroshi Aoki1791, Nadezda Apostolova2007,
\nSaveria Aquila1500, Katia Aquilano1876, Koichi Araki292, Eli Arama2098, Agustin Aranda456, Jun Araya591,
\nAlexandre Arcaro1472, Esperanza Arias26, Hirokazu Arimoto1225, Aileen R Ariosa1749, Jane L Armstrong1930,
\nThierry Arnould1773, Ivica Arsov2120, Katsuhiko Asanuma675, Valerie Askanas1924, Eric Asselin1867, Ryuichiro Atarashi794,
\nSally S Atherton369, Julie D Atkin713, Laura D Attardi1131, Patrick Auberger1787, Georg Auburger379, Laure Aurelian1727,
\nRiccardo Autelli1992, Laura Avagliano1029,1755, Maria Laura Avantaggiati364, Limor Avrahami1166, Suresh Awale1986,
\nNeelam Azad404, Tiziana Bachetti568, Jonathan M Backer28, Dong-Hun Bae1933, Jae-sung Bae677, Ok-Nam Bae409,
\nSoo Han Bae2117, Eric H Baehrecke1729, Seung-Hoon Baek17, Stephen Baghdiguian1368,
\nAgnieszka Bagniewska-Zadworna2, Hua Bai90, Jie Bai667, Xue-Yuan Bai1133, Yannick Bailly884,
\nKithiganahalli Narayanaswamy Balaji473, Walter Balduini2002, Andrea Ballabio316, Rena Balzan1711, Rajkumar Banerjee239,
\nG abor B anhegyi1052, Haijun Bao2109, Benoit Barbeau1363, Maria D Barrachina2007, Esther Barreiro467, Bonnie Bartel997,
\nAlberto Bartolom e222, Diane C Bassham550, Maria Teresa Bassi1046, Robert C Bast Jr1273, Alakananda Basu1798,
\nMaria Teresa Batista1578, Henri Batoko1336, Maurizio Battino970, Kyle Bauckman2085, Bradley L Baumgarner1909,
\nK Ulrich Bayer1594, Rupert Beale1553, Jean-Fran¸cois Beaulieu1360, George R. Beck Jr48,294, Christoph Becker336,
\nJ David Beckham1595, Pierre-Andr e B edard749, Patrick J Bednarski301, Thomas J Begley1135, Christian Behl1419,
\nChristian Behrends757, Georg MN Behrens406, Kevin E Behrns1627, Eloy Bejarano26, Amine Belaid490,
\nFrancesca Belleudi1041, Giovanni B enard497, Guy Berchem706, Daniele Bergamaschi983, Matteo Bergami1401,
\nBen Berkhout1441, Laura Berliocchi714, Am elie Bernard1749, Monique Bernard1354, Francesca Bernassola1880,
\nAnne Bertolotti791, Amanda S Bess272, S ebastien Besteiro1351, Saverio Bettuzzi1828, Savita Bhalla913,
\nShalmoli Bhattacharyya973, Sujit K Bhutia838, Caroline Biagosch1159, Michele Wolfe Bianchi520,1378,1381,
\nMartine Biard-Piechaczyk210, Viktor Billes298, Claudia Bincoletto1314, Baris Bingol350, Sara W Bird1128, Marc Bitoun1112,
\nIvana Bjedov1258, Craig Blackstone843, Lionel Blanc1183, Guillermo A Blanco1496, Heidi Kiil Blomhoff1812,
\nEmilio Boada-Romero1297, Stefan B€ockler1464, Marianne Boes1423, Kathleen Boesze-Battaglia1835, Lawrence H Boise286,287,
\nAlessandra Bolino2063, Andrea Boman693, Paolo Bonaldo1823, Matteo Bordi897, J€urgen Bosch608, Luis M Botana1308,
\nJoelle Botti1375, German Bou1405, Marina Bouch e1038, Marion Bouchecareilh1331, Marie-Jos ee Boucher1901,
\nMichael E Boulton481, Sebastien G Bouret1926, Patricia Boya133, Micha€el Boyer-Guittaut1345, Peter V Bozhkov1141,
\nNathan Brady374, Vania MM Braga469, Claudio Brancolini1997, Gerhard H Braus353, Jos e M Bravo-San Pedro299,393,508,1374,
\nLisa A Brennan322, Emery H Bresnick2022, Patrick Brest490, Dave Bridges1939, Marie-Agn es Bringer124, Marisa Brini1822,
\nGlauber C Brito1311, Bertha Brodin631, Paul S Brookes1872, Eric J Brown352, Karen Brown1690, Hal E Broxmeyer480,
\nAlain Bruhat486,1339, Patricia Chakur Brum1893, John H Brumell446, Nicola Brunetti-Pierri315,1171,
\nRobert J Bryson-Richardson781, Shilpa Buch1777, Alastair M Buchan1819, Hikmet Budak1022, Dmitry V Bulavin118,505,1789,
\nScott J Bultman1792, Geert Bultynck665, Vladimir Bumbasirevic1470, Yan Burelle1356, Robert E Burke216,217,
\nMargit Burmeister1750, Peter B€utikofer1473, Laura Caberlotto1987, Ken Cadwell896, Monika Cahova112, Dongsheng Cai24,
\nJingjing Cai2099, Qian Cai1018, Sara Calatayud2007, Nadine Camougrand1343, Michelangelo Campanella1700,
\nGrant R Campbell1525, Matthew Campbell1249, Silvia Campello556,1876, Robin Candau1769, Isabella Caniggia1983,
\nLavinia Cantoni560, Lizhi Cao116, Allan B Caplan1656, Michele Caraglia1051, Claudio Cardinali1043, Sandra Morais Cardoso1579, Jennifer S Carew208, Laura A Carleton874, Cathleen R Carlin101, Silvia Carloni2002,
\nSven R Carlsson1267, Didac Carmona-Gutierrez1643, Leticia AM Carneiro312, Oliana Carnevali971, Serena Carra1318,
\nAlice Carrier120, Bernadette Carroll900, Caty Casas1324, Josefina Casas1116, Giuliana Cassinelli324, Perrine Castets1462,
\nSusana Castro-Obregon214, Gabriella Cavallini1841, Isabella Ceccherini568, Francesco Cecconi253,555,1884,
\nArthur I Cederbaum459, Valent ın Ce~na199,1281, Simone Cenci1323,2064, Claudia Cerella444, Davide Cervia1996,
\nSilvia Cetrullo1478, Hassan Chaachouay2028, Han-Jung Chae187, Andrei S Chagin634, Chee-Yin Chai626,628,
\nGopal Chakrabarti1502, Georgios Chamilos1601, Edmond YW Chan1142, Matthew TV Chan181, Dhyan Chandra1003,
\nPallavi Chandra548, Chih-Peng Chang818, Raymond Chuen-Chung Chang1653, Ta Yuan Chang345, John C Chatham1434,
\nSaurabh Chatterjee1910, Santosh Chauhan527, Yongsheng Che62, Michael E Cheetham1263, Rajkumar Cheluvappa1783,
\nChun-Jung Chen1153, Gang Chen598,1676, Guang-Chao Chen9, Guoqiang Chen1078, Hongzhuan Chen1077, Jeff W Chen1514,
\nJian-Kang Chen370,371, Min Chen249, Mingzhou Chen2104, Peiwen Chen1823, Qi Chen1674, Quan Chen172,
\nShang-Der Chen138, Si Chen325, Steve S-L Chen10, Wei Chen2125, Wei-Jung Chen829, Wen Qiang Chen979, Wenli Chen1113,
\nXiangmei Chen1133, Yau-Hung Chen1157, Ye-Guang Chen1250, Yin Chen1447, Yingyu Chen953,955, Yongshun Chen2135,
\nYu-Jen Chen712, Yue-Qin Chen1145, Yujie Chen1208, Zhen Chen339, Zhong Chen2123, Alan Cheng1702,
\nChristopher HK Cheng184, Hua Cheng1728, Heesun Cheong814, Sara Cherry1836, Jason Chesney1703,
\nChun Hei Antonio Cheung817, Eric Chevet1359, Hsiang Cheng Chi140, Sung-Gil Chi656, Fulvio Chiacchiera308,
\nHui-Ling Chiang958, Roberto Chiarelli1826, Mario Chiariello235,567,577, Marcello Chieppa835, Lih-Shen Chin290,
\nMario Chiong1285, Gigi NC Chiu878, Dong-Hyung Cho676, Ssang-Goo Cho650, William C Cho982, Yong-Yeon Cho105,
\nYoung-Seok Cho1064, Augustine MK Choi2095, Eui-Ju Choi656, Eun-Kyoung Choi387,400,685, Jayoung Choi1563,
\nMary E Choi2093, Seung-Il Choi2116, Tsui-Fen Chou412, Salem Chouaib395, Divaker Choubey1574, Vinay Choubey1936,
\nKuan-Chih Chow822, Kamal Chowdhury730, Charleen T Chu1856, Tsung-Hsien Chuang827, Taehoon Chun657,
\nHyewon Chung652, Taijoon Chung978, Yuen-Li Chung1194, Yong-Joon Chwae18, Valentina Cianfanelli254,
\nRoberto Ciarcia1775, Iwona A Ciechomska886, Maria Rosa Ciriolo1876, Mara Cirone1042, Sofie Claerhout1694,
\nMichael J Clague1698, Joan Cl aria1457, Peter GH Clarke1687, Robert Clarke361, Emilio Clementi1045,1398, C edric Cleyrat1781,
\nMiriam Cnop1366, Eliana M Coccia574, Tiziana Cocco1459, Patrice Codogno1375, J€orn Coers271, Ezra EW Cohen1533,
\nDavid Colecchia235,567,577, Luisa Coletto25, N uria S Coll123, Emma Colucci-Guyon516, Sergio Comincini1829,
\nMaria Condello578, Katherine L Cook2073, Graham H Coombs1929, Cynthia D Cooper2076, J Mark Cooper1395,
\nIsabelle Coppens601, Maria Tiziana Corasaniti1387, Marco Corazzari485,1884, Ramon Corbalan1566,
\nElisabeth Corcelle-Termeau251, Mario D Cordero1899, Cristina Corral-Ramos1289, Olga Corti507,1109, Andrea Cossarizza1767,
\nPaola Costelli1993, Safia Costes1518, Susan L Cotman721, Ana Coto-Montes946, Sandra Cottet566,1688, Eduardo Couve1301,
\nLori R Covey1015, L Ashley Cowart762, Jeffery S Cox1536, Fraser P Coxon1427, Carolyn B Coyne1846, Mark S Cragg1919,
\nRolf J Craven1679, Tiziana Crepaldi1995, Jose L Crespo1300, Alfredo Criollo1285, Valeria Crippa558, Maria Teresa Cruz1576,
\nAna Maria Cuervo26, Jose M Cuezva1277, Taixing Cui1907, Pedro R Cutillas987, Mark J Czaja27, Maria F Czyzyk-Krzeska1572,
\nRuben K Dagda2068, Uta Dahmen1404, Chunsun Dai800, Wenjie Dai1187, Yun Dai2059, Kevin N Dalby1940,
\nLuisa Dalla Valle1822, Guillaume Dalmasso1340, Marcello D’Amelio557, Markus Damme188, Arlette Darfeuille-Michaud1340,
\nCatherine Dargemont950, Victor M Darley-Usmar1433, Srinivasan Dasarathy205, Biplab Dasgupta202, Srikanta Dash1254,
\nCrispin R Dass242, Hazel Marie Davey8, Lester M Davids1560, David D avila227, Roger J Davis1731, Ted M Dawson604,
\nValina L Dawson606, Paula Daza1898, Jackie de Belleroche470, Paul de Figueiredo1180,1182,
\nRegina Celia Bressan Queiroz de Figueiredo135, Jos e de la Fuente1023, Luisa De Martino1775,
\nAntonella De Matteis1171, Guido RY De Meyer1443, Angelo De Milito631, Mauro De Santi2002,

Geant4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Over the past several years, major changes have been made to the toolkit in order to accommodate the needs of these user communities, and to efficiently exploit the growth of computing power made available by advances in technology. The adaptation of Geant4 to multithreading, advances in physics, detector modeling and visualization, extensions to the toolkit, including biasing and reverse Monte Carlo, and tools for physics and release validation are discussed here.

Recently, a growing interest has been seen in deep learning-based semantic segmentation. UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation. Combining multi-scale features is one of important factors for accurate segmentation. UNet++ was developed as a modified Unet by designing an architecture with nested and dense skip connections. However, it does not explore sufficient information from full scales and there is still a large room for improvement. In this paper, we propose a novel UNet 3+, which takes advantage of full-scale skip connections and deep supervisions. The full-scale skip connections incorporate low-level details with high-level semantics from feature maps in different scales; while the deep supervision learns hierarchical representations from the full-scale aggregated feature maps. The proposed method is especially benefiting for organs that appear at varying scales. In addition to accuracy improvements, the proposed UNet 3+ can reduce the network parameters to improve the computation efficiency. We further propose a hybrid loss function and devise a classification-guided module to enhance the organ boundary and reduce the over-segmentation in a non-organ image, yielding more accurate segmentation results. The effectiveness of the proposed method is demonstrated on two datasets. The code is available at: github.com/ZJUGiveLab/UNet-Version.

This article reports the development and validation of a 10-item international Positive and Negative Affect Schedule (PANAS) Short Form (I-PANAS-SF) in English. A qualitative study ( N = 18) and then an exploratory quantitative study ( N = 407), each using informants from a range of cultural backgrounds, were used to identify systematically which 10 of the original 20 PANAS items to retain or remove. A same-sample retest study ( N = 163) was used in an initial examination of the new 10-item international PANAS's psychometric properties and to assess its correlation with the full, 20-item, original PANAS. In a series of further validation studies ( N = 1,789), the cross-sample stability, internal reliability, temporal stability, cross-cultural factorial invariance, and convergent and criterion-related validities of the I-PANAS-SF were examined and found to be psychometrically acceptable.

One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to incorporate into existing implementations. We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.

Over the past few years, there has been an increased interest in automatic facial behavior analysis and understanding. We present OpenFace - an open source tool intended for computer vision and machine learning researchers, affective computing community and people interested in building interactive applications based on facial behavior analysis. OpenFace is the first open source tool capable of facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation. The computer vision algorithms which represent the core of OpenFace demonstrate state-of-the-art results in all of the above mentioned tasks. Furthermore, our tool is capable of real-time performance and is able to run from a simple webcam without any specialist hardware. Finally, OpenFace allows for easy integration with other applications and devices through a lightweight messaging system.

The optical properties of wurtzite-structured InN grown on sapphire substrates by molecular-beam epitaxy have been characterized by optical absorption, photoluminescence, and photomodulated reflectance techniques. These three characterization techniques show an energy gap for InN between 0.7 and 0.8 eV, much lower than the commonly accepted value of 1.9 eV. The photoluminescence peak energy is found to be sensitive to the free-electron concentration of the sample. The peak energy exhibits very weak hydrostatic pressure dependence, and a small, anomalous blueshift with increasing temperature.

Abstract In this first worldwide synthesis of in situ and satellite‐derived lake data, we find that lake summer surface water temperatures rose rapidly (global mean = 0.34°C decade −1 ) between 1985 and 2009. Our analyses show that surface water warming rates are dependent on combinations of climate and local characteristics, rather than just lake location, leading to the counterintuitive result that regional consistency in lake warming is the exception, rather than the rule. The most rapidly warming lakes are widely geographically distributed, and their warming is associated with interactions among different climatic factors—from seasonally ice‐covered lakes in areas where temperature and solar radiation are increasing while cloud cover is diminishing (0.72°C decade −1 ) to ice‐free lakes experiencing increases in air temperature and solar radiation (0.53°C decade −1 ). The pervasive and rapid warming observed here signals the urgent need to incorporate climate impacts into vulnerability assessments and adaptation efforts for lakes.

The singularity problem is an inherent problem in controlling robot manipulators with articulated configuration. In this paper, we propose to determine the joint motion for the requested motion of the endeffector by evaluating the feasibility of the joint motion. The determined joint motion is called an inverse kinematic solution with singularity robustness, because it denotes feasible solution even at or in the neighborhood of singular points. The singularity robust inverse (SR-inverse) is introduced as an alternative to the pseudoinverse of the Jacobian matrix. The SR-inverse of the Jacobian matrix provides us with an approximating motion close to the desired Cartesian trajectory of the endeffector, even when the inverse kinematic solution by the inverse or the pseudoinverse of the Jacobian matrix is not feasible at or in the neighborhood of singular points. The properties of the SR-inverse are clarified by comparing it with the inverse and the pseudoinverse. The computational complexity of the SR-inverse is considered to discuss its implementability. Several simulation results are also shown to illustrate the singularity problem and the effectiveness of the inverse kinematic solution with singularity robustness.

Heterostructured materials are an emerging class of materials with superior performances that are unattainable by their conventional homogeneous counterparts. They consist of heterogeneous zones with dramatic (>100%) variations in mechanical and/or physical properties. The interaction in these hetero-zones produces a synergistic effect where the integrated property exceeds the prediction by the rule-of-mixtures. The heterostructured materials field explores heterostructures to control defect distributions, long-range internal stresses, and nonlinear inter-zone interactions for unprecedented performances. This paper is aimed to provide perspectives on this novel field, describe the state-of-the-art of heterostructured materials, and identify and discuss key issues that deserve additional studies.

Perovskite quantum dots (QDs) as a new type of colloidal nanocrystals have gained significant attention for both fundamental research and commercial applications owing to their appealing optoelectronic properties and excellent chemical processability. For their wide range of potential applications, synthesizing colloidal QDs with high crystal quality is of crucial importance. However, like most common QD systems such as CdSe and PbS, those reported perovskite QDs still suffer from a certain density of trapping defects, giving rise to detrimental nonradiative recombination centers and thus quenching luminescence. In this paper, we show that a high room-temperature photoluminescence quantum yield of up to 100% can be obtained in CsPbI3 perovskite QDs, signifying the achievement of almost complete elimination of the trapping defects. This is realized with our improved synthetic protocol that involves introducing organolead compound trioctylphosphine–PbI2 (TOP–PbI2) as the reactive precursor, which also leads to a significantly improved stability for the resulting CsPbI3 QD solutions. Ultrafast kinetic analysis with time-resolved transient absorption spectroscopy evidence the negligible electron or hole-trapping pathways in our QDs, which explains such a high quantum efficiency. We expect the successful synthesis of the “ideal” perovskite QDs will exert profound influence on their applications to both QD-based light-harvesting and -emitting devices.

Abstract Simulation of large deformation and post‐failure of geomaterial in the framework of smoothed particle hydrodynamics (SPH) are presented in this study. The Drucker–Prager model with associated and non‐associated plastic flow rules is implemented into the SPH code to describe elastic–plastic soil behavior. In contrast to previous work on SPH for solids, where the hydrostatic pressure is often estimated from density by an equation of state , this study proposes to calculate the hydrostatic pressure of soil directly from constitutive models. Results obtained in this paper show that the original SPH method, which has been successfully applied to a vast range of problems, is unable to directly solve elastic–plastic flows of soil because of the so‐called SPH tensile instability. This numerical instability may result in unrealistic fracture and particles clustering in SPH simulation. For non‐cohesive soil, the instability is not serious and can be completely removed by using a tension cracking treatment from soil constitutive model and thereby give realistic soil behavior. However, the serious tensile instability that is found in SPH application for cohesive soil requires a special treatment to overcome this problem. In this paper, an artificial stress method is applied to remove the SPH numerical instability in cohesive soil. A number of numerical tests are carried out to check the capability of SPH in the current application. Numerical results are then compared with experimental and finite element method solutions. The good agreement obtained from these comparisons suggests that SPH can be extended to general geotechnical problems. Copyright © 2008 John Wiley & Sons, Ltd.

The study of school bullying has recently assumed an international dimension, but is faced with difficulties in finding terms in different languages to correspond to the English word bullying. To investigate the meanings given to various terms, a set of 25 stick-figure cartoons was devised, covering a range of social situations between peers. These cartoons were shown to samples of 8- and 14-year-old pupils (N = 1,245; n = 604 at 8 years, n = 641 at 14 years) in schools in 14 different countries, who judged whether various native terms cognate to bullying, applied to them. Terms from 10 Indo-European languages and three Asian languages were sampled. Multidimensional scaling showed that 8-year-olds primarily discriminated nonaggressive and aggressive cartoon situations; however, 14-year-olds discriminated fighting from physical bullying, and also discriminated verbal bullying and social exclusion. Gender differences were less appreciable than age differences. Based on the 14-year-old data, profiles of 67 words were then constructed across the five major cartoon clusters. The main types of terms used fell into six groups: bullying (of all kinds), verbal plus physical bullying, solely verbal bullying, social exclusion, solely physical aggression, and mainly physical aggression. The findings are discussed in relation to developmental trends in how children understand bullying, the inferences that can be made from cross-national studies, and the design of such studies.

By using the ATLAS detector, observations have been made of a centrality-dependent dijet asymmetry in the collisions of lead ions at the Large Hadron Collider. In a sample of lead-lead events with a per-nucleon center of mass energy of 2.76 TeV, selected with a minimum bias trigger, jets are reconstructed in fine-grained, longitudinally segmented electromagnetic and hadronic calorimeters. The transverse energies of dijets in opposite hemispheres are observed to become systematically more unbalanced with increasing event centrality leading to a large number of events which contain highly asymmetric dijets. This is the first observation of an enhancement of events with such large dijet asymmetries, not observed in proton-proton collisions, which may point to an interpretation in terms of strong jet energy loss in a hot, dense medium.

Abstract SLAM is an abbreviation for simultaneous localization and mapping, which is a technique for estimating sensor motion and reconstructing structure in an unknown environment. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM) because it is based on visual information only. vSLAM can be used as a fundamental technology for various types of applications and has been discussed in the field of computer vision, augmented reality, and robotics in the literature. This paper aims to categorize and summarize recent vSLAM algorithms proposed in different research communities from both technical and historical points of views. Especially, we focus on vSLAM algorithms proposed mainly from 2010 to 2016 because major advance occurred in that period. The technical categories are summarized as follows: feature-based, direct, and RGB-D camera-based approaches.

We study the intrinsic spin Hall conductivity (SHC) in various $5d$ transition metals (Ta, W, Re, Os, Ir, Pt, and Au) and $4d$ transition metals (Nb, Mo, Tc, Ru, Rh, Pd, and Ag) based on the Naval Research Laboratory tight-binding model, which enables us to perform quantitatively reliable analysis. In each metal, the obtained intrinsic SHC is independent of resistivity in the low resistive regime $(\ensuremath{\rho}<50\phantom{\rule{0.3em}{0ex}}\ensuremath{\mu}\ensuremath{\Omega}\phantom{\rule{0.2em}{0ex}}\mathrm{cm})$ whereas it decreases in proportion to ${\ensuremath{\rho}}^{\ensuremath{-}2}$ in the high resistive regime. In the low resistive regime, the SHC takes a large positive value in Pt and Pd, both of which have approximately nine $d$ electrons per ion $({n}_{d}=9)$. On the other hand, the SHC takes a large negative value in Ta, Nb, W, and Mo, where ${n}_{d}<5$. In transition metals, a conduction electron acquires the trajectory-dependent phase factor that originates from the atomic wave function. This phase factor, which is reminiscent of the Aharonov--Bohm phase, is the origin of the SHC in paramagnetic metals and that of the anomalous Hall conductivity in ferromagnetic metals. Furthermore, each transition metal shows huge and positive $d$-orbital Hall conductivity (OHC), independent of the strength of the spin-orbit interaction. Since the OHC is much larger than the SHC, it will be possible to realize an orbitronics device made of transition metals.

Abstract Financial inclusion is a key element of social inclusion, particularly useful in combating poverty and income inequality by opening blocked advancement opportunities for disadvantaged segments of the population. This study intends to investigate the impact of financial inclusion on reducing poverty and income inequality, and the determinants and conditional effects thereof in 116 developing countries. The analysis is carried out using an unbalanced annual panel data for the period of 2004–2016. For this purpose, we construct a novel index of financial inclusion using a broad set of financial sector outreach indicators, finding that per capita income, ratio of internet users, age dependency ratio, inflation, and income inequality significantly influence the level of financial inclusion in developing countries. Furthermore, the results provide robust evidence that financial inclusion significantly reduces poverty rates and income inequality in developing countries. The findings are in favor of further promoting access to and usage of formal financial services by marginalized segments of the population in order to maximize society’s overall welfare.

Traditional classification algorithms usually provide poor accuracy on the prediction of the minority class of imbalanced data sets. This paper proposes a new method for dealing with imbalanced data sets by over-sampling the borderline minority class instances. A Support Vector Machine (SVM) classifier is then trained to predict future instances. Compared with other over-sampling methods, the proposed method focuses only on the minority class instances residing along the decision boundary, due to the fact that this region is the most crucial for establishing the decision boundary. Furthermore, the artificial minority instances are generated in such a way that the regions of the minority class with fewer majority class instances would be expanded by extrapolation, otherwise the current boundary of the minority class would be consolidated by interpolation. Experimental results show that the proposed method achieves a better performance than other over-sampling methods.

AIM: To examine the relationship between calf circumference and muscle mass, and to evaluate the suitability of calf circumference as a surrogate marker of muscle mass for the diagnosis of sarcopenia among middle-aged and older Japanese men and women. METHODS: A total of 526 adults aged 40-89 years participated in the present cross-sectional study. The maximum calf circumference was measured in a standing position. Appendicular skeletal muscle mass was measured using dual-energy X-ray absorptiometry, and the skeletal muscle index was calculated as appendicular skeletal muscle mass divided by the square of the height (kg/m(2)). The cut-off values for sarcopenia were defined as a skeletal muscle index of less than -2 standard deviations of the mean value for Japanese young adults, as defined previously. RESULTS: Calf circumference was positively correlated with appendicular skeletal muscle (r = 0.81 in men, r = 0.73 in women) and skeletal muscle index (r = 0.80 in men, r = 0.69 in women). In receiver operating characteristic analysis, the optimal calf circumference cut-off values for predicting sarcopenia were 34 cm (sensitivity 88%, specificity 91%) in men and 33 cm (sensitivity 76%, specificity 73%) in women. CONCLUSIONS: Calf circumference was positively correlated with appendicular skeletal muscle mass and skeletal muscle index, and could be used as a surrogate marker of muscle mass for diagnosing sarcopenia. The suggested cut-off values of calf circumference for predicting low muscle mass are <34 cm in men and <33 cm in women.