PUBLICATIONS
CHRISTOPH M. KIRSCH
<EMAIL_ADDRESS>www.cs.uni-salzburg.at/~ck
CONFERENCE AND WORKSHOP PAPERS
T. Hütter et al. “JEDI: These aren’t the JSON documents you’re looking for...” In: Proc. International Conference on Management of Data (SIGMOD). ACM, 2022. Click here for PDF file.
A. Goel et al. “What We Eval in the Shadows: A Large-Scale Study of Eval in R Programs”. In: Proc. ACM SIGPLAN Conference on Systems, Programming, Languages, and Applications: Software for Humanity (OOPSLA). ACM, 2021. Click here for PDF file.
A.S. Abyaneh and C.M. Kirsch. “ASE: A Value Set Decision Procedure for Symbolic Execution”. In: Proc. IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE/ACM, 2021. Click here for PDF file.
A.S. Abyaneh et al. “Selfie: Towards Minimal Symbolic Execution”. In: Online Proc. Workshop on Modern Language Runtimes, Ecosystems, and VMs (MoreVMs). 2018. Click here for PDF file.
P. Donat-Bouillud and C.M. Kirsch. “Work in Progress: Adaptive Scheduling with Approximate Computing of Audio Graphs”. In: Proc. IEEE Real-Time Systems Symposium (RTSS). 2017. Click here for PDF file.
C.M. Kirsch. “Selfie and the Basics”. In: Proc. ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!) ACM, 2017. Click here for PDF file.
A. Haas et al. “Local Linearizability for Concurrent Container-Type Data Structures”. In: Proc. International Conference on Concurrency Theory (CONCUR). Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2016, 6:1–6:15. DOI: 10.4230/LIPIcs.CONCUR.2016.6. Click here for PDF file.
M. Aigner et al. “Fast, Multicore-Scalable, Low-Fragmentation Memory Allocation through Large Virtual Memory and Global Data Structures”. In: Proc. ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA). ACM, 2015. Click here for PDF file.
E. Pereira et al. “A Runtime System for Logical-Space Programming”. In: Proc. Workshop on the Swarm at the Edge of the Cloud (SWEC). 2015. Click here for PDF file.
M. Dodds, A. Haas, and C.M. Kirsch. “A Scalable, Correct Time-Stamped Stack”. In: Proc. Symposium on Principles of Programming Languages (POPL). ACM, 2015. Click here for PDF file.
M. Aigner et al. “ACDC-JS: Explorative Benchmarking of JavaScript Memory Management”. In: Proc. Dynamic Languages Symposium (DLS). ACM, 2014. Click here for PDF file.
C. Krainer and C.M. Kirsch. “Cyber-Physical Cloud Computing Implemented as PaaS”. In: Proc. Workshop on Design, Modeling, and Evaluation of Cyber-Physical Systems (CyPhy). ACM, 2014. Click here for PDF file.
C.M. Kirsch, M. Lippautz, and H. Payer. “Fast and Scalable, Lock-free k-FIFO Queues”. In: Proc. International Conference on Parallel Computing Technologies (PaCT). LNCS. Springer, 2013. Click here for PDF file.
M. Aigner et al. “Analysis of Portfolio-Style Parallel SAT Solving on Current Multi-Core Architectures”. In: Proc. Workshop on Pragmatics of SAT (PoS). EPiC. EasyChair, 2013. Click here for PDF file.
M. Aigner and C.M. Kirsch. “ACDC: Towards a Universal Mutator for Benchmarking Heap Management Systems”. In: Proc. International Symposium on Memory Management (ISMM). ACM, 2013. Click here for PDF file.
E. Pereira et al. “Modeling and Controlling the Structure of Heterogeneous Mobile Robotic Systems: A BigActor Approach”. In: International Systems Conference (SysCon). IEEE, 2013. Click here for PDF file.
E. Pereira et al. “BigActors - A Model for Structure-aware Computation”. In: Proc. International Conference on Cyber-Physical Systems (ICCPS). ACM, 2013. Click here for PDF file.
T.A. Henzinger et al. “Quantitative Relaxation of Concurrent Data Structures”. In: Proc. Symposium on Principles of Programming Languages (POPL). ACM, 2013. Click here for PDF file.
S.S. Craciunas and C.M. Kirsch. “The Power of Isolation”. In: Proc. International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2012. Click here for PDF file.
A. Haas et al. “How FIFO is Your Concurrent FIFO Queue?” In: Proc. OOPSLA Workshop on Relaxing Synchronization for Multicore and Manycore Scalability (RACES). 2012. Click here for PDF file.
C.M. Kirsch et al. “Performance, Scalability, and Semantics of Concurrent FIFO Queues”. In: Proc. International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP). LNCS. Springer, 2012. Click here for PDF file.
C.M. Kirsch and H. Payer. “Incorrect Systems: It’s not the Problem, It’s the Solution”. In: Proc. Design Automation Conference (DAC). ACM, 2012. Click here for PDF file.
C.M. Kirsch et al. “Cyber-Physical Cloud Computing: The Binding and Migration Problem”. In: Proc. International Conference on Design, Automation and Test in Europe (DATE). 2012. Click here for PDF file.
C.M. Kirsch, H. Payer, and H. Röck. “Hierarchical PLABs, CLABs, TLABs in Hotspot”. In: Proc. International Conference on Systems (ICONS). 2012. Click here for PDF file.
H. Chen et al. “Cloud Computing on Wings: Applications to Air Quality”. In: Proc. American Astronautical Society Guidance and Control Conference (AASGNC). AAS, 2012. Click here for PDF file.
C.M. Kirsch et al. “Runtime Programming through Model-Preserving, Scalable Runtime Patches”. In: Proc. International Conference on Application of Concurrency to System Design (ACSD). IEEE, 2011, pp. 77–86. Click here for PDF file.
C.M. Kirsch et al. “Brief Announcement: Scalability versus Semantics of Concurrent FIFO Queues”. In: Proc. Symposium on Principles of Distributed Computing (PODC). ACM, 2011, pp. 331–332. Click here for PDF file.
M. Aigner et al. “Short-term Memory for Self-collecting Mutators”. In: Proc. International Symposium on Memory Management (ISMM). ACM, 2011. Click here for PDF file.
C.M. Kirsch et al. “Runtime Programming through Model-Preserving, Scalable Runtime Patches”. In: Proc. International Workshop on Formal Aspects of Component Software (FACS), Doctoral Track. Vol. 6921. LNCS. Springer, 2010, pp. 290–294. Click here for PDF file.
S.S. Craciunas, C.M. Kirsch, and A. Sokolova. “Power-aware Temporal Isolation with Variable-Bandwidth Servers”. In: Proc. International Conference on Embedded Software (EMSOFT). ACM, 2010. Click here for PDF file.
S.S. Craciunas et al. “Information-Acquisition-as-a-Service for Cyber-Physical Cloud Computing”. In: Proc. Workshop on Hot Topics in Cloud Computing (HotCloud). USENIX, 2010. Click here for PDF file.
S.S. Craciunas, C.M. Kirsch, and A. Sokolova. “Response Time versus Utilization in Scheduler Overhead Accounting”. In: Proc. Real-Time and Embedded Technology and Applications Symposium (RTAS). IEEE, 2010. Click here for PDF file.
T.A. Henzinger et al. “Distributed, Modular HTL”. In: Proc. Real-Time Systems Symposium (RTSS). IEEE, 2009. Click here for PDF file.
S.S. Craciunas, C.M. Kirsch, and A. Sokolova. “A Workload-oriented Programming Model for Temporal Isolation with VBS”. In: Online Proc. Workshop on Reconciling Performance with Predictability (RePP). 2009. Click here for PDF file.
H. Röck et al. “Avoiding Unbounded Priority Inversion in Barrier Protocols Using Gang Priority Management”. In: Proc. International Workshop on Java Technologies for Real-time and Embedded Systems (JTRES). ACM, 2009. Click here for PDF file.
S.S. Craciunas et al. “Programmable Temporal Isolation through Variable-Bandwidth Servers”. In: Proc. Symposium on Industrial Embedded Systems (SIES). IEEE, 2009. Click here for PDF file.
K. Hedrick et al. “CSL: A Language to Specify and Re-Specify Mobile Sensor Network Behaviors”. In: Proc. Real-Time and Embedded Technology and Applications Symposium (RTAS). IEEE, 2009. Click here for PDF file.
S.S. Craciunas et al. “Programmable Temporal Isolation in Real-Time and Embedded Execution Environments”. In: Proc. Workshop on Isolation and Integration in Embedded Systems (IIES). ACM, 2009. Click here for PDF file.
H. Payer et al. “Combo Drive: Optimizing Cost and Performance in a Heterogeneous Storage Device”. In: Proc. Workshop on Integrating Solid-state Memory into the Storage Hierarchy (WISH). 2009. Click here for PDF file.
S.S. Craciunas et al. “The JAviator: A High-Payload Quadrotor UAV with High-Level Programming Capabilities”. In: Proc. AIAA Guidance, Navigation and Control Conference (GNC). 2008. Click here for PDF file.
S.S. Craciunas et al. “A Compacting Real-Time Memory Management System”. In: Proc. USENIX Annual Technical Conference. 2008. Click here for PDF file.
C.M. Kirsch, L. Lopes, and E.R.B. Marques. “Semantics-Preserving and Incremental Runtime Patching of Real-Time Programs”. In: Proc. Workshop on Adaptive and Reconfigurable Embedded Systems (APRES). 2008. Click here for PDF file.
K. Chatterjee et al. “Logical Reliability of Interacting Real-Time Tasks”. In: Proc. International Conference on Design, Automation and Test in Europe (DATE). 2008. Click here for PDF file.
C.M. Kirsch and R. Wilhelm. “Grand Challenges in Embedded Software”. In: Proc. International Conference on Embedded Software (EMSOFT). ACM, 2007, pp. 2–6.
A. Ghosal et al. “Separate Compilation of Hierarchical Real-Time Programs into Linear-Bounded Embedded Machine Code”. In: Online Proc. Workshop on Automatic Program Generation for Embedded Systems (APGES). 2007. Click here for PDF file.
J. Auerbach et al. “Java Takes Flight: Time-portable Real-time Programming with Exotasks”. In: Proc. ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES). ACM, 2007. Click here for PDF file.
A. Ghosal et al. “A Hierarchical Coordination Language for Interacting Real-Time Tasks”. In: Proc. International Conference on Embedded Software (EMSOFT). ACM, 2006. Click here for PDF file.
C.M. Kirsch, M.A.A. Sanvido, and T.A. Henzinger. “A Programmable Microkernel for Real-Time Systems”. In: Proc. ACM/USENIX Conference on Virtual Execution Environments (VEE). ACM, 2005. Click here for PDF file.
T.A. Henzinger, C.M. Kirsch, and S. Matic. “Composable Code Generation for Distributed Giotto”. In: Proc. ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES). ACM, 2005. Click here for PDF file.
C.M. Kirsch. “Threading by Appointment”. In: Proc. Monterey Workshop. CRC Press, 2004. Click here for PDF file.
T.A. Henzinger and C.M. Kirsch. “A Typed Assembly Language for Real-Time Programs”. In: Proc. International Conference on Embedded Software (EMSOFT). ACM, 2004, pp. 104–113. Click here for PDF file.
A. Ghosal et al. “Event-driven Programming with Logical Execution Times”. In: Proc. International Workshop on Hybrid Systems: Computation and Control (HSCC). Vol. 2993. LNCS. Springer, 2004, pp. 357–371. Click here for PDF file.
T.A. Henzinger, C.M. Kirsch, and S. Matic. “Schedule-Carrying Code”. In: Proc. International Conference on Embedded Software (EMSOFT). Vol. 2855. LNCS. Springer, 2003, pp. 241–256. Click here for PDF file.
T.A. Henzinger and C.M. Kirsch. “The Embedded Machine: Predictable, Portable Real-Time Code”. In: Proc. ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI). ACM, 2002, pp. 315–326. Click here for PDF file.
T.A. Henzinger et al. “Time Safety Checking for Embedded Programs”. In: Proc. International Workshop on Embedded Software (EMSOFT). Vol. 2491. LNCS. Springer, 2002, pp. 76–92. Click here for PDF file.
C.M. Kirsch et al. “A Giotto-Based Helicopter Control System”. In: Proc. International Workshop on Embedded Software (EMSOFT). Vol. 2491. LNCS. Springer, 2002, pp. 46–60. Click here for PDF file.
T.A. Henzinger, B. Horowitz, and C.M. Kirsch. “Giotto: A Time-triggered Language for Embedded Programming”. In: Proc. International Workshop on Embedded Software (EMSOFT). Vol. 2211. LNCS. Springer, 2001, pp. 166–184. Click here for PDF file.
T.A. Henzinger, B. Horowitz, and C.M. Kirsch. “Embedded Control Systems Development with Giotto”. In: Proc. ACM SIGPLAN Workshop on Languages, Compilers, and Tools for Embedded Systems (LCTES). ACM, 2001. Click here for PDF file.
T.B. Brown et al. “A Reusable and Platform-Independent Framework for Distributed Control Systems”. In: Proc. Digital Avionics Systems Conference (DASC). IEEE, 2001. Click here for PDF file.
H. Ganzinger, C. Meyer, and M. Veanes. “The Two-Variable Guarded Fragment with Transitive Relations”. In: Proc. Symposium on Logic in Computer Science (LICS). IEEE, 1999. Click here for PDF file.
H. Ganzinger et al. “A Resolution-Based Decision Procedure for Extensions of K4”. In: Proc. Workshop on Advances in Modal Logic (AiML). Vol. 2. Lecture Notes. CSLI Publications, Stanford, CA, 1998. Click here for PDF file.
F. Jacquemard, C. Meyer, and C. Weidenbach. “Unification in Extensions of Shallow Equational Theories”. In: Proc. International Conference on Rewriting Techniques and Applications (RTA). Vol. 1379. LNCS. Springer, 1998. Click here for PDF file.
H. Ganzinger, C. Meyer, and C. Weidenbach. “Soft Typing for Ordered Resolution”. In: Proc. International Conference on Automated Deduction (CADE). Vol. 1249. LNCS. Springer, 1997. Click here for PDF file.
P. Graf and C. Meyer. “Advanced Indexing Operations on Substitution Trees”. In: Proc. International Conference on Automated Deduction (CADE). Vol. 1104. LNCS. Springer, 1996. Click here for PDF file.
JOURNAL PAPERS
R. Ambros et al. “Two-Year Progress of Pilot Research Activities in Teaching Digital Thinking Project (TDT)”. In: Zeitschrift für Hochschulentwicklung 18 (2023). Ed. by Martin Ebner and Charlotte Zwiauer, pp. 117–136. DOI: 10.3217/zfhe-SH-HL/07. URL: https://zfhe.at/index.php/zfhe/article/view/1755. Click here for PDF file.
J. Huang, C.M. Kirsch, and R. Sengupta. “Cloud Computing in Space”. In: INFORMS Journal on Computing 27.4 (2015), pp. 704–717. DOI: 10.1287/ijoc.2015.0652.
S.S. Craciunas et al. “Temporal Isolation in Real-Time Systems: The VBS Approach”. In: Software Tools for Technology Transfer (STTT) 15.3 (2013), pp. 189–209. Click here for PDF file.
A. Ghosal et al. “Separate Compilation of Hierarchical Real-Time Programs into Linear-bounded Embedded Machine Code”. In: Science of Computer Programming 77.2 (2012), pp. 96–112.
J. Auerbach et al. “Low-Latency Time-portable Real-time Programming with Exotasks”. In: ACM Transactions on Embedded Computing Systems (TECS) 8.2 (Jan. 2009), pp. 1–48. Click here for PDF file.
S.S. Craciunas, C.M. Kirsch, and H. Röck. “I/O Resource Management through System Call Scheduling”. In: ACM Operating Systems Review (OSR), Special Issue on Research and Developments in the Linux Kernel (July 2008). Click here for PDF file.
T.A. Henzinger and C.M. Kirsch. “The Embedded Machine: Predictable, Portable Real-Time Code”. In: ACM Transactions on Programming Languages and Systems (TOPLAS) 29.6 (Oct. 2007), pp. 33–61. Click here for PDF file.
T.A. Henzinger, B. Horowitz, and C.M. Kirsch. “Giotto: A Time-triggered Language for Embedded Programming”. In: Proceedings of the IEEE 91.1 (Jan. 2003), pp. 84–99. Click here for PDF file.
T.A. Henzinger et al. “From Control Models to Real-Time Code using Giotto”. In: IEEE Control Systems Magazine (CSM) 23.1 (Feb. 2003), pp. 50–64. Click here for PDF file.
INVITED PAPERS AND ABSTRACTS
C.M. Kirsch. “On the Self in Selfie (Invited Talk)”. In: Proc. ACM SIGPLAN International Workshop on Virtual Machines and Intermediate Languages (VMIL). ACM, 2018. Click here for PDF file.
C.M. Kirsch. “From Logical Execution Time to Principled Systems Engineering”. In: Dagstuhl Reports 8.2 (2018). Ed. by Rolf Ernst et al., p. 133. ISSN: 2192-5283. DOI: 10.4230/DagRep.8.2.122. URL: http://drops.dagstuhl.de/opus/volltexte/2018/9293. Click here for PDF file.
A.S. Abyaneh and C.M. Kirsch. “You can program what you want but you cannot compute what you want”. In: Edward A. Lee Festschrift. Vol. 10760. LNCS. Springer, 2018. Click here for PDF file.
A. Haas et al. “Scal: A Benchmarking Suite for Concurrent Data Structures”. In: Proc. International Conference on Networked Systems (NETYS). LNCS. Springer, 2015. Click here for PDF file.
A. Haas et al. “Distributed Queues in Shared Memory—Multicore Performance and Scalability through Quantitative Relaxation”. In: Proc. International Conference on Computing Frontiers. ACM, 2013. Click here for PDF file.
D.F. Bacon et al. “High-Level Real-Time Programming in Java”. In: Proc. International Conference on Embedded Software (EMSOFT). ACM, 2005. Click here for PDF file.
C.M. Kirsch. “Principles of Real-Time Programming”. In: Proc. International Workshop on Embedded Software (EMSOFT). Vol. 2491. LNCS. Springer, 2002, pp. 61–75. Click here for PDF file.
PROCEEDINGS AND SPECIAL ISSUES
Christoph M. Kirsch and Ben L. Titzer, eds. Proc. ACM SIGPLAN International Symposium on Memory Management (ISMM). Barcelona, Spain: ACM, 2017.
“Introduction to Special Section on Probabilistic Embedded Computing”. In: ACM Trans. Embed. Comput. Syst. 12.2s (May 2013). Ed. by C.M. Kirsch and V. Mooney, 86:1–86:2. ISSN: 1539-9087. DOI: 10.1145/2465787.2465788. URL: http://doi.acm.org/10.1145/2465787.2465788. Click here for PDF file.
Gernot Heiser and Christoph M. Kirsch, eds. ACM European Conference on Computer Systems, EuroSys 2011, Salzburg, Austria, April 10 - 13, 2011, Proceedings. New York, NY, USA: ACM, 2011.
Christoph M. Kirsch and Mahmut T. Kandemir, eds. ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems, LCTES 2009, Dublin, Ireland, June 19 - 20, 2009, Proceedings. New York, NY, USA: ACM, 2009.
Christoph M. Kirsch and Reinhard Wilhelm, eds. ACM & IEEE International Conference on Embedded Software, EMSOFT 2007, Salzburg, Austria, September 30 - October, 3, 2007, Proceedings. New York, NY, USA: ACM, 2007.
Thomas A. Henzinger and Christoph M. Kirsch, eds. Embedded Software, First International Workshop, EMSOFT 2001, Tahoe City, CA, USA, October, 8-10, 2001, Proceedings. Vol. 2211. LNCS. Springer, 2001.
BOOK CHAPTERS
C.M. Kirsch and A. Sokolova. “The Logical Execution Time Paradigm”. In: Advances in Real-Time Systems. 2012, pp. 103–120.
“Handbook of Real-Time and Embedded Systems”. In: ed. by I. Lee, J. Leung, and S.H. Son. CRC Press, 2007. Chap. The Evolution of Real-Time Programming.
“Software-Enabled Control: Information Technology for Dynamical Systems”. In: ed. by T. Samad and G. Balas. IEEE Press and Wiley-Interscience, 2003. Chap. "Embedded Control Systems Development with Giotto".
SYSTEM PAPERS
R. Alur et al. “jMocha: A Model Checking Tool that Exploits Design Structure”. In: Proc. International Conference on Software Engineering (ICSE). 2001. Click here for PDF file.
C. Weidenbach et al. “SPASS v0.77”. In: Journal of Automated Reasoning 21.1 (1998).
SHORT TALKS AND POSTERS
M. Dodds, A. Haas, and C.M. Kirsch. Fairness vs. Linearizability in a Concurrent FIFO Queue. Short Talk at the Joint Euro-TM/MEDIAN Workshop on Dependable Multicore and Transactional Memory Systems (DMTM). 2014.
E. Pereira et al. A Networked Robotic System and its Use in an Oil Spill Monitoring Exercise. Short Talk at the International Workshop on the Swarm at the Edge of the Cloud. 2013. Click here for PDF file.
J. Huang, C.M. Kirsch, and R. Sengupta. Scalability of Vehicle Networks through Vehicle Virtualization. Poster at the International Workshop on the Swarm at the Edge of the Cloud. 2013. Click here for PDF file.
H. Payer, H. Röck, and C.M. Kirsch. Get What You Pay For: Providing Performance Isolation in Virtualized Execution Environments. Poster at the ACM SIGOPS European Systems Conference (EuroSys). 2010. Click here for PDF file.
S.S. Craciunas et al. Everyone Virtualizes Everything But Time. Poster at the IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS). 2009. Click here for PDF file.
TECHNICAL REPORTS
M. Dodds, A. Haas, and C.M. Kirsch. Fast Concurrent Data-Structures Through Explicit Timestamping. Tech. rep. 2014-03. Department of Computer Science, University of Salzburg, Feb. 2014. Click here for PDF file.
C.M. Kirsch, M. Lippautz, and H. Payer. Fast and Scalable k-FIFO Queues. Tech. rep. 2012-04. Department of Computer Science, University of Salzburg, June 2012. Click here for PDF file.
T.A. Henzinger et al. Quantitative Relaxation of Concurrent Data Structures. Tech. rep. 2012-03. Department of Computer Science, University of Salzburg, May 2012. Click here for PDF file.
C.M. Kirsch et al. Performance, Scalability, and Semantics of Concurrent FIFO Queues. Tech. rep. 2011-03. Department of Computer Science, University of Salzburg, Sept. 2011. Click here for PDF file.
S.S. Craciunas, C.M. Kirsch, and A. Sokolova. The Power of Isolation. Tech. rep. 2011-02. Department of Computer Science, University of Salzburg, July 2011. Click here for PDF file.
C.M. Kirsch et al. Runtime Programming through Model-Preserving, Scalable Runtime Patches. Tech. rep. 2010-08. Department of Computer Science, University of Salzburg, Dec. 2010. Click here for PDF file.
C.M. Kirsch, H. Payer, and H. Röck. ScalA: Non-Linearizable Computing Breaks the Scalability Barrier. Tech. rep. 2010-07. Department of Computer Science, University of Salzburg, Nov. 2010. Click here for PDF file.
M. Aigner et al. Short-term Memory for Self-collecting Mutators - Revised Version. Tech. rep. 2010-06. Department of Computer Science, University of Salzburg, Oct. 2010. Click here for PDF file.
M. Aigner et al. Short-term Memory for Self-collecting Mutators. Tech. rep. 2010-03. Department of Computer Science, University of Salzburg, Apr. 2010. Click here for PDF file.
S.S. Craciunas, C.M. Kirsch, and A. Sokolova. Power-aware Temporal Isolation with Variable-Bandwidth Servers. Tech. rep. 2010-02. Department of Computer Science, University of Salzburg, Apr. 2010. Click here for PDF file.
S.S. Craciunas, C.M. Kirsch, and A. Sokolova. Response Time versus Utilization in Scheduler Overhead Accounting. Tech. rep. 2009-03. Department of Computer Science, University of Salzburg, Aug. 2009. Click here for PDF file.
S.S. Craciunas et al. Concurrency and Scalability versus Fragmentation and Compaction with Compact-fit. Tech. rep. 2009-02. Department of Computer Science, University of Salzburg, Apr. 2009. Click here for PDF file.
S.S. Craciunas et al. Real-Time Scheduling for Workload-oriented Programming. Tech. rep. 2008-02. Department of Computer Science, University of Salzburg, Sept. 2008. Click here for PDF file.
S.S. Craciunas, C.M. Kirsch, and H. Röck. Shaping Process Semantics. Tech. rep. 2007-01. Department of Computer Science, University of Salzburg, Apr. 2007. Click here for PDF file.
M. Törngren et al. Co-Design of Control Systems and Their Real-Time Implementation — A Tool Survey. Tech. rep. KTH/MMK/R-06/11-SE. Stockholm, Sweden: Department of Machine Design, Royal Institute of Technology (KTH), Sept. 2006. Click here for PDF file.
A. Ghosal et al. Hierarchical Timing Language. Tech. rep. UCB/EECS-2006-79. EECS Department, University of California, Berkeley, May 2006. Click here for PDF file.
C.M. Kirsch and H. Röck. Traffic Shaping System Calls Using Threading by Appointment. Tech. rep. T009. Department of Computer Science, University of Salzburg, Aug. 2005. Click here for PDF file.
C.M. Kirsch. Threading by Appointment. Tech. rep. T003. Department of Computer Science, University of Salzburg, Sept. 2004. Click here for PDF file.
C.M. Kirsch, T.A. Henzinger, and M.A.A. Sanvido. A Programmable Microkernel for Real-Time Systems. Tech. rep. UCB//CSD-03-1250. California: University of California at Berkeley, June 2003. Click here for PDF file.
T.A. Henzinger, C.M. Kirsch, and S. Matic. Schedule-Carrying Code. Tech. rep. UCB//CSD-03-1230. California: University of California at Berkeley, Feb. 2003. Click here for PDF file.
C.M. Kirsch. The Embedded Machine. Tech. rep. UCB//CSD-01-1137. California: University of California at Berkeley, Mar. 2001. Click here for PDF file.
T.A. Henzinger, B. Horowitz, and C.M. Kirsch. Giotto: A Time-Triggered Language for Embedded Programming. Tech. rep. UCB//CSD-00-1121. California: University of California at Berkeley, 2000. Click here for PDF file.
F. Jacquemard, C. Meyer, and C. Weidenbach. Unification in Extensions of Shallow Equational Theories. Tech. rep. MPI-I-98-2-002. Saarbrücken, Germany: Max Planck Institute for Computer Science, Jan. 1998. Click here for PDF file.
P. Graf and C. Meyer. Extended Path-Indexing. Tech. rep. MPI-I-93-253. Saarbrücken, Germany: Max Planck Institute for Computer Science, Dec. 1993. Click here for PDF file.
THESES
C. Meyer. “Soft Typing for Clausal Inference Systems”. PhD Thesis. Saarbrücken, Germany: Saarland University, 1999. Click here for PDF file.
C. Meyer. “Parallel Unit Resulting Resolution”. Master’s Thesis. Saarbrücken, Germany: Saarland University, 1996. Click here for PDF file.
SOFTWARE
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cf.Field.moving_window¶
-
Field.
moving_window
(method, window_size=None, axis=None, weights=None, mode=None, cval=None, origin=0, scale=None, radius='earth', great_circle=False, inplace=False)[source]¶ Perform moving window calculations along an axis.
Moving mean, sum, and integral calculations are possible.
By default moving means are unweighted, but weights based on the axis cell sizes, or custom weights, may applied to the calculation via the weights parameter.
By default moving integrals must be weighted.
When appropriate, a new cell method construct is created to describe the calculation.
Note
The
moving_window
method can not, in general, be emulated by theconvolution_filter
method, as the latter i) can not change the window weights as the filter passes through the axis; and ii) does not update the cell method constructs.New in version 3.3.0.
See also
- Parameters
- method:
str
Define the moving window method. The method is given by one of the following strings (see https://ncas-cms.github.io/cf-python/analysis.html#collapse-methods for precise definitions):
method
Description
Weighted
'sum'
The sum of the values.
Never
'mean'
The weighted or unweighted mean of the values.
May be
'integral'
The integral of values.
Always
Methods that are “Never” weighted ignore the weights parameter, even if it is set.
Methods that “May be” weighted will only be weighted if the weights parameter is set.
Methods that are “Always” weighted require the weights parameter to be set.
- window_size:
int
Specify the size of the window used to calculate the moving window.
- Parameter example:
A 5-point moving window is set with
window_size=5
.
- axis:
str
orint
Select the domain axis over which the filter is to be applied, defined by that which would be selected by passing the given axis description to a call of the field construct’s
domain_axis
method. For example, for a value of'X'
, the domain axis construct returned byf.domain_axis('X')
is selected.- weights: optional
Specify the weights for the moving window. The weights are, those that would be returned by this call of the field construct’s
weights
method:f.weights(weights, axes=axis, radius=radius, great_circle=great_circle, data=True)
. See the axis, radius and great_circle parameters andcf.Field.weights
for details.Note
By default weights is
None
, resulting in unweighted calculations.Note
Setting weights to
True
is generally a good way to ensure that the moving window calculations are appropriately weighted according to the field construct’s metadata. In this case, if it is not possible to create weights for the selected axis then an exception will be raised.- Parameter example:
To specify weights on the cell sizes of the selected axis:
weights=True
.
- mode:
str
, optional The mode parameter determines how the input array is extended when the filter overlaps an array border. The default value is
'constant'
or, if the dimension being convolved is cyclic (as ascertained by theiscyclic
method),'wrap'
. The valid values and their behaviours are as follows:mode
Description
Behaviour
'reflect'
The input is extended by reflecting about the edge
(c b a | a b c | c b a)
'constant'
The input is extended by filling all values beyond the edge with the same constant value (
k
), defined by the cval parameter.(k k k | a b c | k k k)
'nearest'
The input is extended by replicating the last point
(a a a | a b c | c c c)
'mirror'
The input is extended by reflecting about the centre of the last point.
(c b | a b c | b a)
'wrap'
The input is extended by wrapping around to the opposite edge.
(a b c | a b c | a b c)
The position of the window relative to each value can be changed by using the origin parameter.
- cval: scalar, optional
Value to fill past the edges of the array if mode is
'constant'
. Ignored for other modes. Defaults toNone
, in which case the edges of the array will be filled with missing data. The only other valid value is0
.- Parameter example:
To extend the input by filling all values beyond the edge with zero:
cval=0
- origin:
int
, optional Controls the placement of the filter. Defaults to 0, which is the centre of the window. If the window size, defined by the window_size parameter, is even then then a value of 0 defines the index defined by
window_size/2 -1
.- Parameter example:
For a window size of 5, if
origin=0
then the window is centred on each point. Iforigin=-2
then the window is shifted to include the previous four points. Iforigin=1
then the window is shifted to include the previous point and the and the next three points.
- radius: optional
Specify the radius used for calculating the areas of cells defined in spherical polar coordinates. The radius is that which would be returned by this call of the field construct’s
radius
method:f.radius(radius)
. See thecf.Field.radius
for details.By default radius is
'earth'
which means that if and only if the radius can not found from the datums of any coordinate reference constructs, then the default radius taken as 6371229 metres.- great_circle:
bool
, optional If True then allow, if required, the derivation of i) area weights from polygon geometry cells by assuming that each cell part is a spherical polygon composed of great circle segments; and ii) and the derivation of line-length weights from line geometry cells by assuming that each line part is composed of great circle segments.
- scale: number, optional
If set to a positive number then scale the weights so that they are less than or equal to that number. By default the weights are scaled to lie between 0 and 1 (i.e. scale is 1).
Ignored if the moving window method is not weighted. The scale parameter can not be set for moving integrals.
- Parameter example:
To scale all weights so that they lie between 0 and 0.5:
scale=0.5
.
- inplace:
bool
, optional If True then do the operation in-place and return
None
.
- method:
- Returns
Examples
>>> f = cf.example_field(0) >>> print(f) Field: specific_humidity (ncvar%q) ---------------------------------- Data : specific_humidity(latitude(5), longitude(8)) 1 Cell methods : area: mean Dimension coords: latitude(5) = [-75.0, ..., 75.0] degrees_north : longitude(8) = [22.5, ..., 337.5] degrees_east : time(1) = [2019-01-01 00:00:00] >>> print(f.array) [[0.007 0.034 0.003 0.014 0.018 0.037 0.024 0.029] [0.023 0.036 0.045 0.062 0.046 0.073 0.006 0.066] [0.11 0.131 0.124 0.146 0.087 0.103 0.057 0.011] [0.029 0.059 0.039 0.07 0.058 0.072 0.009 0.017] [0.006 0.036 0.019 0.035 0.018 0.037 0.034 0.013]] >>> print(f.coordinate('X').bounds.array) [[ 0. 45.] [ 45. 90.] [ 90. 135.] [135. 180.] [180. 225.] [225. 270.] [270. 315.] [315. 360.]] >>> f.iscyclic('X') True >>> f.iscyclic('Y') False
Create a weighted 3-point running mean for the cyclic ‘X’ axis:
>>> g = f.moving_window('mean', 3, axis='X', weights=True) >>> print(g) Field: specific_humidity (ncvar%q) ---------------------------------- Data : specific_humidity(latitude(5), longitude(8)) 1 Cell methods : area: mean longitude(8): mean Dimension coords: latitude(5) = [-75.0, ..., 75.0] degrees_north : longitude(8) = [22.5, ..., 337.5] degrees_east : time(1) = [2019-01-01 00:00:00] >>> print(g.array) [[0.02333 0.01467 0.017 0.01167 0.023 0.02633 0.03 0.02 ] [0.04167 0.03467 0.04767 0.051 0.06033 0.04167 0.04833 0.03167] [0.084 0.12167 0.13367 0.119 0.112 0.08233 0.057 0.05933] [0.035 0.04233 0.056 0.05567 0.06667 0.04633 0.03267 0.01833] [0.01833 0.02033 0.03 0.024 0.03 0.02967 0.028 0.01767]] >>> print(g.coordinate('X').bounds.array) [[-45. 90.] [ 0. 135.] [ 45. 180.] [ 90. 225.] [135. 270.] [180. 315.] [225. 360.] [270. 405.]]
Create an unweighted 3-point running mean for the cyclic ‘X’ axis:
>>> g = f.moving_window('mean', 3, axis='X')
Create an weighted 4-point running integral for the non-cyclic ‘Y’ axis:
>>> g = f.moving_window('integral', 4, axis='Y', weights=True) >>> g.Units <Units: 0.0174532925199433 rad> >>> print(g.array) [[ -- -- -- -- -- -- -- --] [ 8.37 11.73 10.05 13.14 8.88 11.64 4.59 4.02] [ 8.34 11.79 10.53 13.77 8.88 11.64 4.89 3.54] [ -- -- -- -- -- -- -- --] [ -- -- -- -- -- -- -- --]] >>> print(g.coordinate('Y').bounds.array) [[-90. 30.] [-90. 60.] [-60. 90.] [-30. 90.] [ 30. 90.]] >>> g = f.moving_window('integral', 4, axis='Y', weights=True, cval=0) >>> print(g.array) [[ 7.5 9.96 8.88 11.04 7.14 9.48 4.32 3.51] [ 8.37 11.73 10.05 13.14 8.88 11.64 4.59 4.02] [ 8.34 11.79 10.53 13.77 8.88 11.64 4.89 3.54] [ 7.65 10.71 9.18 11.91 7.5 9.45 4.71 1.56] [ 1.05 2.85 1.74 3.15 2.28 3.27 1.29 0.9 ]]


British Islamic State Fighters Could Be Brought Back From Syria To Be Prosecuted
The UK's national security advisor said some detainees could be brought back to the UK if it's the best place to put them on trial.
Captured British Islamic State fighters will be brought back to the UK if they can be put on trial, the UK's national security advisor said.
Sir Mark Sedwill suggested some detainees held in Syria could be repatriated if they can be prosecuted in the UK.
But he said that should only if the UK is the best place for them to face justice.
Speaking at an international security conference in Bahrain, he said it may be better for British IS fighters to be put on trial in the countries where they committed their crimes.
He told the International Institute for Strategic Studies' Manama Dialogue:
"We want foreign fighters, who have been captured, dealt with through the criminal justice system but they have to be dealt with through the justice system in the most appropriate place possible.
"So if we can bring them back to the UK and put them through our justice system and deal with them appropriately, we will.
"If it is better done elsewhere, then we will work with allies to do that."
Sir Mark further indicated more British children, whose parents were members of IS, could be repatriated after a group of orphans was brought back to the UK.
It was the first time that the Government had agreed to bring the families of suspected jihadists back to Britain.
"If others find themselves in the same circumstances and it is possible to do so then the same principle will apply," Sir Mark said.
"But people should not underestimate the practical challenges and security challenges of doing that safely.
"The primary responsibility, of course, for the welfare of these children is the parents, who in effect abused and victimised them by taking them to a war zone and allowed them to be indoctrinated in the first place.
"But they are Brits and we have a responsibility to them," he added.
There are still as many as 60 British children that remain stranded in Syria.
Last month, the two suspected British IS terrorists dubbed The Beatles were taken from Syria into American custody.
Alexanda Kotey and El Shafee Elsheikh, both in their 30s, were moved by US security services to an "undisclosed location" amid fears they could escape custody as Turkish troops invaded the Syrian Kurdish-held region of north-eastern Syria.
Shamima Begum, the Bethnal Green schoolgirl who fled to Syria to join IS in 2015, was stripped of her British citizenship, prompting her to take legal action against the UK.
In August, 24-year-old "Jihadi" Jack Letts was also stripped of if British citizenship, after leaving his Oxfordshire home to join IS in Syria in 2014 at the age of 18.


|
keigeki
1.0.0
傾撃
|
#import <CCLayer.h>
Instance Methods | |
| (id) | - initWithColor:fadingTo: |
| (id) | - initWithColor:fadingTo:alongVector: |
| Instance Methods inherited from CCLayerColor | |
| (id) | - initWithColor:width:height: |
| (id) | - initWithColor: |
| (void) | - changeWidth: |
| (void) | - changeHeight: |
| (void) | - changeWidth:height: |
| Instance Methods inherited from <CCRGBAProtocol> | |
| (void) | - setColor: |
| (ccColor3B) | - color |
| (GLubyte) | - opacity |
| returns the opacity | |
| (void) | - setOpacity: |
| (void) | - setOpacityModifyRGB: |
| (BOOL) | - doesOpacityModifyRGB |
| Instance Methods inherited from <CCBlendProtocol> | |
| (void) | - setBlendFunc: |
| (ccBlendFunc) | - blendFunc |
Class Methods | |
| (id) | + layerWithColor:fadingTo: |
| (id) | + layerWithColor:fadingTo:alongVector: |
| Class Methods inherited from CCLayerColor | |
| (id) | + layerWithColor:width:height: |
| (id) | + layerWithColor: |
Protected Attributes | |
| GLubyte | startOpacity_ |
| GLubyte | endOpacity_ |
| CGPoint | vector_ |
| BOOL | compressedInterpolation_ |
| Protected Attributes inherited from CCLayerColor | |
| GLubyte | opacity_ |
| ccColor3B | color_ |
| ccVertex2F | squareVertices_ [4] |
| ccColor4F | squareColors_ [4] |
| ccBlendFunc | blendFunc_ |
Properties | |
| ccColor3B | endColor_ |
| ccColor3B | startColor |
| ccColor3B | endColor |
| GLubyte | startOpacity |
| GLubyte | endOpacity |
| CGPoint | vector |
| BOOL | compressedInterpolation |
| Properties inherited from CCLayerColor | |
| GLubyte | opacity |
| ccColor3B | color |
| ccBlendFunc | blendFunc |
CCLayerGradient is a subclass of CCLayerColor that draws gradients across
the background.
All features from CCLayerColor are valid, plus the following new features:
Color is interpolated between the startColor and endColor along the given vector (starting at the origin, ending at the terminus). If no vector is supplied, it defaults to (0, -1) – a fade from top to bottom.
If 'compressedInterpolation' is disabled, you will not see either the start or end color for non-cardinal vectors; a smooth gradient implying both end points will be still be drawn, however.
If ' compressedInterpolation' is enabled (default mode) you will see both the start and end colors of the gradient.
| - (id) initWithColor: | (ccColor4B) | start | |
| fadingTo: | (ccColor4B) | end | |
Initializes the CCLayer with a gradient between start and end.
| - (id) initWithColor: | (ccColor4B) | start | |
| fadingTo: | (ccColor4B) | end | |
| alongVector: | (CGPoint) | v | |
Initializes the CCLayer with a gradient between start and end in the direction of v.
| + (id) layerWithColor: | (ccColor4B) | start | |
| fadingTo: | (ccColor4B) | end | |
Creates a full-screen CCLayer with a gradient between start and end.
| + (id) layerWithColor: | (ccColor4B) | start | |
| fadingTo: | (ccColor4B) | end | |
| alongVector: | (CGPoint) | v | |
Creates a full-screen CCLayer with a gradient between start and end in the direction of v.
|
readwritenonatomicassign |
Whether or not the interpolation will be compressed in order to display all the colors of the gradient both in canonical and non canonical vectors Default: YES
|
readwritenonatomicassign |
The ending color.
|
readwritenonatomicassign |
The ending color.
|
readwritenonatomicassign |
The starting color.
|
readwritenonatomicassign |
The starting opacity.
|
readwritenonatomicassign |
The vector along which to fade color.


Stats
User Management
Category Management
Customer Management
Product Management
WareHouse Management
Invoice Management
Company Management
Inventory Management
Purchase Order
Sales Order
Stock Balance
Stock Ledger


My Awesome Books
Add a Book
Contact Information
Do you have any questions or just want to say "Hello!"?
You can reach out to us:
- Our email<EMAIL_ADDRESS>- Our phone: 001 5378 7372
- Our address: Books Ave. #33, myCity, myCountry
Do you have any questions or just want to say "Hello!"?
You can reach out to us:


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/coercion.20from.20.E2.84.9A.20to.20.E2.84.9D.html ---
What's the canonical name for the coercion from rat
to real
? I had imagined coe
, but
instance : is_semiring_hom (coe : ℚ → ℝ) := by apply_instance
times out.
instance : is_semiring_hom (rat.cast : ℚ → ℝ) := by apply_instance
works fine, but I need that it's injective and rat.cast_injective
works with coe
. Which one am I supposed to be using?
import data.set.basic set_theory.schroeder_bernstein import data.set.countable import ring_theory.algebraic import data.polynomial import data.rat.default import data.real.basic data.real.cardinality import tactic.linarith tactic.fin_cases noncomputable theory local attribute [instance] classical.prop_decidable
might be relevant here (this is work of a student)
In such an algebraic context I would say rat.cast
is what you want. Here you are really using that ℝ
is a division ring, not the fact that it contains ℚ
.
coe
is defeq to rat.cast
It is written using coe
, but the lemmas have rat.cast
based names
I know they're defeq but I got that timeout. Is it a missing instance then?
A student wants to pass from Q[X] to R[X] a lot
it's a missing instance
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


University '19
Merit
Your targeted college
{% for i in inst%}
{{ i | upper }}
{% endfor %}
Other college to compare with
{% for i in inst%}
{{ i | upper }}
{% endfor %}
The possible cutoff position of other colleges
Submit


Denver Foodie Concierge
Find all things tastey in Denver!
Home
Breakfast
Lunch
Dinner
Follow me on twitter
Favorite Breakfasts
Best Coffee
Sweet Bloom Coffee
Best Bagel
Rosenbergs


I am a Chicago-based Ruby on Rails developer currently hacking away at OneMain Financial. I am a true Midwesterner through and through, hailing from the north (or farther north - Minnesota to be exact).
If you want to get to know me professionally, check me out on
LinkedIn,
Instagram if you want to see life through my eyes, and
GitHub if reading code is your thing.


CommandStudio is an editor that let you write complex sequences of command blocks and easily import them into Minecraft. It has a lot of convenient tools which will make your life easier.
In CommandStudio you work with code instead of command blocks. In your code, each line represents a command block.
If you compile the following code:
CommandStudio will give you a single command that you have to paste into Minecraft. Once activated this command will place all your command blocks in vertical piles. The pile generated by this code will be:
Note:
Commands are in a reversed order because command blocks are oriented upward.
CommandStudio is designed to work with vertical pile of command blocks. Each blocks of the pile is pointing upward.
By default, all command blocks will be chained command blocks that don't need redstone to be activated. You can change a block attributes by beginning a line with a mask like this one:
This command block will be a repeating command block that needs redstone to be active.
You can use all those characters to change attributes of a command block:
| Modifier | Effect |
|---|---|
| i | The command block will be an impulse command block |
| r | The command block will be an repeating command block |
| c | The command block will be an chain command block |
| 0 | The command block will need redstone to be active |
| 1 | The command block will always be active |
| ? | The command block will be conditionnal |
| ! | The command block will be unconditionnal |
By default a block is: c1!. But you can change it using the keyword default:
Will compile to :
By default command blocks spawn right above the command block that execute the summon command. You can tell the compiler to spawn a block pile at a certain place using the keyword position:
If you write very long commands you might want to split them on multiple lines. You can do it by adding spaces at the beginning of lines you want to join. For example:
Will compile to a single command block containing:
By default, a space is inserted between joined lines. To avoid this behavior, insert a + before your line.
Will compile to a single command block containing:
You can perform addition on coordinates using this syntax:
Will compile to:
Variables let you save data and re-use them later. All variable names must be preceded by a $.
Will compile to:
A procedure will let you re-use one or more commands. You can define procedures using the def keyword. All procedures name must be preceded by a ^.
Will compile to:
You can prevent characters from being interpreted. To do it, insert a \ before it.
Will compile to:
You can add comments to your file using the // syntax.
Will compile to:
You can import other files into a compiling file using the include keyword.
Will compile to:


Bosozoku E30 BMW 3 Series Has JDM Dustpan Aero and Exhaust
by Mihnea RaduThe most successful touring car of its era, maybe ever, the anchor for the whole M sedan family - the BMW E30 series is many things. However, at the end of the day, people just love the shape of it.
It's an iconic design, recognized even by people who don't like BMWs or have never driven anything in their lives. Basically, it's the shape of Bavarian car design in the 80s, which flat panels leading the eyes to that black nose and quad lights. Of course, many of the cars from that era are shaped this way, partly due to the limited shapes metal could be stamped in.
We've seen all kinds of E30 modifications and honestly enjoy most of them. You can have something super-clean with some period-specific wheels or swap the motor out for a modern twin-turbo and make a drift machine. Somebody even turned it into an EV. However, the shape of the early 3 Series is very forgiving, and we're pleased to finally show something revolting, something so ugly it provokes thought, the fundamental requirement for any form of artistic expression.
Our criticism is unusually cruel, but that's what the Bosozoku style is meant to do. It's the Japanese tuning scene's middle finger to society and its rules. Obviously, this isn't a real car, but a piece by render.richter. E30 3D models should be quite easy to find, though this one was obviously modified extensively.
 The fender extensions are mild, but that's always been the Bosozoku way, just like the exhaust pipe that sticks out further than it should. A little externally mounted radiator is something else often seen on these projects.
If you'd like to know a little more about Bosozoku from our coverstory.


@(message: String) @main("Trans*H4CK") {
A gathering place for LGBTQ student activists and institutional memory archive for colleges and universities.


LTPs Plant cuticle lipid polymorpha transfer Arabidopsis gene {border-style cuticular LTP plant px;} wax #ffffff;} Marchantia moss plants proteins Cell fatty protein cutin Biol. Physiol. land liverwort patens Kohchi expression plants, cell hydrophobic involved mosses waxes Ishizaki function genes model structure vascular LTPg Mol. Physcomitrella bryophyte px; role surface {font-family Jetter Lipid Nishihama Samuels acids binding font-size non-specific similar surfaces ./s--- ./tpc.. Cuticle Edqvist Kunst acids, calyptra cluster double; evolution export liverworts monomers mutant plants. polymer seed shown suberin Figure J.P. LTPd Lee New PpLTPg Res. Sci. border-color border-width compounds cuticle, different epidermis flowering high ltpg number plasma previously required showed solid; studies types {font-style {font-weight ./pp.. Blomqvist Bot. Bowman Brassica Cuticular E.A. Edstam Funaria GPI-anchored Hence, J.L. K.T. M.M. Mosses Plants Salminen Shin Suh Transfer Yamato [], accumulation aerial apoplastic assembly assembly. bold bryophytes components courier; covered development em; families family formed functions hygrometrica identified increased major membrane molecular mosses, polyesters pt;} rice species stems suggesting systems total transporters


"x" "1" "WATANABE,T, HANASAKI,Y, HIRAYAMA,T AND FUKUI,S, MUTAGENICITY OF NITRO-AND AMINO-SUBSTITUTED PHENAZINES IN SALMONELLA TYPHIMURIUM, MUTAT. RES.225(3):75-82, 1989"


India Vs Bangladesh: Twitter Turns Rohit Sharma's Catch Into Hilarious Memes
He dismissed Bangladesh skipper Mominul Haque in the 11th over.
by Akshita JainBangladesh was reduced to 73 for 6 at lunch on the opening day of the Day/Night Test at Eden Gardens and Rohit Sharma’s stunning catch left Twitter obsessed.
He dismissed Bangladesh skipper Mominul Haque in the 11th over. The ball was going towards captain Virat Kohli when Sharma jumped in and took a one-hand catch.
Twitter was quick to turn Sharma’s catch into hilarious memes:
Some even took a dig at the captaincy of the Indian men’s team. Yuvraj Singh had suggested split captaincy to ease Kohli’s workload. In September, Singh had said Sharma can be considered for T20 captaincy if Kohli feels “overloaded” by leading the side in all three formats.
Sharma did lead India in the Twenty20 International (T20I) series against Bangladesh this month which India won 2-1.
Some could not help but remember the 2019 World Cup which England won on account of superior boundary count after the final and the Super Over ended in a tie.


Minecraft Banner From Image
Tolerance:
Population Size:
Max Layers:
Reference
Start →
Number of generations
0
Number of bests found
0
Current Best
Bests evolution


Project WAN
A server / toolkit to normalize WeChat articles into plain webpages.
Why
WeChat has built its own system of social networking, and facilitated their own platform of web publishing. WeChat Public Platforms, are very popular among Chinese netizens, thus we can't miss our audience on WeChat. WeChat articles are basically made with HTML markups, but with very hard limits on external links, embed content and actually you can't interact with this article outside WeChat's ecosystem. With some "WeChat article editors"(e.g. Xiumi.us), most WeChat articles have terrible HTML structures and markups, making it hard to be crawled by search engines and webpage optimizers(like Telegram's Instant View).
So What?
This project aims to capture, normalize, purify and serve WeChat articles into standard webpages.
- Capture: capture the HTML and assets served by WeChat platform.
- Normalize: Remove
poweredby
and other useless attributes, unwrap elements which was far too wrapped by elements, etc. - Purify(optional): You may need much pure HTML documents. This process
reconstruct all elements based on pure content. Only crucial styles will be
preserved, like
text-align
. Decorational widgets & tiny images removed. - Serve(optional): You may want to push your WeChat articles to your website, and style them with your website's styles. No problem.
Docs
Hosted: https://laosb.github.io/wan
For local, See /docs
. Or use npm run doc
when you tweaked a bit.
Status
In progress. Don't push me too hard.
Legal
This project itself is licensed under MIT. We don't touch with the content you've extracted by this project. Use this at your own risk.


 That is, dark, rich, malty, with some flavors typically associated with breakfast - sometimes maple, sometimes coffee, sometimes smoky/meaty flavors. Sometimes, some heavenly combination of all three. While Central Waters' Brewhouse Stout doesn't tout all of the breakfast-y flavors, it's still a formidable offering. And when you make breakfast with a breakfast stout, you get....
|
|
|
BREAKFAST-CEPTION. BRAAAAAWWWWWWMMMM!
|
Prep/Cooking:
The beer itself pours a silky, smooth black with rich coffee aromas. The flavor matches the dark, rich complexity of the pour, and doubles-down on the coffee found in the aroma. This is possibly one of the best coffee stouts I've ever had. The coffee flavoring is rich and powerful but not overwhelming. It's still beer, after all.
|
|
|
So dark. So delicious.
|
The batter itself came out just as deliciously dark and powerful as the beer. In some aspects it was almost smoother than the beer itself, but that happens often when it comes to batter - the battery goodness smoothes out some of the potentially rough parts of the beer. Maybe "fills in" is a better term for it.
Results:
Admittedly, this entire segment was a bit in a rush given this was the morning of the Radicals' first playoff game, so I had to make them and get out the door relatively quickly, so they didn't quite get the TLC and attention I usually give my pancakes. That being said...
|
|
|
Beercakes!
|
All to often with any sort of dark, flavored beer, the pancakes turn out much worse than expected. It's usually truer with anything smoked, but unfortunately the authentic roasty goodness of the coffee beans came through a little too strong/roasty on this one, and the pancakes tasted a bit burnt. A huge disappointment given how amazing this beer is.
The Ternary Scale:
Pancakes - 0. Don't waste this amazing beer (which gets better as it warms up a bit, mind you) on pancakes. It's amazing in beer form. In pancake form, it's mediocre at best.
Syrup - 0. Don't bother. It masked the burnt flavor, and made them a bit sweeter, but at the end of the day it wasn't a good experience at all.
|
|
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Sad panda
|
{% include twitter.html tweetAt=" from @CWBrewing" %}


public interface SerialPortDevice extends Device
The serial port device object is obtained from the POSTerminal
, its' as follows:
SerialPortDevice serialPortDevice = (SerialPortDevice) POSTerminal.getInstance().getDevice("cloudpos.device.serialport");Among them, the "cloudpos.device.serialport" string is to identify the serial port device, defined by the specific implementation.
Uses the serial port device object to control the operation of the serial port device.
An app can request permission to access the device, as shown below:
<uses-permission android:name="android.permission.CLOUDPOS_SERIAL"/>
Device
| Modifier and Type | Field and Description |
|---|---|
static int |
DATABITS_5
5 data bit format
|
static int |
DATABITS_6
6 data bit format
|
static int |
DATABITS_7
7 data bit format
|
static int |
DATABITS_8
8 data bit format
|
static int |
FLOWCONTROL_NONE
flow control off
|
static int |
FLOWCONTROL_RTSCTS_IN
RTS/CTS flow control on input
|
static int |
FLOWCONTROL_RTSCTS_IN_OUT
RTS/CTS flow control on input and output
|
static int |
FLOWCONTROL_RTSCTS_OUT
RTS/CTS flow control on output
|
static int |
FLOWCONTROL_XONXOFF_IN
XON/XOFF flow control on input
|
static int |
FLOWCONTROL_XONXOFF_IN_OUT
XON/XOFF flow control on input and output
|
static int |
FLOWCONTROL_XONXOFF_OUT
XON/XOFF flow control on output
|
static int |
PARITY_EVEN
EVEN parity scheme.
|
static int |
PARITY_NONE
no parity bits
|
static int |
PARITY_ODD
ODD parity scheme.
|
static int |
STOPBITS_1
1 stop bit
|
static int |
STOPBITS_1_5
1-1/2 stop bits.
|
static int |
STOPBITS_2
2 stop bits
|
| Modifier and Type | Method and Description |
|---|---|
void |
changeFlowControlMode(int flowControl)
Changes the flow control mode.
|
void |
changeRTS(boolean rts)
Sets or clears the RTS (Request To Send) bit in the UART.
|
void |
changeSerialPortParams(int baudrate,
int dataBits,
int stopBits,
int parity)
Changes the serial port parameters.
|
int |
getBaudRate()
Gets the currently configured baud rate.
|
int |
getDataBits()
Gets the currently configured number of data bits.
|
int |
getFlowControlMode()
Gets the currently configured flow control mode.
|
int |
getParity()
Gets the currently configured parity setting.
|
int |
getStopBits()
Gets the currently defined stop bits.
|
void |
listenForRead(int len,
OperationListener listener,
int timeout)
Reads up to byteCount bytes from this serial control and stores them in
the byte array buffer starting at byteOffset.
|
void |
open(int logicID)
Connects the serial communication control to a specific serial port.
|
boolean |
retrieveCTS()
Retrieves the state of the CTS (Clear To Send) bit in the UART.
|
boolean |
retrieveRTS()
Retrieves the state of the RTS (Request To Send) bit in the UART.
|
SerialPortOperationResult |
waitForRead(int len,
int timeout)
The synchronous method of
listenForRead(int,OperationListener,int) . |
void |
write(byte[] data,
int offset,
int len)
Writes
len bytes from the specified byte array starting at
offset to the open output stream. |
cancelRequest, close, getFailCount, getUsageCount, open
static final int DATABITS_5
static final int DATABITS_6
static final int DATABITS_7
static final int DATABITS_8
static final int STOPBITS_1
static final int STOPBITS_2
static final int STOPBITS_1_5
static final int PARITY_NONE
static final int PARITY_ODD
static final int PARITY_EVEN
static final int FLOWCONTROL_NONE
static final int FLOWCONTROL_RTSCTS_IN
static final int FLOWCONTROL_RTSCTS_OUT
static final int FLOWCONTROL_RTSCTS_IN_OUT
static final int FLOWCONTROL_XONXOFF_IN
static final int FLOWCONTROL_XONXOFF_OUT
static final int FLOWCONTROL_XONXOFF_IN_OUT
void open(int logicID) throws DeviceException
Opens and initializes the platform serial port specified in parameter.
logicID
- the logical ID of the serial port to openDeviceException
- for the standard reasons that are documented in
DeviceException
.int getBaudRate() throws DeviceException
DeviceException
- for the standard reasons that are documented in
DeviceException
.int getDataBits() throws DeviceException
DeviceException
- for the standard reasons that are documented in
DeviceException
.int getStopBits() throws DeviceException
DeviceException
- for the standard reasons that are documented in
DeviceException
.int getParity() throws DeviceException
DeviceException
- for the standard reasons that are documented in
DeviceException
.void changeRTS(boolean rts) throws DeviceException
rts
- true
to set RTS, false
to clear RTSDeviceException
- for the standard reasons that are documented in
DeviceException
.boolean retrieveRTS() throws DeviceException
DeviceException
- for the standard reasons that are documented in
DeviceException
.boolean retrieveCTS() throws DeviceException
DeviceException
- for the standard reasons that are documented in
DeviceException
.void changeFlowControlMode(int flowControl) throws DeviceException
flowControl
- a constant as defined in SerialControl:
DeviceException
- for the standard reasons that are documented in
DeviceException
.int getFlowControlMode() throws DeviceException
DeviceException
- for the standard reasons that are documented in
DeviceException
.void changeSerialPortParams(int baudrate, int dataBits, int stopBits, int parity) throws DeviceException
baudrate
- the desired data communication speeddataBits
- stopBits
- parity
- DeviceException
- for the standard reasons that are documented in
DeviceException
.void write(byte[] data, int offset, int len) throws DeviceException
len
bytes from the specified byte array starting at
offset
to the open output stream.
This method is synchronous.
data
- the data to be written to the outputoffset
- the starting offset in the data bufferlen
- the number of bytes to be writtenDeviceException
- for the standard reasons that are documented in
DeviceException
.void listenForRead(int len, OperationListener listener, int timeout) throws DeviceException
len
bytes of data.
This method is asynchronous. On success, the read data can be
retrieved using SerialOperationResult
's
getBuffer()
method.
Because this method uses a timeout parameter, the device receiving this request MUST be able to
successfully respond to cancelRequest()
and abort the operation.
In the case that a timeout occurs, the buffer in the operation event
contains the bytes received so far, up to the requested length. This
operation event has code = ERR_TIMEOUT
regardless of
whether any data has been received.
SerialOperationResult.getDataLength()
can be used to check
how much data, if any, have been received.
len
- the number of bytes to be readlistener
- the OperationListener to handle the result.timeout
- the maximum sleeping time of the method, in milliseconds.
Note that the timeout will begin when the request begins to be
processed. The timeout
parameter is then only
indicative. There are two special cases for
timeout
, as follows:
timeout == FOREVER
, the method will wait
until something is available on the associated service's
input. timeout == IMMEDIATE
, the method
will immediately read the input
DeviceException
- for the standard reasons that are documented in
DeviceException
.OperationListener.handleResult(com.cloudpos.OperationResult)
,
SerialPortOperationResult
,
TimeConstants.FOREVER
,
TimeConstants.IMMEDIATE
SerialPortOperationResult waitForRead(int len, int timeout) throws DeviceException
listenForRead(int,OperationListener,int)
.len
- the number of bytes to be readtimeout
- the maximum sleeping time of the method, in milliseconds.
Note that the timeout will begin when the request begins to be
processed. The timeout
parameter is then only
indicative. There are two special cases for
timeout
, as follows:
timeout == FOREVER
, the method will wait
until something is available on the associated service's
input.
timeout == IMMEDIATE
, the method will
immediately read the input
SerialOperationResult
of this operation.DeviceException
- for the standard reasons that are documented in
DeviceException
.SerialPortOperationResult
,
TimeConstants.FOREVER
,
TimeConstants.IMMEDIATE


{% extends "pulse_search/base.html" %} {% block content %} {% load static %}
meet the crew
Computer Science student at CU Boulder with special enthusiasm for systems level computing and software development.
Find Me On: GitHubAlso a Computer Science student a CU Boulder. Always on the ongoing pursuit for mastery, security and efficiency in the deployment of infrastructure and code.
Find Me On: GitHubComputer Science major at University of Colorado Boulder. Also working on a minor in Mathematics focusing on combinatorics and a minor in Astronomy.
Find Me On: GitHub


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Users: Alice, Bob
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flask.wrappers
~~~~~~~~~~~~~~
Implements the WSGI wrappers (request and response).
:copyright: (c) 2011 by Armin Ronacher.
:license: BSD, see LICENSE for more details.
from werkzeug.wrappers import Request as RequestBase, Response as ResponseBase
from werkzeug.utils import cached_property
from .exceptions import JSONBadRequest
from .debughelpers import attach_enctype_error_multidict
from . import json
from .globals import _request_ctx_stack
The request object used by default in Flask. Remembers the
class Request(RequestBase):
matched endpoint and view arguments.
It is what ends up as :class:~flask.request
. If you want to replace
the request object used you can subclass this and set
:attr:~flask.Flask.request_class
to your subclass.
The request object is a :class:~werkzeug.wrappers.Request
subclass and
provides all of the attributes Werkzeug defines plus a few Flask
specific ones.
: the internal URL rule that matched the request. This can be
: useful to inspect which methods are allowed for the URL from
: a before/after handler (request.url_rule.methods
) etc.
:
: .. versionadded:: 0.6
url_rule = None
: a dict of view arguments that matched the request. If an exception
: happened when matching, this will be None
.
view_args = None
: if matching the URL failed, this is the exception that will be
: raised / was raised as part of the request handling. This is
: usually a :exc:~werkzeug.exceptions.NotFound
exception or
: something similar.
routing_exception = None
switched by the request context until 1.0 to opt in deprecated module functionality
_is_old_module = False
Read-only view of the MAX_CONTENT_LENGTH
config key.
@property
def max_content_length(self):
ctx = _request_ctx_stack.top
if ctx is not None:
return ctx.app.config['MAX_CONTENT_LENGTH']
The endpoint that matched the request. This in combination with
@property
def endpoint(self):
:attr:view_args
can be used to reconstruct the same or a
modified URL. If an exception happened when matching, this will
be None
.
if self.url_rule is not None:
return self.url_rule.endpoint
The name of the current module if the request was dispatched
@property
def module(self):
to an actual module. This is deprecated functionality, use blueprints instead.
from warnings import warn
warn(DeprecationWarning('modules were deprecated in favor of '
'blueprints. Use request.blueprint '
'instead.'), stacklevel=2)
if self._is_old_module:
return self.blueprint
The name of the current blueprint
@property
def blueprint(self):
if self.url_rule and '.' in self.url_rule.endpoint:
return self.url_rule.endpoint.rsplit('.', 1)[0]
If the mimetype is application/json
this will contain the
@cached_property
def json(self):
parsed JSON data. Otherwise this will be None
.
This requires Python 2.6 or an installed version of simplejson.
if self.mimetype == 'application/json':
request_charset = self.mimetype_params.get('charset')
try:
if request_charset is not None:
return json.loads(self.data, encoding=request_charset)
return json.loads(self.data)
except ValueError, e:
return self.on_json_loading_failed(e)
Called if decoding of the JSON data failed. The return value of
def on_json_loading_failed(self, e):
this method is used by :attr:json
when an error occurred. The default
implementation raises a :class:JSONBadRequest
, which is a subclass of
:class:~werkzeug.exceptions.BadRequest
which sets the
Content-Type
to application/json
and provides a JSON-formatted
error description::
{"description": "The browser (or proxy) sent a request that \
this server could not understand."}
.. versionchanged:: 0.9
Return a :class:JSONBadRequest
instead of a
:class:~werkzeug.exceptions.BadRequest
by default.
.. versionadded:: 0.8
raise JSONBadRequest()
def _load_form_data(self):
RequestBase._load_form_data(self)
in debug mode we're replacing the files multidict with an ad-hoc subclass that raises a different error for key errors.
ctx = _request_ctx_stack.top
if ctx is not None and ctx.app.debug and \
self.mimetype != 'multipart/form-data' and not self.files:
attach_enctype_error_multidict(self)
The response object that is used by default in Flask. Works like the
class Response(ResponseBase):
response object from Werkzeug but is set to have an HTML mimetype by
default. Quite often you don't have to create this object yourself because
:meth:~flask.Flask.make_response
will take care of that for you.
If you want to replace the response object used you can subclass this and
set :attr:~flask.Flask.response_class
to your subclass.
default_mimetype = 'text/html'


|
JWTXX
C++ library for JWT
|
Validation functions are here. More...
Functions | |
| Validator | exp (std::time_t now=std::time(nullptr)) noexcept |
| Constructs validator for 'exp' claim. More... | |
| Validator | nbf (std::time_t now=std::time(nullptr)) noexcept |
| Constructs validator for 'nbf' claim. More... | |
| Validator | iat (std::time_t now=std::time(nullptr)) noexcept |
| Constructs validator for 'iat' claim. More... | |
| Validator | iss (std::string issuer) noexcept |
| Constructs validator for 'iss' claim. More... | |
| Validator | aud (std::string audience) noexcept |
| Constructs validator for 'aud' claim. More... | |
| Validator | sub (std::string subject) noexcept |
| Constructs validator for 'sub' claim. More... | |
Validation functions are here.
|
noexcept |
Constructs validator for 'aud' claim.
| audience | valid audience. |
|
noexcept |
Constructs validator for 'exp' claim.
| now | current time, may be overriden. |
|
noexcept |
Constructs validator for 'iat' claim.
| now | current time, may be overriden. |
|
noexcept |
Constructs validator for 'iss' claim.
| issuer | valid issuer name. |
|
noexcept |
Constructs validator for 'nbf' claim.
| now | current time, may be overriden. |
|
noexcept |
Constructs validator for 'sub' claim.
| subject | valid subject name. |


{% extends 'layouts/layout-bsa-design-system.html' %} {% block pageTitle %} Start page template - NHS.UK prototype kit {% endblock %} {% block beforeContent %} {% endblock %} {% block content %}
Help your service meet it's accessibility requirements.
If your service isn't accessible it doesn't meet NHSBSA standards.


{% extends "layout.html" %} {% load uni_form_tags %} {% block title %}{{ client.name }}{% endblock %} {% block content %}
{{ error_description }}
This client has been issued the following access tokens. Click the refresh token button to make a refresh request.
| Token | Refresh Token | Expire | Scope | Refreshable |
|---|---|---|---|---|
| {{ token.token }} | {{ token.expire }} | {% for access_range in token.scope.all %} {{ access_range.key }} {% endfor %} | {{ token.refreshable }} |
This client has no access tokens.
{% endif %} {% if codes %}This client has been issued the following authorization code. Click the authorize code button to make an authorization request.
| Key | Expire | Scope |
|---|---|---|
| {{ code.expire }} | {% for access_range in code.scope.all %} {{ access_range.key }} {% endfor %} |
This client has no authorization codes.
{% endif %}


Call for Papers
PDF Version
Accepted papers are listed here.
ACPR 2019 solicits high-quality original research for publication in its main conference and co-located workshops. All accepted papers will be published in
Springer's Lecture Notes in Computer Science (LNCS, indexed in the ISI Conference Proceedings Citation Index, Scopus, EI Engineering Index, Google Scholar, DBLP, etc.). Topics of interest include all aspects of pattern recognition including, but not limited to:
Track 1: Computer Vision and Robot Vision
- Biologically motivated vision
- Physics-based vision
- Perceptual organisation
- Computational photography
- Motion analysis and tracking
- Object detection / recognition
- Scene understanding
- Cognitive and embodied vision
Track 2: Pattern Recognition and Machine Learning
- Artificial neural networks
- Dimensionality reduction and manifold learning
- Classification and clustering
- Support vector machines and kernel methods
- Deep learning, Reinforcement Learning, Transfer Learning
Track 3: Signal Processing
- Signal and image processing
- Audio and acoustic processing and analysis
- Spoken language processing
- Texture and colour analysis
- Segmentation and descriptors
- Enhancement and restoration
- Coding, compression, and super-resolution
- Automatic speech and speaker recognition
Track 4: Media Processing and Interaction
- Bioinformatics, Surveillance and Security
- Industrial image analysis
- Mixed and augmented reality
- Human computer interaction
- Document understanding
- Medical image and signal analysis
- Affective computing
- Biometric systems
The abstract of a submitted manuscript must summarise the contribution the paper makes to the field. Paper length is limited to 14 pages in
LNCS format. All papers will be peer reviewed single-blind (authors are named, reviewers are anonymous).
Important Dates
Abstract Submission: 24 July 2019 (submission of title and abstract in EasyChair)
Paper Submission: 1 August 2019 (no extensions)
Supplementary Materials: 8 August 2019
Notification to Authors: 1 September 2019
Camera Ready Papers Due: 1 October 2019
Conference Dates: 26-29 November 2019


How to make the Mail app load faster on your iPhone with a single setting change
by Emma Witman, Business Insider US- You can make the Mail app load faster on your iPhone by getting rid of the preview lines when emails appear in your inbox.
- Usually, Apple’s Mail app with show a few lines of preview text before you open the email, but you can edit these settings however you wish.
- Visit Business Insider’s homepage for more stories.
Holidays mean overstuffed bellies and stockings.
But they also mean email inboxes filled with Black Friday deals, shipping confirmations for gifts, and RSVPs for holiday parties.
This throws into stark view one of the most frustrating aspects of checking email on an iPhone’s Mail app – your messages don’t always load very quickly in your inbox.
That is, unless you implement a setting change to make your Mail app prioritize the new messages you have, rather than having it throw heaps of preview text at you and bogging down the buffer time to refresh your inbox.
Check out the products mentioned in this article:
iPhone 11 (From $699.99 at Best Buy)
How to make the Mail app faster on your iPhone
Here’s how you can streamline your iPhone’s email service so that it loads messages faster, in a few easy steps:
1. Open the Settings app.
2. Scroll to the “Mail” page. Tap it.
3. Under “Message List” is a setting for “Preview.” Open it.
4. Select “None.” If you’d rather be able to get the gist of a message without opening it at all, you could also set several lines of preview text – but the more lines you set, the longer it will take to load.
Now that you’ll only view recipients and subject lines in your inbox, your iPhone won’t have to take quite so long to load new messages.
Related coverage from How To Do Everything: Tech:
-
How to attach a file in an iPhone email using the new format bar in Mail with iOS 13, or in the Gmail app
-
iOS 13 allows you to mute email threads in the Mail app on your iPhone – here’s how to do it
-
How to search for an email on your iPhone, in the Mail or Gmail app
-
How to set an automatic ‘out of office’ email response on your iPhone in the Mail, Outlook, or Gmail app
-
How to easily delete all of your emails on an iPhone


MatchdayPool
Home
(current)
Public Pools
Search
Create a Matchday
Start Day
Finish Day
Choose the 2 teams for the match
Team 1
Choose...
Team 2
Choose...
Create the match
Matches
Delete match
Create Matchday


UoA MediaplayerPlus Online Services let you watch lectures and study together with your classmates.
This is the domain name of the server you want to connect to. The official server of this project is at tct.pythonanywhere.com. You can also enter localhost:8000 if you're running the server on your own machine. Make sure to add this to your manifest.json file as well!
Whether you want to develop, test, give feedback, or just stay up-to-date with our work, we welcome you to join the discussion on Discord.
UoA Mediaplayer Plus
Developed by acoollevel & contributors
If this extension has helped you in any way, please consider leaving a review or sharing it with your friends


Salmon Store Daily Sales Projections
HOME
ABOUT
MERCHANDISE
SALES
Salmon Cookie Sales
Add a new store
Name
Minimum Customers
Maximum Customers
Average Cookie Sale
Add a Store
Staff Projection


express-mongoose-crud example Please feel free to experiment and try the following links: Authors (JSON) Books (JSON)


When enabled the current cursor position (or selection) is synchronized between the hex window and the comparison window.
If there are inserted/deleted bytes then the position/selection in one window may not be able to be represented in the other. HexEdit will move the cursor as close to the corresponding position as possible. For example a small selection in one window may translate to a much larger selection in the other if it includes deleted bytes.


New Mercedes-AMG GLS 63 Debuts, Is Waiting for the BMW X7 M60i
by Mihnea RaduDespite going into EVs and compacts, Mercedes is still almost synonymous with big, powerful SUVs, filled with amazing gadgets. Today, new benchmarks are set by the 2021 Mercedes-AMG GLS 63.
It's a new generation of an older concept, the GL 63. This was never hugely popular, but German 3-row luxury vehicles are very cool right now for some reason. The AMG GLS 63 4Matic+ is a flagship of sorts, though we do expect to see a Maybach GLS 680 within the year.
While it basically has no direct rivals right now, BMW is reportedly working on the X7 M60i, which is not a true M car but has a thumping V12 under its hood.
Just like the smaller GLE 63 that came out only moments ago, the performance version of the GLS comes with a 4-liter bi-turbo V8 that also takes advantage of EQ Boost mild-hybrid technology. Even though its 22 hp will fill out gaps in output, the number you're looking for is still the same as the E63 S: 603 horsepower... or 612 if you're German.
This, along with 627 lb-ft (850 Nm) of torque goes to all four wheels via a 9-speed AMG-tuned gearbox. The 0 to 62mph sprint takes 4.2 seconds, a few tenths slower than the GLE of the same engine output. While we can't imagine this being hooned around a track, some crazy Russian will probably find a way to drift it around Moscow for sure, assisted by the AMG electronic rear-axle locking differential.
We'd describe the exterior makeover as "tastefully restrained." It feels like just another GLS, but with unusual trim like the Panamericana grille or the slightly bigger lower intake. Wheel sizes do go up to 23 inches, but that seems normal for such a large SUV.
Aside from the AMG-specific steering wheel with the little display dial, the GLE 63 also has different menus for the MBUX infotainment and can be configured with a variety of sporty trim pieces. But at the end of the day, it's still a super-luxurious, technologically advanced 7-seater that just happens to sprint as fast a sports car. 🏁


WITNESS LIST State Affairs Committee April 28, 2005 -11:00A HB 256 FOR: Battey, Phil (Promontory InterFinancial Network), Alexandria, VA Scurlock, Steve (Independent Bankers Association of Texas), Austin, TX ON: Jobe, Everette General Counsel (Texas Department of Banking), Austin, TX Registering, but not testifying: For: Dennis, Rachel (Texas Bankers Association), Austin, TX HB 755 Registering, but not testifying: For: Saxon, Lucinda (Texas Association of Business), Austin, TX HB 1285 Registering, but not testifying: For: Day, Aaron (City of Fort Worth), Fort Worth, TX Scneider, Michael (Texas Association of Broadcasters), Austin, TX On: Cary, Missy (Office of the Attorney General), Austin, TX Darby, Robert (Dallas/Ft. Worth Int'l Airport), Plano, TX Hastings, Kay (Attorney General's Office), Austin, TX Schloss, Hadassah (Self), Austin, TX HB 1602 Registering, but not testifying: For: Green, Shelton (Texas Association of Business), Austin, TX Manross, Lee (Texas Association of Health Underwriters), Austin, TX Rummel, Leah (Self), Austin, TX Simonton, Kay (BlueCross and Blue Shield of Texas), Austin, TX On: Bingham, Bill (Texas Department of Insurance), Austin, TX SB 309 AGAINST: Getting, Roy (Texas Fathers Alliance), Irving, TX Registering, but not testifying: On: McCleskey, Gene (Office of Attorney General), Austin, TX Millholland, Herman Chief Crime Victim Services Division (Office of Attorney General), Austin, TX SB 623 FOR: Pagan, Kevin (City of McAllen), McAllen, TX SB 1738 Registering, but not testifying: On: Bednar, Cindy (Department of State Health Services), Elgin, TX SB 1739 FOR: Galecke, Robert M. (CARES), Irving, TX Registering, but not testifying: For: Haley, Bill (Texas Christian University), Buda, TX On: Brady, Kevin (Texas Department of Insurance), Austin, TX


Framework
Release Date
Latest Version
Ruby on Rails
Decemeber 13, 2005
4.1.5
Node JS
May 27, 2009
0.10.30
Play
May 2007
2.3.3


{% extends "base.html" %} {% block content %}
You get a new entry every day at 4 AM local time.
You are shown your old entries in the following intervals: 1 day, 7 days, 30 days, 90 days, and 365 days.
You can use Markdown to format your entries.
Issue tracker


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
checkpoints_dir = './checkpoints'
data_dir = './data'
figures_dir = './figures'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
!mkdir ./checkpoints
!mkdir ./figures
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
import matplotlib.pyplot as plt
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
real_dim = (image_height, image_width, image_channels)
inputs_real = tf.placeholder(tf.float32, shape=(None, *real_dim), name='input_real')
inputs_z = tf.placeholder(tf.float32, shape=(None, z_dim), name='input_z')
lr = tf.placeholder(tf.float32)
return inputs_real, inputs_z, lr
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False, alpha=0.2):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
with tf.variable_scope('discriminator', reuse=reuse):
# layer 1
# -> in: 28x28x3
x = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
x = tf.maximum(alpha * x, x)
# print('x1: {}'.format(x)) # shape=(?, 14, 14, 64)
# layer 2
# -> in: 14x14x64
x = tf.layers.conv2d(x, 128, 5, strides=2, padding='same')
x = tf.layers.batch_normalization(x, training=True)
x = tf.maximum(alpha * x, x)
# print('x2: {}'.format(x)) # shape=(?, 7, 7, 128)
# layer 3
# -> in: 7x7x128
x = tf.layers.conv2d(x, 256, 5, strides=2, padding='same')
x = tf.layers.batch_normalization(x, training=True)
x = tf.maximum(alpha * x, x)
# print('x3: {}'.format(x)) # shape=(?, 4, 4, 256)
# flatten
# -> in: 4x4x256
x = tf.reshape(x, (-1, 4*4*256))
# print('flatten: {}'.format(x)) # shape=(?, 4096)
# outputs
logits = tf.layers.dense(x, 1)
# print('logits: {}'.format(logits)) # shape=(?, 1)
out = tf.sigmoid(logits)
# print('out: {}'.format(out)) # shape=(?, 1)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
References:
-> https://discussions.udacity.com/t/how-tf-layers-conv2d-and-tf-layers-conv2d-transpose-work/249624/2
-> https://classroom.udacity.com/nanodegrees/nd101/parts/f6d74d04-6d65-424a-9f3a-d58f4da7d406/modules/ca299d5f-35f6-4520-a2b0-74c6878008b3/lessons/2fd24529-215c-47b5-a644-2c23650493f6/concepts/4f028128-6c6a-41e1-80cd-58f54189615d
"""
with tf.variable_scope('generator', reuse=not is_train):
# First fully connected layer
x = tf.layers.dense(z, 4*4*512)
# print('x1: {}'.format(x)) # shape=(?, 8192)
# Reshape it to start the convolutional stack
x = tf.reshape(x, (-1, 4, 4, 512))
x = tf.layers.batch_normalization(x, training=is_train)
x = tf.maximum(alpha * x, x)
# 4x4x512 now
# print('x1: {}'.format(x)) # shape=(?, 4, 4, 512)
x = tf.layers.conv2d_transpose(x, 256, 4, strides=1, padding='valid')
x = tf.layers.batch_normalization(x, training=is_train)
x = tf.maximum(alpha * x, x)
# 7x7x256 now
# print('x2: {}'.format(x)) # shape=(?, 7, 7, 256)
x = tf.layers.conv2d_transpose(x, 128, 5, strides=2, padding='same')
x = tf.layers.batch_normalization(x, training=is_train)
x = tf.maximum(alpha * x, x)
# 14x14x128 now
# print('x3: {}'.format(x)) # shape=(?, 14, 14, 128)
# Output layer
logits = tf.layers.conv2d_transpose(x, out_channel_dim, 5, strides=2, padding='same')
# 28x28xout_channel_dim now
# print('logits: {}'.format(logits)) # shape=(?, 28, 28, 5)
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
g_model = generator(input_z, out_channel_dim, alpha=alpha)
d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# Get weights and bias to update
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
import os.path
from datetime import datetime
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
fig = pyplot.imshow(images_grid, cmap=cmap)
file_name = '_'.join(
[datetime.now().strftime('%Y%m%d_%H%M%S'), 'samples.png'])
file_path = os.path.join(figures_dir, file_name)
plt.savefig(file_path)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count,
batch_size,
z_dim,
learning_rate,
beta1,
get_batches,
data_shape,
data_image_mode,
print_every=10,
show_every=100,
n_images=25):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# set ivars
losses = []
steps_i = 0
out_channel_dim = 1 if data_image_mode == 'L' else 3
# build model
input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2],
data_shape[3], z_dim)
d_loss, g_loss = model_loss(input_real, input_z, out_channel_dim)
d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
# train
print('Training...')
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
images_i = 0
for epoch_i in range(epoch_count):
batches_i = 0
for batch_images in get_batches(batch_size):
steps_i += 1
batches_i += 1
images_i += len(batch_images)
# adjust batch images to be in the range (-1, 1)
batch_images *= 2
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(
d_opt,
feed_dict={
input_real: batch_images,
input_z: batch_z,
lr: learning_rate})
_ = sess.run(
g_opt,
feed_dict={
input_z: batch_z,
input_real: batch_images,
lr: learning_rate})
if batches_i % print_every == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({
input_z: batch_z,
input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch: {}/{} ".format(epoch_i + 1, epoch_count),
"Batches: {:>4,} ".format(batches_i),
"Images seen: {:>6,}".format(images_i),
"Discriminator Loss: {:.4f} ".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Save losses to view after training
losses.append((train_loss_d, train_loss_g))
if batches_i % show_every == 0:
show_generator_output(sess, n_images, input_z,
out_channel_dim, data_image_mode)
# show final sample
show_generator_output(sess, n_images, input_z,
out_channel_dim, data_image_mode)
saver.save(sess, checkpoints_dir + '/generator.ckpt')
plt.plot(losses)
plt.show()
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 1e-4
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 32
z_dim = 100
learning_rate = 1e-4
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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klf-200-api
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"KLF200-API/GW_SYSTEMTABLE_DATA"
ActuatorType
Enumeration ActuatorType
Index
Enumeration members
Awning
Beacon
Blind
Curtain
Track
Dual
Shutter
Exterior
Heating
External
Ventian
Blind
Garage
Opener
Gate
Opener
Heat
Pump
Heating
Temperature
Interface
Horizontal
Awning
Intrusion
Alarm
Light
Lock
Louvre
Blind
NO_
TYPE
On
Off
Switch
Roller
Shutter
Rolling
Door
Opener
Swinging
Shutter
Venetian
Blind
Ventilation
Point
Window
Opener
Enumeration members
Awning
Awning
:
= 3
Beacon
Beacon
:
= 12
Blind
Blind
:
= 10
Curtain
Track
Curtain
Track
:
= 19
Dual
Shutter
Dual
Shutter
:
= 13
Exterior
Heating
Exterior
Heating
:
= 21
External
Ventian
Blind
External
Ventian
Blind
:
= 17
Garage
Opener
Garage
Opener
:
= 5
Gate
Opener
Gate
Opener
:
= 7
Heat
Pump
Heat
Pump
:
= 22
Heating
Temperature
Interface
Heating
Temperature
Interface
:
= 14
Horizontal
Awning
Horizontal
Awning
:
= 16
Intrusion
Alarm
Intrusion
Alarm
:
= 23
Light
Light
:
= 6
Lock
Lock
:
= 9
Louvre
Blind
Louvre
Blind
:
= 18
NO_
TYPE
NO_
TYPE
:
= 0
On
Off
Switch
On
Off
Switch
:
= 15
Roller
Shutter
Roller
Shutter
:
= 2
Rolling
Door
Opener
Rolling
Door
Opener
:
= 8
Swinging
Shutter
Swinging
Shutter
:
= 24
Venetian
Blind
Venetian
Blind
:
= 1
Ventilation
Point
Ventilation
Point
:
= 20
Window
Opener
Window
Opener
:
= 4
Globals
"KLF200-
API/GW_
SYSTEMTABLE_
DATA"
Actuator
Type
Awning
Beacon
Blind
Curtain
Track
Dual
Shutter
Exterior
Heating
External
Ventian
Blind
Garage
Opener
Gate
Opener
Heat
Pump
Heating
Temperature
Interface
Horizontal
Awning
Intrusion
Alarm
Light
Lock
Louvre
Blind
NO_
TYPE
On
Off
Switch
Roller
Shutter
Rolling
Door
Opener
Swinging
Shutter
Venetian
Blind
Ventilation
Point
Window
Opener
Change
Key
Status
Controller
Copy
Mode
Manufacturer
Node
Operating
State
Node
Variation
Power
Save
Mode
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Actuator
Alias
System
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