| [Agr95] |
R. Agrawal (1995).
Sample mean based index policies by O(logn)
regret for the Multi-Armed Bandit problem.
Advances in Applied Probability, 27(4):1054--1078. [ bib ] |
| [ALK19] |
P. Alatur, K. Y. Levy, and A. Krause (2019).
Multi-Player Bandits: The Adversarial Case.
arXiv preprint
arXiv:1902.08036.
https://arxiv.org/abs/1902.08036. [ bib | http ] |
| [AFM17] |
R. Allesiardo, R. Féraud, and O.-A. Maillard (2017).
The Non-Stationary Stochastic Multi-Armed Bandit Problem.
International Journal of Data Science and Analytics,
3(4):267--283. [ bib ] |
| [AMTA11] |
A. Anandkumar, N. Michael, A. K. Tang, and S. Agrawal (2011).
Distributed Algorithms for Learning and Cognitive Medium
Access with Logarithmic Regret.
Journal on Selected Areas in Communications, 29(4):731--745. [ bib ] |
| [AVW87a] |
V. Anantharam, P. Varaiya, and J. Walrand (1987).
Asymptotically efficient allocation rules for the Multi-Armed
Bandit problem with multiple plays - Part I: IID rewards.
Transactions on Automatic Control, 32(11):968--976. [ bib ] |
| [AVW87b] |
V. Anantharam, P. Varaiya, and J. Walrand (1987).
Asymptotically efficient allocation rules for the Multi-Armed
Bandit problem with multiple plays - Part II: Markovian rewards.
Transactions on Automatic Control, 32(11):977--982. [ bib ] |
| [AHK12] |
S. Arora, E. Hazan, and S. Kale (2012).
The Multiplicative Weights Update Method: a Meta-Algorithm and
Applications.
Theory of Computing, 8(1):121--164. [ bib ] |
| [AE61] |
K. J. Arrow and A. C. Enthoven (1961).
Quasi-Concave Programming.
Econometrica, 29(4):779--800. [ bib ] |
| [ACBF02] |
P. Auer, N. Cesa-Bianchi, and P. Fischer (2002).
Finite-time Analysis of the Multi-armed Bandit Problem.
Machine Learning, 47(2):235--256. [ bib | DOI ] |
| [ACBFS02] |
P. Auer, N. Cesa-Bianchi, Y. Freund, and R. Schapire (2002).
The Non-Stochastic Multi-Armed Bandit Problem.
SIAM journal on computing, 32(1):48--77. [ bib ] |
| [AO10] |
P. Auer and R. Ortner (2010).
UCB Revisited: Improved Regret Bounds For The Stochastic
Multi-Armed Bandit Problem.
Periodica Mathematica Hungarica, 61(1-2):55--65. [ bib ] |
| [AGO18] |
P. Auer, P. Gajane, and R. Ortner (2018).
Adaptively Tracking the Best Arm with an Unknown Number of
Distribution Changes.
European Workshop on Reinforcement Learning.
https://ewrl.files.wordpress.com/2018/09/ewrl_14_2018_paper_28.pdf. [ bib | .pdf ] |
| [AM15] |
O. Avner and S. Mannor (2015).
Learning to Coordinate Without Communication in Multi-User
Multi-Armed Bandit Problems.
arXiv preprint
arXiv:1504.08167.
https://arxiv.org/abs/1504.08167. [ bib | http ] |
| [AM18] |
O. Avner and S. Mannor (2018).
Multi-User Communication Networks: A Coordinated Multi-Armed
Bandit Approach.
arXiv preprint
arXiv:1808.04875.
https://arxiv.org/abs/1808.04875. [ bib | http ] |
| [BMM14] |
A. Baransi, O.-A. Maillard, and S. Mannor (2014).
Sub-sampling for Multi-armed Bandits.
Proceedings of the European Conference on Machine Learning.
https://hal.archives-ouvertes.fr/hal-01025651. [ bib | http ] |
| [BK19a] |
L. Besson and E. Kaufmann (August 2019).
Analyse non asymptotique d'un test séquentiel de détection
de ruptures et application aux bandits non stationnaires.
GRETSI.
https://hal.archives-ouvertes.fr/hal-02152243,
https://hal.archives-ouvertes.fr/hal-02152243. [ bib | http | .pdf ] |
| [BMP18] |
R. Bonnefoi, C. Moy, and J. Palicot (2018).
Improvement of the LPWAN AMI backhaul's latency thanks to
reinforcement learning algorithms.
EURASIP Journal on Wireless Communications and Networking,
2018(1):34. [ bib | DOI ] |
| [BP18] |
E. Boursier and V. Perchet (2018).
SIC-MMAB: Synchronisation Involves Communication in
Multiplayer Multi-Armed Bandits.
arXiv preprint
arXiv:1809.08151.
https://arxiv.org/abs/1809.08151. [ bib | http ] |
| [BCB12] |
S. Bubeck and N. Cesa-Bianchi (2012).
Regret Analysis of Stochastic and Non-Stochastic Multi-Armed
Bandit Problems.
Foundations and Trends in Machine Learning,
5(1):1--122. [ bib ] |
| [BK96] |
A. N. Burnetas and M. N. Katehakis (1996).
Optimal Adaptive Policies for Sequential Allocation
Problems.
Advances in Applied Mathematics, 17(2):122--142. [ bib ] |
| [CVZZ16] |
M. Centenaro, L. Vangelista, A. Zanella, and M. Zorzi (2016).
Long-range communications in unlicensed bands: the rising stars
in the IoT and smart city scenarios.
Wireless Communications, 23(5):60--67. [ bib | DOI ] |
| [CMR14] |
O. Chapelle, E. Manavoglu, and R. Rosales (2014).
Simple and Scalable Response Prediction For Display
Advertising.
Transactions on Intelligent Systems and Technology. [ bib ] |
| [DMP16] |
S. J. Darak, C. Moy, and J. Palicot (2016).
Proof-of-Concept System for Opportunistic Spectrum Access in
Multi-user Decentralized Networks.
EAI Endorsed Transactions on Cognitive Communications,
2:1--10. [ bib ] |
| [DH18] |
S. J. Darak and M. K. Hanawal (2018).
Distributed Learning and Stable Orthogonalization in Ad-Hoc
Networks with Heterogeneous Channels.
arXiv preprint
arXiv:1812.11651.
https://arxiv.org/abs/1812.11651. [ bib | http ] |
| [GBV18] |
G. Gautier, R. Bardenet, and M. Valko (2018).
DPPy: Sampling Determinantal Point Processes with Python.
arXiv preprint
arXiv:1809.07258.
https://arxiv.org/abs/1809.07258, code at
https://github.com/guilgautier/DPPy. Documentation at
https://dppy.readthedocs.io. [ bib | http ] Keywords: Computer Science - Machine Learning, Computer Science - Mathematical Software, Statistics - Machine Learning |
| [GMS16] |
A. Garivier, P. Ménard, and G. Stoltz (2016).
Explore First, Exploit Next: The True Shape of Regret in
Bandit Problems.
arXiv preprint
arXiv:1602.07182.
https://arxiv.org/abs/1602.07182. [ bib | http ] |
| [GHMS18] |
A. Garivier, H. Hadiji, P. Menard, and G. Stoltz (2018).
KL-UCB-switch: optimal regret bounds for stochastic bandits
from both a distribution-dependent and a distribution-free viewpoints.
arXiv preprint
arXiv:1805.05071.
https://arxiv.org/abs/1805.05071. [ bib | http ] |
| [Hay05] |
S. Haykin (2005).
Cognitive Radio: Brain-Empowered Wireless Communications.
Journal on Selected Areas in Communications, 23(2):201--220. [ bib ] |
| [H+16] |
E. Hazan et al. (2016).
Introduction to online convex optimization.
Foundations and Trends in Optimization,
2(3-4):157--325. [ bib ] |
| [Hon19] |
J. Honda (2019).
A Note on KL-UCB+ Policy for the Stochastic Bandit.
arXiv preprint
arXiv:1903.07839.
https://arxiv.org/abs/1903.07839. [ bib | http ] |
| [JMP12] |
W. Jouini, C. Moy, and J. Palicot (2012).
Decision Making for Cognitive Radio Equipment: Analysis of the
First 10 Years of Exploration.
EURASIP Journal on Wireless Communications and Networking,
2012(1). [ bib ] |
| [KK18] |
E. Kaufmann and W. M. Koolen (2018).
Mixture Martingales Revisited with Applications to Sequential
Tests and Confidence Intervals.
arXiv preprint
arXiv:1811.11419.
https://arXiv.org/abs/1811.11419. [ bib | http ] |
| [CGM+13] |
O. Cappé, A. Garivier, O.-A. Maillard, R. Munos, and G. Stoltz (2013).
Kullback-Leibler Upper Confidence Bounds For Optimal
Sequential Allocation.
Annals of Statistics, 41(3):1516--1541. [ bib ] |
| [KG17] |
E. Kaufmann and A. Garivier (2017).
Learning The Distribution With Largest Mean: Two Bandit
Frameworks.
arXiv preprint
arXiv:1702.00001.
https://arxiv.org/abs/1702.00001. [ bib | http ] |
| [KM19] |
E. Kaufmann and A. Mehrabian (2019).
New Algorithms for Multiplayer Bandits when Arm Means Vary
Among Players.
arXiv preprint
arXiv:1902.01239.
https://arxiv.org/abs/1902.01239. [ bib | http ] |
| [KDI18] |
N. Keriven, G. Damien, and P. Iacopo (2018).
NEWMA: a new method for scalable model-free online
change-point detection.
arXiv preprint
arXiv:1805.08061.
https://arxiv.org/abs/1805.08061, code at
https://github.com/lightonai/newma. [ bib | http ] |
| [KT19] |
B. Kim and A. Tewari (2019).
On the Optimality of Perturbations in Stochastic and
Adversarial Multi-Armed Bandit Problems.
arXiv preprint
arXiv:1902.00610.
https://arxiv.org/abs/1902.00610. [ bib | http ] |
| [KL51] |
S. Kullback and R.A. Leibler (1951).
On Information and Sufficiency.
The Annals of Mathematical Statistics, 22(1):79--86. [ bib ] |
| [KDH+19] |
R. Kumar, S. J. Darak, M. K. Hanawal, A. K. Sharma, and R. K. Tripathi (2019).
Distributed Algorithm for Learning to Coordinate in
Infrastructure-Less Network.
IEEE Communications Letters, 23(2):362--365.
ISSN 1089-7798. [ bib | DOI ] |
| [LR85] |
T. L. Lai and H. Robbins (1985).
Asymptotically Efficient Adaptive Allocation Rules.
Advances in Applied Mathematics, 6(1):4--22. [ bib ] |
| [LX10] |
T. L. Lai and H. Xing (2010).
Sequential change-point detection when the pre-and post-change
parameters are unknown.
Sequential Analysis, 29(2):162--175. [ bib ] |
| [Lat16b] |
T. Lattimore (2016).
Regret Analysis of the Anytime Optimally Confident UCB
Algorithm.
arXiv preprint
arXiv:1603.08661.
https://arxiv.org/abs/1603.08661. [ bib | http ] |
| [Lat18] |
T. Lattimore (2018).
Refining the confidence level for optimistic bandit
strategies.
The Journal of Machine Learning Research, 19(1):765--796. [ bib ] |
| [LJ18] |
L. Li and K. Jamieson (2018).
Hyperband: A Novel Bandit-Based Approach to Hyperparameter
Optimization.
Journal of Machine Learning Research, 18:1--52.
https://arxiv.org/abs/1603.06560. [ bib | http ] |
| [LKC17] |
A. Luedtke, E. Kaufmann, and A. Chambaz (2017).
Asymptotically Optimal Algorithms for Budgeted Multiple Play
Bandits.
Machine Learning, pages 1--31.
https://arxiv.org/abs/1606.09388. [ bib | http ] |
| [Lue68] |
D. G. Luenberger (1968).
Quasi-Convex Programming.
SIAM Journal on Applied Mathematics, 16(5):1090--1095. [ bib ] |
| [LM18] |
G. Lugosi and A. Mehrabian (2018).
Multiplayer Bandits Without Observing Collision Information.
arXiv preprint
arXiv:1808.08416.
https://arxiv.org/abs/1808.08416. [ bib | http ] |
| [MH16] |
S. Maghsudi and E. Hossain (2016).
Multi-Armed Bandits with application to 5G small cells.
Wireless Communications, 23(3):64--73. [ bib | DOI ] |
| [MGMM+15] |
L. Melián-Gutiérrez, N. Modi, C. Moy, F. Bader,
I. Pérez-Álvarez, and S. Zazo (2015).
Hybrid UCB-HMM: A Machine Learning Strategy for Cognitive
Radio in HF Band.
IEEE Transactions on Cognitive Communications and Networking,
1(3):347--358. [ bib ] |
| [MM99] |
J. Mitola and G. Q. Maguire (1999).
Cognitive Radio: making software radios more personal.
Personal Communications, 6(4):13--18. [ bib ] |
| [MMM17] |
N. Modi, P. Mary, and C. Moy (2017).
QoS driven Channel Selection Algorithm for Cognitive Radio
Network: Multi-User Multi-Armed Bandit Approach.
Transactions on Cognitive Communications and Networking,
3(1):49--66. [ bib ] |
| [Nie11] |
F. Nielsen (2011).
Chernoff Information of Exponential Families.
arXiv preprint
arXiv:1102.2684.
https://arxiv.org/abs/1102.2684. [ bib | http ] |
| [PGNN19] |
V. Patil, G. Ghalme, V. Nair, and Y. Narahari (2019).
Stochastic Multi-Armed Bandits with Arm-specific Fairness
Guarantees.
arXiv preprint
arXiv:1905.11260.
https://arxiv.org/abs/1905.11260. [ bib | http ] |
| [RKS17] |
U. Raza, P. Kulkarni, and M. Sooriyabandara (2017).
Low Power Wide Area Networks (LPWAN): An Overview.
Communications Surveys Tutorials, 19(2):855--873. [ bib | DOI ] |
| [Rob52] |
H. Robbins (1952).
Some Aspects of the Sequential Design of Experiments.
Bulletin of the American Mathematical Society,
58(5):527--535. [ bib ] |
| [Rob75] |
L. G. Roberts (1975).
ALOHA Packet System With and Without Slots and Capture.
SIGCOMM Computer Communication Review, 5(2):28--42. [ bib ] |
| [SLC+19] |
J. Seznec, A. Locatelli, A. Carpentier, A. Lazaric, and M. Valko (2019).
Rotting Bandits Are No Harder Than Stochastic Ones.
International Conference on Artificial Intelligence and
Statistics.
https://arxiv.org/abs/1811.11043. [ bib | http ] |
| [AHK17] |
S. Adish, H. Hassani, and A. Krause (2017).
Learning to Use Learners' Advice.
arXiv preprint
arXiv:1702.04825.
https://arxiv.org/abs/1702.04825. [ bib | http ] |
| [Sli19] |
A. Slivkins (June 2019).
Introduction to Multi-Armed Bandits.
arXiv preprint
arXiv:1904.07272v3.
https://arxiv.org/abs/1904.07272v3. [ bib | http ] |
| [Tho33] |
W. R. Thompson (1933).
On the Likelihood that One Unknown Probability Exceeds Another
in View of the Evidence of Two Samples.
Biometrika, 25. [ bib ] |
| [Z. 19] |
Z. Tian and J. Wang and J. Wang and J. Song (2019).
Distributed NOMA-Based Multi-Armed Bandit Approach for Channel
Access in Cognitive Radio Networks.
IEEE Wireless Communications Letters, pages 1--4.
ISSN 2162-2337. [ bib | DOI ] |
| [TdSCC13] |
F. S. Truzzi, V. F. da Silva, A. H. Reali Costa, and F. Gagliardi Cozman
(2013).
AdBandit: a New Algorithm for Multi-Armed Bandits.
ENIAC, 2013(1). [ bib ] |
| [Wal45] |
A. Wald (1945).
Some Generalizations of the Theory of Cumulative Sums of
Random Variables.
The Annals of Mathematical Statistics, 16(3):287--293. [ bib ] |
| [Whi88] |
P. Whittle (1988).
Restless bandits: Activity allocation in a changing world.
Journal of Applied Probability, 25(A):287--298. [ bib ] |
| [WCN+19] |
F. Wilhelmi, C. Cano, G. Neu, B. Bellalta, A. Jonsson, and
S. Barrachina-Muñoz (2019).
Collaborative Spatial Reuse In Wireless Networks Via Selfish
Multi-Armed Bandits.
Ad Hoc Networks. [ bib ] |
| [WBMB+19] |
F. Wilhelmi, S. Barrachina-Muñoz, B. Bellalta, C. Cano, A. Jonsson, and
G. Neu (2019).
Potential and Pitfalls of Multi-Armed Bandits for Decentralized
Spatial Reuse in WLANs.
Journal of Network and Computer Applications, 127:26--42. [ bib ] |
| [Wil38] |
S. S. Wilks (1938).
The large-sample distribution of the likelihood ratio for
testing composite hypotheses.
The Annals of Mathematical Statistics, 9(1):60--62. [ bib ] |
| [Yaa77] |
M. E. Yaari (1977).
A Note on Separability and Quasiconcavity.
Econometrica, 45(5):1183--1186. [ bib ] |
| [ZS07] |
Q. Zhao and B. M. Sadler (2007).
A Survey of Dynamic Spectrum Access.
Signal Processing magazine, 24(3):79--89. [ bib ] |
| [LZ10] |
K. Liu and Q. Zhao (2010).
Distributed Learning in Multi-Armed Bandit with Multiple
Players.
Transaction on Signal Processing, 58(11):5667--5681. [ bib ] |
| [ALVM06] |
I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty (2006).
NeXt Generation, Dynamic Spectrum Access, Cognitive Radio
Wireless Networks: A Survey.
Computer Networks, 50(13):2127--2159. [ bib ] |
| [AB10] |
J.-Y. Audibert and S. Bubeck (2010).
Regret Bounds And Minimax Policies Under Partial Monitoring.
Journal of Machine Learning Research, 11:2785--2836. [ bib ] |
| [Bar59] |
G.A. Barnard (1959).
Control charts and stochastic processes.
Journal of the Royal Statistical Society. Series B
(Methodological), pages 239--271. [ bib ] |
| [BR19] |
D. Bouneffouf and I. Rish (2019).
A Survey on Practical Applications of Multi-Armed and
Contextual Bandits.
arXiv preprint
arXiv:1904.10040, under review by
IJCAI 2019 Survey.
https://arxiv.org/abs/1904.10040. [ bib | http ] |
| [C+52] |
H. Chernoff et al. (1952).
A Measure of Asymptotic Efficiency for Tests of a Hypothesis
Based on The Sum of Observations.
The Annals of Mathematical Statistics, 23(4):493--507. [ bib ] |
| [Che81] |
H. Chernoff (1981).
A Note on an Inequality Involving the Normal Distribution.
The Annals of Probability, pages 533--535. [ bib ] |
| [GHRZ19] |
Z. Gao, Y. Han, Z. Ren, and Z. Zhou (2019).
Batched Multi-armed Bandits Problem.
arXiv preprint
arXiv:1904.01763.
https://arxiv.org/abs/1904.01763. [ bib | http ] |
| [GB12] |
A. Garhwal and P. P. Bhattacharya (2012).
A Survey on Dynamic Spectrum Access Techniques for Cognitive
Radio.
International Journal of Next-Generation Networks (IJNGN),
3(4).
https://arxiv.org/abs/1201.1964. [ bib | DOI | http ] |
| [HR90] |
T. Hagerup and C. Rüb (1990).
A Guided Tour of Chernoff Bounds.
Information processing letters, 33(6):305--308. [ bib ] |
| [Hoe63] |
W. Hoeffding (1963).
Probability Inequalities for Sums of Bounded Random Variables.
Journal of the American statistical association,
58(301):13--30. [ bib ] |
| [PG07] |
F. Pérez and B. E. Granger (May 2007).
IPython: a System for Interactive Scientific Computing.
Computing in Science and Engineering, 9(3):21--29.
https://ipython.org. [ bib | http ] |
| [KG19] |
A. Kolnogorov and S. Garbar (2019).
Multi-Armed Bandit Problem and Batch UCB Rule.
arXiv preprint
arXiv:1902.00214.
https://arxiv.org/abs/1902.00214. [ bib | http ] |
| [KDY+16] |
R. Kumar, S. J. Darak, A. Yadav, A. K. Sharma, and R. K. Tripathi (2016).
Two-stage Decision Making Policy for Opportunistic Spectrum
Access and Validation on USRP Testbed.
Wireless Networks, pages 1--15. [ bib ] |
| [KDY+17] |
R. Kumar, S. J. Darak, A. Yadav, A. K. Sharma, and R. K. Tripathi (2017).
Channel Selection for Secondary Users in Decentralized Network
of Unknown Size.
Communications Letters, 21(10):2186--2189. [ bib ] |
| [LLL19] |
H. Li, J. Luo, and C. Liu (2019).
Selfish Bandit based Cognitive Anti-jamming Strategy for
Aeronautic Swarm Network in Presence of Multiple Jammert.
IEEE Access. [ bib ] |
| [MM12] |
J. Marinho and E. Monteiro (2012).
Cognitive Radio: Survey on Communication Protocols Spectrum
Decision Issues and Future Research Directions.
Wireless Networks, 18(2):147--164. [ bib ] |
| [Hun07] |
J. D. Hunter (2007).
Matplotlib: a 2D Graphics Environment.
Computing In Science & Engineering, 9(3):90--95. [ bib | DOI ] |
| [vdWCV11] |
S. van der Walt, C. S. Colbert, and G. Varoquaux (March 2011).
The NumPy Array: A Structure for Efficient Numerical
Computation.
Computing in Science & Engineering, 13(2):22--30. [ bib | DOI ] |
| [VPSE16] |
P. Vianney, R. Philippe, C. Sylvain, and S. Erik (04 2016).
Batched Bandit Problems.
The Annals of Statistics, 44(2):660--681.
https://doi.org/10.1214/15-AOS1381. [ bib | DOI | http ] |
| [SV95] |
D. Siegmund and E.S. Venkatraman (1995).
Using the Generalized Likelihood Ratio Statistic for
Sequential Detection of a Change Point.
The Annals of Statistics, pages 255--271. [ bib ] |
| [SB11] |
M. Subhedar and G. Birajdar (2011).
Spectrum Sensing Techniques in Cognitive Radio Networks: a
Survey.
International Journal of Next-Generation Networks,
3(2):37--51. [ bib ] |
| [TZZ19] |
C. Tao, Q. Zhang, and Y. Zhou (2019).
Collaborative Learning with Limited Interaction: Tight Bounds
for Distributed Exploration in Multi-Armed Bandits.
arXiv preprint
arXiv:1904.03293.
https://arxiv.org/abs/1904.03293. [ bib | http ] |
| [WHCW19] |
Y. Wang, J. Hu, X. Chen, and L. Wang (2019).
Distributed Bandit Learning: How Much Communication is Needed
to Achieve (Near) Optimal Regret.
arXiv preprint
arXiv:1904.06309.
https://arxiv.org/abs/1904.06309. [ bib | http ] |
| [YA09] |
T. Yucek and H. Arslan (2009).
A Survey of Spectrum Sensing Algorithms for Cognitive Radio
Applications.
IEEE Communications Surveys & Tutorials, 11(1):116--130. [ bib ] |
| [YRJW17] |
F. Yang, A. Ramdas, K. Jamieson, and M. Wainwright (2017).
A framework for Multi-A(rmed)/B(andit) Testing with Online FDR
Control.
In Advances in Neural Information Processing Systems, pages
5957--5966. Curran Associates, Inc. [ bib ] |
| [CL11] |
O. Chapelle and L. Li (2011).
An Empirical Evaluation of Thompson Sampling.
In Advances in Neural Information Processing Systems, pages
2249--2257. Curran Associates, Inc. [ bib ] |
| [Abr70] |
N. Abramson (1970).
The ALOHA System: Another Alternative for Computer
Communications.
In Proceedings of the November 17-19, 1970, Fall Joint Computer
Conference, AFIPS '70 (Fall), pages 281--285. ACM, New York, NY, USA. [ bib | DOI ] |
| [ACE09] |
D. Agarwal, B. Chen, and P. Elango (2009).
Explore Exploit Schemes For Web Content Optimization.
In International Conference on Data Mining. IEEE. [ bib ] |
| [ALNS17] |
A. Agarwal, H. Luo, B. Neyshabur, and R. E. Schapire (2017).
Corralling a Band of Bandit Algorithms.
In Conference on Learning Theory, pages 12--38. PMLR. [ bib ] |
| [AG12] |
S. Agrawal and N. Goyal (2012).
Analysis of Thompson sampling for the Multi-Armed Bandit
problem.
In Conference On Learning Theory, pages 36--65. PMLR. [ bib ] |
| [AMF17] |
R. Alami, O.-A. Maillard, and R. Féraud (2017).
Memory Bandits: Towards the Switching Bandit Problem Best
Resolution.
In Conference on Neural Information Processing Systems. [ bib ] |
| [AF15] |
R. Allesiardo and R. Féraud (2015).
Exp3 with Drift Detection for the Switching Bandit Problem.
In International Conference on Data Science and Advanced
Analytics, pages 1--7. IEEE. [ bib ] |
| [AMT10] |
A. Anandkumar, N. Michael, and A. K. Tang (2010).
Opportunistic Spectrum Access with multiple users: Learning
under competition.
In International Conference on Computer Communications.
IEEE. [ bib ] |
| [AMS07] |
J.-Y. Audibert, R. Munos, and C. Szepesvári (2007).
Tuning Bandit Algorithms in Stochastic Environments.
In Algorithmic Learning Theory, pages 150--165. Springer,
Sendai, Japan. [ bib ] |
| [AB09] |
J-Y. Audibert and S. Bubeck (2009).
Minimax Policies for Adversarial and Stochastic Bandits.
In Conference on Learning Theory, pages 217--226. PMLR. [ bib ] |
| [AC16] |
P. Auer and C.-K. Chiang (2016).
An Algorithm with Nearly Optimal Pseudo Regret for Both
Stochastic and Adversarial Bandits.
In Conference on Learning Theory, pages 116--120. PMLR. [ bib ] |
| [AM16] |
O. Avner and S. Mannor (2016).
Multi-User Lax Communications: a Multi-Armed Bandit
Approach.
In International Conference on Computer Communications.
IEEE. [ bib ] |
| [AC18] |
A. Azari and C. Cavdar (December 2018).
Self-organized Low-power IoT Networks: A Distributed Learning
Approach.
In Global Communications Conference. IEEE, Abu Dhabi, UAE. [ bib ] |
| [BGZ14] |
O. Besbes, Y. Gur, and A. Zeevi (2014).
Stochastic Multi-Armed Bandit Problem with Non-Stationary
Rewards.
In Advances in Neural Information Processing Systems, pages
199--207. [ bib ] |
| [BK18a] |
L. Besson and E. Kaufmann (2018).
Multi-Player Bandits Revisited.
In M. Mohri and K. Sridharan, editor, Algorithmic Learning
Theory. Lanzarote, Spain.
https://hal.archives-ouvertes.fr/hal-01629733. [ bib | http ] |
| [BKM18] |
L. Besson, E. Kaufmann, and C. Moy (2018).
Aggregation of Multi-Armed Bandits Learning Algorithms for
Opportunistic Spectrum Access.
In Wireless Communications and Networking Conference. IEEE,
Barcelona, Spain.
https://hal.archives-ouvertes.fr/hal-01705292. [ bib | http ] |
| [BBM19] |
L. Besson, R. Bonnefoi, and C. Moy (April 2019).
GNU Radio Implementation of MALIN: “Multi-Armed bandits
Learning for Internet-of-things Networks”.
In Wireless Communications and Networking Conference. IEEE,
Marrakech, Morocco.
https://hal.archives-ouvertes.fr/hal-02006825,
following a Demonstration presented at International Conference on
Telecommunications (ICT) 2018. [ bib | http ] |
| [BL18] |
I. Bistritz and A. Leshem (2018).
Distributed Multi-Player Bandits: a Game Of Thrones
Approach.
In Advances in Neural Information Processing Systems, pages
7222--7232. [ bib ] |
| [BMP16] |
R. Bonnefoi, C. Moy, and J. Palicot (2016).
Advanced metering infrastructure backhaul reliability
improvement with Cognitive Radio.
In International Conference on Communications, Control, and
Computing Technologies for Smart Grids, pages 230--236. [ bib | DOI ] |
| [BBM+17] |
R. Bonnefoi, L. Besson, C. Moy, E. Kaufmann, and J. Palicot (2017).
Multi-Armed Bandit Learning in IoT Networks: Learning Helps
Even in Non-Stationary Settings.
In 12th EAI Conference on Cognitive Radio Oriented Wireless
Network and Communication. Lisboa, Portugal. [ bib ] |
| [BBMVM19] |
R. Bonnefoi, L. Besson, J. C. Manco-Vasquez, and C. Moy (April 2019).
Upper-Confidence Bound for Channel Selection in LPWA Networks
with Retransmissions.
In MOTIoN Workshop. IEEE, Marrakech, Morocco.
https://hal.archives-ouvertes.fr/hal-02049824. [ bib | http ] |
| [BS12] |
S. Bubeck and A. Slivkins (2012).
The Best Of Both Worlds Stochastic And Adversarial Bandits.
In Conference on Learning Theory, pages 42--1. PMLR. [ bib ] |
| [CN18] |
C. Cano and G. Neu (2018).
Wireless Optimisation via Convex Bandits: Unlicensed LTE/WiFi
Coexistence.
In Proceedings of the 2018 Workshop on Network Meets AI & ML,
NetAI'18, pages 41--47. ACM, New York, NY, USA.
ISBN 978-1-4503-5911-5. [ bib | DOI ] |
| [CZKX19] |
Y. Cao, W. Zheng, B. Kveton, and Y. Xie (2019).
Nearly Optimal Adaptive Procedure for Piecewise-Stationary
Bandit: a Change-Point Detection Approach.
In International Conference on Artificial Intelligence and
Statistics. Okinawa, Japan. [ bib ] |
| [CLLW19] |
Y. Chen, C. Lee, H. Luo, and C. Wei (2019).
A New Algorithm for Non-stationary Contextual Bandits:
Efficient, Optimal, and Parameter-free.
In A. Beygelzimer and D. Hsu, editor, Conference on Learning
Theory, volume 99, pages 1--30. PMLR.
https://arxiv.org/abs/1902.00980. [ bib | http ] |
| [CMP17] |
R. Combes, S. Magureanu, and A. Proutiere (2017).
Minimal Exploration in Structured Stochastic Bandits.
In Advances in Neural Information Processing Systems, pages
1761--1769. [ bib ] |
| [CGH+96] |
R. Corless, G. Gonnet, D. Hare, D. Jeffrey, and D. Knuth (1996).
On the Lambert W Function.
In Advances in Computational Mathematics, pages 329--359. [ bib ] |
| [DP16] |
R. Degenne and V. Perchet (2016).
Anytime Optimal Algorithms In Stochastic Multi Armed
Bandits.
In International Conference on Machine Learning, pages
1587--1595. [ bib ] |
| [GC11] |
A. Garivier and O. Cappé (2011).
The KL-UCB Algorithm for Bounded Stochastic Bandits and
Beyond.
In Conference on Learning Theory, pages 359--376. PMLR. [ bib ] |
| [GM11] |
A. Garivier and E. Moulines (2011).
On Upper-Confidence Bound Policies For Switching Bandit
Problems.
In Algorithmic Learning Theory, pages 174--188. PMLR. [ bib ] |
| [GK16] |
A. Garivier and E. Kaufmann (2016).
Optimal Best Arm Identification with Fixed Confidence.
In PMLR, volume 49 of Conference on Learning
Theory. [ bib ] |
| [GKL16] |
A. Garivier, E. Kaufmann, and T. Lattimore (2016).
On Explore-Then-Commit Strategies.
In PMLR, volume 29 of Advances in Neural Information
Processing Systems. [ bib ] |
| [GGCA11] |
N. Gupta, O. Granmo-Christoffer, and A. Agrawala (2011).
Thompson Sampling for Dynamic Multi Armed Bandits.
In International Conference on Machine Learning and
Applications Workshops, pages 484--489. IEEE. [ bib ] |
| [HGB+06] |
C. Hartland, S. Gelly, N. Baskiotis, O. Teytaud, and M. Sebag (2006).
Multi-Armed Bandit, Dynamic Environments and Meta-Bandits.
In NeurIPS 2006 Workshop, Online Trading Between Exploration
And Exploitation. [ bib ] |
| [HT10] |
J. Honda and A. Takemura (2010).
An Asymptotically Optimal Bandit Algorithm for Bounded Support
Models.
In Conference on Learning Theory, pages 67--79. PMLR. [ bib ] |
| [JKYD18] |
H. Joshi, R. Kumar, A. Yadav, and S. J. Darak (2018).
Distributed Algorithm for Dynamic Spectrum Access in
Infrastructure-Less Cognitive Radio Network.
In 2018 IEEE Wireless Communications and Networking Conference
(WCNC), pages 1--6.
ISSN 1558-2612. [ bib | DOI ] |
| [JEMP09] |
W. Jouini, D. Ernst, C. Moy, and J. Palicot (2009).
Multi-Armed Bandit Based Policies for Cognitive Radio's
Decision Making Issues.
In International Conference Signals, Circuits and Systems.
IEEE. [ bib ] |
| [JEMP10] |
W. Jouini, D. Ernst, C. Moy, and J. Palicot (2010).
Upper Confidence Bound Based Decision Making Strategies and
Dynamic Spectrum Access.
In International Conference on Communications, pages 1--5.
IEEE. [ bib | DOI ] |
| [KNJ12] |
D. Kalathil, N. Nayyar, and R. Jain (2012).
Decentralized Learning for Multi-Player Multi-Armed Bandits.
In Conference on Decision and Control. IEEE. [ bib ] |
| [KCG12] |
E. Kaufmann, O. Cappé, and A. Garivier (2012).
On Bayesian Upper Confidence Bounds for Bandit Problems.
In International Conference on Artificial Intelligence and
Statistics, pages 592--600. [ bib ] |
| [KKM12] |
E. Kaufmann, N. Korda, and R. Munos (2012).
Thompson Sampling: an Asymptotically Optimal Finite-Time
Analysis.
In Algorithmic Learning Theory, pages 199--213. PMLR. [ bib ] |
| [KCG14] |
E. Kaufmann, O. Cappé, and A. Garivier (2014).
On the Complexity of A/B Testing.
In Conference on Learning Theory, pages 461--481. PMLR. [ bib ] |
| [KAF+18] |
R. Kerkouche, R. Alami, R. Féraud, N. Varsier, and P. Maillé (2018).
Node-based optimization of LoRa transmissions with Multi-Armed
Bandit algorithms.
In International Conference on Telecommunications. J. Palicot
and R. Pyndiah, Saint-Malo, France. [ bib ] |
| [KS06] |
L. Kocsis and C. Szepesvári (2006).
Discounted UCB.
In 2nd PASCAL Challenges Workshop. [ bib ] |
| [KHN15] |
J. Komiyama, J. Honda, and H. Nakagawa (2015).
Optimal Regret Analysis of Thompson Sampling in Stochastic
Multi-Armed Bandit Problem with Multiple Plays.
In International Conference on Machine Learning, volume 37,
pages 1152--1161. PMLR. [ bib ] |
| [TRY17] |
K. Tomer, L. Roi, and M. Yishay (2017).
Bandits with Movement Costs and Adaptive Pricing.
In Conference on Learning Theory, volume 65, pages
1242--1268. PMLR. [ bib ] |
| [KYDH18] |
R. Kumar, A. Yadav, S. J. Darak, and M. K. Hanawal (2018).
Trekking Based Distributed Algorithm for Opportunistic
Spectrum Access in Infrastructure-Less Network.
In 2018 16th International Symposium on Modeling and
Optimization in Mobile, Ad-Hoc, and Wireless Networks (WiOpt), pages 1--8. [ bib | DOI ] |
| [KSGB19] |
B. Kveton, C. Szepesvari, M. Ghavamzadeh, and C. Boutilier (2019).
Perturbed-History Exploration in Stochastic Multi-Armed
Bandits.
In 28th International Joint Conference on Artificial
Intelligence (IJCAI 2019).
https://arxiv.org/abs/1902.10089. [ bib | http ] |
| [KPV17] |
J. Kwon, V. Perchet, and C. Vernade (2017).
Sparse Stochastic Bandits.
In Conference on Learning Theory, pages 1269--1270. [ bib ] |
| [LVC16] |
P. Lagrée, C. Vernade, and O. Cappé (2016).
Multiple-Play Bandits in the Position-Based Model.
In Advances in Neural Information Processing Systems, pages
1597--1605. [ bib ] |
| [Lat16c] |
T. Lattimore (2016).
Regret Analysis Of The Finite Horizon Gittins Index Strategy
For Multi Armed Bandits.
In Conference on Learning Theory, pages 1214--1245. PMLR. [ bib ] |
| [LM09] |
A. Lazaric and R. Munos (2009).
Hybrid Stochastic-Adversarial On-Line Learning.
In Conference on Learning Theory. [ bib ] |
| [LCLS10] |
L. Li, W. Chu, J. Langford, and R. E. Schapire (2010).
A Contextual-Bandit Approach to Personalized News Article
Recommendation.
In International Conference on World Wide Web, pages 661--670.
ACM. [ bib ] |
| [LPSY18] |
D. Liau, E. Price, Z. Song, and G. Yang (2018).
Stochastic Multi-Armed Bandits in Constant Space.
In International Conference on Artificial Intelligence and
Statistics. [ bib ] |
| [LZ08] |
K. Liu and Q. Zhao (2008).
A Restless Bandit Formulation of Opportunistic Access:
Indexablity and Index Policy.
In Annual Communications Society Conference on Sensor, Mesh
and Ad-Hoc Communications and Networks Workshops. IEEE. [ bib ] |
| [LLS18] |
F. Liu, J. Lee, and N. Shroff (2018).
A Change-Detection based Framework for Piecewise-stationary
Multi-Armed Bandit Problem.
In The Thirty-Second AAAI Conference on Artificial
Intelligence (AAAI 2018). [ bib ] |
| [LBCU+09] |
M. López-Benítez, F. Casadevall, A. Umbert, J. Pérez-Romero,
R. Hachemani, J. Palicot, and C. Moy (2009).
Spectral Occupation Measurements and Blind Standard
Recognition Sensor for Cognitive Radio Networks.
In 2009 4th International Conference on Cognitive Radio
Oriented Wireless Networks and Communications, pages 1--9. IEEE. [ bib ] |
| [LRC+16] |
J. Louëdec, L. Rossi, M. Chevalier, A. Garivier, and J. Mothe (2016).
Algorithme de bandit et obsolescence : un modèle pour la
recommandation.
In 18ème Conférence francophone sur l'Apprentissage
Automatique, 2016 (Marseille, France). [ bib ] |
| [LWAL18] |
H. Luo, C. Wei, A. Agarwal, and J. Langford (2018).
Efficient Contextual Bandits in Non-stationary Worlds.
In S. Bubeck, V. Perchet, and P. Rigollet, editors, Proceedings
of the 31st Conference On Learning Theory, volume 75 of Proceedings of
Machine Learning Research, pages 1739--1776. PMLR.
https://http://proceedings.mlr.press/v75/luo18a.html. [ bib | .html | .pdf ] |
| [MM11] |
O.-A. Maillard and R. Munos (2011).
Adaptive Bandits: Towards the best history-dependent
strategy.
In International Conference on Artificial Intelligence and
Statistics, pages 570--578. [ bib ] |
| [Mai19] |
O.-A. Maillard (2019).
Sequential change-point detection: Laplace concentration of
scan statistics and non-asymptotic delay bounds.
In Algorithmic Learning Theory. [ bib ] |
| [MS13] |
J. Mellor and J. Shapiro (2013).
Thompson Sampling in Switching Environments with Bayesian
Online Change Detection.
In Artificial Intelligence and Statistics, pages 442--450. [ bib ] |
| [MG17] |
P. Ménard and A. Garivier (2017).
A Minimax and Asymptotically Optimal Algorithm for Stochastic
Bandits.
In Algorithmic Learning Theory, volume 76, pages 223--237.
PMLR. [ bib ] |
| [MTC+16] |
A. Maskooki, V. Toldov, L. Clavier, V. Loscrí, and N. Mitton (February
2016).
Competition: Channel Exploration/Exploitation Based on a
Thompson Sampling Approach in a Radio Cognitive Environment.
In International Conference on Embedded Wireless Systems and
Networks (dependability competition). Graz, Austria. [ bib ] |
| [MM17] |
J. Mourtada and O.-A. Maillard (2017).
Efficient Tracking of a Growing Number of Experts.
In Algorithmic Learning Theory, volume 76 of
Proceedings of Algorithmic Learning Theory, pages 1--23. Tokyo, Japan. [ bib ] |
| [MB19] |
C. Moy and L. Besson (May 2019).
Decentralized Spectrum Learning for IoT Wireless Networks
Collision Mitigation.
In ISIoT workshop. Santorin, Greece.
https://sites.google.com/view/ISIoT2019/. [ bib | http | .pdf ] |
| [Moy14] |
C. Moy (2014).
Reinforcement Learning Real Experiments for Opportunistic
Spectrum Access.
In WSR'14, page 10. Karlsruhe, Germany.
https://hal-supelec.archives-ouvertes.fr/hal-00994975. [ bib | http ] |
| [NC17] |
O. Naparstek and K. Cohen (2017).
Deep Multi-User Reinforcement Learning for Dynamic Spectrum
Access in Multichannel Wireless Networks.
In GLOBECOM 2017 - 2017 IEEE Global Communications Conference,
pages 1--7. [ bib | DOI ] |
| [RMZ14] |
C. Robert, C. Moy, and H. Zhang (2014).
Opportunistic Spectrum Access Learning Proof of Concept.
In SDR-WinnComm'14, page 8. Schaumburg, United States.
https://hal-supelec.archives-ouvertes.fr/hal-00994940. [ bib | http ] |
| [RSS16] |
J. Rosenski, O. Shamir, and L. Szlak (2016).
Multi-Player Bandits -- A Musical Chairs Approach.
In International Conference on Machine Learning, pages
155--163. PMLR. [ bib ] |
| [SLM12] |
A. Sani, A. Lazaric, and R. Munos (2012).
Risk-Aversion In Multi-Armed Bandits.
In Advances in Neural Information Processing Systems, pages
3275--3283. [ bib ] |
| [SKHD18] |
S. Sawant, R. Kumar, M. K. Hanawal, and S. J. Darak (2018).
Learning to Coordinate in a Decentralized Cognitive Radio
Network in Presence of Jammers.
In 16th International Symposium on Modeling and Optimization
in Mobile, Ad Hoc, and Wireless Networks (WiOpt). IEEE, Shanghai, China.
https://arxiv.org/abs/1803.06810. [ bib | DOI | http ] |
| [SL17] |
Y. Seldin and G. Lugosi (2017).
An Improved Parametrization and Analysis of the EXP3++
Algorithm for Stochastic and Adversarial Bandits.
In Conference on Learning Theory, volume 65, pages 1--17.
PMLR. [ bib ] |
| [TL11] |
C. Tekin and M. Liu (2011).
Performance and Convergence of Multi-User Online Learning.
In International Conference on Game Theory for Networks,
pages 321--336. Springer Berlin Heidelberg. [ bib ] |
| [TL12] |
C. Tekin and M. Liu (2012).
Online Learning in Decentralized Multi-User Spectrum Access
with Synchronized Explorations.
In Military Communications Conference. IEEE. [ bib ] |
| [TPHD19] |
H. Tibrewal, S. Patchala, M. K. Hanawal, and S. J. Darak (2019).
Distributed Learning and Optimal Assignment in Multiplayer
Heterogeneous Networks.
In IEEE Conference on Computer Communications (INFOCOM
2019), pages 1693--1701. IEEE.
https://arxiv.org/abs/1901.03868. [ bib | http ] |
| [TCLM16] |
V. Toldov, L. Clavier, V. Loscrí, and N. Mitton (September 2016).
A Thompson Sampling Approach to Channel Exploration
Exploitation Problem in Multihop Cognitive Radio Networks.
In PIMRC, pages 1--6. Valencia, Spain. [ bib | DOI ] |
| [WS18b] |
L. Wei and V. Srivatsva (2018).
On Abruptly-Changing And Slowly-Varying Multi-Armed Bandit
Problems.
In American Control Conference, pages 6291--6296. IEEE. [ bib ] |
| [WS18a] |
L. Wei and V. Srivastava (2018).
On Distributed Multi-player Multi-Armed Bandit Problems in
Abruptly-Changing Environment.
In Conference on Decision and Control, pages 5783--5788. IEEE. [ bib ] |
| [YFE12] |
X. Yang, A. Fapojuwo, and E. Egbogah (September 2012).
Performance Analysis and Parameter Optimization of Random
Access Backoff Algorithm in LTE.
In Vehicular Technology Conference, pages 1--5. IEEE. [ bib | DOI ] |
| [YM09] |
J. Y. Yu and S. Mannor (2009).
Piecewise-Stationary Bandit Problems with Side Observations.
In International Conference on Machine Learning, pages
1177--1184. ACM. [ bib ] |
| [ZBLN19] |
S. M. Zafaruddin, I. Bistritz, A. Leshem, and D. Niyato (2019).
Distributed Learning for Channel Allocation Over a Shared
Spectrum.
In 20th IEEE International Workshop on SignalProcessing
Advances in Wireless Communications (SPAWC). Cannes, France.
https://arxiv.org/abs/1902.06353. [ bib | http ] |
| [ZS19] |
J. Zimmert and Y. Seldin (2019).
An Optimal Algorithm for Stochastic and Adversarial Bandits.
In K. Chaudhuri and M. Sugiyama, editors, Proceedings of
Machine Learning Research, volume 89 of Proceedings of Machine
Learning Research, pages 467--475. PMLR.
http://proceedings.mlr.press/v89/zimmert19a.html. [ bib | .html | .pdf ] |
| [ABM10] |
J-Y. Audibert, S. Bubeck, and R. Munos (2010).
Best Arm Identification in Multi-Armed Bandits.
In Conference on Learning Theory, page 13. PMLR. [ bib ] |
| [ACBFS95] |
P. Auer, N. Cesa-Bianchi, Y. Freund, and R. Schapire (1995).
Gambling in a Rigged Casino: The Adversarial Multi-Armed
Bandit Problem.
In Annual Symposium on Foundations of Computer Science, pages
322--331. IEEE. [ bib ] |
| [BV19] |
M. Bande and V. V. Veeravalli (2019).
Adversarial Multi-User Bandits for Uncoordinated Spectrum
Access.
In IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP), pages 4514--4518. IEEE. [ bib ] |
| [DNMP16] |
S. J. Darak, A. Nafkha, C. Moy, and J. Palicot (2016).
Is Bayesian Multi Armed Bandit Algorithm Superior? Proof of
Concept for Opportunistic Spectrum Access in Decentralized Networks.
In 11th EAI Conference on Cognitive Radio Oriented Wireless
Network and Communication. Grenoble, France. [ bib ] |
| [K+16] |
T. Kluyver et al. (2016).
Jupyter Notebooks -- a publishing format for reproducible
computational workflows.
In F. Loizides and B. Schmidt, editors, Positioning and Power
in Academic Publishing: Players, Agents and Agendas, pages 87--90. IOS
Press. [ bib ] |
| [DMNM16] |
S. J. Darak, N. Modi, A. Nafkha, and C. Moy (2016).
Spectrum Utilization and Reconfiguration Cost Comparison of
Various Decision Making Policies for Opportunistic Spectrum Access Using Real
Radio Signals.
In 11th EAI Conference on Cognitive Radio Oriented Wireless
Network and Communication. Grenoble, France. [ bib ] |
| [PPS11] |
K. Patil, R. Prasad, and K. Skouby (2011).
A Survey of Worldwide Spectrum Occupancy Measurement Campaigns
for Cognitive Radio.
In 2011 International Conference on Devices and Communications
(ICDeCom), pages 1--5. IEEE. [ bib ] |
| [VMB+10] |
V. Valenta, R. Maršálek, G. Baudoin, M. Villegas, M. Suarez, and
F. Robert (2010).
Survey on spectrum utilization in Europe: Measurements,
analyses and observations.
In 5th EAI Conference on Cognitive Radio Oriented Wireless
Network and Communication, pages 1--5. IEEE. [ bib ] |
| [Chi18] |
F. Chiusano (2018).
Breakpoint Prediction for the Abruptly-Switching
Non-Stationary Multi-Armed Bandit Problem.
Master's thesis, Politecnico Di Milano, AI & R Lab, Laboratorio di
Intelligenza Artificiale e Robotica del Politecnico di Milano. [ bib ] |
| [Wei17] |
E. W. Weisstein (2017).
Exponential Integral.
Online at
https://mathworld.wolfram.com/ExponentialIntegral.html.
From MathWorld -- A Wolfram Web Resource. [ bib ] |
| [Col17] |
Collective (2017).
Exponential Integral.
Online at
https://en.wikipedia.org/wiki/Exponential_integral.
From Wikipedia, The Free Encyclopedia. [ bib ] |
| [GNUa] |
GNU Radio Companion Documentation and Website.
Online at
https://wiki.gnuradio.org/index.php/GNURadioCompanion.
Accessed: 2018-09-25. [ bib ] |
| [GNUb] |
GNU Radio Documentation and Website.
Online at https://www.gnuradio.org/about/.
Accessed: 2018-09-25. [ bib ] |
| [Jor10] |
M. Jordan (2010).
Stat 260/CS 294.
Online at
https://people.eecs.berkeley.edu/~jordan/courses/260-spring10/.
Chapter 8 covers the exponential family. [ bib ] |
| [Oct] |
OctoClock Clock Distribution Module with GPSDO - Ettus Research.
Online at
https://www.ettus.com/product/details/OctoClock-G.
Accessed: 2018-09-25. [ bib ] |
| [Bes19] |
L. Besson (2016--2019).
SMPyBandits: an Open-Source Research Framework for Single and
Multi-Players Multi-Arms Bandits (MAB) Algorithms in Python.
Code at https://GitHub.com/SMPyBandits/SMPyBandits/,
documentation at https://SMPyBandits.GitHub.io/. [ bib ] |
| [Tol14a] |
J. Toledano (June 2014).
Executive Summary: Dynamic Spectrum Management For Innovation
And Growth.
Online at
https://www.economie.gouv.fr/files/files/PDF/french-spectrum-mission-executive-summary-2014-06-25.pdf.
https://www.ladocumentationfrancaise.fr/rapports-publics/144000381/index.shtml. [ bib | http | .pdf ] |
| [Tol14b] |
J. Toledano (June 2014).
Une gestion dynamique du spectre pour l'innovation et la
croissance.
Online at
https://www.economie.gouv.fr/files/files/PDF/rapport-gestion-dynamique-spectre-2014-06-30.pdf.
https://www.ladocumentationfrancaise.fr/rapports-publics/144000381/index.shtml. [ bib | http | .pdf ] |
| [Lat16a] |
T. Lattimore (2016).
Library for Multi-Armed Bandit Algorithms.
Online at: https://github.com/tor/libbandit.
https://github.com/tor/libbandit. [ bib | http ] |
| [Ett] |
Ettus.
USRP Hardware Driver and USRP Manual.
Online at
https://files.ettus.com/manual/page_usrp2.html.
Accessed: 2018-09-25. [ bib ] |
| [Raj17] |
V. Raj (2017).
A Julia Package for providing Multi Armed Bandit
Experiments.
Online at: https://github.com/v-i-s-h/MAB.jl.
https://github.com/v-i-s-h/MAB.jl. [ bib | http ] |
| [BBS+19] |
S. Behnel, R. Bradshaw, Dag S. Seljebotn, G. Ewing, W. Stein, G. Gellner,
et al. (2019).
Cython: C-Extensions for Python.
Online at: https://cython.org.
https://cython.org. [ bib | http ] |
| [C+18] |
A. Collette et al. (2018).
h5py: HDF5 for Python.
Online at: https://www.h5py.org.
https://www.h5py.org. [ bib | http ] |
| [Var17] |
G. Varoquaux (March 2017).
Joblib: running Python functions as pipeline jobs.
Online at: https://joblib.readthedocs.io.
https://joblib.readthedocs.io. [ bib | http ] |
| [I+17] |
Anaconda Inc. et al. (2017).
Numba, NumPy aware dynamic Python compiler using LLVM.
Online at: https://numba.pydata.org.
https://numba.pydata.org. [ bib | http ] |
| [CGK12] |
O. Cappé, A. Garivier, and E. Kaufmann (2012).
pymaBandits.
https://mloss.org/software/view/415, online at:
https://mloss.org/software/view/415. [ bib | http ] |
| [Fou17] |
Python Software Foundation (October 2017).
Python Language Reference, version 3.6.
Online at: https://www.python.org.
https://www.python.org. [ bib | http ] |
| [JOP+01] |
E. Jones, T. E. Oliphant, P. Peterson, et al. (2001).
SciPy: Open source scientific tools for Python.
Online at: https://www.scipy.org.
https://www.scipy.org. [ bib | http ] |
| [W+17] |
M. Waskom et al. (September 2017).
Seaborn: statistical data visualization.
Online at: https://seaborn.pydata.org.
https://seaborn.pydata.org. [ bib | DOI | http ] |
| [B+18] |
G. Brandl et al. (2018).
Sphinx: Python documentation generator.
Online at: https://sphinx-doc.org.
https://sphinx-doc.org. [ bib | http ] |
| [BP+16] |
I. Bicking, PyPA, et al. (November 2016).
Virtualenv: a tool to create isolated Python environments.
Online at: https://virtualenv.pypa.io.
https://virtualenv.pypa.io. [ bib | http ] |
| [Bod17] |
Q. Bodinier (2017).
Coexistence of Communication Systems Based on Enhanced
Multi-Carrier Waveforms with Legacy OFDM Networks.
Ph.D. thesis, CentraleSupélec.
https://www.theses.fr/2017REN1S091. [ bib | http ] |
| [Jou17] |
W. Jouini (2017).
Contribution to Learning and Decision Making under Uncertainty
for Cognitive Radio.
Ph.D. thesis, CentraleSupélec, IETR, Rennes.
https://www.theses.fr/2012SUPL0010. [ bib | http ] |
| [Kau14] |
E. Kaufmann (2014).
Analysis of Bayesian and Frequentist Strategies for Sequential
Resource Allocation.
Ph.D. thesis, Telecom ParisTech.
https://www.theses.fr/2014ENST0056. [ bib | http ] |
| [Mod17] |
N. Modi (2017).
Machine Learning and Statistical Decision Making for Green
Radio.
Ph.D. thesis, CentraleSupélec, IETR, Rennes.
https://www.theses.fr/2017SUPL0002. [ bib | http ] |
| [Tol17] |
V. Toldov (2017).
Adaptive MAC Layer for Interference Limited WSN.
Ph.D. thesis, Université Lille 1 Sciences et technologies.
https://www.theses.fr/2017LIL10002. [ bib | http ] |
| [Val16] |
M. Valko (2016).
Bandits on Graphs and Structures.
Habilitation thesis to supervise research, École normale
supérieure de Cachan. [ bib ] We investigate the structural properties of certain sequential decision-making problems with limited feedback (bandits) in order to bring the known algorithmic solutions closer to a practical use. In the first part, we put a special emphasis on structures that can be represented as graphs on actions, in the second part we study the large action spaces that can be of exponential size in the number of base actions or even infinite. We show how to take advantage of structures over the actions and (provably) learn faster. |
| [BK18b] |
L. Besson and E. Kaufmann (February 2018).
What Doubling Trick Can and Can't Do for Multi-Armed
Bandits.
https://hal.archives-ouvertes.fr/hal-01736357,
preprint, https://hal.archives-ouvertes.fr/hal-01736357. [ bib | http | www: ] |
| [BBM18] |
L. Besson, R. Bonnefoi, and C. Moy (June 2018).
Multi-Arm Bandit Algorithms for Internet of Things Networks: A
TestBed Implementation and Demonstration that Learning Helps.
https://ict-2018.org/demos,youtu.be/HospLNQhcMk,
Demonstration presented at International Conference on Telecommunications. [ bib | http ] |
| [BK19b] |
L. Besson and E. Kaufmann (February 2019).
Combining the Generalized Likelihood Ratio Test and kl-UCB for
Non-Stationary Bandits.
https://hal.archives-ouvertes.fr/hal-02006471,
preprint, https://hal.archives-ouvertes.fr/hal-02006471, arXiv
preprint arXiv:1902.01575. [ bib | http ] |
| [MBDT19] |
C. Moy, L. Besson, G. Delbarre, and L. Toutain (July 2019).
Decentralized Spectrum Learning for Radio Collision Mitigation
in Ultra-Dense IoT Networks: LoRaWAN Case Study and Measurements.
https://hal.inria.fr/hal-XXX, Submitted for a
special volume on Machine Learning for Intelligent Wireless Communications
and Networking, Annals of Telecommunications. [ bib | http | .pdf ] |
| [MM19] |
S. Mukherje and O.-A. Maillard (2019).
Improved Changepoint Detection for Piecewise i.i.d Bandits.
https://subhojyoti.github.io/pdf/aistats_2019.pdf,
working paper or preprint. [ bib | .pdf ] |
| [RK17] |
V. Raj and S. Kalyani (2017).
Taming Non-Stationary Bandits: a Bayesian Approach.
https://arxiv.org/abs/1707.09727. [ bib | http ] |
| [Bes18] |
L. Besson (2018).
SMPyBandits: an Experimental Framework for Single and
Multi-Players Multi-Arms Bandits Algorithms in Python.
https://hal.archives-ouvertes.fr/hal-01840022,
preprint, submitted to JMLR MLOSS,
https://hal.archives-ouvertes.fr/hal-01840022. [ bib | http | www: ] |
| [BN93] |
M. Basseville and I. Nikiforov (1993).
Detection of Abrupt Changes: Theory And Application, volume
104.
Prentice Hall Englewood Cliffs. [ bib ] |
| [Bón11] |
M. Bóna (2011).
A Walk Through Combinatorics: an Introduction to Enumeration
and Graph Theory.
World Scientific. [ bib ] |
| [BLM13] |
S. Boucheron, G. Lugosi, and P. Massart (2013).
Concentration Inequalities: A Nonasymptotic Theory of
Independence.
Oxford university press. [ bib ] |
| [BV04] |
S. Boyd and L. Vandenberghe (2004).
Convex Optimization.
Cambridge Univ. Press. [ bib ] |
| [CBL06] |
N. Cesa-Bianchi and G. Lugosi (2006).
Prediction, Learning, and Games.
Cambridge University Press. [ bib ] |
| [LS19] |
T. Lattimore and C. Szepesvári (2019).
Bandit Algorithms.
Cambridge University Press.
https://tor-lattimore.com/downloads/book/book.pdf,
draft of Wednesday 1st of May, 2019,
https://tor-lattimore.com/downloads/book/book.pdf. [ bib | .pdf ] |
| [Nor98] |
J. R. Norris (1998).
Markov Chains, volume 2 of Cambridge Series in
Statistical and Probabilistic Mathematics.
Cambridge University Press, Cambridge. [ bib ] |
| [Sie13] |
D. Siegmund (2013).
Sequential analysis: tests and confidence intervals.
Springer Science & Business Media. [ bib ] |
| [SB18] |
R. S. Sutton and A. G. Barto (2018).
Reinforcement Learning: An introduction.
MIT press. [ bib ] |
| [TNB15] |
A. Tartakovsky, I. Nikiforov, and M. Basseville (2015).
Sequential Analysis. Hypothesis Testing and Changepoint
Detection.
CRC Press. [ bib ] |
This file was generated by bibtex2html 1.99.


Howdy Ho!
Home
Work
About
Portfolio
phone
+46702673920
location_on
Gothenburg
mail_outline
Email me
Portfolio
Have a look friend!


Menu
Home
View
Visit
Visualize
Scenes from the City
Filmmaking in New York: 1966-2006
X
Movie Title
Year:
1966
Director:
Sidney Lumet
Rating:
4.5
Votes:
32,556
Plot:
Desc....
Powered by


' out posts '
Safe blood Save lives
The campaign theme for this year's World Blood Donor Day is “Safe blood saves lives” with the slogan “Give blood and make the world a healthier place”. The idea is to focus on the contribution an individual giver can make to improve health of others in the community.
Stay home Stay safe
Official Public Service Announcement on Coronavirus from the World Health Organization. Learn about Symptoms, Prevention, Travel Advisory and Common FAQs on Coronavirus. Advice for the public. Official info on COVID-19. Your questions answered
Stop Together
Let's Stop HIV Together. The Let's Stop HIV Together campaign (formerly known as Act Against AIDS) includes resources and partnerships aimed at stopping HIV ...


What it does
The following examples show the Context Menu plugin in action. Just make a right-click as you are used to in your faviourite OS. The first list item has a context menu defined as a JSON object and passed to the plugin within the JavaScrip. The second one has a menu defined in the HTML accordion to the menu structure for the Foundation DropdownMenu.
For the basic setup, just take any HTML element you want and add an data-context-menu
attribute to it. The value depends on the way you want to use this plugin. The possibilities can be found below.
For further information please visit the Project on GitHub.
Currently this plugin is still in development, and thus in a version 0.0.x, which works well with Foundation for sites 6.2.3. It is planned to release the first stable version of this plugin once Foundation for sites version 6.3 is released.
HTML Example
This example uses a predefined HTML list, formed according to the Foundation Dropdown Menu syntax to hold the context menu. You only need to give your context menu list an ID and then reference it in the list's data-context-menu
attribute. Notice the leading #
to indicate that it points to an HTML element.
- Some document
- Another document
- Important presentation

Copy

Move

Delete

JSON Example
You can also define the context menu using JSON. Just register the menu type in the plugin and then reference the registered name in the list's data-context-menu
attribute.
- Some document
- Another document
- Important presentation
Foundation.ContextMenu.prototype.addConfig('listitem', {
accessible: true,
single: true,
structure: [{
text: 'Move to',
help: 'Alt + M',
key: 'ALT_M',
click: function($item) {
alert('Moving item: ' + $item.index());
}
}, {
icon: 'fa fa-paper-plane',
text: 'Send via mail',
click: function($item) {
alert('Sending a mail!');
}
}, {
cssclass: 'divider'
}, {
text: 'More...',
children: [{
text: 'Delete',
isDisabled: true,
}, {
text: 'Download...',
children: [{
text: 'Save as PDF'
}, {
text: 'Save as PNG'
}]
}]
}]
});
Creating Context Menus with JS
It is also possible to open context menus on the fly without attaching them to an actual HTML element. This approach is based on the JSON method shown above. Creating the plugin instance on demand might me helpful when dealing with dynamic applications that keep on re-rendering parts of the page.
- Some document
- Another document
- Important presentation
$('#javascript-example-list li').on('contextmenu', function(event) {
event.preventDefault();
var jsContextMenu = new Foundation.ContextMenu($(this), {
position: event,
structure: [{
text: 'Move to',
click: function($item) {
alert('Moving item: ' + $item.index());
}
}, {
icon: 'fa fa-paper-plane',
text: 'Send via mail',
click: function($item) {
alert('Sending a mail!');
}
}]
});
// Destroy context menu once it's closed
$(this).one('hide.zf.contextmenu', function() {
jsContextMenu.destroy();
});
});
Interacting
To actually do something with the user's interaction, you can use the custom contextselect.zf.contextmenu
-event. This will be triggered for the menu's anchor element (i.e. the element containing the data-context-menu
-attribute) once an option out of the context menu is chosen. The event carries an object of data that helps you handling the selection:
element
: The menu's anchor element (li
-element).option
: The selected context menu entry (a
-element).
Options
The following options can be used.
-
single
If only one menu should be open at a time.
Default istrue
. -
accessible
If ARIA attributes should be added.
Default istrue
. -
touchdelay
Delay for touch event before the menu opens.
Default is400
. -
screenOffset
Minimum offset to the screen.
Default is10
. -
closeOnClick
If the menu should be closed after clicking an option.
Default istrue
. -
emptyEntryWillCloseMenu
If the menu should be closed if an option without a click function was clicked.
Default istrue
. -
position
Event to display the context menu directly when using pure JS.
Default isnull
.


Cloakspace is an example of anonymity where opinions are always welcome. No one should feel restrained here.
However, although we are an anonymous site, we have to maintain some guidelines and consider these while determining which posts should be considered as inappropriate.
We encourage everyone to share their opinions here without any fear but at the same time, we understand that this does not come with the freedom to type anything that might be extreme. Cloakspace neither promotes any hate speech, cyber bullying nor does it have any affiliation towards any group, community, society be it political or social. If we find any post which engages in activities including BUT NOT limited to using ethnic slurs, obscenity, racism, cyber violence, we reserve the right to remove that post permanently. We will look at each post objectively and adhere to the guidelines we have mentioned above. Extreme posts be it abusive or bullying, shall be removed if they disturb the order of the website.
In addition to this, we have a report action which we urge people to use in case someone finds any post abusive or unusual. We would have a look at it as soon as we receive a report. You can also use the feedback to notify us of any indecent activities that might be going on to help us keep in track. You can also inform the creators directly.
Cloakspace should remain an optimistic platform encouraging people to come forward with their thoughts and we shall always try to keep it that way.
Stay happy. Keep posting!


Select the three actions on this page in order.
This step will compare the performance differences between TRUNCATE and DELETE.
Run TRUNCATE time test
Truncate rows in the 1k, 5k, 10k, 50k and 100k tables and record the time required to perform each operation.
Graph TRUNCATE times
Plot the TRUNCATE performance data on the graph.
Compare DELETE vs. TRUNCATE times
Adds performance data from previous DELETE operations to the graph for comparison.
After running the three actions you should see a graph in the lower right panel comparing the performance differences between the two commands. Note that TRUNCATE performs consistently even with larger data sets.


 This is to move as much code into this file as possible to allow flexibility with future changes.
- Parameters
-
| string | $rootDir | Root of XF install |
- Returns
- XFUpgraderWeb
The documentation for this class was generated from the following file:
- C:/Users/Michel/Downloads/XenForo-2.1.0-RC1/XenForo-2.1.0-RC1/upload/src/XF/Install/_upgrader/core.php


Sprint 1: Getting Started, Software Engineering Methods, Software as a Service, and Tools of the Trade
Welcome to Software Engineering! By this point in your software development journey
I'm sure that you are confident in writing moderately complicated software programs. You've
learned about structured programming techniques like sequence, selection, and repetition. You've
learned how to break complicated programs down into more manageable pieces by using functions.
You've even understand Object-Oriented Programming can help you develope more complex
applications by utilizing encapsulation, inheritance, and polymorphism.
That's wonderful, however to create sophisticated modern programs we need to master some
additional skills that allow us to effectively work across teams to predictably develop, enhance,
and maintain complex systems that meet real users needs. These additional Software Engineering
skills are included in what we call the Software Development Life Cycle and include topics like
Project Management, Architecture, Requirements, Testing, Support, and Maintenance. These are some of
the topics that we will be exploring as part of this Software Engineering course.
You have likely noticed that I am using the term “sprint 1” to describe our first set of course
activities and assignments. Sprints are part of the Scrum development process. The term is used to
describe a unit of work which will be delivered in a given amount of time. We will be learning
more about sprints, Scrum, and Agile development in the coming days. For now it is sufficient to
know that our sprint 1 activities listed below are expected to take approximately 20 hours of
focused effort to complete.
Let's start with what we hope to learn in sprint 1. By the end of sprint 1 we will be able to:
describe the basic steps in the Software Development Life Cycle (SDLC)
compare and contrast software life cycle methodologies
explain why agile development has become the preferred software development process
define software quality
describe software as a service and its benefits
explain legacy code
list the pros and cons of cloud computing
describe client-server, three-tier and MVC design patterns
Below is our sprint 1 activities list. Although you are welcome to pursue the activities in any order, they are
listed in the order I think will be the most beneficial for you to work through the topics. Each item is expected to
take 10 to 30 minutes of focused time unless otherwise noted. Highlighted activities are
directly related to graded assignments.
Review our course syllabus
[pdf]
and familiarize yourself with our Blackboard course structure
Review our week 1 / sprint 1 assignments in Blackboard that include two discussion boards, quiz 1, lab
1, and a journal entry
Understand how Blended Learning, Flipped Classroom, and Online class formats relate by reviewing "Blended
Learning & Flipped Classroom"
[video]
Develop a basic understanding of Scrum by reviewing “Introduction to Scrum in 7 Minutes”
[video]
Read Chapter 1 of “Engineering Software as a Service: An Agile Approach Using Cloud Computing” by Armando Fox
and David Patterson (Fox) and review our class lecture on Chapter 1
[video] ~2 hours
Review everything that came before Chapter 1 including Preface in our Fox textbook
[video]
Consistently work on your sprint 1 assignments throughout the sprint ~6 hours
Complete discussion board initial posts (DB* & DB1) by the middle of the sprint
Read Chapter 2 of Fox and review our class lecture on Chapter 2
[video]
Review navigation of your computer local file system utilizing MacOS terminal,
[video],
MS Windows PowerShell
[video],
or Linux
Complete and submit all sprint 1 assignments including Lab 1, Quiz 1, final DB posts, and your Journal entry
Additional references:
Activate the Microsoft 365 account that is available to you free through Lewis University by going to the
Office 365 website
[link] and signing in with your Lewis University ID
<EMAIL_ADDRESS>password
Sprint 2: Node.js, JavaScript, Ruby on Rails, and Azure
Welcome to sprint 2. We covered a lot of material in sprint 1. However, we didn't get a
chance to write much code. This sprint get your code editors out. We are going to start practicing some of the
concepts we learned.
By the end of sprint 2 we will be able to:
Write simple HTML, CSS, Javascript, and Node.js applications and install them to Azure
Understand simple Ruby programs using basic control structures and data structures
Write and interpret regular expressions
Comfortably be able to utilize single-developer workflow with Git and GitHub
Below is the list of activities that we will need to complete this sprint. Once again you are welcome to
pursue the activities in any order you prefer. However, they are listed in the order I think will be the most
beneficial for you to work through the topics. The time commitment for each line item is generally 20 to 40
minutes unless otherwise noted.
Review our sprint 2 assignments in Blackboard that include discussion board 2, quiz 2, lab 2 and a
journal entry
Read Chapter 6 of Fox on developing SaaS client applications in JavaScript using technologies like AJAX and
JSON and review the associated class lecture
[video]
Briefly scan Chapters 3, 4, and 5 of Fox and review the associated class lecture
[video]
Sign up for Safari Books Online which is available to you free through Lewis University
[link]
Within Safari Books Online execute searches for combinations of Node.js, Azure, JavaScript, TypeScript,
Express, Git, GitHub, JSON, and maybe Sails and review the results
Analyze and commit to adopting the “How to Be a Success Programmer” suggestions that you feel are the
most important
[link]
Complete the "HTML5 Tutorial" from W3C
[link] while attempting to elegantly format our course
syllabus into an HTML 5 document
Complete the "CSS Tutorial" from W3C
[link] while enhancing our formatted course syllabus using both
HTML and CSS
Consistently work on your sprint 2 assignments throughout the sprint ~12 hours
Complete discussion board initial DB2 post by the middle of the sprint
Activate the Microsoft 365 account that is available to you free through Lewis University by going to the
Office 365 website and install the products as needed to up to 5 devices
[link] and signing in with your Lewis University ID
Start working on the “JavaScript tutorial” also from W3C
[link] with an eye toward how we might utilize
JavaScript and eventually JSON to dynamically create a data driven implementation of our Syllabus
formatter
Review Regular Expressions and how to effectively utilize them within JavaScript
[link]
Read and Understand the article “What is JSON? JavaScript Object Notation explained”
[link]
Read and Understand “JSON Introduction” from W3C
[link]
Read Fox sections "A.6 Getting Started With Git for Version Control" and "A.7 Getting Started with GitHub"
Complete and submit all sprint 2 assignments including Lab 2, Quiz 2, Discussion Board 2, and your Journal entry
Sprint 3: Application Requirements, Behavior-Driven Design, Node.js, and Azure,
Welcome to sprint 3. This sprint we are going to be focused on Node.js as a platform for Saas. We will be
utilizing in a book from our Safari Books Online in order to help us learn more about the Node.js platform. The book
that we are utilizing learn more about Node.js and the Express framework is “Web Development with Node and Express,
2nd Edition” by Ethan Brown. You will also notice that starting in sprint 3 the assignments are becoming more open-ended and less
prescriptive. I also want to encourage you to collaborate more with your peers as you work through some of the open-ended topics.
By the end of sprint 3 we will be able to:
Understand how client-side HTML, CSS, JavaScript, and Bootstrap applications can be hosted using Node.js and Express
Create and modify basic Node.js utilizing server-side JavaScript applications
Migrate all of your HTML, CSS, JavaScript, and Bootstrap applications over to a single Node.js hosted site running locally
Create a basic Node.js application on Azure
Below is the list of activities that we will need to complete this sprint.
Review our sprint 3 assignments in Blackboard that include discussion board 3, quiz 3, lab 3 and a
journal entry 3
Recall signing up for Safari Books Online which is available to you free through Lewis University
[link]
Within Safari Books Online find “Web Development with Node and Express, 2nd Edition” by Ethan Brown (Brown)
and preview the preface and first four chapters of the book
Thoroughly read chapters 1 and 2 of Brown while completing each of the exercises
Consistently work on your sprint 3 assignments throughout the sprint ~12 hours
Complete discussion board initial DB3 post by the middle of the sprint
Thoroughly read chapters 3 and 4 of Brown while completing each of the exercises
Review the “Node.js Tutorial” also from W3C
[link]
Review Section 4.5 of Fox on Debugging
Read Chapter 7 of Fox on Requirements
Complete and submit all sprint 3 assignments including discussion board 3, quiz 3, lab 3 and a journal
entry 3
Sprint 4: Testing, Test-Driven Development, Express, Node.js, and Azure
Welcome to sprint 4. This sprint we are going to be focused focused on learning about Testing and Test-Driven
Development while we add the Express library to our Node.js and Azure a platform for software as a service (Saas).
We will continue to utilize the “Web Development with Node and Express, 2nd Edition” by Ethan Brown from our Safari
Online library to help prepare us for our final projects.
By the end of sprint 4 we will be able to:
Utilize client-side HTML, CSS, JavaScript, and Bootstrap to create client applications
Create and modify basic Node.js, Express, and JavaScript to create server-side applications
Migrate all of our previous HTML, CSS, JavaScript, and Bootstrap applications over to a single Node.js
hosted site in Azure
Below is the list of activities that we will need to complete this sprint.
Review our sprint 4 assignments in Blackboard that include discussion board 4, quiz 4, lab 4, and
journal entry 4
Recall signing up for Safari Books Online which is available to you free through Lewis University
[link]
Within Safari Books Online find “Web Development with Node and Express, 2nd Edition” by Ethan Brown (Brown)
and preview the preface and first four chapters of the book
Thoroughly read chapters 5 of Brown while completing each of the exercises
Thoroughly read chapters 6 of Brown while completing each of the exercises
Consistently work on your sprint 4 assignments throughout the sprint ~12 hours
Complete discussion board initial DB4 post by the middle of the sprint
Review W3C Tutorials on HTML, CSS, JavaScript, Bootstrap, and Node.js as needed
Read Chapter 8 of Fox on Test-Drive Development
Complete and submit all sprint 4 assignments including discussion board 4, quiz 4, lab 4, and journal
entry 4
Sprint 5: Software Maintenance, Final Project Proposal, and production Azure Site Setup
Welcome to sprint 5. This sprint marks the start of our final project. Think of this sprint as your final project
sprint 1 (of 4). In this sprint you will be defining your final project in terms Epics and User Stories, submitting
them as part of Lab 5, and sharing them with your classmates' as part of Discussion Board 5. If you would like to
work on your final project with a partner, this will also be the time for you to find a partner.
Now that we have demonstrated our ability to work in a HTML, CSS, JavaScript, Bootstrap, and Node.js software as
a service (Saas) environment, we will be setting up the production environment for our final project and migrating
all of our previous application/lab assignments to that new site.
Finally, we will be continuing our software engineering process knowledge journey by studying software
maintenance.
By the end of sprint 5 we will be able to:
Utilize our knowledge of Agile requirements to develop our final project proposal utilizing Epics and
Stories
Setup our production Azure Node.js site for our final project and migrate all of our previous application to
the new site
Fully test and verify our production site and its applications
Explain why change is inevitable for software systems and describe the processes involved in software
evolution
Assess whether a legacy system should be scrapped, maintained, re-engineered or replaced
Below is the list of activities that we will need to complete this sprint.
Review our sprint 5 assignments in Blackboard that include discussion board 5, quiz 5, lab 5, and
reflections 5
Develop your final project proposal by providing Agile Epics and Stories that describe the intended application
functionality from a user perspective and by providing it an excellent name
Review the Agile Alliance site [link], analyze
“Agile 101” (you should understand these topics), and perform a quick overview of the “Agile Glossary”
Complete discussion board initial DB5 post by the middle of the sprint
Setup your production final project Node.js based website and migrate all of the applications that you
developed previously in this class to the new site
Create and execute a test plan on the production site that verifies that the site and all migrated
applications are correctly up and running
Read Chapter 9 of Fox on Software Maintenance
Complete and submit all sprint 5 assignments including discussion board 5, quiz 5, lab 5, and
reflections 5
Sprint 6: Project Management, Serverless Computing, and Final Project
Welcome to sprint 6. This sprint will see us splitting our time between working on our final project, implementing
our first serverless computing services utilizing Azure Functions, and learning more about project management.
By the end of this sprint we will be able to:
Utilize your knowledge of Agile development and Saas to deliver the first features of our final project
Understand where serverless computing fits in to modern software architecture
Write your first serverless computing application utilizing Azure Functions
Understand Project Management, Scrum, Pairs, and VCS
Understand the basics of Scaled Agile
Below is the list of activities that we will need to complete this sprint.
Review our sprint 6 assignments in Blackboard that include discussion board 6, quiz 6, lab 6, and
reflections 6
Start working on your final project project and implement the start implementing the functionality that you
identified in the highest priority stories last sprint
Update your final project Requirements for next sprint and document your commitment for what you will
accomplish next sprint
Recall signing up for Safari Books Online which is available to you free through Lewis University
[link]
Complete discussion board initial DB6 post by the middle of the sprint
Within Safari Books Online do a search on “Azure Functions” and “Serverless Computing”
Scan the resulting books and identify what you think is the best source of information on both topics
Read/Watch and Analyze the following cloud services material below:
Build a serverless computing Azure Function utilizing information and tutorials referenced above or possibly
utilizing an “Additional resources” link below
Within Safari Books Online find the book “Scaling Software Agility: Best Practices for Large Enterprises” by
Dean Luffingwell and read chapters 1 and 2 paying close attention to the “Assumptions Underlying The Model”
section
Read Chapter 10 of Fox on Project Management, Scrum, Pairs, and VCS
Complete and submit all sprint 6 assignments including responding back to one of your classmates'
discussion board 6 post, quiz 6, lab 6, and reflections 6
Sprint 7: Design Patterns, Model-View-Controller, Application Architecture, and Final Project
Welcome to sprint 7. This sprint will see us focussing more on our final project while we also take a look
at the important topic of Design Patterns.
By the end of this sprint we will be able to:
Utilize your knowledge of Agile development and Saas to deliver additional features for our final project
Understand Design Patterns with a particular focus on the Model-View-Controller pattern
Understand Application architecture particularly as it relates to Object-Oriented Programming and Databases
Below is the list of activities that we will need to complete this sprint.
Review our sprint 7 assignments in Blackboard that include discussion board 7, quiz 7, lab 7, and
reflections 7
Continue working on your final project project and implement and test additional high priority functionality that you
identified last sprint
Update your final project Requirements for next sprint and document your commitment for what you will
accomplish next sprint
Recall signing up for Safari Books Online which is available to you free through Lewis University
[link]
Within Safari Books Online find the book “Design Patterns: Elements of Reusable Object-Oriented Software” by
Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides and read everything through chapter 1 paying close
attention to the “Guide to Readers” and “Model/View/Controller” sections
Read Chapter 11 of Fox on Design Patterns
Complete "Application Architectures, Object-Oriented Programming, and Databases - Part 1"
[link]
Complete and submit all sprint 7 assignments including your initial discussion board post, responding
back to one of your classmates' discussion board 7 post, quiz 7, lab 7, and reflections 7
Software Engineering
Sprint 8: Final project, Software Architecture, Performance, Releases, Reliability, and Security
Welcome to sprint 8. Stay focused on your final project. We will be learning about a couple other topics, but this
sprint is all about your final project.
It is important to remember that in our final sprint all assignments are due Thursday at 11:59pm CT. You do have a grace
period until 6am CT the morning of Friday, March 6th to submit them.
By the end of this sprint we will be able to:
Utilize your knowledge of Agile development and Saas to deliver a final project that you are proud of creating
Understand Software Architecture including performance, releases, reliability, and security
Enhance your understanding of application architecture, Object-Oriented Programming, and Databases
Below is the list of activities that we will need to complete this sprint.
Review our sprint 8 assignments in Blackboard that include discussion board 8, quiz 8, lab 8, and
reflections 8
Stay focused on your final project by implementing and testing the additional high priority functionality
that you identified last sprint
Read Chapter 12 of Fox on Project Management, Scrum, Pairs, and VCS
Complete "Application Architectures, Object-Oriented Programming, and Databases - Part 2"
[link]
Complete and submit all sprint 8 assignments including quiz 8, lab 8, and reflections
8 by Thursday (March 5th) at 11:59pm CT
Let's finish strong and deliver something that makes us proud.
Calendar
Developed by Eric Pogue
The MIT License
Copyright (c) 2020 Eric Pogue
Permission is hereby granted, free of charge,
to any person obtaining a copy of this software and
associated documentation files (the "Software"), to
deal in the Software without restriction, including
without limitation the rights to use, copy, modify,
merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom
the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice
shall be included in all copies or substantial portions of the Software.

IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


Use this page to import the TextMate/SublimeText 2 bundles, and to map color scheme of IntelliJ IDEA to that of TextMate.
This page appears when the plugin is installed and enabled.
Refer to TextMate Bundles for controls of this page.


[gecode-users] Managing memory footprint for large problems
Christian Schulte
cschulte at kth.se
Tue Apr 13 11:39:52 CEST 2010
Hi,
I wouldn't really know what to say off my head with the level of detail you
provide. Did you check the recent programming part in Modeling and
Programming with Gecode?
Domain sizes only matter if they have holes and are very, very large. Then
the number of variables and propagators matter. So, without some numbers of
how many propagators and variables your problem has I really can't say
anything.
Cheers
Christian
--
Christian Schulte, www.ict.kth.se/~cschulte/
-----Original Message-----
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of Zaremby, David
Sent: Monday, April 12, 2010 5:25 PM
To: Guido Tack; Christian Schulte
Cc: users at gecode.org gecode
Subject: Re: [gecode-users] Managing memory footprint for large problems
This does seem to be just a gist issue. I guess looking at that was throwing
me off. I used the MinimizeScript and it seems to behave as described as far
as cloning. I suppose our issue is just the number of propagators and the
size of the domains. I suppose we will have to look into defining our own
propagators. Do you have any recommendations for what to aim for in defining
our own propagators to reduce the memory usage and get the most bang for our
buck. What actually consumes the memory, common pitfalls, optimization
tricks?
As an example, We are doing things to constrain tasks that are somewhat
complicated such as you can only have 3 tasks or 10 hours worth of usage in
a period of time and these periods repeat over a specified interval.
David Zaremby
Senior Software Engineer
LSS / Strategic Products
Lockheed Martin Simulation, Training & Support
12506 Lake Underhill Road, MP-823
Orlando, FL 32825
-----Original Message-----
From: Guido Tack [mailto:tack at gecode.org]
Sent: Monday, April 12, 2010 10:05 AM
To: cschulte at kth.se
Cc: Zaremby, David; users at gecode.org gecode
Subject: Re: [gecode-users] Managing memory footprint for large problems
That was actually a small bug in Gist that only occurred when using
interactive search. I just fixed it in the trunk version.
Cheers,
Guido
Christian Schulte wrote:
> Dear David,
>
> could you (or Ashis) please check whether you observe the same behavior
when
> not using Gist, but a simple search engine?
>
> Thanks
> Christian
>
> --
> Christian Schulte, www.ict.kth.se/~cschulte/
>
>
> -----Original Message-----
> From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
> Of Zaremby, David
> Sent: Monday, April 12, 2010 3:41 PM
> To: users at gecode.org; Guido Tack
> Subject: [gecode-users] Managing memory footprint for large problems
>
> I work with Ashis and I have been playing with a few experiments. What I
am
> seeing is that the memory is constantly climbing at every node as I
traverse
> down the left hand side despite very high computation parameters int
> c_d=1000000, int a_d=1000000). I have posted the stack trace for the code
> to maybe give some insight. I see the clone method being called for each
> node which seems odd since I believe it should only be doing choice,
status,
> and commits. I have reverted back from our custom brancher to a simple
built
> in of branch(*this, resources, INT_VAR_MIN_MIN, INT_VAL_MIN). If I had to
> venture a guess from exploring the code, it is because the workingspace is
> never being initialized and thus it keeps trying to initialize it for the
> first time. The memory growth seems to scale with the number of
propagators.
> If I reduce my scheduling constraints but leave my number of choice the
same
> I get a smaller memory footprint for each clone. The same behavior happens
> for the defaul
> t c_d and a_d values.
>
> AutomatedScheduler++.exe!Scheduler::Scheduler(bool share=true,
> Scheduler & s={...}) Line 65 C++
> AutomatedScheduler++.exe!Scheduler::copy(bool share=true) Line 79 +
> 0x37 bytes C++
> GecodeKernel-3-3-1-d-x86.dll!Gecode::Space::_clone(bool share=true)
> Line 470 + 0x18 bytes C++
> GecodeGist-3-3-1-d-x86.dll!Gecode::Space::clone(bool share=true,
> Gecode::CloneStatistics & __formal={...}) Line 2369 C++
>>
>
GecodeGist-3-3-1-d-x86.dll!Gecode::Gist::SpaceNode::recompute(Gecode::Gist::
> BestNode * curBest=0x1dde3558, int c_d=1000000, int a_d=1000000) Line 87
+
> 0x14 bytes C++
>
>
GecodeGist-3-3-1-d-x86.dll!Gecode::Gist::SpaceNode::acquireSpace(Gecode::Gis
> t::BestNode * curBest=0x1dde3558, int c_d=1000000, int a_d=1000000) Line
> 173 + 0x14 bytes C++
>
>
GecodeGist-3-3-1-d-x86.dll!Gecode::Gist::SpaceNode::getNumberOfChildNodes(Ge
> code::Support::BlockAllocator<Gecode::Gist::VisualNode,10000> & na={...},
> Gecode::Gist::BestNode * curBest=0x1dde3558, Gecode::Gist::Statistics &
> stats={...}, int c_d=1000000, int a_d=1000000) Line 291 C++
>
>
GecodeGist-3-3-1-d-x86.dll!Gecode::Gist::TreeCanvas::inspectCurrentNode(bool
> fix=true, int inspectorNo=-1) Line 605 + 0x34 bytes C++
>
>
GecodeGist-3-3-1-d-x86.dll!Gecode::Gist::TreeCanvas::mouseDoubleClickEvent(Q
> MouseEvent * event=0x017bd100) Line 1129 C++
>
>
> David Zaremby
> Senior Software Engineer
> LSS / Strategic Products
>
> Lockheed Martin Simulation, Training & Support
> 12506 Lake Underhill Road, MP-823
> Orlando, FL 32825
> -----Original Message-----
> From: Maity, Ashis K
> Sent: Monday, April 12, 2010 8:48 AM
> To: Zaremby, David
> Subject: FW: [gecode-users] Stopping Gecode Engine gracefully
>
> FYI!
>
> -----Original Message-----
> From: Guido Tack [mailto:tack at gecode.org]
> Sent: Saturday, April 10, 2010 5:41 AM
> To: Maity, Ashis K
> Subject: Re: [gecode-users] Stopping Gecode Engine gracefully
>
> Hi Ashis,
>
> would you mind posting your message to the mailing list? I think this
might
> interest others, too. Anyway, here's my answer.
>
> Maity, Ashis K wrote:
>> Yes, that was also my understanding in terms of setting c_d and a_d. I
was
> not clear in my write up. I mean to say that I am setting higher c_d so
that
> it can use less memory and not run out of memory. But one thing you made
> clear is that even if I do not set a_d it will default to 2. I probably
> missed seeing that in documentation. I was hoping the default behavior of
> a_d will be like setting it higher than c_d (that is a_d is not used).
>
> The defaults are independent of each other (i.e. c_d is always 8 and a_d
is
> always 2). We tried to come up with something more automatic, but it's
not
> clear how that's supposed to work.
>
>> In any event, yes my problem runs out of memory with default c_d and a_d.
> And it does run out with other combination of c_d and a_d as well (say c_d
=
> 20000 and a_d = 10000 or c_d = 200 and a_d = 100). Strange thing is even
> with varied c_d & a_d combination, my memory consumption graph rises
almost
> similar way (as observed from Windows Task Manager). To give you some
> background, I am working on Scheduler problems that contain hundreds of
> tasks with large start domains and have many choices on resource.
Previously
> I was using Mozart/Oz, but recently transferred my code to Gecode hoping
it
> will help my memory problem. It appeared to do so when it ran a problem
with
> 600 tasks rather quickly. But when I try to run a problem with 1000 tasks,
> it crashes within a couple of minutes saying "heap memory is exhausted".
It
> may be that my memory problem is exhausting before it comes to choices and
> that's why c_d and a_d value are not making much of a difference! Can you
> please comment o
> n!
>> this?
>
>
> You could try running your problem in Gist and double-click the root node
> (that will create exactly one instance of your problem and not perform any
> search).
> If that exhausts the memory, it's clearly a problem in the model itself.
> Actually, if that doesn't exhaust the memory, then setting c_d/a_d very
high
> should definitely at least change the memory behavior, because with a very
> high c_d/a_d, the required memory is pretty much exactly twice the memory
of
> the root node.
>
> If it turns out to be a problem in the root node already, there's several
> things one could try. First of all, you should check how many variables
you
> create. If it's quadratic in the problem size (e.g. one Boolean for every
> pair of tasks could be typical for scheduling problems), that would
explain
> the difference between 600 and 1000 tasks - I'm also working on scheduling
> problems right now and I've run into similar issues. Being very careful
not
> to create too many temporary variables is very important in this case.
> Then, it's sometimes possible to replace a set of propagators with one
> custom propagator. That can help a lot if there's a large number (say,
> quadratically many) of these propagators.
> Finally, reformulating the problem might help, or trying to solve a
> relaxation (e.g. aggregating several tasks into bigger meta-tasks to make
> the problem smaller), but that's of course very problem-specific and
> sometimes simply impossible.
>
> Cheers,
> Guido
>
> --
> Guido Tack, http://people.cs.kuleuven.be/~guido.tack/
>
>
>
>
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org
> https://www.gecode.org/mailman/listinfo/gecode-users
>
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org
> https://www.gecode.org/mailman/listinfo/gecode-users
--
Guido Tack, http://people.cs.kuleuven.be/~guido.tack/
_______________________________________________
Gecode users mailing list
users at gecode.org
https://www.gecode.org/mailman/listinfo/gecode-users
More information about the users
mailing list


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/Definitions.20for.20binder.20notations.html ---
If I am a learner and I see X ≃ Y
and I want to know what it means, I can type #print notation ≃
. If I am a learner and I see {x : ℝ | x > 0}
and I type #print {
I just get a confusing answer. I guess this also happens for (
and [
. Of course { and [ are defined in Lean somewhere -- but a beginner is unlikely to be able to find them. Finally, if I try and figure out what Π
or λ
mean, which to a beginner look as much like notation as ≃
, #print notation
just tells me that actually these aren't notation at all. I was going to write some brief docs about this. What other symbols need to be covered? Oh -- ∃!
and ∃
. Hmm -- ∀
has no notation again, I'm surprised it's not defined to be Pi via a standard notation definition.
I guess a trick which works well with { and [ is to simply switch notation off in your Lean file. But this has an irritating consequence of switching _all_ your notation off, rather than just that for the brackets.
[
(assuming you're referring to the notation for lists) and the existential quantifiers are defined as notation, the other things you mention are not. I assume they must be built in to the Lean parser.
You can also use jump-to-definition to find out the meaning of notation, sort of. It takes you to the definition of the thing that the notation represents (e.g., the definition of list
itself), but often the notation
declaration is nearby.
Last updated: Apr 24 2020 at 19:02 UTC


--- ---
Hi! My name is Gabriela. I'm originally from Slovakia, a tiny country in the middle of Europe, but living currently in San Francisco. I'm communicative, positive and outgoing person with ability to quickly learn new things.
During the college I started to look for job opportunities. I volunteered in the shelter for homeless people and later I got a job in the field I studied.
Volunteering in the homeless shelter.
Financial advice in mortgage application, insurance polices, saving products.
Administrative duties, warehouse management, reports and variety of ad hoc duties.
I graduated with masters degree in Financial management.
Before graduating I got a job as executive assistant for the CEO. After 3 months I got promoted to project manager.
Daily administrative work, correspondence, reports, dealing with business travel and accommodation, office management.
Project Manager for EU funds aimed at Operational Program Employment, Social Inclusion and Operational Program Education.
Preparing applications for grants, setting a schedule of activities, tracking spending budget, reporting, public procurement and responsibility for project planning.
I wanted to learn english. The best way how to do that is to move to english speaking country. So I did that and with my basic english I started from the beginning. After my english improved, I got a job as customer support.
Cashpoint operations, focusing on customers service and satisfaction, dealing with customer complaints, shift scheduling, building sales strategies based on customers surveys and participating in meetings about further education in retail business.
Technical support for incoming service inquiries, solving issues, troubleshooting, investigating. Coaching employees and demonstrating of commitment to personal improvement. Focusing on daily tasks, preparation of personal schedule and reports for team manager.
My husband got an offer to relocate to San Francisco. It's once in a lifetime opportunity so we decided to leave Ireland and move to California.
Temporary position for financial company.
Transferring data from clients statements to the new program, correcting wrong informations in Addepar and ensuring all informations from the statements are in Addepar and are coded correctly
Final checking that all historical data are matching with automatic data and can be connected in time, when program went live.


{% extends "layout/main.html" %} {% block content %}
Your password has been set. You may go ahead and log in now.
Log in


Feel free to contact me through email at<EMAIL_ADDRESS>or through Twitter for support for any of my projects, or just to talk!


Why??
 Also, Markov chains are absurd, and hilarious.
So to my way of thinking, they're a perfect combination. That said, I have a strange sense of humour;
so perhaps you won't find this as hilarious. Your loss if you don't, however I really do hope this
provides you with some manner of amusing distraction from your otherwise dull and dreary life. ENJOY!
Instructions/Notes:
The markov chain generator will try numerous times to assemble what is meant to pass for a
logical sentence. However, generally speaking, this value SHOULD be above 1000 to avoid an error,
and the default value of 10000 should be fine enough, however if you start seeing duplicate cards,
you may want to increase this number.
Additionally, you can pick the number of cards you'd like to generate.
Once you've generated the cards, you can simply refresh the browser to get a new set of cards using
the same parameters, or click the "Start Again" button to return to this page.


Woodrow, John Essenden “Essie”
| Birth Name | Woodrow, John Essenden “Essie” |
| Gender | male |
| Age at Death | 70 years, 6 months, 29 days |
Narrative
Joy Murrin - 21/3/03.
Essie was born at Sorrell Street, Parramatta, the home of his grandmother! His father was 30yrs old, a Printer by trade & born in Grafton.!!!
1925 E.Rolls - John lived at 44 Smith St, Summer Hill, he was a Soldier, before he was married.
SMH -WOODROW, John Essendon - 15 June, 1966, at his residence, 54 Holborow St, Croydon, late of Marion St, Leichhardt. Beloved husband of Thulla, loved father of James, Cecil, Ron & Christine, father-in-law of Kevin, Val & Patricia, & loved grandfather of Michael, Donna, John & David. Aged 70yrs.
SMH - WOODROW, John Essendon - 15 June, 1966, loved brother & brother-in-law of Thyra (dec), Madeline (Mrs Martin), Isabel Holman & Walter (dec), Claude (dec), & Faris, Cecile & Bess, Gwen, Doris & George Hatton, & loved uncle of their children.
FUNERAL - The rels & friends of Mrs. T. Woodrow & family are invited to attend the funeral of their Dear husband & father, JOHN ESSENDON WOODROW - to leave Leslie Andrews Chapel, 319 Liverpool Rd, Ashfield, today Friday, after a service at 9.50am, for Rookwood C.of E. Cemetery.
Events
| Event | Date | Place | Description | Sources |
|---|---|---|---|---|
| Birth | 1895-11-16 | Sorrell St., Parramatta NSW #6256 | ||
|
|
||||
| Death | 1966-06-15 | Holborow St Croydon NSW (70yrs) | ||
|
|
||||
| Burial | 1966-06-17 | Rookwood CofE Cemetery NSW # 226932 Grave 2900 | ||
|
|
Parents
| Relation to main person | Name | Birth date | Death date | Relation within this family (if not by birth) |
|---|---|---|---|---|
| Father | Woodrow, John Brown (Compositor) | 1866 | 1941-07-08 | |
| Mother | Bongers, Christina | 1873-09-21 | 1936-05-13 | |
| Woodrow, John Essenden “Essie” | 1895-11-16 | 1966-06-15 | ||
| Sister | Woodrow, Adora Thyra “Dora” | 1898-02-26 | ||
| Sister | Woodrow, Madeline Mildred or “Millie” | 1901-12-12 | 1987-10-15 | |
| Sister | Woodrow, Isabella Beatrice | 1903-04-23 | 1972-07-19 | |
| Sister | Woodrow, Priscilla Faris | 1905 | ||
| Brother | Woodrow, Claude Hamilton | 1906-10-23 | 1944-08-01 | |
| Brother | Woodrow, Cecil Frazer | about 1910 | 1986-02-11 | |
| Sister | Woodrow, Gwendoline Aurora | 1912 | 1986-01-08 | |
| Sister | Woodrow, Doris Lillian | 1914-10-19 | 1998-08-20 |
Families
Family of Woodrow, John Essenden “Essie” and Maresso, Thulla Thelma |
|||||||||||||||||
| Married | Wife | Maresso, Thulla Thelma ( * 1902 + 1972-08-20 ) | |||||||||||||||
|
|||||||||||||||||
| Children |
| Name | Birth Date | Death Date |
|---|---|---|
| Woodrow, [Living] | ||
| Woodrow, [Living] | ||
| Woodrow, [Living] | ||
| Woodrow, [Living] |
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | 71AC84D8E054D7119853E9C145094A7D3FEF |
Attributes
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | 62AC84D8E054D7119853E9C145094A7D30FF |


Explore Imagery with terracotta
¶
For quick exploration of imagery in the browser, we can use
terracotta
.
This document captures how to serve a folder of images and explore it
interactively.
Build terracotta
¶
A containerised version of terracotta
can be built using the
Dockerfile
:
docker built -t terracotta -f ./Dockerfile.terracotta .
This is a relatively lightweight image based off the Alpine version of
miniconda3 (v.4.8.2-alpine
).
Serving¶
The approach follows a client/server model so, first, we need to spin up a server that makes the imagery available. For that, we first launch the server container:
docker run --rm \
-ti \
-p 5000:5000 \
-v /media/dani/DataStore/GHS-composite-S2/:/data \
terracotta sh
This needs to be run off the same machine where the imagery is stored.
Once inside the container, launch the server with:
/opt/conda/bin/terracotta serve \
--allow-all-ips \
-r /path/to/data/folder/S2_percentile_UTM_{tile}-{scene1}-{scene2}_osgb.tif
This should open a Flask server that will listen to port 5000 for requests.
Exploring¶
Once the imagery is being served, we can launch a web app through a separate (local or remote) container:
docker run --rm \
-ti \
-p 5005:5005 \
-v /path/to/data/folder/:/data \
--network host \
terracotta sh
Note that although the command is similar to that we use for the server, here
we set --network host
so the client can be reached from the guest’s browser.
Inside the container, then launch the app:
/opt/conda/bin/terracotta connect --port 5005 localhost:5000
Then point your browser to localhost:5005
.
Optimising¶
The above process “works”, but if the imagery is too heavy, it’ll choke and
will struggle to be responsive enough for exploration. If that is the case,
consider pre-optimising the images. Following the terracotta
tutorial, we can generate optimised versions by:
/opt/conda/bin/terracotta optimize-rasters -o /target/folder \
--reproject \
/path/to/data/*.tif
Once this finishes, you can serve as above, with the modification of the data path.


Welcome to the Rewards Shop. Here you can spend trophies
for things that you can use as ingredients to craft bigger
prices.
Help
Skills
You will need some skills first. Try to create the goals that each
skill will set. When you are stuck, simply look up the goals in
the dictionary and find out how they are crafted.
Challenges
Skills
none
{{skill.name}}
Achieve your goals to unlock further skills
You can buy new skills:
{{skill.name}} ({{skill.price}} trophytrophies)
{{skill.name}} ({{skill.price}} trophytrophies)
Goals achieved
none
Goals
none
{{authentication.user.rewardlocation}}
Workbench
{{authentication.user.trophies}} trophies
You can buy
You have to buy skills first
Your Inventory
none
{{item.amount}}x
You can buy
Recipe
none
{{item.amount}}x
Product
none
Items Dictionary
There are plenty of items that you can craft. Use this search to find
out more of what you can build out of what materials.


{% extends 'main/base.html' %} {% block title%} Contact Us Form {% endblock title %} {% block content %}
If you are having an issue with the service or have any comments that you would like us to know, fill out the contact form below and someone will respond shortly.


We value each and every person who is coming to our event and for the conference we want to get the best experience for everyone. Thus we would kindly ask you to make yourself familiar with our Code of Conduct if you want to attend the conference.
Get there
By plane
If you’re flying to the conference, choose a local train (S-Bahn) to Cologne Central station or Hansaring, then choose the S-Bahn to "Köln Ehrenfeld" and switch off there. From there, the venue is within a walking distance (see below).
By train
Arriving at Cologne Central Station ("Köln Hbf"), choose the S-Bahn (S12, S13) on track 11 (2 stops) to "Köln Ehrenfeld" and switch off there. If you’re taking a local cable car, switch off at "Köln Ehrenfeld / Venloer Str./Gürtel" (Tram lines 3, 4, 13 or Bus 141, 142, 143). From there, the venue is within a walking distance (see below).
Please make sure to buy a train ticket (costs 2.40 Euro). The ride and walk takes about 15 minutes, trains leave every 10 minutes.
Walk
Starting from the train stations mentioned above, walk the Ehrenfeld Gürtel (which is the street where all the small restaurants are) straight on the road to the south. There’s a Burger King on half the way to the venue. It’s a 6 minutes walk from the station and easy to find. Here’s a Link to the venue on Google Maps.
Schedule
Find the schedule of the evening (yes, all times are p.m.) here:
Doors open |
|
KeynoteAral Balkan |
|
TBA.Nico Hagenburger |
|
Change of perspective – Reflecting UXJasmin Boje |
|
Dumping native <select>Alice Bartlett |
|
Establishing Performance ContextsIan Feather |
|
Designing Meaningful ThingsSteffen Süpple |
|
Thank you & Good ByeThe Nightlybuild team |
|
After PartyThere’s an open after party location (free and open for everyone) at a bar around the corner. This is not an official thing but we’re happy to continue the great evening and end it with a few drinks and good conversations. |