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# zbMATH — the first resource for mathematics

## IEEE Transactions on Signal Processing

 Short Title: IEEE Trans. Signal Process. Publisher: Institute of Electrical and Electronics Engineers (IEEE), New York, NY ISSN: 1053-587X Online: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78 Predecessor: IEEE Transactions on Acoustics, Speech, and Signal Processing
 Documents Indexed: 8,905 Publications (since 1991)
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#### Authors

 102 Giannakis, Georgios B. 83 Eldar, Yonina Chana 81 Poor, Harold Vincent 68 Lau, Vincent K. N. 66 Palomar, Daniel Pérez 64 Nehorai, Arye 63 Vaidyanathan, Palghat P. 62 Sayed, Ali H. 62 Sidiropoulos, Nicholas D. 62 Varshney, Pramod K. 58 Stoica, Petre Gheorghe 47 Ottersten, Björn E. 46 Willett, Peter K. 45 Moura, José M. F. 45 So, Hing-Cheung 43 Leus, Geert 43 Wang, Xiaodong 42 De Maio, Antonio 42 Gershman, Alex B. 42 Hero, Alfred O. III 42 Ribeiro, Alejandro R. 41 Rao, Bhaskar D. 40 Moonen, Marc S. 39 Zoubir, Abdelhak M. 38 Boche, Holger 38 Heath, Robert W. jun. 38 Li, Jian 38 Luo, Zhi-Quan 36 Krishnamurthy, Vikram 36 Li, Hongbin 36 Pei, Soochang 36 Unser, Michael A. 36 Vo, Ba-Ngu 35 Ma, Wing-Kin 34 Tong, Lang 34 Vorobyov, Sergiy A. 33 Besson, Olivier 33 Swindlehurst, Arnold Lee 32 Liu, An 31 Scharf, Louis L. 31 Shahbazpanahi, Shahram 31 Xie, Lihua 29 Petropulu, Athina P. 29 Schniter, Philip 29 Vandendorpe, Luc 29 Xia, Xianggen 28 Koivunen, Visa 28 Tourneret, Jean-Yves 28 Utschick, Wolfgang 27 Amin, Moeness G. 27 Blum, Rick S. 27 Ricci, Giuseppe 27 Vetterli, Martin 26 Orlando, Danilo 26 van der Schaar, Mihaela 26 Weiss, Anthony J. 26 Wu, Yik-Chung 25 Ding, Zhi 25 Hassibi, Babak 25 Hua, Yingbo 25 Vo, Ba-Tuong 24 Davidson, Timothy N. 24 Jorswieck, Eduard Axel 24 Liu, K. J. Ray 24 Marano, Stefano 24 Selesnick, Ivan W. 24 Walden, Andrew T. 23 Abramovich, Yuri I. 23 Adali, Tülay 23 Blu, Thierry 23 Chang, Tsung-Hui 23 Djurić, Petar M. 23 Friedlander, Benjamin 23 Matta, Vincenzo 23 Tugnait, Jitendra K. 23 Wiesel, Ami 22 Babu, Prabhu 22 Bershad, Neil J. 22 Chi, Chong-Yung 22 Dougherty, Edward R. 22 Haardt, Martin 22 Hlawatsch, Franz 22 Mandic, Danilo P. 21 Larzabal, Pascal 21 Nguyen, Truong Quang 21 Swami, Ananthram 21 Teo, Kok Lay 21 Tuan, Hoang Duong 21 Yamada, Isao 20 Barbarossa, Sergio 20 Bermudez, José Carlos Moreira 20 De Lathauwer, Lieven 20 Farina, Alfonso 20 Gao, Feifei 20 Huang, Lei 20 Huang, Yongwei 20 Kozat, Suleyman Serdar 20 Mitra, Urbashi 20 Murthy, Chandra Ramabhadra 20 Singer, Andrew C. ...and 9,905 more Authors
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#### Fields

 7,828 Information and communication theory, circuits (94-XX) 1,301 Statistics (62-XX) 823 Systems theory; control (93-XX) 584 Operations research, mathematical programming (90-XX) 505 Numerical analysis (65-XX) 336 Computer science (68-XX) 233 Probability theory and stochastic processes (60-XX) 231 Harmonic analysis on Euclidean spaces (42-XX) 129 Linear and multilinear algebra; matrix theory (15-XX) 89 Biology and other natural sciences (92-XX) 85 Optics, electromagnetic theory (78-XX) 82 Game theory, economics, finance, and other social and behavioral sciences (91-XX) 71 Combinatorics (05-XX) 46 Approximations and expansions (41-XX) 29 Integral transforms, operational calculus (44-XX) 24 Special functions (33-XX) 20 Quantum theory (81-XX) 18 Number theory (11-XX) 14 Dynamical systems and ergodic theory (37-XX) 13 Calculus of variations and optimal control; optimization (49-XX) 8 Real functions (26-XX) 8 Functions of a complex variable (30-XX) 8 Operator theory (47-XX) 8 Convex and discrete geometry (52-XX) 8 Fluid mechanics (76-XX) 7 Ordinary differential equations (34-XX) 7 Functional analysis (46-XX) 7 Astronomy and astrophysics (85-XX) 6 Field theory and polynomials (12-XX) 6 Partial differential equations (35-XX) 6 Difference and functional equations (39-XX) 5 Group theory and generalizations (20-XX) 5 Measure and integration (28-XX) 5 Integral equations (45-XX) 5 Geometry (51-XX) 5 Geophysics (86-XX) 4 Abstract harmonic analysis (43-XX) 4 Statistical mechanics, structure of matter (82-XX) 2 Commutative algebra (13-XX) 2 Topological groups, Lie groups (22-XX) 2 Potential theory (31-XX) 2 Differential geometry (53-XX) 1 General and overarching topics; collections (00-XX) 1 Algebraic geometry (14-XX) 1 Several complex variables and analytic spaces (32-XX) 1 Sequences, series, summability (40-XX)

#### Citations contained in zbMATH Open

4,242 Publications have been cited 19,051 times in 10,734 Documents Cited by Year
Matching pursuits with time-frequency dictionaries. Zbl 0842.94004
Mallat, Stéphane G.; Zhang, Zhifeng
1993
Sparse reconstruction by separable approximation. Zbl 1391.94442
Wright, Stephen J.; Nowak, Robert D.; Figueiredo, Mário A. T.
2009
K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. Zbl 1375.94040
Aharon, M.; Elad, M.; Bruckstein, A.
2006
Robust $$H_{\infty}$$ filtering for stochastic time-delay systems with missing measurements. Zbl 1373.94729
Wang, Zidong; Yang, Fuwen; Ho, D. W. C.; Liu, Xiaohui
2006
A delay-dependent approach to robust $$H_{\infty}$$ filtering for uncertain discrete-time state-delayed systems. Zbl 1369.93175
Gao, Huijun; Wang, Changhong
2004
Robust $$\mathcal H_{\infty}$$ filtering for Markovian jump systems with randomly occurring nonlinearities and sensor saturation: the finite-horizon case. Zbl 1391.93234
Dong, Hongli; Wang, Zidong; Ho, Daniel W. C.; Gao, Huijun
2011
Embedded image coding using zerotrees of wavelet coefficients. Zbl 0841.94020
Shapiro, Jerome M.
1993
Robust $$H_{\infty}$$ filtering for a class of nonlinear networked systems with multiple stochastic communication delays and packet dropouts. Zbl 1392.94183
Dong, Hongli; Wang, Zidong; Gao, Huijun
2010
New approach to mixed $$\mathcal H_2/\mathcal H_\infty$$ filtering for polytopic discrete-time systems. Zbl 1370.93274
Gao, Huijun; Lam, James; Xie, Lihua; Wang, Changhong
2005
Robust $$H_{\infty}$$ filtering for nonlinear stochastic systems. Zbl 1370.93293
Zhang, Weihai; Chen, Bor-Sen; Tseng, Chung-Shi
2005
Sampling signals with finite rate of innovation. Zbl 1369.94309
Vetterli, Martin; Marziliano, Pina; Blu, Thierry
2002
Distributed filtering for a class of time-varying systems over sensor networks with quantization errors and successive packet dropouts. Zbl 1391.93232
Dong, Hongli; Wang, Zidong; Gao, Huijun
2012
Robust $$H_{\infty}$$ filter design of uncertain descriptor systems with discrete and distributed delays. Zbl 1370.93111
Yue, Dong; Han, Qing-Long
2004
A survey of convergence results on particle filtering methods for practitioners. Zbl 1369.60015
Crisan, Dan; Doucet, Arnaud
2002
Optimal linear filtering under parameter uncertainty. Zbl 0988.93082
Geromel, José C.
1999
Correntropy: properties and applications in non-Gaussian signal processing. Zbl 1390.94277
Liu, Weifeng; Pokharel, Puskal P.; Principe, Jose C.
2007
Nonfragile $$H_{\infty}$$ filtering of continuous-time fuzzy systems. Zbl 1391.93134
Chang, Xiao-Heng; Yang, Guang-Hong
2011
Distributed consensus algorithms in sensor networks with imperfect communication: link failures and channel noise. Zbl 1391.94263
Kar, Soummya; Moura, José M. F.
2009
Wavelets and filter banks: Theory and design. Zbl 0825.94059
Vetterli, Martin; Herley, Cormac
1992
Robust $$\mathcal H_{\infty}$$ filter design for uncertain linear systems with multiple time-varying state delays. Zbl 1369.93667
de Souza, Carlos E.; Palhares, Reinaldo Martinez; Peres, Pedro Luis Dias
2001
A block-iterative surrogate constraint splitting method for quadratic signal recovery. Zbl 1369.94121
Combettes, Patrick L.
2003
Recovering sparse signals with a certain family of nonconvex penalties and DC programming. Zbl 1391.90489
Gasso, Gilles; Rakotomamonjy, Alain; Canu, Stéphane
2009
A sparse signal reconstruction perspective for source localization with sensor arrays. Zbl 1370.94191
Malioutov, Dmitry; Çetin, Müjdat; Willsky, Alan S.
2005
Shifting inequality and recovery of sparse signals. Zbl 1392.94117
Cai, T. Tony; Wang, Lie; Xu, Guangwu
2010
High-resolution radar via compressed sensing. Zbl 1391.94236
Herman, Matthew A.; Strohmer, Thomas
2009
Variance-constrained $$\mathcal H_{\infty}$$ filtering for a class of nonlinear time-varying systems with multiple missing measurements: the finite-horizon case. Zbl 1391.93233
Dong, Hongli; Wang, Zidong; Ho, Daniel W. C.; Gao, Huijun
2010
Block-sparse signals: uncertainty relations and efficient recovery. Zbl 1392.94195
Eldar, Yonina C.; Kuppinger, Patrick; Bölcskei, Helmut
2010
Online learning with kernels. Zbl 1369.68281
Kivinen, Jyrki; Smola, Alexander J.; Williamson, Robert C.
2004
Sparse solutions to linear inverse problems with multiple measurement vectors. Zbl 1372.65123
2005
Gain-constrained recursive filtering with stochastic nonlinearities and probabilistic sensor delays. Zbl 1393.94261
Hu, Jun; Wang, Zidong; Shen, Bo; Gao, Huijun
2013
Robust $$\mathcal H_{\infty}$$ filtering for uncertain discrete-time state-delayed systems. Zbl 1369.93641
Martinez Palhares, Reinaldo; de Souza, Carlos E.; Peres, Pedro Luis Dias
2001
Reduced-order $$H_{\infty}$$ filtering for stochastic systems. Zbl 1369.94325
Xu, Shengyuan; Chen, Tongwen
2002
B-spline signal processing. I: Theory. Zbl 0825.94086
Unser, Michael; Aldroubi, Akram; Eden, Murray
1993
Sampling moments and reconstructing signals of finite rate of innovation: Shannon meets strang-fix. Zbl 1391.94598
Dragotti, Pier Luigi; Vetterli, Martin; Blu, Thierry
2007
Sensor selection via convex optimization. Zbl 1391.90679
Joshi, Siddharth; Boyd, Stephen
2009
Cardinal exponential splines. I: Theory and filtering algorithms. Zbl 1370.94262
Unser, Michael; Blu, Thierry
2005
The discrete wavelet transform: Wedding the à trous and Mallat algorithms. Zbl 0825.94053
Shensa, Mark J.
1992
Tensor decomposition for signal processing and machine learning. Zbl 1415.94232
Sidiropoulos, Nicholas D.; de Lathauwer, Lieven; Fu, Xiao; Huang, Kejun; Papalexakis, Evangelos E.; Faloutsos, Christos
2017
Nonuniform fast Fourier transforms using min-max interpolation. Zbl 1369.94048
Fessler, Jeffrey A.; Sutton, Bradley P.
2003
Fixed-order robust $$\_{\infty}$$ filter design for Markovian jump systems with uncertain switching probabilities. Zbl 1373.94736
Xiong, Junlin; Lam, J.
2006
Cubature Kalman filtering for continuous-discrete systems: theory and simulations. Zbl 1391.93223
Arasaratnam, Ienkaran; Haykin, Simon; Hurd, Thomas R.
2010
Fault detection filter design for Markovian jump singular systems with intermittent measurements. Zbl 1392.94544
Yao, Xiuming; Wu, Ligang; Zheng, Wei Xing
2011
Bayesian compressive sensing. Zbl 1390.94231
Ji, Shihao; Xue, Ya; Carin, Lawrence
2008
Efficient implementation of quaternion Fourier transform convolution, and correlation by 2-D complex FFT. Zbl 1369.65190
Pei, Soo-Chang; Ding, Jian-Jiun; Chang, Ja-Han
2001
A delay-dependent approach to robust $$H_{\infty}$$ filtering for uncertain distributed delay systems. Zbl 1370.93109
Xu, Shengyuan; Lam, James; Chen, Tongwen; Zou, Yun
2005
The Zak transform and sampling theorems for wavelet subspaces. Zbl 0841.94011
Janssen, Augustus J. E. M.
1993
Optimization algorithms exploiting unitary constraints. Zbl 1369.90169
Manton, Jonathan H.
2002
On the linear convergence of the ADMM in decentralized consensus optimization. Zbl 1394.94532
Shi, Wei; Ling, Qing; Yuan, Kun; Wu, Gang; Yin, Wotao
2014
Hypercomplex signals – a novel extension of the analytic signal to the multidimensional case. Zbl 1369.94099
Bülow, Thomas; Sommer, Gerald
2001
The monogenic signal. Zbl 1369.94139
Felsberg, Michael; Sommer, Gerald
2001
ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems. Zbl 1369.94238
Neelamani, Ramesh; Choi, Hyeokho; Baraniuk, Richard
2004
An improved delay-dependent $$H_{\infty}$$ filtering of linear neutral systems. Zbl 1369.93623
Fridman, Emilia; Shaked, Uri
2004
Performance analysis of estimation algorithms of nonstationary ARMA processes. Zbl 1373.94569
Ding, Feng; Shi, Yang; Chen, Tongwen
2006
Distributed consensus algorithms in sensor networks: quantized data and random link failures. Zbl 1392.94269
Kar, Soummya; Moura, José M. F.
2010
Consensus in ad hoc WSNs with noisy links. I: Distributed estimation of deterministic signals. Zbl 1390.94395
Schizas, Ioannis D.; Ribeiro, Alejandro; Giannakis, Georgios B.
2008
Distributing the Kalman filter for large-scale systems. Zbl 1390.94242
Khan, Usman A.; Moura, José M. F.
2008
Induced $$l_2$$ and generalized $$H_2$$ filtering for systems with repeated scalar nonlinearities. Zbl 1370.94125
Gao, Huijun; Lam, James; Wang, Changhong
2005
Using iterated function systems to model discrete sequences. Zbl 0753.58015
Mazel, David S.; Hayes, Monson H.
1992
Network-based robust $$H_{\infty}$$ filtering for uncertain linear systems. Zbl 1373.93111
Yue, D.; Han, Q.-l.
2006
Blind separation of speech mixtures via time-frequency masking. Zbl 1369.94383
Yılmaz, Özgür; Rickard, Scott
2004
Sensitivity to basis mismatch in compressed sensing. Zbl 1392.94144
Chi, Yuejie; Scharf, Louis L.; Pezeshki, Ali; Calderbank, A. Robert
2011
Phase retrieval using alternating minimization. Zbl 1394.94421
Netrapalli, Praneeth; Jain, Prateek; Sanghavi, Sujay
2015
Distributed average consensus with dithered quantization. Zbl 1390.94083
Aysal, Tuncer Can; Coates, Mark J.; Rabbat, Michael G.
2008
On the reconstruction of block-sparse signals with an optimal number of measurements. Zbl 1391.94402
Stojnic, Mihailo; Parvaresh, Farzad; Hassibi, Babak
2009
Geodesic entropic graphs for dimension and entropy estimation in manifold learning. Zbl 1369.68278
Costa, Jose A.; Hero, Alfred O.
2004
The discrete fractional Fourier transform. Zbl 1011.65102
Candan, Çãgaty; Kutay, M. Alper; Ozaktas, Haldun M.
2000
$$H_\infty$$ filtering of 2-D discrete systems. Zbl 0996.93089
Du, Chunling; Xie, Lihua; Soh, Yeng Chai
2000
The quaternion LMS algorithm for adaptive filtering of hypercomplex processes. Zbl 1391.93261
Took, Clive Cheong; Mandic, Danilo P.
2009
The Gaussian mixture probability hypothesis density filter. Zbl 1374.94614
Vo, B.-N.; Ma, W.-K.
2006
Adaptive Kalman filtering in networked systems with random sensor delays, multiple packet dropouts and missing measurements. Zbl 1391.93252
Moayedi, Maryam; Foo, Yung K.; Soh, Yeng C.
2010
Alternating projections and Douglas-Rachford for sparse affine feasibility. Zbl 1394.94227
Hesse, Robert; Luke, D. Russell; Neumann, Patrick
2014
A new $$H_{\infty}$$ filter design for linear time delay systems. Zbl 1369.93622
Fridman, E.; Shaked, Uri
2001
Gaussian particle filtering. Zbl 1369.94195
Kotecha, Jayesh H.; Djurić, Petar M.
2003
Sparse Bayesian learning for basis selection. Zbl 1369.94318
Wipf, David P.; Rao, Bhaskar D.
2004
An affine scaling methodology for best basis selection. Zbl 0984.94010
1999
Diffusion LMS strategies for distributed estimation. Zbl 1392.94123
Cattivelli, Federico S.; Sayed, Ali H.
2010
Diffusion least-mean squares over adaptive networks: formulation and performance analysis. Zbl 1390.94283
Lopes, Cassio G.; Sayed, Ali H.
2008
A new fault detection scheme for networked control systems subject to uncertain time-varying delay. Zbl 1390.94584
Wang, Yongqiang; Ding, Steven X.; Ye, Hao; Wang, Guizeng
2008
Hypercomplex correlation techniques for vector images. Zbl 1369.94032
Moxey, C. Eddie; Sangwine, Stephen J.; Ell, Todd A.
2003
Robust $$H_{\infty}$$ filtering for uncertain 2-D continuous systems. Zbl 1370.93292
Xu, Shengyuan; Lam, James; Zou, Yun; Lin, Zhiping; Paszke, Wojciech
2005
Theoretical results on sparse representations of multiple-measurement vectors. Zbl 1375.94051
Chen, Jie; Huo, Xiaoming
2006
Atomic norm denoising with applications to line spectral estimation. Zbl 1394.94079
2013
GESPAR: efficient phase retrieval of sparse signals. Zbl 1394.94522
Shechtman, Yoav; Beck, Amir; Eldar, Yonina C.
2014
Robust wideband beamforming by the hybrid steepest descent method. Zbl 1390.94416
2007
A fast approach for overcomplete sparse decomposition based on smoothed $$\ell^0$$ norm. Zbl 1391.94325
Mohimani, Hosein; Babaie-Zadeh, Massoud; Jutten, Christian
2009
Relations between fractional operations and time-frequency distributions, and their applications. Zbl 1369.94258
Pei, Soo-Chang; Ding, Jian-Jiun
2001
High-order balanced multiwavelets: theory, factorization, and design. Zbl 1369.42031
Lebrun, Jérôme; Vetterli, Martin
2001
Kruskal’s permutation lemma and the identification of CANDECOMP/PARAFAC and bilinear models with constant modulus constraints. Zbl 1369.94186
Jiang, Tao; Sidiropoulos, Nicholas D.
2004
Theory of regular $$M$$-band wavelet bases. Zbl 0841.94021
Steffen, Peter; Heller, Peter N.; Gopinath, Ramesh A.; Burrus, C. Sidney
1993
Multiresolution representations using the autocorrelation functions of compactly supported wavelets. Zbl 0841.94019
Saito, Naoki; Beylkin, Gregory
1993
Structured compressed sensing: from theory to applications. Zbl 1392.94188
Duarte, Marco F.; Eldar, Yonina C.
2011
One or two frequencies? The empirical mode decomposition answers. Zbl 1390.94382
Rilling, Gabriel; Flandrin, Patrick
2008
Blind separation of instantaneous mixtures of nonstationary sources. Zbl 1369.94262
Pham, Dinh-Tuan; Cardoso, Jean-François
2001
An uncertainty principle for real signals in the fractional Fourier transform domain. Zbl 1369.94287
2001
Nonlinear filtering for state delayed systems with Markovian switching. Zbl 1369.94314
Wang, Zidong; Lam, James; Liu, Xiaohui
2003
The kernel recursive least-squares algorithm. Zbl 1369.68280
Engel, Yaakov; Mannor, Shie; Meir, Ron
2004
Spatial diversity in radars-models and detection performance. Zbl 1373.94779
Fishler, E.; Haimovich, A.; Blum, R. S.; Cimini, L. J.; Chizhik, D.; Valenzuela, R. A.
2006
Quaternion-MUSIC for vector-sensor array processing. Zbl 1373.94667
Miron, S.; Le Bihan, N.; Mars, J. I.
2006
State/noise estimator for descriptor systems with application to sensor fault diagnosis. Zbl 1373.94594
Gao, Zhiwei; Ho, D. W. C.
2006
Optimal infinite-horizon control for probabilistic Boolean networks. Zbl 1374.94952
Pal, R.; Datta, A.; Dougherty, E. R.
2006
Nonconvex optimization meets low-rank matrix factorization: an overview. Zbl 07123429
Chi, Yuejie; Lu, Yue M.; Chen, Yuxin
2019
A framework of adaptive multiscale wavelet decomposition for signals on undirected graphs. Zbl 1458.94160
Zheng, Xianwei; Tang, Yuan Yan; Zhou, Jiantao
2019
Exact diffusion for distributed optimization and learning. I: Algorithm development. Zbl 1414.90277
Yuan, Kun; Ying, Bicheng; Zhao, Xiaochuan; Sayed, Ali H.
2019
Analysis of distributed ADMM algorithm for consensus optimization in presence of node error. Zbl 1458.90515
Majzoobi, Layla; Lahouti, Farshad; Shah-Mansouri, Vahid
2019
A novel robust Gaussian-Student’s $$t$$ mixture distribution based Kalman filter. Zbl 07123311
Huang, Yulong; Zhang, Yonggang; Zhao, Yuxin; Chambers, Jonathon A.
2019
A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates. Zbl 07123375
Li, Zhi; Shi, Wei; Yan, Ming
2019
Noise benefits in combined nonlinear Bayesian estimators. Zbl 07123384
Duan, Fabing; Pan, Yan; Chapeau-Blondeau, François; Abbott, Derek
2019
Hybrid-spline dictionaries for continuous-domain inverse problems. Zbl 07123517
Debarre, Thomas; Aziznejad, Shayan; Unser, Michael
2019
Exact diffusion for distributed optimization and learning. II: Convergence analysis. Zbl 1414.90278
Yuan, Kun; Ying, Bicheng; Zhao, Xiaochuan; Sayed, Ali H.
2019
Graph topology inference based on sparsifying transform learning. Zbl 1458.94333
Sardellitti, Stefania; Barbarossa, Sergio; Di Lorenzo, Paolo
2019
Parallel transport on the cone manifold of SPD matrices for domain adaptation. Zbl 1458.65051
Yair, Or; Ben-Chen, Mirela; Talmon, Ronen
2019
Low-rank data completion with very low sampling rate using Newton’s method. Zbl 1458.65049
Ashraphijuo, Morteza; Wang, Xiaodong; Zhang, Junwei
2019
Online primal-dual methods with measurement feedback for time-varying convex optimization. Zbl 1458.90508
Bernstein, Andrey; Dall’anese, Emiliano; Simonetto, Andrea
2019
Optimal spectral initialization for signal recovery with applications to phase retrieval. Zbl 1458.94111
Luo, Wangyu; Alghamdi, Wael; Lu, Yue M.
2019
Learning ReLU networks on linearly separable data: algorithm, optimality, and generalization. Zbl 1458.68185
Wang, Gang; Giannakis, Georgios B.; Chen, Jie
2019
Source resolvability of spatial-smoothing-based subspace methods: a Hadamard product perspective. Zbl 1458.94152
Yang, Zai; Stoica, Petre; Tang, Jinhui
2019
Eigendecomposition-free sampling set selection for graph signals. Zbl 1458.94123
Sakiyama, Akie; Tanaka, Yuichi; Tanaka, Toshihisa; Ortega, Antonio
2019
Hypercomplex widely linear estimation through the lens of underpinning geometry. Zbl 07123339
Nitta, Tohru; Kobayashi, Masaki; Mandic, Danilo P.
2019
Tests for the weights of the global minimum variance portfolio in a high-dimensional setting. Zbl 07123374
Bodnar, Taras; Dmytriv, Solomiia; Parolya, Nestor; Schmid, Wolfgang
2019
MMSE approximation for sparse coding algorithms using stochastic resonance. Zbl 07123383
Simon, Dror; Sulam, Jeremias; Romano, Yaniv; Lu, Yue M.; Elad, Michael
2019
Super resolution phase retrieval for sparse signals. Zbl 07123401
Baechler, Gilles; Kreković, Miranda; Ranieri, Juri; Chebira, Amina; Lu, Yue M.; Vetterli, Martin
2019
A novel Kullback-Leibler divergence minimization-based adaptive Student’s $$t$$-filter. Zbl 07123441
Huang, Yulong; Zhang, Yonggang; Chambers, Jonathon A.
2019
Secure state estimation against integrity attacks: a Gaussian mixture model approach. Zbl 1414.62349
Guo, Ziyang; Shi, Dawei; Quevedo, Daniel E.; Shi, Ling
2019
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text:
1.2k views

Which of the following assertions are CORRECT?

• P: Adding $7$ to each entry in a list adds $7$ to the mean of the list
• Q: Adding $7$ to each entry in a list adds $7$ to the standard deviation of the list
• R: Doubling each entry in a list doubles the mean of the list
• S: Doubling each entry in a list leaves the standard deviation of the list unchanged
1. $P$, $Q$
2. $Q$, $R$
3. $P$, $R$
4. $R$, $S$
edited | 1.2k views

Suppose we double each entry of a list

Initial Mean $(M_I)=\frac{ \sum_{i=1}^{n} x_i}{n}$

New Mean $(M_N)= \frac{\sum_{i=1}^{n}2\times x_i}{n}$

$\quad \quad \quad =\frac{2}{n} \sum_{i=1}^{n}x_i$

So, when each entry in the list is doubled, mean also gets doubled.

Standard Deviation $\sigma_I = \sqrt {\frac{1}{N}\sum_{i=1}^{n} \left(M_I - x_i\right)^2}$

New Standard Deviation $\sigma_N = \sqrt {\frac{1}{N}\sum_{i=1}^{n} \left(M_N - 2 \times x_i\right)^2}$

$\quad \quad \quad= \sqrt {\frac{1}{N}\sum_{i=1}^{n} \left(2 \times \left(M_I - x_i\right)\right)^2}$

$\quad \quad \quad= 2 \sigma_I$

So, when each entry is doubled, standard deviation also gets doubled.

When we add a constant to each element of the list, it gets added to the mean as well. This can be seen from the formula of mean.

When we add a constant to each element of the list, the standard deviation (or variance) remains unchanged. This is because, the mean also gets added by the same constant and hence the deviation from the mean remains the same for each element.

So, here P and R are correct.

Correct Answer: $C$

edited
+1
Mean = (x1+x2+x3+...+xn)/n
The answer has "the division by n" part missing.
0
Thanks :) Corrected now..
+1
Arjun sir you missed dividing by n in the SD formulae.

anyway, that doesn't affect the answer here.

But people my mistake the formulae.
0
Thanks. Corrected.
C. P,R are correct.

1

format: plain


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# Solving Linear Equation by Guessing

Algebra Level 2

Given that $$f(x) = mx+b$$ such that $$f(2)=0$$, $$f(x_1)=f_1$$, and $$f(x_2)=f_2$$. Find the value of $\frac{f_1x_2 – f_2x_1}{f_1-f_2}.$

×


text:
ATTENTION: We have to do a short maintenance with downtime on Wed 19 Oct 2022, 9:00 - 10:00 CEST. Please finish your work in time to prevent data loss.

EPS-HEP2021 conference

Jul 26 – 30, 2021
Zoom
Europe/Berlin timezone

Latest dark sector searches at the Belle Experiment

Jul 26, 2021, 5:30 PM
15m
Zoom

Zoom

Parallel session talk Flavour Physics and CP Violation

Speaker

Seokhee Park (BELLE (BELLE II Experiment))

Description

The Belle experiment has accumulated close to $1\,{\rm ab}^{-1}$ of data in electron-positron collisions at center-of-mass energies around various $\Upsilon(nS)$ resonances. These data can be used to perform a number of new physics searches in the context of dark sector with an unprecedented precision. We present for the first time the results of a search of the dark photon in $B$-meson decays, the search for dark matter in bottomonium decays, as well as the latest results in the search for dark forces, via direct production, or in the decay of mesons. These competitive results can be used to severely constrain new physics scenarios.

First author S. Nishida shohei.nishida@kek.jp Belle

Primary author

Shohei Nishida (BELLE (BELLE II Experiment))


text:
Is saying "the rate of change is five-thirds meters per second" equivalent to saying "the rate of change is five meters every three seconds"?

I am a teaching assistant for a winter school course in basic algebra and I was explaining the meaning of the slope of a line to a student today using the following example: $$y = \frac{5}{3}x$$ where $$y$$ is measured in units of meters and $$x$$ is measured in units of time. I told them that the slope of the line is the rate of change of $$y$$ with respect to $$x$$, however, I then followed up by saying for this line in particular the rate of change is "five-thirds meters per second". This got me thinking, is this the same thing as saying "the rate of change is five meters every three seconds"? I concluded that these two statements do in fact mean the same thing since $$\frac{5}{3} \cdot 3 = 5$$ which precisely what you should have if you think about "your position" after $$3$$ seconds has elapsed.

I understand this is a little informal, but the concept to new students is important to me. Any feedback is welcome.

• Yes, you can say that. Jan 13, 2021 at 22:51
• A derivative is an expression of rate of change at and instant. There is no interval of $3$ seconds or $1$ second of $\frac 12$ a second or $7$ seconds. In this case $1$ second is just a unit of time. If you went to another planet where all their clocks measured time in a unit called the "three second" and it was three times as long $\frac 5{3}$ per second would equal $5$ per three second. Likewise you could go to a strange country where they have something called "minutes" that are $20$ times slower than a "three-second" then it would be $100$ per minute.. .. wooo..... Jan 14, 2021 at 0:17
• Yes, you would say exactly that. Jan 14, 2021 at 0:41
• ... assuming the function is one whose input are second units of time and whose output are meter units of measure. Jan 14, 2021 at 0:43
• But "five meters per 3 seconds" is okay (but weird) . Also $\frac 53$ per second is okay, but $\frac 53$ every second is not correct. Jan 14, 2021 at 1:07

Is saying “the rate of change is five-thirds meters per second” equivalent to saying “the rate of change is five meters every three seconds”?

Yes. To make things even simpler, the following two notions are equivalent:

• five-thirds meters per second
• five meters every three seconds

In general, if $$p$$ and $$q$$ are two positive integers,

• $$p/q$$ meters per second
• $$p$$ meters every $$q$$ seconds

are equivalent. Essentailly, you are looking at the function $$y=\frac{p}{q}x$$ in two different way by using two different "time unit":

• $$\displaystyle y\ (\textrm{meters})=\frac{p}{q}\ (\textrm{meter}/\textrm{second})\cdot x(\textrm{seconds})$$
• $$\displaystyle y\ (\textrm{meters})=p\ (\textrm{meter}/\color{red}{q\ \textrm{seconds}})\cdot x(\textrm{seconds})$$

If you want to use the second interpretation, the "slope" needs to be rational. Imagine how you want to interpret $$y=\pi x$$.

• Thank you for your feedback. Quick follow up: I’ve noticed with the derivative of a curve at a point we simply say “$m/n$ meters per second” rather than “$m$ meters every $n$ seconds”; why is that? Jan 13, 2021 at 23:05
• @TaylorRendon: I edited my answer. Saying (a) “𝑚/𝑛 meters per second” or (b)“𝑚 meters every 𝑛 seconds” depends on what "time unit" one wishes to use: in (a) the time unit is "one second" while in (b) the time unit is "n seconds". In physics, they are simply two different ways to measure things while in mathematics, they are equivalent.
– user9464
Jan 13, 2021 at 23:10
• @TaylorRendon: well, they are equivalent as you observe. (b) works only when you have a rational number if you require the denominator to be an integer. (a) sticks to the original time unit, which is more desirable I suppose, since everybody can agree on one uniformly used. This is particularly useful when one adds two velocities.
– user9464
Jan 13, 2021 at 23:18
• @TaylorRendon: JimJim was probably talking about $y=\frac{5}{3}x^2$. But your function is linear.
– user9464
Jan 13, 2021 at 23:27
• @TaylorRendon: in the nonlinear case, you are then talking about "instant rate change" and the slope of the tangent line. The two are still equivalent.
– user9464
Jan 13, 2021 at 23:34

Yes, the two statements are equivalent. Since you can think of the unit for rate of change in this case as fraction with meters divided by seconds. So the measurement $$\frac{5}{3}$$ mps is the same as $$\frac{5 \text{meters}}{3 \text{seconds}}$$, or 5 meters per 3 seconds as you said.


text:
## ICSE Class 9 Maths Sample Question Paper 4 with Answers

Section – A
(Attempt all questions from this Section)

Question 1.
(a) Expand : $$\left(\frac{2}{3} x-\frac{3}{2 x}-1\right)^{2}$$

(b) A person invests ₹ 10,000 for two years at a certain rate of interest, compounded annually. At the end of one year, this sum amounts to ₹ 11,200. Calculate :
(i) The rate of interest p. a.
(ii) The amount at the end of second year.

(c) Factorize : 64x6 – 729y

Question 2.
(a) If a2 + b2 = 7ab, prove that 2 log (a + b) = log 9 + log a + log b.
Given : a2 + b2 .= 7ab
⇒ a2 + b2 – 9ab – 2ab
⇒ a2 + b2 + 2ab = 9 ab
⇒ (a + b)2 = 9 ab
Taking log of both sides, we get
log (a + b)2 = log (9ab)
⇒ 2 log (a + b) = log 9 + log a + log b.

(b) Prove that: $$\tan ^{2} \theta-\frac{1}{\cos ^{2} \theta}+1=0$$
To prove:
$$\tan ^{2} \theta-\frac{1}{\cos ^{2} \theta}+1=0$$
Consider L.H.S. = $$\tan ^{2} \theta-\frac{1}{\cos ^{2} \theta}+1$$
= tan2 θ- sec2 θ +1
= (1 + tan2 θ) – sec2 θ (∵ 1 + tan2 0 = sec2 0)
= sec2 θ – sec2 θ = θ= R.H.S.
Hence Proved

(c) In the given figure, two chords AB and CD of a circle intersect at P. If AB = CD, prove that arc AD = arc CB.
AB = CD
Minor arc AB = Minor arc CD
Minor arc AB – minor arc BD = Minor arc CD – Minor arc BD
⇒ arc AD = arc CB.
Hence Proved.

Question 3.
(a) In the given figure, ∠ BCD = ∠ADC and ∠BCA = ∠ADB.
Show that: (i)ΔACD ≅ ΔBDC (ii) BC = AD (iii) ∠A = ∠B.

=> ∠BCA + ∠BCD = ∠ADB + ∠BCD
=> ∠ACD = ∠BDC
In ΔACD and ΔBDC
CD = CD (Common side)
⇒ ∠ACD = ∠BDC (Proved above)
∴ ΔACD ≅ ΔBDC (ASA axiom)
(iii) ∴ ∠A = ∠B. (c.p.c.t.)
Hence Proved.

(b) $$a=\frac{2-\sqrt{5}}{2+\sqrt{5}} \text { and } b=\frac{2+\sqrt{5}}{2-\sqrt{5}}, \text { find } a^{2}-b^{2}$$

(c) Prove that the points A (2, 3), B (-2, 2), C (-1, -2) and D (3, – 1) are the vertices of a square ABCD.

Question 4.
(a) The mean height of 10 girls in a class is 1.38 m and the mean height of 40 boys is 1.44 m. Find the mean height of 50 students of the class.
Given : Mean height of 10 girls = 1.38 m
∴ Sum of heights of 10 girls = 1.38 x 10 = 13.8 m
and Mean height of 40 boys = 1.44 m
∴ Sum of heights of 40 boys = 1.44 x 40 = 57.6 m.
∴ Sum of heights of 50 students = 13.8 +57.6 = 71.4 m.
∴ Mean heights of 50 students $$\frac{71.4}{50}$$
=1.428m

(b) In the given figure, AABC is a right triangle with ∠C = 90° and D is mid-point of side BC. Prove that AB2 = 4AD2 – 3AC2.

Given ∠C = 90°, D is the mid-point of BC.
∴ CD = BD
∴  In ΔABC,
AB2 = AC2 + BC(Pythagoras theorem)
= AC2 + (2CD)2 (∵ CD = BD = $$\frac{1}{2}$$ BC)
AB2 = AC2 + 4 CD2  …(i)
In ΔACD,
AD2 = AC2 + CD2  (Pythagoras theorem)
⇒ CD2 = AD2 – AC2 …(ii)
From equations (i) and (ii), we get
AB2 = AC2 + 4 (AD2 – AC2)
= AC2 + 4AD2 – 4AC2
Hence Proved.

(c) The following observation have been arranged in ascending order.
3, 6, 7, 10, x, x + 4, 19, 20, 25, 28 . If the median of the data is 13, find the value of x.
Given : Numbers in ascending order : 3, 6, 7, 10, x, x + 4, 19, 20, 25, 28.
Median = 13
Here, n = 20.

Section – B
(Attempt any four questions from this Section)

Question 5.
(a) Factorize : 5x2 + 17xy – 12y2.
5x2 + 17xy – 12y2 = 5x2 + (20 – 3) xy – 12y2
= 5x2 + 20xy – 3xy – 12y2
= 5x(x + 4y) – 3y (x + 4y)
= (x + 4y) (5x – 3y).

(b) If twice the son’s age in years is added to the father’s age, the sum is 70. But if twice the  father’s age is added to the son’s age, the sum is 95. Find the ages of father and son.
Let father’s age be x years and that of son’s be y years.
By 1st condition, x + 2y = 70 …(i)
By 2nd condition, 2x + y = 95 …(ii)
Multiplying equation (ii) by 2, we get
4x + 2y = 190
Subtracting equation (iii) from equation (i), we get

⇒ 40 +2y = 70
⇒ 2y = 70 – 40
⇒ $$y=\frac{30}{2}=15$$
∴ Father’s age is 40 years and son’s age is 15 years.

(c) In a ΔABC, E and F are the mid-points of AC and AB respectively. The altitude AP to BC intersects FE at Q. Prove that AQ = QP.
Given: E, F are mid-points of sides AC and AB, respectively.

We know, the line joining the mid-points of any two sides of a triangle is parallel to the third side
FE || BC
⇒ FQ || BP
Now, since F is mid-point of AB and FQ || BP
∴ By converse of mid-point theorem,
⇒ Q is the mid-point of AP.
AQ = QP. Hence Proved.

Question 6.
(a) If a + $$\frac{1}{a} = p,$$ prove that $$a^{3}+\frac{1}{a^{3}}=p\left(p^{2}-3\right).$$

(b) The side of a square exceeds the side of another square by 3 cm and the sum of the areas of the two squares is 549 cm2. Find the perimeters of the squares.
Let the side of one square be x cm
Then, side of other square = (x + 3) cm.
Area of two squares are x2 cm2 and (x + 3)2 cm2, respectively.
According to the question,
x2 + (x + 3)2 = 549 ⇒ x2 + x2 + 6x + 9 = 549
⇒ 2x2 + 6x – 540 = 0
⇒ x2 + 3x – 270 = 0
⇒ x2 + (18 – 15)x – 270 =0
⇒ x2 + 15x – 15x – 270 =0
⇒ x (x + 18) – 15 (x + 18) =0
⇒ (x + 18) (x – 15) =0
⇒ x = – 18 or 15
∴ x – 15      (∵ x cannot be negative)
∴ x+ 3 =15+ 3 = 18
∴ Perimeter of one square = 4 x 15 = 60 cm
and Perimeter of other square = 4 x 18 = 72 cm

(c) Simplify :
$$\frac{\cos \theta}{\sin \left(90^{\circ}-\theta\right)}+\frac{\cos \left(90^{\circ}-\theta\right)}{\sec \left(90^{\circ}-\theta\right)}-3 \tan ^{2} 30^{\circ}$$

Question 7.
(a) If log10 a = b, express 102b3 in terms of a.

(b) ΔABC and ΔDBC are on the same base BC with A, D on opposite sides of BC. If area of ΔABC = area of ΔDBC, prove that BC bisects AD.

(c) If cos θ + sec θ = 2, show that cos8 θ + sec8 θ = 2.

Question 8.
(a) If each interior angle is double the exterior angle, find the number of sides.

(b) Solve by the substitution method :
5x + 4y – 4 = 0; x – 20 = 12y.
Given: 5x+4y – 4 =0 ………..(i)
and x – 20 =12y ………… (2)
From (ii), x = 12y + 20 ……. (3)
Putting x = 12 + 20 in equation (i), we have
5(12y+20)+4y – 4 =0
⇒ 60y+100+4y – 4=0
⇒ 64y = – 96
⇒ $$y=-\frac{96}{64}=-\frac{3}{2}$$
From (iii), x = 12 x $$\left(\frac{-3}{2}\right)$$ +20 = 2
x = 2,y = $$\frac{-3}{2}$$

(c) If x = 2 + √3 , find the value of x – $$\frac{1}{x}$$

Question 9.
(a) Evaluate : $$x^{2 / 3} \cdot y^{-1} \cdot z^{1 / 2}$$ when x – 8, y = 4 and z = 25

(b) Construct a ΔABC is which base AB 5 cm, ∠A = 30° and AC – BC – 2.5 cm.

Steps of construction :
(1) Draw base AB = 5 cm.
(2) Draw ∠BAX = 30°
(3) From AX, cut-off AD = 2.5 cm
(4) Join BD.
(5) Draw the perpendicular bisector of BD to cut AX at C.
(6) Join BC.
Thus, ABC in the required triangle.

(c) Simplify: $$\left(2 x-\frac{1}{2 x}\right)^{2}-\left(2 x+\frac{1}{2 x}\right)\left(2 x-\frac{1}{2 x}\right)$$

Question 10.
(a) Simplify: $$\left(a^{m-n}\right)^{m+n} \cdot\left(a^{n-l}\right)^{n+l} \cdot\left(a^{l-m}\right)^{l+m}$$

(b) If area of a semi-circular region is 1232 cm2, find its perimeter.

(c) If x = 15°, evaluate : 8 sin cos 4x. sin 6x.
Given: x=15°
∴ 8 sin 2xcos 4x.sin 6x = 8 sin (2 x 15°). cos (4 x 15°) . sin (6 x 15°)
= 8 sin 30° . cos 60° . sin 90°
$$=8 \times \frac{1}{2} \times \frac{1}{2} \times 1=2$$

Question 11.
(a) A farmer increases his output of wheat in his farm every year by 8%. This year he produced 2187 quintals of wheat. What was his yearly produce of wheat 2 years ago?


text:
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A bag contains 5 black, 7 red ...

# A bag contains 5 black, 7 red and 3 white balls. A ball is drawn from the bag at random.
 Find the probability that the ball drawn is :
 (i) red (ii) black or white (iii) not black.

Updated On: 27-06-2022

UPLOAD PHOTO AND GET THE ANSWER NOW!

Text Solution

Solution : Total number of balls = 5 + 7 +3 = 15
 (i) Number of red balls = 7
 therefore "P(drawing a red ball) "=(7)/(15)
 (ii) Number of black or white balls = 5 + 3 = 8
 therefore "P(drawing a black or white ball) "=(8)/(15)
 (iii) Number of balls which are not black = 15 - 5 = 10
 therefore "P(drawing a ball that is not black) "=(10)/(5)=(2)/(3)
 Hence, the probability of getting a red ball, a black or white ball and not black ball are (7)/(15),(8)/(15)and (2)/(3)" respectively."

Step by step solution by experts to help you in doubt clearance & scoring excellent marks in exams.

Transcript

yah to question is a bag contains 500073 white was a ball is drawn from the back at random find the probability that the ball is drawn red black or white not like that probability is equals to Pagal Hai of red ball we have to find the probability of that was equal to forget your number of favourable condition number of favourable outcomes / number of Total outcomes sunao ke number of Total balls is here 7 + 3 + 57 + 3 + 5 of 8 + 7 + 8 so that is equal to 15 so the total number of vowels are there 15 to find the probability of that was that was here 7 so here we get a purple ts7 by total Wazir 15787 by 15 that is that is a possibility of red was

now we have to find the black or white beauty of Black Or White balls and probability of black or white ball is equal to get black and white black black balls are there 5 white balls Arya 365 + 308 that are the terrible outcomes and the total number of balls isliye 1515 that is not have to find that is not that means they will either red or other white Furious 7 plus theatre the seven bowler red and itchy white she was here White spirit 750 that is your tan no you get the probability not black that is equals to hear 7 plus three that is divided by 15 sunao ever get 52 and 53 Ja 15 Surya 2 by 3 this is the probability of red ball this is the probability of lack of fibre and this is the probability of not

black ball so this is our answer thank you


text:
Question: RinGO problem
0
10.9 years ago by
Jianping Jin890
Jianping Jin890 wrote:
Dear list, I ran "runExonerate.sh" and had the per-chromosome output files condensed into one single file (see below, I read in the file just for viewing): > str(allchranno) 'data.frame': 390388 obs. of 6 variables: $SEQ_ID : Factor w/ 388250 levels "chr1:10000013-10000070",..: 31687 28404 33240 34681 29011 26915 ...$ PROBE_ID : Factor w/ 373478 levels "5313_0001_0001",..: 138251 325230 15265 268671 45500 270116 ... $CHROMOSOME: Factor w/ 21 levels "1","10","11",..: 2 2 2 2 2 2 2 2 2 2 ...$ POSITION : int 75573476 4540877 79390517 80647222 5734395 30338873 82085749 7386228 61247293 ... $LENGTH : int 50 50 50 60 50 60 50 50 50 52 ...$ MISMATCHES: int 0 0 0 0 0 0 0 0 0 0 ... But when I tried to map probes to the genome I got an error and warnings: probeAnno <- posToProbeAnno("C:/from_DriveD/Chip- chip/Bultman/allChromExonerateOut_scott.txt") Creating probeAnno mapping for chromosome 1 10 11 12 13 14 15 16 17 18 19 2 3 4 5 6 7 8 9 X Y Done. Error in validObject(.Object) : invalid class "probeAnno" object: FALSE In addition: Warning message: In validityMethod(object) : Some match positions end before they actually start. Please check elements 1.start and 1.end . Appreciate it if you can help! Jianping FYI: > sessionInfo() R version 2.8.0 (2008-10-20) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 attached base packages: [1] splines tools stats graphics grDevices utils datasets methods base other attached packages: [1] Ringo_1.6.0 SparseM_0.78 RColorBrewer_1.0-2 vsn_3.8.0 affy_1.20.0 [6] limma_2.16.3 geneplotter_1.20.0 annotate_1.20.1 xtable_1.5-4 AnnotationDbi_1.4.2 [11] lattice_0.17-15 genefilter_1.22.0 survival_2.34-1 Biobase_2.2.1 loaded via a namespace (and not attached): [1] affyio_1.10.1 DBI_0.2-4 grid_2.8.0 KernSmooth_2.22-22 preprocessCore_1.4.0 [6] RSQLite_0.7-1 ################################## Jianping Jin Ph.D. Bioinformatics scientist Center for Bioinformatics Room 3133 Bioinformatics building CB# 7104 University of Chapel Hill Chapel Hill, NC 27599 Phone: (919)843-6105 FAX: (919)843-3103 E-Mail: jjin at email.unc.edu
• 750 views
modified 10.9 years ago by Joern Toedling720 • written 10.9 years ago by Jianping Jin890
0
10.9 years ago by
Joern Toedling720 wrote:
Hi Jianping, I am not completely sure what the source of the error is yet, so bear with me as I am trying to find out. First, something could be wrong with the merged Exonerate output file. Which version of Exonerate are you using? Can you also please tell me what the output of summary(allchranno$LENGTH) and summary(allchranno$POSITION) are? Another issue could be the use of factors for probe and chromosome identifiers. When reading in the output file using read.table, read.delim etc., please try the argument "as.is=TRUE", which will prevent the conversion of character vectors into factors. You can then directly supply the data.frame allchranno to the function posToProbeAnno. probeAnno <- posToProbeAnno(allchranno) Please tell me whether the error message still persists. If so could you please provide me with the file allChromExonerateOut_scott.txt or an excerpt thereof (please do not attach it to the mail but provide it for download on some server) such that I can further look into the issue. Best regards, Joern Jianping Jin wrote: > Dear list, > > I ran "runExonerate.sh" and had the per-chromosome output files > condensed into one single file (see below, > I read in the file just for viewing): > >> str(allchranno) > 'data.frame': 390388 obs. of 6 variables: > $SEQ_ID : Factor w/ 388250 levels "chr1:10000013-10000070",..: > 31687 28404 33240 34681 29011 26915 ... >$ PROBE_ID : Factor w/ 373478 levels "5313_0001_0001",..: 138251 > 325230 15265 268671 45500 270116 ... > $CHROMOSOME: Factor w/ 21 levels "1","10","11",..: 2 2 2 2 2 2 2 2 2 > 2 ... >$ POSITION : int 75573476 4540877 79390517 80647222 5734395 30338873 > 82085749 7386228 61247293 ... > $LENGTH : int 50 50 50 60 50 60 50 50 50 52 ... >$ MISMATCHES: int 0 0 0 0 0 0 0 0 0 0 ... > > But when I tried to map probes to the genome I got an error and warnings: > > probeAnno <- > posToProbeAnno("C:/from_DriveD/Chip- chip/Bultman/allChromExonerateOut_scott.txt") > > Creating probeAnno mapping for chromosome 1 10 11 12 13 14 15 16 17 18 > 19 2 3 4 5 6 7 8 9 X Y Done. > Error in validObject(.Object) : invalid class "probeAnno" object: FALSE > In addition: Warning message: > In validityMethod(object) : > Some match positions end before they actually start. > Please check elements 1.start and 1.end . > > Appreciate it if you can help! > > Jianping > -- Joern Toedling EMBL - European Bioinformatics Institute Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD United Kingdom Phone +44(0)1223 492566 Email toedling at ebi.ac.uk
Hi Joern, Please see below, --On Monday, January 12, 2009 5:44 PM +0000 Joern Toedling <toedling at="" ebi.ac.uk=""> wrote: > Hi Jianping, > > I am not completely sure what the source of the error is yet, so bear > with me as I am trying to find out. > First, something could be wrong with the merged Exonerate output file. > Which version of Exonerate are you using? exonerate-2.2.0-x86_64 > > Can you also please tell me what the output of > > summary(allchranno$LENGTH) > summary(allchranno$LENGTH) Min. 1st Qu. Median Mean 3rd Qu. Max. -72.00 50.00 50.00 50.13 51.00 75.00 > > and > > summary(allchranno$POSITION) > summary(allchranno$POSITION) Min. 1st Qu. Median Mean 3rd Qu. Max. 133400 37680000 75400000 77490000 112700000 197100000 > > are? Another issue could be the use of factors for probe and chromosome > identifiers. When reading in the output file using read.table, > read.delim etc., please try the argument "as.is=TRUE", which will > prevent the conversion of character vectors into factors. You can then > directly supply the data.frame allchranno to the function posToProbeAnno. > > probeAnno <- posToProbeAnno(allchranno) > > Please tell me whether the error message still persists. If so could you > please provide me with the file allChromExonerateOut_scott.txt > or an excerpt thereof (please do not attach it to the mail but provide > it for download on some server) such that I can further look into the > issue. > Yes. The problem is the same. I put up the data file on <http: seattle.med.unc.edu="" jjin=""/>. You can check that out. Thanks, Jianping > Best regards, > Joern > > Jianping Jin wrote: >> Dear list, >> >> I ran "runExonerate.sh" and had the per-chromosome output files >> condensed into one single file (see below, >> I read in the file just for viewing): >> >>> str(allchranno) >> 'data.frame': 390388 obs. of 6 variables: >> $SEQ_ID : Factor w/ 388250 levels "chr1:10000013-10000070",..: >> 31687 28404 33240 34681 29011 26915 ... >>$ PROBE_ID : Factor w/ 373478 levels "5313_0001_0001",..: 138251 >> 325230 15265 268671 45500 270116 ... >> $CHROMOSOME: Factor w/ 21 levels "1","10","11",..: 2 2 2 2 2 2 2 2 2 >> 2 ... >>$ POSITION : int 75573476 4540877 79390517 80647222 5734395 30338873 >> 82085749 7386228 61247293 ... >> $LENGTH : int 50 50 50 60 50 60 50 50 50 52 ... >>$ MISMATCHES: int 0 0 0 0 0 0 0 0 0 0 ... >> >> But when I tried to map probes to the genome I got an error and warnings: >> >> probeAnno <- >> posToProbeAnno("C:/from_DriveD/Chip- chip/Bultman/allChromExonerateOut_sc >> ott.txt") >> >> Creating probeAnno mapping for chromosome 1 10 11 12 13 14 15 16 17 18 >> 19 2 3 4 5 6 7 8 9 X Y Done. >> Error in validObject(.Object) : invalid class "probeAnno" object: FALSE >> In addition: Warning message: >> In validityMethod(object) : >> Some match positions end before they actually start. >> Please check elements 1.start and 1.end . >> >> Appreciate it if you can help! >> >> Jianping >> > > -- > Joern Toedling > EMBL - European Bioinformatics Institute > Wellcome Trust Genome Campus > Hinxton, Cambridge CB10 1SD > United Kingdom > Phone +44(0)1223 492566 > Email toedling at ebi.ac.uk > ################################## Jianping Jin Ph.D. Bioinformatics scientist Center for Bioinformatics Room 3133 Bioinformatics building CB# 7104 University of Chapel Hill Chapel Hill, NC 27599 Phone: (919)843-6105 FAX: (919)843-3103 E-Mail: jjin at email.unc.edu
Hello, well, the culprit(s) is/are the matches with a negative entry in LENGTH, as these are not supposed to happen. I am not sure how these came about, but it might have to do with changes in Exonerate (the scripts were written for Exonerate version 2.0.0). I shall investigate this further and get back to you. But for the moment, I am afraid you will have either to discard these lines with negative length matches before calling posToProbeAnno (how many are these?) or find a way to correct them in the Exonerate output file. Regards, Joern Jianping Jin wrote: > Hi Joern, > > Please see below, > > --On Monday, January 12, 2009 5:44 PM +0000 Joern Toedling > <toedling at="" ebi.ac.uk=""> wrote: > >> Hi Jianping, >> >> I am not completely sure what the source of the error is yet, so bear >> with me as I am trying to find out. >> First, something could be wrong with the merged Exonerate output file. >> Which version of Exonerate are you using? > > exonerate-2.2.0-x86_64 >> >> Can you also please tell me what the output of >> >> summary(allchranno$LENGTH) > >> summary(allchranno$LENGTH) > Min. 1st Qu. Median Mean 3rd Qu. Max. > -72.00 50.00 50.00 50.13 51.00 75.00 -- Joern Toedling EMBL - European Bioinformatics Institute Wellcome Trust Genome Campus Hinxton, Cambridge CB10 1SD United Kingdom Phone +44(0)1223 492566 Email toedling at ebi.ac.uk
Hi Joern, There are 6247 probes with LENGTH <= 0 (actually <= -50). The minus sign may refer to the genome strain. I checked the demo data set which also contains minus values for LENGTH. The only difference I can tell is the format of PROBE_ID. In the demo file it is something beginning with MM, e.g. MM5000P01479955, while in my file it is something like 5313_0514_0052. Jianping --On Monday, January 12, 2009 6:46 PM +0000 Joern Toedling <toedling at="" ebi.ac.uk=""> wrote: > Hello, > > well, the culprit(s) is/are the matches with a negative entry in LENGTH, > as these are not supposed to happen. I am not sure how these came about, > but it might have to do with changes in Exonerate (the scripts were > written for Exonerate version 2.0.0). I shall investigate this further > and get back to you. But for the moment, I am afraid you will have > either to discard these lines with negative length matches before > calling posToProbeAnno (how many are these?) or find a way to correct > them in the Exonerate output file. > > Regards, > Joern > > Jianping Jin wrote: >> Hi Joern, >> >> Please see below, >> >> --On Monday, January 12, 2009 5:44 PM +0000 Joern Toedling >> <toedling at="" ebi.ac.uk=""> wrote: >> >>> Hi Jianping, >>> >>> I am not completely sure what the source of the error is yet, so bear >>> with me as I am trying to find out. >>> First, something could be wrong with the merged Exonerate output file. >>> Which version of Exonerate are you using? >> >> exonerate-2.2.0-x86_64 >>> >>> Can you also please tell me what the output of >>> >>> summary(allchranno$LENGTH) >> >>> summary(allchranno$LENGTH) >> Min. 1st Qu. Median Mean 3rd Qu. Max. >> -72.00 50.00 50.00 50.13 51.00 75.00 > > -- > Joern Toedling > EMBL - European Bioinformatics Institute > Wellcome Trust Genome Campus > Hinxton, Cambridge CB10 1SD > United Kingdom > Phone +44(0)1223 492566 > Email toedling at ebi.ac.uk > ################################## Jianping Jin Ph.D. Bioinformatics scientist Center for Bioinformatics Room 3133 Bioinformatics building CB# 7104 University of Chapel Hill Chapel Hill, NC 27599 Phone: (919)843-6105 FAX: (919)843-3103 E-Mail: jjin at email.unc.edu


text:
IndependenceTestStatistic-class {coin} R Documentation

## Class `"IndependenceTestStatistic"` and its subclasses

### Description

Objects of class `"IndependenceTestStatistic"` and its subclasses `"MaxTypeIndependenceTestStatistic"`, `"QuadTypeIndependenceTestStatistic"` and `"ScalarIndependenceTestStatistic"` represent the test statistic, the linear statistic, and the transformed and original data structures corresponding to an independence problem.

### Objects from the Class

Class `"IndependenceTestStatistic"` is a virtual class, so objects cannot be created from it directly.

Objects can be created by calls of the form

```     new("MaxTypeIndependenceTestStatistic", object,
alternative = c("two.sided", "less", "greater"), \dots),

new("QuadTypeIndependenceTestStatistic", object, paired = FALSE, \dots)```

and

```     new("ScalarIndependenceTestStatistic", object,
alternative = c("two.sided", "less", "greater"), paired = FALSE, \dots)```

where `object` is an object of class `"IndependenceLinearStatistic"`, `alternative` is a character specifying the direction of the alternative hypothesis and `paired` is a logical indicating that paired data have been transformed in such a way that the (unstandardized) linear statistic is the sum of the absolute values of the positive differences between the paired observations.

### Slots

For objects of classes `"IndependenceTestStatistic"`, `"MaxTypeIndependenceTestStatistic"`, `"QuadTypeIndependenceTestStatistic"` or `"ScalarIndependenceTestStatistic"`:

`teststatistic`:

Object of class `"numeric"`. The test statistic.

`standardizedlinearstatistic`:

Object of class `"numeric"`. The standardized linear statistic.

`linearstatistic`:

Object of class `"matrix"`. The linear statistic for each block.

`expectation`:

Object of class `"matrix"`. The expectation of the linear statistic for each block.

`covariance`:

Object of class `"matrix"`. The lower triangular elements of the covariance of the linear statistic for each block.

`xtrans`:

Object of class `"matrix"`. The transformed `x`.

`ytrans`:

Object of class `"matrix"`. The transformed `y`.

`xtrafo`:

Object of class `"function"`. The regression function for `x`.

`ytrafo`:

Object of class `"function"`. The influence function for `y`.

`x`:

Object of class `"data.frame"`. The variables `x`.

`y`:

Object of class `"data.frame"`. The variables `y`.

`block`:

Object of class `"factor"`. The block structure.

`weights`:

Object of class `"numeric"`. The case weights.

Additionally, for objects of classes `"MaxTypeIndependenceTest"` or `"ScalarIndependenceTest"`:

`alternative`:

Object of class `"character"`. The direction of the alternative hypothesis.

Additionally, for objects of class `"QuadTypeIndependenceTest"`:

`covarianceplus`:

Object of class `"numeric"`. The lower triangular elements of the Moore-Penrose inverse of the covariance of the linear statistic.

`df`:

Object of class `"numeric"`. The rank of the covariance matrix.

Additionally, for objects of classes `"QuadTypeIndependenceTest"` or `"ScalarIndependenceTest"`:

`paired`:

Object of class `"logical"`. The indicator for paired test statistics.

### Extends

For objects of class `"IndependenceTestStatistic"`:
Class `"IndependenceLinearStatistic"`, directly.
Class `"IndependenceTestProblem"`, by class `"IndependenceLinearStatistic"`, distance 2.
Class `"IndependenceProblem"`, by class `"IndependenceLinearStatistic"`, distance 3.

For objects of classes `"MaxTypeIndependenceTestStatistic"`, `"QuadTypeIndependenceTestStatistic"` or `"ScalarIndependenceTestStatistic"`:
Class `"IndependenceTestStatistic"`, directly.
Class `"IndependenceLinearStatistic"`, by class `"IndependenceTestStatistic"`, distance 2.
Class `"IndependenceTestProblem"`, by class `"IndependenceTestStatistic"`, distance 3.
Class `"IndependenceProblem"`, by class `"IndependenceTestStatistic"`, distance 4.

### Known Subclasses

For objects of class `"IndependenceTestStatistic"`:
Class `"MaxTypeIndependenceTestStatistic"`, directly.
Class `"QuadTypeIndependenceTestStatistic"`, directly.
Class `"ScalarIndependenceTestStatistic"`, directly.

### Methods

ApproxNullDistribution

`signature(object = "MaxTypeIndependenceTestStatistic")`: See the documentation for `ApproxNullDistribution` for details.

ApproxNullDistribution

`signature(object = "QuadTypeIndependenceTestStatistic")`: See the documentation for `ApproxNullDistribution` for details.

ApproxNullDistribution

`signature(object = "ScalarIndependenceTestStatistic")`: See the documentation for `ApproxNullDistribution` for details.

AsymptNullDistribution

`signature(object = "MaxTypeIndependenceTestStatistic")`: See the documentation for `AsymptNullDistribution` for details.

AsymptNullDistribution

`signature(object = "QuadTypeIndependenceTestStatistic")`: See the documentation for `AsymptNullDistribution` for details.

AsymptNullDistribution

`signature(object = "ScalarIndependenceTestStatistic")`: See the documentation for `AsymptNullDistribution` for details.

ExactNullDistribution

`signature(object = "QuadTypeIndependenceTestStatistic")`: See the documentation for `ExactNullDistribution` for details.

ExactNullDistribution

`signature(object = "ScalarIndependenceTestStatistic")`: See the documentation for `ExactNullDistribution` for details.

covariance

`signature(object = "QuadTypeIndependenceTestStatistic")`: See the documentation for `covariance` for details.

initialize

`signature(.Object = "IndependenceTestStatistic")`: See the documentation for `initialize` (in package methods) for details.

initialize

`signature(.Object = "MaxTypeIndependenceTestStatistic")`: See the documentation for `initialize` (in package methods) for details.

initialize

`signature(.Object = "QuadTypeIndependenceTestStatistic")`: See the documentation for `initialize` (in package methods) for details.

initialize

`signature(.Object = "ScalarIndependenceTestStatistic")`: See the documentation for `initialize` (in package methods) for details.

statistic

`signature(object = "IndependenceTestStatistic")`: See the documentation for `statistic` for details.

[Package coin version 1.4-1 Index]


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QUESTION

# Choose the following Ethics Problems and answer it to the very best of your ability. These questions are fairly complex, and will require a minimum of 3 full pages (double spaced, not including the ti

Choose the following Ethics Problems and answer it to the very best of your ability. These questions are fairly complex, and will require a minimum of 3 full pages (double spaced, not including the title/header) pages to answer completely.

Chapter 7: Ethics problem - Who gets to stay on the lifeboat? on Page 136

Ecologist Garratt Hardin (1915-2003) proposed the metaphor of a lifeboat to help us think about resource distribution in a world fast approaching limits.

Garret Hardin, “Lifeboat Ethics: The case Against Helping the Poor,” Psychology Today, September 8, 1974, 38-43.

Hardin’s view, called “Lifeboat Ethics,” looks at trends such as population increase, resource depletion, and the carrying capacity of land to make a case against helping the poor. Although we all have basic needs, some people must be denied. Lifeboats, metaphorically, refer to wealthy nations.

Consider the example of a lifeboat with a capacity of 50 persons. The ship is sinking and there are not enough lifeboats. You are safe on a lifeboat filled to capacity, but there are still 100 people in the water. What do you do? All need to get in the lifeboat, or they will not survive. But, the lifeboat is full. Who should stay and who should go, and why? What criteria do we use to make decisions in a world of limited resources? Do an internet search on lifeboat ethics and also see Alfred Hitchcock’s film Lifeboat.

Files: Chapter 7.pdf
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Tutor has posted answer for $25.00. See answer's preview$25.00


text:
# Find email addresses in text using TextCases? "Recursion limit problem"

Posted 1 year ago
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 Windows 10 MM11.1I'm mining through some email and would like to retrieve email addresses. Wanted to use the easy TexCases but it seems that when I use this it generates a recursion limit problem. With a lot of outlook mails you often see the following format: "Lab_Wolfram_Interest_Group " When I use TextCases TextCases["Lab_Wolfram_Interest_Group ", "EmailAddress"] RegularExpression::maxrec: Recursion limit exceeded; positive match might be missed. It seems that the string before the < creates the problem when we use an _ character but the length of the string seems also important. TextCases["abcdefg_hijklmnopqrst ", "EmailAddress"] generates an error.The following seems ok: TextCases["abcdefg_hijklmnopqrs ", "EmailAddress"] Any thoughts?
15 Replies
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Posted 1 year ago
 I've looked at the code of TextCases a bit but can't say 100% sure what it does for the type "EmailAddress", however I do see that some types simply get routed to Interpreter: Interpreter[type][strings] If you try this: Interpreter["EmailAddress"]["Lab_Wolfram_Interest_Group "] "Lab_Wolfram_Interest_Group@groups.wolfram.com" it does work.
Posted 1 year ago
 If you follow the command with your input: str="Lab_Wolfram_Interest_Group " TextCases[str,"EmailAddress"] calls: TextCases[str,{"EmailAddress"}] which calls: NaturalLanguageProcessingiTextCases[str,{"EmailAddress"}->"String"] which calls: TextPosition[str,{"EmailAddress"}]
Posted 1 year ago
 And after some deep digging, you get the string pattern: NaturalLanguageProcessing$TextPatternTable["EmailAddress"] StringPosition[str, %] How that is converted to regular expressions I'm not sure, I'll leave that puzzle to other people: NaturalLanguageProcessing$TextPatternTable["EmailAddress"] StringPatternPatternConvert[%] {(?ms)(?:(?:[[:alnum:]\x{f6b2}-\x{f6b5}\x{f6b7}\x{f6b9}-\x{f6bc}\x{f6be}-\x{f6bf}\x{f6c1}-\x{f700}\x{f730}-\x{f731}\x{f770}\x{f772}-\x{f773}\x{f776}\x{f779}-\x{f77a}\x{f77d}-\x{f780}\x{f782}-\x{f78b}\x{f78d}-\x{f790}\x{f793}-\x{f79a}\x{f79c}-\x{f7a2}\x{f7a4}-\x{f7bd}\x{f800}-\x{f844}\x{f846}-\x{f84c}\x{f854}-\x{f86c}\x{f874}-\x{f875}\x{f878}-\x{f879}\x{f87d}-\x{f886}\x{f88a}_%+\-])+\.{0,1})+@(?:[[:alnum:]\x{f6b2}-\x{f6b5}\x{f6b7}\x{f6b9}-\x{f6bc}\x{f6be}-\x{f6bf}\x{f6c1}-\x{f700}\x{f730}-\x{f731}\x{f770}\x{f772}-\x{f773}\x{f776}\x{f779}-\x{f77a}\x{f77d}-\x{f780}\x{f782}-\x{f78b}\x{f78d}-\x{f790}\x{f793}-\x{f79a}\x{f79c}-\x{f7a2}\x{f7a4}-\x{f7bd}\x{f800}-\x{f844}\x{f846}-\x{f84c}\x{f854}-\x{f86c}\x{f874}-\x{f875}\x{f878}-\x{f879}\x{f87d}-\x{f886}\x{f88a}.\-])+\.(?:[[:alpha:]\x{f6b2}-\x{f6b5}\x{f6b7}\x{f6b9}-\x{f6bc}\x{f6be}-\x{f6bf}\x{f6c1}-\x{f700}\x{f730}-\x{f731}\x{f770}\x{f772}-\x{f773}\x{f776}\x{f779}-\x{f77a}\x{f77d}-\x{f780}\x{f782}-\x{f78b}\x{f78d}-\x{f790}\x{f793}-\x{f79a}\x{f79c}-\x{f7a2}\x{f7a4}-\x{f7bd}\x{f800}-\x{f844}\x{f846}-\x{f84c}\x{f854}-\x{f86c}\x{f874}-\x{f875}\x{f878}-\x{f879}\x{f87d}-\x{f886}\x{f88a}]){2,4},{},{},Hold[None]}
Posted 1 year ago
 Would splitting the string be acceptable? Flatten[TextCases[#, "EmailAddress"] & /@ StringSplit["Lab_Wolfram_Interest_Group "], 1]
Posted 1 year ago
 This is a strange but annoying problem! I noticed you can do a stack trace. Is this the pattern used to find mail addresses?So when I even use the standard StringCases I run into the same max recursion problem. I would think this must be a bug..? str = "Lab_Wolfram_Interest_Group "; StringCases[str, ((WordCharacter | "_" | "%" | "+" | "-") ..~~Repeated[Verbatim["."], {0, 1}]) ..~~Verbatim["@"] ~~WordCharacter | "." | "-") .. ~~ Verbatim["."] ~~Repeated[LetterCharacter, {2, 4}], Overlaps -> False]
Posted 1 year ago
 I didn't use the stack trace actually, that would be much easier haha. I did it in another way, anyhow, yes that is the email-pattern. The patterns for various things can be found here: NaturalLanguageProcessing$TextPatternTable // Keys // Sort // Column NaturalLanguageProcessingPackageScope$WordSplitterPatterns // Keys // Sort // Column both of them are associations with a bunch of patterns as values.
Posted 1 year ago
 Hi Sander, great to know! btw.. How do you know all this stuff? can't find it in any book. You should write one :)
Posted 1 year ago
 Well, I don't know these things by heart, but you can do: Needs["GeneralUtilities"] PrintDefinitions@NaturalLanguageProcessingTextPositionPackagePrivateiTextPosition to see the internals of TextPosition and click on the various links you will see in that document and go deeper in to the code and so on...
Posted 1 year ago
 It seem to me the pattern used is "wrong" When I change it to f[x_] := StringCases[x, (WordCharacter | "_" | "%" | "+" | "-" | ".") .. ~~Verbatim["@"] ~~ (WordCharacter | "." | "-") .. ~~ Verbatim["."] ~~Repeated[LetterCharacter, {2, 4}], Overlaps -> False] it seems to work like a charm.
Posted 1 year ago
 That would allow for email addresses starting with a . which is not allowed as far as I know...
Posted 1 year ago
 You're right. But the addresses have already been evaluated (otherwise I wouldn't have received them) But what about if I force a wordcharacter first. Gives the same result but resolves your point. f[x_] := StringCases[x, WordCharacter ~~ (WordCharacter | "_" | "%" | "+" | "-" | ".") .. ~~ Verbatim["@"] ~~ (WordCharacter | "." | "-") .. ~~ Verbatim["."] ~~ Repeated[LetterCharacter, {2, 4}], Overlaps -> False] `
Posted 1 year ago
 Well, I think now you can still have multiple . in a row; I think the original stringpattern (stringexpression) is correct, it is either the conversion to RegularExpression that goes wrong, or the evaluation of the regularexpression... I would call this a bug indeed, do you mind sending it in as product feedback?
Posted 1 year ago
 Hi Sander, I submitted a case. This is in my opinion a bug indeed. I'll post the outcome.


text:
OpenStudy (anonymous):

Which of the following is a true statement? a.All square roots are irrational. b. All decimals are irrational. c. All non-perfect squares have irrational square roots. d.All irrational numbers can be written as ratios of two integers.

OpenStudy (anonymous):

c

OpenStudy (across):

A is false because: $\sqrt{64}=8$B is false because: $10/3\approx3.333...$C is correct, for example: $\sqrt{2}$D is false because it can be proven otherwise.


text:
# mcookie

Generates random 128 bit hexadecimal numbers.

• Generate a random number:

mcookie

• Generate a random number, using the contents of a file as a seed for the randomness:

mcookie --file {{path/to/file}}

• Generate a random number, using a specific number of bytes from a file as a seed for the randomness:

mcookie --file {{path/to/file}} --max-size {{number_of_bytes}}

• Print the details of the randomness used, such as the origin and seed for each source:

mcookie --verbose


text:
# Two Kinds of Equal Opportunity Outcomes

Robinson, Azerrad, and Matthews have each argued that equality of opportunity would represent a “dystopian, totalitarian nightmare” and even that it is “impossible.”1

The underlying argument seems to be the following:

1. In the relevant population, there is diversity of body, character, parents, etc. at some time period, $$t$$.
2. Difference between individuals A and B at time, $$t$$, means they do not have equality of opportunity for outcome Y at time, $$t+k$$ (e.g., usually jobs or income).
3. Eliminating these differences among A and B at time, $$t$$, let alone ALL individuals, would require eliminating differences in (a) genes,2 (b) parental investment in their children,3 etc.
4. Eliminating these differences in (a) and (b) would be a symptom of a “dystopian, totalitarian nightmare”.
5. Eliminating these differences in (a) and (b) is, technologically speaking, currently impossible.

Real equality of opportunity is inconceivable. It would mean that nobody had any genetic disadvantages, everybody had equally good parents, schools, communities. And that’s just within a country.4

Yet, there are two very different types of outcomes to which we might apply equal opportunity. On one hand, you have empirical outcomes. This is frequently top of mind due to the use of “race” or other sports metaphors.

It’s all well and good to ensure that the rules are applied equally once the starting gun has been fired, liberals say, but it really isn’t much of a race when some have been training for years while others couldn’t even afford a pair of running shoes.5

Here we are invited to believe that the outcome is just insofar as there was no cheating during the game’s timeline.

On the other hand, you have reward outcomes. This type of outcome depends on what the individual did in the past—it is because of something we observed during the race about individual A that we now wish to give them this reward. As should be immediately apparent, interventions on a reward look nothing need not look like interventions on genes or parental activities. If during the race, you earned $$\100$$ and we want to reward you with $$\200$$, we can do it by simply using boring old taxes. Thus, equality of opportunity for a reward outcome is neither dystopian nor totalitarian. Further, presupposing the reward exists and can be (re)distributed (e.g., money, land, gold medals), equality of opportunity for a reward outcome is, technologically speaking, currently feasible.

Equality of opportunity is not a goal to be abandoned lightly.

# Code

library(tidyverse)
library(magrittr)
set.seed(10204)
df <- data.frame(A = rnorm(100, 5, 1),
B = rnorm(100, 20, 3))

gg <- df %>%
gather() %>%
ggplot() +
geom_density(aes(x = value, fill = key)) +
theme_classic() +
scale_fill_manual(values = c("#4caf50", "#2b94c3")) +
labs(fill = "Individual")

ggsave(filename = "original.png", plot = gg)

1. “… it would require all of us to have the same genes.” https://justdeserts.org/post/azerrad-how-equal-should-opportunities-be/

2. “… any actions taken by affluent families meant to help their kids get ahead are prima facie illegitimate.” https://justdeserts.org/post/matthews-the-case-against-equality-of-opportunity/


text:
# Implementing mkdir in C/ C++

#### Software Engineering C C++

Ever thought to make directories without the usual right-click functionality in the operating system?, you can do that by another way which is through command line! The "mkdir" command is exactly made for that!. The "mkdir" command stands for "make directory/ies" . This is an inbuilt command in the UNIX based filesystems and is widely used for saving time by following the usual right-click creation method, let's dive into implementation of the "mkdir" command,

# Initial setup

The initial setup follows the usual setting up of C++ file, so create a file named "mkdir.cpp" and here we will write our implementation code,

After the correct setup the directory tree will look like this,

# Implementation

For the implementation of the logic we will need several header files, namely,

• algorithm : For using the algorithms required in the program.
• sys/stat.h : Used by the existing mkdir function
• sys/types.h : Used for error types and existing mkdir function.

Then let's move to the implementation part,

//Including header files
#include <algorithm>
#include <sys/stat.h>
#include <sys/types.h>
using namespace std;

Now we'll require command line arguments for the successful execution of the command. So we'll define the main function with arguments as,

int main(int argc, char **argv){
//Logic implementation
}

What this does is allows the user to pass the names through command line, such as the name of the directory to be created, so at the time of execution we could pass the name as,

$./filename argument1  Here argv signifies the list of arguments passed, Now we'll use the prebuilt function mkdir in C++ to create a directory, this function takes two arguments, the first one being the filename and the second one is the file permissions for user,group and others. int main(int argc, char **argv){ //Logic implementation if(mkdir(argv[1],0777) == -1) cerr << "Error : " << strerror(errno) << endl; }  The permissions 0777 stands for the permission type, 7 stands for the user permissions that the user could read, write, and excute the file. If the file is not present the function will create a directory, else will return -1. The argument argv[1] stands for the directory name that you want to be created here for example we are going to use test as the argv[1] but you could use it as anything you wish for. Now what if the directory doesn't exist? You guessed it right! We'll create one! int main(int argc, char **argv) { if (mkdir(argv[1], 0777) == -1) cerr << "Error : " << strerror(errno) << endl; else cout << "Directory created" << endl; }  So the complete implementation of the file will look like, // C++ program to create a directory in Linux #include <algorithm> #include <sys/stat.h> #include <sys/types.h> using namespace std; int main(int argc, char **argv) { if (mkdir(argv[1], 0777) == -1) cerr << "Error : " << strerror(errno) << endl; else cout << "Directory created"; }  For running the file, we'll use terminal, or you could do it with any compiler of your choice, Here I'll use terminal and make command to compile the file, Then to run the file we'll use ./mkdir along with the filename, $ ./mkdir test

Now by listing the filesystem we could see that the directory named test has been created!

So here our implementation is concluded and is working successfully in creating the directories!

#### Sahil Silare

B.Tech Student in Computer Science at National Institute of Technology (NIT), Raipur (2019 - 2023) | Intern at OpenGenus

Vote for Sahil Silare for Top Writers 2021:


text:
# Residual mean square

Jump to: navigation, search

In statistics, the residual mean square is defined as

${\displaystyle \mathrm {RMS} ={\frac {\mathrm {WRSS} }{\mathrm {df} }}}$

where df is the degree of freedom. In many simple cases, df = N − γ where N is the number of discrete data points fitted and γ is the number of identifiable and independently fitted parameters.