CHATTANOOGA, Tenn. (AP) — CHATTANOOGA, Tenn. (AP) — Astec Industries Inc. (ASTE) on Wednesday reported a third-quarter loss of $6.6 million, after reporting a profit in the same period a year earlier. The Chattanooga, Tennessee-based company said it had a loss of 29 cents per share. Losses, adjusted for non-recurring costs, were 1 cent per share.

Astec Industries: Q3 Earnings Snapshot

The milling machine is used to grind the top layer of asphalt pavement before resurfacing. Astec Industries reported a $6.6 million loss in the third quarter on Wednesday. Astec Industries Inc. reported Wednesday a third-quarter loss of $6.6 million after ...

Astec Industries loses $6.6 million in the third quarter

Astec Industries (ASTE) delivered earnings and revenue surprises of -101.56% and 8.01%, respectively, for the quarter ended September 2023. Do the numbers hold clues to what lies ahead for the stock?

Astec Industries (ASTE) Reports Q3 Loss, Lags Revenue Estimates

Below are the earnings highlights for Astec Industries (ASTE): Earnings: -$6.6 million in Q3 vs. $0.7 million in the same period

Astec Industries Q3 Earnings Summary

Nov. 01, 2023 (GLOBE NEWSWIRE) -- Astec Industries, Inc. (Nasdaq: ASTE) announced today its financial results for the third quarter of 2023. "During the third quarter, order activity normalized amidst ongoing positive sentiment from customers" said Jaco ...

Astec Reports Third Quarter 2023 Results

Investors will have to decide whether strong performance is enough to overcome their fears about the global economy. Machinery giant Caterpillar is slated to report third-quarter earnings on Tuesday morning. Wall Street is looking for earnings per share of $4.80 from sales of $16.6 billion, according to FactSet.

Astec Industries, Inc. (NASDAQ:ASTE) Stock's Been Sliding But Fundamentals Look Decent: Will The Market Correct The Share Price In The Future?

Astec (ASTE) declares $0.13/share quarterly dividend, in line with previous. Forward yield 1.29% Payable Dec. 1; for shareholders of record Nov. 10; ex-div Nov.

Astec declares $0.13 dividend

CHATTANOOGA, Tenn., Oct. 30, 2023 (GLOBE NEWSWIRE) --  (NASDAQ:ASTE) announced that its Board of Directors declared a quarterly dividend of $0.13 per share. The dividend is to be paid on or about December 1,

Astec Announces Quarterly Dividend

Astec Industries Inc. is a company that has been at the forefront of innovation in infrastructure solutions for over 50 years. Founded in 1972 by J. Don Brock, the company has grown from a small asphalt plant manufacturer to a global leader in the industry.

Astec Industries Inc.: Pioneering Innovation in Infrastructure Solutions

Astec Industries Inc. has long been at the forefront of innovation in the industrial equipment manufacturing industry. With a rich history spanning several decades, the company has consistently pushed the boundaries of what is possible, introducing ...

A Closer Look at Astec Industries Inc.’s Groundbreaking Technologies

Astec Industries

rexcon

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix VII. CONCLUSION In this work, we have carried out a full-blown analysis and experimental implementation of a Slice Reconciliation scheme applied to a specific CV QKD protocol (with post-selection) under simulated channel conditions anticipated for satelliteto-Earth channels. We have provided the optimal solution for the classical reconciliation process for this CV-QKD protocol in the context of massive parallelisation under the finite key regime. More specifically, we have identified the optimal block length when a large-code block is to be subdivided so as to improve the final secure key rate in bits per second. Although our results were based on a specific CV-QKD protocol and a specific GPU architecture, the type of analysis we have introduced here will apply in general terms a large suite of CV-QKD protocols run over any form of architecture that offers massive parallelisation. Our results, therefore, pave the way to optimal reconciliation system design for a wide range of practical CV-QKD systems that operate in the finite key regime. As the demand on the finite key size grows (better security thresholds), and technology advances lead to larger quantum signalling rates, the importance of optimised multithreaded CV-QKD reconciliation will grow. REFERENCES [1] N. Hosseinidehaj, Z. Babar, R. Malaney, S. X. Ng, and L. Hanzo, “Satellite-Based Continuous-Variable Quantum Communications: Stateof-the-Art and a Predictive Outlook,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, 881–919, 2018. [2] C. H. Bennett and G. Brassard, “Quantum Cryptography: Public Key Distribution and Coin Tossing,” in Proceedings of IEEE International Conference on Computers, Systems and Signal Processing,, vol. 175, 8, 1984. [3] H. Weinfurter, “Quantum Communication Experiments with Discrete Variables,” Quantum Information: From Foundations to Quantum Technology Applications, 369–381, 2016. [4] L. Gy ¨ongy ¨osi, L. Bacsardi, and S. Imre, “A Survey on Quantum Key Distribution,” Infocommunications Journal, vol. 11, no. 2, 14–21, 2019. [5] S. Pirandola, U. L. Andersen, L. Banchi, M. Berta, D. Bunandar, R. Colbeck, D. Englund, T. Gehring, C. Lupo, C. Ottaviani, et al., “Advances in Quantum Cryptography,” Advances in Optics and Photonics, vol. 12, no. 4, 1012–1236, 2020. [6] B. Korzh, C. C. W. Lim, R. Houlmann, N. Gisin, M. J. Li, D. Nolan, B. Sanguinetti, R. Thew, and H. Zbinden, “Provably Secure and Practical Quantum Key Distribution over 307 km of Optical Fibre,” Nature Photonics, vol. 9, no. 3, 163–168, 2015. [7] T. A. Eriksson, T. Hirano, B. J. Puttnam, G. Rademacher, R. S. Lu´ıs, M. Fujiwara, R. Namiki, Y. Awaji, M. Takeoka, N. Wada, et al., “Wavelength Division Multiplexing of Continuous Variable Quantum Key Distribution and 18.3 Tbit/s Data Channels,” Communications Physics, vol. 2, no. 1, 1–8, 2019. [8] S.-Y. Shen, M.-W. Dai, X.-T. Zheng, Q.-Y. Sun, G.-C. Guo, and Z.- F. Han, “Free-Space Continuous-Variable Quantum Key Distribution of Unidimensional Gaussian Modulation Using Polarized Coherent States in an Urban Environment,” Physical Review A, vol. 100, 012325, 2019. [9] L. Gyongyosi and S. Imre, “Secret Key Rates of Free-Space Optical Continuous-Variable Quantum Key Distribution,” International Journal of Communication Systems, vol. 32, no. 18, e4152, 2019. [10] P. Jouguet, S. Kunz-Jacques, and A. Leverrier, “Long-Distance Continuous-Variable Quantum Key Distribution with a Gaussian Modulation,” Physical Review A, vol. 84, no. 6, 062317, 2011. [11] P. Jouguet, S. Kunz-Jacques, T. Debuisschert, S. Fossier, E. Diamanti, R. All´eaume, R. Tualle-Brouri, P. Grangier, A. Leverrier, P. Pache, et al., “Field Test of Classical Symmetric Encryption with Continuous Variables Quantum Key Distribution,” Optics Express, vol. 20, no. 13, 14030–14041, 2012. [12] Q. Liao, Y. Guo, D. Huang, P. Huang, and G. Zeng, “Long-Distance Continuous-Variable Quantum Key Distribution Using Non-Gaussian State-Discrimination Detection,” New Journal of Physics, vol. 20, no. 2, 023015, 2018. [13] D. Guo, C. He, T. Guo, Z. Xue, Q. Feng, and J. Mu, “Comprehensive High-Speed Reconciliation for Continuous-Variable Quantum Key Distribution,” Quantum Information Processing, vol. 19, no. 9, 1–19, 2020. [14] Y. Zhang, Z. Chen, S. Pirandola, X. Wang, C. Zhou, B. Chu, Y. Zhao, B. Xu, S. Yu, and H. Guo, “Long-Distance Continuous-Variable Quantum Key Distribution over 202.81 km of Fiber,” Physical Review Letters, vol. 125, no. 1, 010502, 2020. [15] A. Leverrier, F. Grosshans, and P. Grangier, “Finite-Size Analysis of a Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 81, no. 6, 062343, 2010. [16] F. Furrer, T. Franz, M. Berta, A. Leverrier, V. B. Scholz, M. Tomamichel, and R. F. Werner, “Continuous Variable Quantum Key Distribution: Finite-Key Analysis of Composable Security against Coherent Attacks,” Physical Review Letters, vol. 109, no. 10, 100502, 2012. [17] S. Kish, E. Villase˜nor, R. Malaney, K. Mudge, and K. Grant, “Feasibility Assessment for Practical Continuous Variable Quantum Key Distribution over the Satellite-to-Earth Channel,” Quantum Engineering, vol. 2, no. 3, e50, 2020. [18] M. Tomamichel, C. C. W. Lim, N. Gisin, and R. Renner, “Tight FiniteKey Analysis for Quantum Cryptography,” Nature Communications, vol. 3, no. 1, 1–6, 2012. [19] Z. Yuan, A. Plews, R. Takahashi, K. Doi, W. Tam, A. Sharpe, A. Dixon, E. Lavelle, J. Dynes, A. Murakami, et al., “10-Mb/s Quantum Key Distribution,” Journal of Lightwave Technology, vol. 36, no. 16, 3427– 3433, 2018. [20] S.-K. Liao, W.-Q. Cai, W.-Y. Liu, L. Zhang, Y. Li, J.-G. Ren, J. Yin, Q. Shen, Y. Cao, Z.-P. Li, et al., “Satellite-to-Ground Quantum Key Distribution,” Nature, vol. 549, no. 7670, 43–47, 2017. [21] D. Lin, D. Huang, P. Huang, J. Peng, and G. Zeng, “High Performance Reconciliation for Continuous-Variable Quantum Key Distribution with LDPC Code,” International Journal of Quantum Information, vol. 13, no. 02, 1550010, 2015. [22] X. Wang, Y. Zhang, S. Yu, B. Xu, Z. Li, and H. Guo, “Efficient Rate-Adaptive Reconciliation for Continuous-Variable Quantum Key Distribution,” Quantum Information & Computation, vol. 17, no. 13– 14, 1123–1134, 2017. [23] C. Zhou, X. Wang, Y. Zhang, Z. Zhang, S. Yu, and H. Guo, “ContinuousVariable Quantum Key Distribution with Rateless Reconciliation Protocol,” Physical Review Applied, vol. 12, no. 5, 054013, 2019. [24] M. Milicevic, C. Feng, L. M. Zhang, and P. G. Gulak, “Quasi-Cyclic Multi-Edge LDPC Codes for Long-Distance Quantum Cryptography,” NPJ Quantum Information, vol. 4, no. 1, 1–9, 2018. [25] S.-S. Yang, Z.-G. Lu, and Y.-M. Li, “High-Speed Post-Processing in Continuous-Variable Quantum Key Distribution Based on FPGA Implementation,” Journal of Lightwave Technology, vol. 38, no. 15, 3935–3941, 2020. [26] H. Li and Y. Pang, “FPGA-Accelerated Quantum Computing Emulation and Quantum Key Distillation,” IEEE Micro, vol. 41, no. 4, 49–57, 2021. [27] C. Weedbrook, A. M. Lance, W. P. Bowen, T. Symul, T. C. Ralph, and P. K. Lam, “Quantum Cryptography without Switching,” Physical Review Letters, vol. 93, no. 17, 170504, 2004. [28] N. Hosseinidehaj, A. M. Lance, T. Symul, N. Walk, and T. C. Ralph, “Finite-Size Effects in Continuous-Variable Quantum Key Distribution with Gaussian Postselection,” Physical Review A, vol. 101, no. 5, 052335, 2020. [29] D. Dequal, L. T. Vidarte, V. R. Rodriguez, G. Vallone, P. Villoresi, A. Leverrier, and E. Diamanti, “Feasibility of Satellite-to-Ground Continuous-Variable Quantum Key Distribution,” NPJ Quantum Information, vol. 7, no. 1, 1–10, 2020. [30] M. Bloch, A. Thangaraj, and S. W. McLaughlin, “Efficient Reconciliation of Correlated Continuous Random Variables Using LDPC Codes,” arXiv preprint cs/0509041, 2005. [31] P. Jouguet, D. Elkouss, and S. Kunz-Jacques, “High-Bit-Rate Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 90, no. 4, 042329, 2014. [32] H. Mani, T. Gehring, P. Grabenweger, B. Omer, C. Pacher, and ¨ U. L. Andersen, “Multiedge-Type Low-Density Parity-Check Codes for Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 103, no. 6, 062419, 2021. [33] H. Imai and S. Hirakawa, “A New Multilevel Coding Method Using Error-Correcting Codes,” IEEE Transactions on Information Theory, vol. 23, no. 3, 371–377, 1977. [34] U. Wachsmann, R. F. Fischer, and J. B. Huber, “Multilevel codes: Theoretical concepts and practical design rules,” IEEE Transactions on Information Theory, vol. 45, no. 5, 1361–1391, 1999. [35] R. D. Wesel, X. Liu, J. M. Cioffi, and C. Komninakis, “Constellation Labeling for Linear Encoders,” IEEE Transactions on Information Theory, vol. 47, no. 6, 2417–2431, 2001. [36] M. Bloch, A. Thangaraj, S. W. McLaughlin, and J.-M. Merolla, “LDPCBased Gaussian Key Reconciliation,” in IEEE Information Theory Workshop-ITW’06 Punta del Este, 116–120, IEEE, 2006. [37] A. Leverrier, R. All´eaume, J. Boutros, G. Z´emor, and P. Grangier, “Multidimensional Reconciliation for a Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 77, no. 4, 042325, 2008. [38] J. Fiur´aˇsek and N. J. Cerf, “Gaussian Postselection and Virtual Noiseless Amplification in Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 86, no. 6, 060302, 2012. [39] F. Darema, “The SPMD Model: Past, Present and Future,” in European Parallel Virtual Machine/Message Passing Interface Users’ Group Meeting, 1–1, Springer, 2001. [40] M. Pharr and W. R. Mark, “A SPMD Compiler for High-Performance CPU Programming,” in Innovative Parallel Computing (InPar), 1–13, IEEE, 2012. [41] J. Lodewyck, M. Bloch, R. Garc´ıa-Patr ´on, S. Fossier, E. Karpov, E. Diamanti, T. Debuisschert, N. J. Cerf, R. Tualle-Brouri, S. W. McLaughlin, et al., “Quantum Key Distribution over 25 km with an AllFiber Continuous-Variable System,” Physical Review A, vol. 76, no. 4, 042305, 2007. [42] F. Laudenbach, C. Pacher, C.-H. F. Fung, A. Poppe, M. Peev, B. Schrenk, M. Hentschel, P. Walther, and H. H ¨ubel, “Continuous-Variable Quantum Key Distribution with Gaussian Modulation—The Theory of Practical Implementations,” Advanced Quantum Technologies, vol. 1, no. 1, 1800011, 2018. [43] Y. Polyanskiy, H. V. Poor, and S. Verd ´u, “Channel Coding Rate in the Finite Blocklength Regime,” IEEE Transactions on Information Theory, vol. 56, no. 5, 2307–2359, 2010. [44] D. Slepian and J. Wolf, “Noiseless Coding of Correlated Information Sources,” IEEE Transactions on Information Theory, vol. 19, no. 4, 471–480, 1973. [45] T. Richardson and R. Urbanke, Modern Coding Theory. Cambridge University Press, 2008. [46] X. Ai, R. Malaney, and S. X. Ng, “A Reconciliation Strategy for RealTime Satellite-Based QKD,” IEEE Communications Letters, vol. 24, no. 5, 1062–1066, 2020. [47] S.-Y. Chung, T. J. Richardson, and R. L. Urbanke, “Analysis of SumProduct Decoding of Low-Density Parity-Check Codes Using a Gaussian Approximation,” IEEE Transactions on Information Theory, vol. 47, no. 2, 657–670, 2001. [48] V. A. Chandrasetty and S. M. Aziz, “FPGA Implementation of an LDPC Decoder Using a Reduced Complexity Message Passing Algorithm,” Journal of Networks, vol. 6, no. 1, 36, 2011. [49] A. Leverrier, “Composable Security Proof for Continuous-Variable Quantum Key Distribution with Coherent States,” Physical Review Letters, vol. 114, no. 7, 070501, 2015. [50] C. Lupo, C. Ottaviani, P. Papanastasiou, and S. Pirandola, “ContinuousVariable Measurement-Device-Independent Quantum Key Distribution: Composable Security against Coherent Attacks,” Physical Review A, vol. 97, no. 5, 052327, 2018. [51] N. Hosseinidehaj, N. Walk, and T. C. Ralph, “Optimal Realistic Attacks in Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 99, no. 5, 052336, 2019. [52] S. Pirandola, “Limits and Security of Free-Space Quantum Communications,” Physical Review Research, vol. 3, no. 1, 013279, 2021. [53] S. Pirandola, “Satellite Quantum Communications: Fundamental Bounds and Practical Security,” Physical Review Research, vol. 3, no. 2, 023130, 2021. [54] J. H. Mathews and K. D. Fink, Numerical Methods Using MATLAB, vol. 4. Pearson Prentice Hall Upper Saddle River, NJ, 2004. [55] A. Leverrier, “Security of Continuous-Variable Quantum Key Distribution via a Gaussian de Finetti Reduction,” Physical Review Letters, vol. 118, no. 20, 200501, 2017. [56] E. Villase˜nor, R. Malaney, K. A. Mudge, and K. J. Grant, “Atmospheric Effects on Satellite-to-Ground Quantum Key Distribution Using Coherent States,” in GLOBECOM 2020-2020 IEEE Global Communications Conference, 1–6, IEEE, 2020. [57] J. Mateo, Efficient Information Reconciliation for Quantum Key Distribution. PhD thesis, Universidad Politecnica de Madrid, 2011. [58] T. Tian, C. R. Jones, J. D. Villasenor, and R. D. Wesel, “Selective Avoidance of Cycles in Irregular LDPC Code Construction,” IEEE Transactions on Communications, vol. 52, no. 8, 1242–1247, 2004. [59] D. V. Nguyen, S. K. Chilappagari, M. W. Marcellin, and B. Vasic, “On the Construction of Structured LDPC Codes Free of Small Trapping Sets,” IEEE Transactions on Information Theory, vol. 58, no. 4, 2280– 2302, 2012. [60] S. Landner and O. Milenkovic, “Algorithmic and Combinatorial Analysis of Trapping Sets in Structured LDPC Codes,” in International Conference on Wireless Networks, Communications and Mobile Computing, vol. 1, 630–635, IEEE, 2005. [61] X. Wang, Y. Zhang, S. Yu, and H. Guo, “High Speed Error Correction for Continuous-Variable Quantum Key Distribution with Multi-Edge Type LDPC Code,” Scientific Reports, vol. 8, no. 1, 1–7, 2018. [62] Y. Li, X. Zhang, Y. Li, B. Xu, L. Ma, J. Yang, and W. Huang, “HighThroughput GPU Layered Decoder of Quasi-Cyclic Multi-Edge Type Low Density Parity Check Codes in Continuous-Variable Quantum Key Distribution Systems,” Scientific Reports, vol. 10, no. 1, 1–11, 2020. [63] F. Grosshans, “Collective Attacks and Unconditional Security in Continuous Variable Quantum Key Distribution,” Physical Review Letters, vol. 94, 020504, 2005. [64] M. Navascu´es, F. Grosshans, and A. Acin, “Optimality of Gaussian Attacks in Continuous-Variable Quantum Cryptography,” Physical Review Letters, vol. 97, no. 19, 190502, 2006. [65] S. Fossier, E. Diamanti, T. Debuisschert, R. Tualle-Brouri, and P. Grangier, “Improvement of Continuous-Variable Quantum Key Distribution Systems by Using Optical Preamplifiers,” Journal of Physics B: Atomic, Molecular and Optical Physics, vol. 42, no. 11, 114014, 2009. [66] P. Jouguet, S. Kunz-Jacques, E. Diamanti, and A. Leverrier, “Analysis of Imperfections in Practical Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 86, no. 3, 032309, 2012.

Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks: Conclusion & References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix VI. DISCUSSION CV-QKD. High-speed implementations realised in [61], [62] have used multidimensional reconciliation [37]. This multidimensional scheme is preferred for low SNR - but not so for the higher SNRs available via the Gaussian post-selection technique - a technique likely to be more useful in the satellite context [1]. 1) Reconciliation schemes for satellite-based In CV-QKD, Alice and Bob have to discard a block of reconciled bits if they detect a reconciliation failure (coding error) for that block. To compensate for the discarded bits, additional quantum signals need to be transmitted and reconciled, causing unwanted delays. Such delays can be problematic for satellitebased systems since the satellite is not always visible to the ground station. The FPGA-based reconciliation of [25] may suffer from this problem due to limited precision of arithmetic operations leading to higher reconciliation failures. As shown in many GPU-based works (including this work), GPU-based reconciliation offers less probability of reconciliation failure. 2) Low probability of reconciliation failures.

Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks: Discussion

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix APPENDIX

Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks: Appendix

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix V. EXPERIMENTAL RESULTS A. Decoding Error and Time B. The Optimal NR For this experiment, we pre-built a database to store LDPC codes with their code rates ranging from 0.01 to 0.8 and their block lengths ranging from 106 to 109 . For code rates less than 0.1, Multi-Edge-Type LDPC codes (degree distributions outlined in [22]) were used. These achieve a lower pDecode compared to irregular LDPC codes with the same code rate. For code rates greater than or equal to 0.1, we adopted the irregular LDPC codes (degree distributions outlined in [57]). At such code rates, these latter codes have the same pDecode performance as the Multi-Edge-Type counterparts, but allow for faster code construction. C. Final-key Effect for a Given No [15] Recall, we are particularly interested in the satellite-to-Earth channel. As in other works, we assume losses for this channel are dominated by diffraction effects, and therefore the transmissivity can be held constant. We further assume post-selections, using a bright classical beam sent along with the quantum signals (but different polarisation), remove any significant transmissivity deviations. As discussed elsewhere [17], some receiver/transmitter apertures, coupled to detailed phase-screen simulations of satellite downlink channels, render the constant-transmissivity assumption reasonable [56]. If the transmissivity is highly variable the optimal block length, NR, can be calculated by an expectation over the transmissivity density function. [16] We adopted the following method to determine ch. For m LDPC codes with NR = 106 and T = 0.9, we obtained the total number of arithmetic operations for those codes. Next, we measured the elapsed time to reconcile a block of 106 quadrature values. We then obtained ch by dividing the number of arithmetic operations to this measured elapsed time. [17] These trapping sets are the primary reason that additional decoding iterations are consumed for only a marginal decrease of the decoding error, i.e. the error floor effect [58].

Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks: Experimental Results

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix IV. FINAL KEY RATE We now present the penalty incurred for the division of N in the finite-key regime, and then propose an optimisation procedure to find the optimal NR which maximises the final key rate in bits per second. A. Analysis of the Final Key Rate For the protocol considered, in the finite-key regime the final key rate in bits per pulse, K, under the assumption of Gaussian collective attacks is adopted as [28], [49], [50][11] Thus far, we have been analysing the final key rate in bits per pulse. However, the final key rate in bits per second is more interesting in our context - the system will not be viable if the reconciliation takes too long to complete. From this point forward, we use a dashed symbol to distinguish a final key rate that is given in bits per second. Taking the decoding complexity into account, and ignoring the time taken to acquire the quantum signals, we can write the final key rate, K′ Finite, as B. Optimised LDPC Blocklength for CV-QKD Reconciliation where Eq. 30 can be solved via a numerical root-finding algorithm [54]. If we consider the standard CV-QKD settings, we obtain a stationary point at NR = 3.6×107 bits (the value of K′ Finite at this NR is discussed later). [11] This key-rate formulation was developed in [49] with a typographical error corrected in [50]. Eq. 19 is from [28] which acknowledged the correction and simplified the final key rate formulation (see footnote 2 of [51] for more details). A general discussion on the use of other key-rate formulations (e.g. [52], [53]) within our framework is given later. [12] Recalling Eq. 18, we note that for a given LDPC code, Ej is only dependent on the degree distribution pairs and Dj is only a function of the degree distribution pairs and γ. [13] In using this technique it is important to check that the inequality holds for the chosen parameter range of interest. This is done for all calculations we show here, but also for a much wider range not shown. For example, we find for ǫ = 10−9 , N = 109 and m = 5, the inequality 28 holds for any combination of the remaining parameters selected from the ranges VA ∈ [1, 34], T ∈ [0, 1] and ξ = [0, 0.05]. [14] The values of ξch and ξd in the standard CV-QKD settings are predicted values after accounting for all noise terms [17].

Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks: Final Key Rate

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix III. OVERCOMING THE LIMITATIONS OF KEY RECONCILIATION A. GPU-based SR The process of SR is to reconcile mN bits. One can naively use m LDPC matrices with NR = N for each matrix. However, due to practical hardware limitations, the process is better implemented by dividing N into Nd blocks of some smaller NR so that the same LDPC decoders can reconcile these blocks in parallel. This process resembles the idea of Single Program Multiple Data (see [39], [40] for more details). As illustrated in Fig. 2, we implement SR by creating Nd LDPC decoders loaded with the same LDPC matrix on Nd GPU threads and let these decoders reconcile Nd blocks in parallel. This helps to reduce the SR timescale and assists in meeting the time constraints, such as those posed in satellite-based scenarios. Section V-A will demonstrate in detail the advantage of using such parallelisation. B. The Penalty of Using Finite-Length LDPC Codes In SR, Bob needs to transmit syndrome bits to Alice based on the selected LDPC matrix with the code rate, Rj , for Sj via classical communications. For a given channel condition, selecting Rj closest to the capacity is the common approach to minimise the number of bits disclosed to the eavesdropper while Alice can still reconstruct Bob’s quantised bits without error [36]. Specifically, for a given T , we can obtain the SNR, γ, as [42] where Y is a vector of Bob’s quadrature values of length NR, M(Y ) is a mN-bit string obtained by applying the quantisation function M(·) to each quadrature value in Y , and Π(M(Y )) is the entropy function of M(Y ). Increasing m to values that render the quantisation error negligible is always possible, but this would require the individual LDPC codes for every j th slice to be near perfect (capacity-achieving) otherwise the efficiency β will be low; m = 5 is found to be a good pragmatic compromise, and is adopted here. Given five slices a constant quantisation size of the real line across 2 5 bins centered on zero is chosen. This size, which is dependent on the adopted γ, optimises β (see [31] for further discussion). The LDPC code rates, Rj , in Eq. 2 are the actual rates of the specific codes used for each slice (of length NR). Normally, in practice, NR is simply set to some value that allows target time-frames to be met, given that the decoding time is an increasing function of the block length [24]. We use Rj to obtain our experimental key rate in Eq. 32. A more nuanced value of that NR that optimises secure key rates is now analysed. Function A is termed the “channel dispersion” since it represents the reduction of the code rate from the channel capacity due to a tolerated decoding error probability. It is the “price to pay” for using a code with finite block length, for a given γ. C. Analysing the Computational Complexity of S With this all in place, it is now possible to solve for Dj as given by Eq. 1 Now we focus on the determination of Ej . When messages are propagated from the variable nodes to the check nodes, there are 2G multiplications and G additions [48]. When messages are propagating back to the variable nodes, there are 4G operations required (2G multiplications and 2G additions) [48]. Therefore, Ej is obtained by [46], [48] The decoding time of the whole reconciliation process, ∆t, is given by [7] We assume the least significant bit is l i 0 . [8] The rationale behind this is that the soft decoding output of Sj−1 provides a priori information on the reliability of each bit in Sj [30] [9] This approximation is accurate if the code achieves more than 80% of the capacity [43]. [10] In a BP decoder, a decoding iteration is one pass through the decoding algorithm

Overcoming the limitation of key reconciliation

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix II. SYSTEM OVERVIEW Although, as just stated, our analysis will apply to most CV-QKD protocols, for detailed quantitative discussion we will consider only one specific CV-QKD protocol - the “noswitching” protocol [27]–[29] based on heterodyne detection. In this protocol, the quantum signal is encoded using Gaussian-modulated coherent states [27]. The main advantage of the no-switching protocol is that Alice and Bob can utlise all measurement results [28] (in most other protocols some results are discarded due to a random quadrature selection). We also adopt a Slice Reconciliation (SR) variant named Multi-Stage Hard Decoding[4] (MSHD) [30], [33], [34] for the classical reconciliation step, where the number of bits derived from each measurement outcome is m. We refer to this variant simply as SR in the following. It is worth noting that the optimisation analysis to follow is to some extent independent of the details of the reconciliation scheme. However, SR [30], [36] can be compared with the other well-known reconciliation scheme for CV-QKD - multidimensional reconciliation [37]. It is known that SR achieves higher reconciliation efficiency when the Signal-to-Noise Ratio (SNR) is greater than 1 [36]. At low SNR the opposite is true. For focus, here we adopt SR (as multistage hard decoding [36]) since in many satellite scenarios post-selection is used to filter out the low SNR quantum signals [1]. Our adopted scheme will be more useful in such scenarios. We now briefly describe the steps of the protocol, a diagram of which shown in Fig. 1. In the following, we assume the quantum signalling rate is much larger than the reconciliation rate. • Alice selects a fixed modulation variance VA. For each quantum signal to be transmitted to Bob, Alice randomly selects a number from a Gaussian distribution, N(0, VA), and then prepares a signal by displacing one of the quadrature components of a vacuum state by this random number. The process is repeated on the signal for the other quadrature. The signal is then transmitted to Bob. Step 1: Signal Preparation. • Bob performs heterodyne detection to obtain the two quadrature values (real numbers) for each received signal. Bob compares each measured quantum signal with a given cut-off threshold and informs Alice to discard her corresponding quantum signal if his measured quantum signal is lower than the threshold[5] . A quantum signal that is lost in transit registers a null signal at Bob. Neglecting null signals, Bob holds 2No quadrature values at the end of this process. Step 2: Heterodyne Detection. • S Bob randomly selects a subset 2Ne from the 2No quadrature values and sends this estimation subset, along with the corresponding time information, to Alice via classical communications (we adopt Ne = 1 2No, unless otherwise stated). Alice uses the timing information to best pair the signals in this subset (and therefore the corresponding quadrature values) sent by her and then estimates the covariance matrix between the shared states. Based on the estimated covariance matrix, Alice determines the channel transmissivity, T , excess noise, ξ, Bob’s SNR, γ, the Holevo Information, χBE, between Bob and the eavesdropper (Eve), and the mutual information between Alice and Bob, IAB. Finally, for a given target reconciliation efficiency β, Alice compares χBE with βIAB. Alice aborts the protocol if χBE ≥ βIAB. Otherwise, Alice informs Bob of the estimation results, i.e. T , ξ, γ, χBE and IAB. tep 3: Parameter Estimation. • Using Gray Labelling, Alice and Bob represent each of the quadrature values embedded in each signal with m bits. Then, for quadrature values selected in the estimation subset, Alice forms a Ne-by-m bit matrix and Bob does the same. Next, Alice and Bob exchange their matrices and compare the j th column of the two matrices to estimate the Bit Error Ratio[6] (BER), pj , j ∈ {0, 1, · · · , m−1}, for all the digits in the j th column. The estimated pj will be used in SR. Finally, Alice and Bob discard all the quadrature values in the estimation subset. At the end of this step, Alice and Bob each hold a mN-bit string. Step 4: Bit Error Estimation for SR. • For each column, Alice and Bob agree on an LDPC code with block length NR that is closest to the capacity determined by pj. Bob forms a new NR-bit string (referred as a “slice” in SR) by selecting the j th digit (bit) of each of the NR quadrature values, encodes the new bit string (the slice) into syndrome bits, and sends those bits to Alice (see III.B for details). Alice then initiates SR to obtain her best estimate of Bob’s string. Alice repeats this process until all her mN bits are reconciled. Finally, Alice and Bob obtain two hashed strings by applying the same hash function to their reconciled strings and exchange the hash results to check whether SR is successful. If successful, Alice holds a mN-bit string identical to Bob’s mN-bit string. Otherwise, they abort the protocol and restart from Step 1. Step 5: Reverse Reconciliation. • Based on Eq. 19, Alice and Bob compute the length of the secret key that can be extracted and then apply a 2-universal hashing function on their reconciled string to obtain two identical and shorter secret key strings about which Eve has effectively no knowledge. Step 6: Privacy Amplification. [4] The slice reconciliation can be implemented with 2 other variants: Bit Interleaved Coded Modulation (BICM) [30] and Multi-Level Coding/MultiStage Decoding (MLC/MSD) [31], [32]. We note that the MLC/MSD takes advantage of the dependence between slices to select the optimal LDPC code rates [30], [32]. However, as a special case of MLC/MSD, MSHD assumes that the slices are independent [30], [33], [34]. Using MSHD leads to a tractable analysis at the expense of sub-optimal selection of LDPC code rates, but such expense is negligible if Gray Labelling [35] is adopted and the number of slices is at least 5 [30], [34], as is the case in this work. [5] The Gaussian post-selection technique at Bob’s side effectively improves the channel conditions between Alice and Bob [38] so that SR is preferred for reconciliation (rather than multidimensional reconciliation). [6] At this step, sources of bit errors include the channel transmission, heterodyne detection, and quantisation.

Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks: System Overview

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix Abstract Designing a practical Continuous Variable (CV) Quantum Key Distribution (QKD) system requires an estimation of the quantum channel characteristics and the extraction of secure key bits based on a large number of distributed quantum signals. Meeting this requirement in short timescales is difficult. On standard processors, it can take several hours to reconcile the required number of quantum signals. This problem is exacerbated in the context of Low Earth Orbit (LEO) satellite CV-QKD, in which the satellite flyover time is constrained to be less than a few minutes. A potential solution to this problem is massive parallelisation of the classical reconciliation process in which a large-code block is subdivided into many shorter blocks for individual decoding. However, the penalty of this procedure on the important final secured key rate is non-trivial to determine and hitherto has not been formally analysed. Ideally, a determination of the optimal reduced block size, maximising the final key rate, would be forthcoming in such an analysis. In this work, we fill this important knowledge gap via detailed analyses and experimental verification of a CV-QKD sliced reconciliation protocol that uses large block-length low-density parity-check decoders. Our new solution results in a significant increase in the final key rate relative to non-optimised reconciliation. In addition, it allows for the acquisition of quantum secured messages between terrestrial stations and LEO satellites within a flyover timescale even using off-the-shelf processors. Our work points the way to optimised global quantum networks secured via fundamental physics. I. INTRODUCTION Continuous Variable (CV) Quantum Key Distribution (QKD) has been intensively studied and significant breakthroughs have been achieved in both theory and experiment (see [1] for review). Compared to Discrete Variable (DV) QKD [2]–[5], CV-QKD can be implemented with well-developed technologies (e.g., homodyne detectors) in commercial fibreoptic networks [6], [7] and free-space optical communications [8], [9], providing it a potential advantage in practical deployments [10]–[14]. Considering the finite-key security of CV-QKD and DV-QKD, there are three critical parameters. These are, No, the number of original quantum signals sent by the transmitter (Alice) that are collected by the receiver (Bob); Ne, the number of quantum signals from which the protocol parameters are estimated;[1] and, ǫ, the probability that a QKD protocol fails to generate secret keys [15], [16]. To satisfy an upper limit on the failure probability of parameter estimation, Alice and Bob set Ne to a large value, which in turn implies a larger No. Despite the advantages in deployment, CV-QKD systems tend to demand a larger No to reach the same ǫ relative to DVQKD protocols. For example, to achieve a final key rate of 0.1 bits per pulse with ǫ = 10−9 , a CV-QKD protocol studied in [17] required No ≈ 109 signals. However, to achieve the same final key rate with ǫ = 10−14, the DV-QKD protocol in [18] required No ≈ 104 signals. This higher number of required signals in CV-QKD can render the classical post-processing (i.e. key reconciliation and privacy amplification[2] ) slow - possibly failing to meet target timescales for reconciliation. The end-users of a CV-QKD system expect the system to deliver two identical and secure keys under a limited time interval. For example, for satellite-based deployments, we would hope that the reconciliation is completed while maintaining a line-of-sight connection with the ground station. For a CV-QKD-enabled satellite with orbital parameters similar to Micius [20], this would mean the reconciliation should be completed in less than a few minutes. For the protocol we use in this work (see later), and for ǫ = 10−9 , this, in turn, would require the data rate of reconciliation to be at least 3.6 × 106 bits per second. For real-time reconciliation (say in subsecond timescales), two orders of magnitude increases in the reconciliation rates would be required. Demands for smaller ǫ will exacerbate the issue. Ideally, the rate of reconciliation should always be faster than the rate of quantum signalling. This all raises the question as to whether current CV-QKD reconciliation schemes are optimised for the highest possible key rates in bits per second. As we show here, this is not the case. Further optimisation is possible on all current schemes. To understand the issue better, we define reconciliation in the context of CV-QKD as a two-step scheme where the inputs to the reconciliation are non-identical N = 2No − 2Ne quadrature values[3] held by Alice and Bob (after parameter estimation), and the output is an identical bit string held by Alice and Bob [21]–[23]. Assuming a reverse reconciliation scheme, Bob first converts the quadrature values encoded by Alice in each signal to m bits. Alice, after converting each of her encoded real numbers also to m bits, then initiates some discrepancy-correction algorithms based on pre-defined errorcorrection codes to ensure her mN bits are identical to Bob’s. In this work, we will adopt Low-Density Parity-Check (LDPC) codes for the error correction. However, as alluded to above, reconciling mN bits within a limited time frame can be challenging. State-of-the-art LDPCbased reconciliation schemes for CV-QKD systems involve parallelised computation on a Graphics Processing Unit (GPU) [13], [24] or Field-Programmable Gate Arrays (FPGAs) [25], [26]. Reconciliation schemes implemented on FPGAs offer more programmable flexibility, but sometimes at the cost of reduced memory access relative to GPUs. For our purposes, both hardware architectures are useful - both offer massive parallelisation opportunities. These parallelisation solutions generally take the following two-step approach: 1) The mN bits are organised as m N-bit blocks to be reconciled. Each N-bit block is divided into multiple shorter blocks of size, say, NR. This is usually just set to a block size that can be processed within some timescale. 2) Then the m NR-bit blocks are reconciled in parallel (via independent processors) using optimally-designed LDPC decoders. However, what is missing in this approach is a proper optimisation analysis as to what the optimal value of NR is. As we show below, simply reducing NR at the cost of additional processing units is not an optimal solution. It transpires that in QKD the “penalty” cost of reducing the code rate (implicit in the use of small block lengths) significantly influences the bit per second final key rate. A more sophisticated analysis is required to determine the optimal reduced block length. Such an analysis is the key contribution of this work. Although we will adopt a specific CV-QKD protocol for our analysis, the key steps of our scheme will apply to any CV-QKD protocol. Our reconciliation scheme will deliver the highest reconciliation rate for a given processor speed - thus allowing for the optimal solution to CV-QKD reconciliation. [1] More precisely, in a CV-QKD protocol, Alice and Bob randomly select a Ne-signal subset from the No signals to estimate the parameters. [2] In this work, we focus on the key reconciliation step because it is the more time-consuming part in the post-processing steps while the privacy amplification involving only bit-wise operations can be easily implemented faster than the reconciliation [19]. [3] No and Ne are multiplied by 2 since Alice and Bob utilise both quadratures from heterodyne detection - the detection process we assume in this work.

Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks: Abstract & Introduction

Hey there. Scott here. Today, let's rip apart some lazy clichés about success (one of my favorite things to do). You know the drill: "Find a mentor." "Surround yourself with the right people." Blah, blah, blah. There's this whole industry built on pimping out "gurus" promising secret keys to the magic kingdom. You'll pay some six-figure mastermind fee to sip bad coffee while some pay-to-speak “thought leader” on stage feeds you recycled platitudes. Here's the newsflash nobody wants to tell you: The fastest way to master something is to TEACH it. You think mentors got to where they are by sitting on their butts waiting for handouts? Hell no. They learned in the trenches, by DOING. They made mistakes, learned from them, and figured out how to do it better next time. Real wisdom isn't downloaded – it's forged in the fire. Teach it to Keep It Let's talk about brain science for a second. There's this thing called the "protégé effect." Turns out, when you EXPECT to teach something, you learn it on a deeper level. Why? Because it forces you to organize your thoughts, spot holes in your understanding, and break things down in a way that makes sense to someone else. You think those business influencers got all their knowledge overnight? Nope. They started out fumbling their way through, probably screwing things up just as much as the rest of us. But somewhere along the way, someone asked them a question, or they started explaining their process, started explaining all the things they figured out by doing…and BOOM. Lightbulb moment. Think about it – have you ever truly understood a concept until you had to explain it to a total newbie? Suddenly, all that fuzzy thinking goes out the window. You have to crystallize that stuff. That’s where the magic happens. The Ultimate Hack: Don't Seek, BE You want a mentor? Flip the script and become one. Find someone a few steps behind you on the path. Offer to help them out. You don't need to be the ultimate expert – just further along than they are. Here's the crazy thing: in the process of mentoring, YOU'LL level up faster than you could have imagined. Suddenly, you're revisiting stuff you thought you knew, only to see it with a whole new perspective. Your mentee asks questions you never thought of, forcing you back to fundamentals. Boom, lightbulb moments all over again. This isn't about charity. It’s pure, calculated selfishness (while simultaneously helping out someone else). "But Scott, I Have Nothing to Teach!" This is the #1 excuse I hear every time. I call BS. Every single one of you has something valuable to share. The trick is that it might not be what you think. Maybe you just: Figured out this crazy productivity hack for squeezing an extra hour into your day. Took your dog through basic training without losing your mind. Mastered a killer 20-minute bodyweight workout. Found a budgeting app that finally stopped you from blowing all your cash every weekend Think that stuff's too basic? Wrong. For someone struggling with those things, your 'basic' skill is worth its weight in gold. Your secret is the starting point someone else is craving. What makes you valuable as a mentor isn't just knowledge, it's relatability. When a 'guru' is on some pedestal, that creates distance. People are afraid to ask the "dumb" questions. however, were just in those beginner shoes. You remember the suck, the confusion, the "why the hell can't I get this" vibes. That makes you approachable. You, The Overflow Effect Here's the part no one tells you about 'pouring into others': it creates this crazy energy loop that comes back to you tenfold. Think about it like this: Every time you invest in helping someone else, you: Solidify your own understanding. Build confidence like a damn muscle. Gain clarity about your own path. Open doors to unexpected opportunities. Form valuable connections with people motivated to grow. Remember the old rule, "Give without expectation of return?" Guess what – the universe isn’t stupid. Your generosity creates this abundance mindset shift. New insights start rolling in. You notice solutions to problems you were stuck on. That side project you had on the back burner suddenly starts picking up steam. Call it karma, call it energetics, whatever. Helping others unlocks something that directly fuels your own success. From Talk to Action Enough philosophizing. You're a doer, right? Okay then, here's your homework. This is how you (start) to be a mentor, build a mentoring loop, and start helping others, while simultaneously, helping yourself. Don't try to be all things to all people. What specific skillset or knowledge have you been geeking out over lately? That's your jam. Don't overthink it – just pick something you genuinely enjoy talking about and feel you can provide a little guidance on. Niche Down: We're not going straight for paid mentoring here. Start small. Could you offer: Low-stakes offers: A 30-minute Q&A session in a relevant Facebook group? A mini-training video on a single, super-specific topic? A one-page PDF cheat sheet that breaks down a complex process? Zoom Office hours every Friday afternoon? Pull people in from LinkedIn. Social media, baby. Tell your network you're open to helping a handful of folks with [your niche topic]. Let your enthusiasm for the subject shine through – that's how you attract like-minded people. Don't worry about looking fancy. People connect with real. Put it out there: Overcoming Your Inner Critic Guess what? Your brain's gonna freak out. Imposter syndrome will throw up roadblocks. You'll hear whispers like: "Who the hell am I to teach this?" "Everyone else knows this stuff already." "What if I give bad advice and look like an idiot?" Here's how you fight back: Flip it: That fresh perspective is gold. "Who am I to teach this?" I'm the perfect person to teach this because I just figured it out myself. Nope. They don't. That's your ego talking. Everyone starts somewhere. Don't underestimate the value of a "beginner's mind." "Everyone else knows this stuff." Dude, relax. No one expects perfection. Focus on being helpful, sincere, and open to learning your mentees. That humility builds trust like nothing else. "Bad advice = idiot" alongside This Is YOUR Learning Lab The beauty of starting small is it takes pressure off. This isn't some permanent contract you're locked into. You're dipping your toes, testing the waters, and evolving right along with the folks you're helping. Don't worry about having all the answers right this second. Be transparent about your journey. People love rooting for the underdog. Your scrappiness and genuine desire to help are your winning qualities. Now you’re wondering, ok - I’ll do it… but I’ve never done this before. No problem, here’s how to start. First Interactions: Set the Stage Your goal isn't to be some all-knowing sage. No, you're the supportive guide on the side – here to provide helpful signposts and cheer your mentees on. With that in mind, here's how to kick things off on the right foot: – Ditch the generic "What do you need help with?" That’s too damn open-ended, especially for newbies who likely don't know where to start. Instead, dive into the specifics: Ask Powerful Questions "What's your biggest pain point with [your niche] right now?" "What's one small win you'd love to achieve by the end of this week?" "What part of this process feels the most overwhelming/confusing?" These force focus and get them thinking strategically rather than flailing around in overwhelmed. – Beginners get obsessed with end results. They want to hit some arbitrary number or get that dream outcome NOW. Your job is to bring them back to Earth: Instead of: "How do I make $10k a month?" ask "What's one concrete thing you can do to move closer to that goal?" Focus on Actions, Not Outcomes today This creates manageable momentum and prevents paralysis. Progress is your real measure. – Let's get real: a lot of people are addicted to the struggle. Watch out for red flags like, "I don't have time," "This won't work for me," and "I've tried all that before." Your job is to call gentle BS. Don't be mean, but be firm: Challenge Excuses "If you found just 15 minutes a day, what could you chip away at?" "Great! What makes your situation different?" "That might be true, what could you try instead? else Don't let them off the hook with their self-limiting story. You're here to crack open possibility, not coddle comfort zones. Beware of Scope Creep Early on, set clear boundaries. Are you offering a one-time Q&A? Ongoing email support? Be specific about what's included and what's not. Your time is valuable. Generosity doesn't mean martyrdom. Also, beware of those who confuse freebie mentoring with therapy. Your job isn't to fix their entire life. Keep steering things back to specific action within your area of expertise. Celebrate Every Damn Win This is HUGE. Most folks spend an unhealthy amount of time fixating on what they accomplished. Flip the script. When they share even the smallest victory, go BIG – shout their praises, share their wins, and give 'em virtual high fives. haven't This wires their brain for success more than anything else you could do. You Are the Ultimate Beneficiary It sounds counterintuitive, right? Shouldn't your focus be on value? That's part of it, but here's where the secret kicks in – mentoring unlocks hidden growth avenues for YOU in ways you don't expect. giving Your mentees won't hold back. They'll tell you which explanations landed brilliantly, and which instructions left them scratching their heads. This kind of direct critique is pure gold for honing your communication skills and pinpointing gaps in your understanding. Feedback Loop from Hell: Remember those "dumb" questions that used to plague you? They make you a better innovator. Every time you're stumped by a newbie's inquiry, you must flex your creative problem-solving muscles to find new ways to explain complex concepts. Forced Innovation: Turns out, having someone count on you is damn motivating. When you promise a mentee you'll tackle a certain topic or show them how to do something, welp, now you gotta walk the walk. No hiding behind lazy procrastination anymore. Accountability on Tap: The universe works in mysterious ways. When you throw out generous energy, those ripples come back multiplied. Maybe a mentee connects you with someone who solves a key problem for your business. Maybe that blog post you cranked out to explain a topic suddenly leads to paid collaboration offers. Unexpected Opportunities: This is pure social dynamics. By actively being the leader, the teacher, and the one with (even a little) more experience, you naturally step into a different social role. People begin to look at you differently. Their belief in fuels belief in yourself. Leveling-Up by Association: you Don't Just Mentor, Find Your OWN Mentors Now, here's the twist: even as you become a rockstar mentor to others, don't think you've outgrown being mentored yourself. Always seek out people a few rungs further up the ladder than you. The game never really ends. Look for people who: Embody the results you want to create. Share your values and energy. Have different skills or areas of expertise than your own. It's tempting to only get involved with the most famous, "successful" folks. Sometimes, the greatest mentoring relationships blossom with someone not that far ahead of you – they still remember the challenges vividly. A Call to Arms Listen, I won't sugarcoat this. Stepping into the mentor role ain't always easy. It requires commitment, vulnerability, and the willingness to confront your own BS alongside those you help. But you know what's harder? Staying stuck in the eternal student loop. Spinning your wheels, forever waiting for that perfect course, magic guru, or secret roadmap to fall from the sky. Here's the truth: the knowledge you need is already inside you. All that's needed is unlocking it. By sharing your journey, embracing the messiness of learning, and pouring into others, you create this unstoppable flow that benefits everyone involved. The world is starving for real. People are sick of the overpolished, "I've got it all figured out" BS. What they crave is someone willing to admit they don't have all the answers and figure things out alongside them. That's where you come in. Your imperfections are your biggest assets. Your willingness to show up, share your wins and losses, and lift others up as you rise – that's what makes a true difference. So, my challenge to you is simple: Pick THAT ONE THING you're most excited about right now, and start talking about it. Find those one or two people who could use your support, and offer a helping hand. Get comfortable being uncomfortable. Mess up. Admit what you don't know. Learn out loud. This is what creates that overflow effect. This is how you tap into something you haven't even fully glimpsed yet. The Anti-Guru Movement This isn't just about self-growth. It's bigger than that. It's about flipping the script on the entire model of success. Imagine a world where knowledge flows freely, where collaboration wins over competition, and where anyone with drive and curiosity can find the support they need to rise. We're the ones who have to create that world. We're the anti-gurus, the knowledge rebels, the ones who pave the way for something bolder, better, and far more sustainable. So go forth, my friends. Teach. Share. Connect. Lift as you climb. The best form of knowledge seeking is knowledge creation – so unleash that inner wisdom you don't even realize have, and watch the magic unfold. That's a wrap, folks! I hope this ignites some fires. I'd love to hear what you think. Have a great weekend! Scott Also published here

The Anti-Guru Movement: Reconsider What Mentorship Is All About

Recently, has been touring the world attempting (perhaps) a regulatory capture of global AI developments. No wonder that OpenAI does not like open-sourced AI, at all. Nevertheless, this post isn’t about AI development, but its security and standardization challenges. Generally, an advanced environment, as well as the defenders’ cyber situation awareness and response capabilities (henceforth referred to together as ‘security automation’ capabilities) are both overwhelmingly driven by automation and AI systems. To get an idea, take something as simple as checking and answering your Gmail today, and enumerate the layers of AI and automation that can constitute securing and orchestrating that simple activity. Sam Altman cyber threat Thus, all organizations of a noticeable size and complexity have to rely on security automation systems to affect their cybersecurity policies. What is often overlooked is that there also exist some cybersecurity “ ” that enable the implementation of these security automation systems. These may include the automated threat data exchange mechanisms, the underlying attribution conventions, and knowledge production/management systems. All these enable a detection and response posture often referred to by marketers and lawyers as “active defense” or “proactive cybersecurity”. However, if you pick up any national cybersecurity policy, you’d be hard-pressed to find anything on these metapolicies – because they are often implicit, brought into national implementations largely by influence and imitation (i.e. network effects) and not so much by formal or strategic deliberations. metapolicies These security automation metapolicies are important to AI governance and security because in the end, all these AI systems, whether completely digital or cyber-physical, exist within the broader cybersecurity and strategic matrix. We need to be asking whether retrofitting the prevalent automation metapolicies would serve well for the future of AI or not. Avoiding Path Dependency Given a tendency towards path dependency in automated information systems, what has worked alright so far is getting further entrenched into the newer and adjunct areas of security automation, like the intelligent/connected vehicle ecosystem. Further, the developments in the security of software-on-wheels are being readily co-opted across a variety of complex automotive systems, from that hold the promise of decreased crew size and increased lethality to standards for automated management and drone transportation systems. Consequently, there is a rise in vehicle SOCs (Security Operations Centers) that operate on the lines of cybersecurity SOCs and use similar data exchange mechanisms, borrowing the same implementations of security automation and information distribution. That would be perfectly fine if the existing means were good enough to blindly retrofit into the emerging threat environment. But they are far from it. fully digitized tanks fleet security For example, most cybersecurity threat data exchanges make use of the Traffic Light Protocol , however TLP itself is only a classification of information – its execution, and any encryption regimes to restrict distribution as intended, is left to the designers of security automation systems. Thus there is a need for not just more fine-grained and richer controls over data sharing with fully or partially automated systems but also for ensuring compliance with it. Much of threat communication policies like the TLP are akin to the infamous Tallinn manual, in the way that these are almost an expression of opinions that cybersecurity vendors may consider implementing, or may not. It gets more problematic when threat data standards are expected to cover automated detection and response (as is the case with automotive and industrial automation) – and may or may not have integrated an appropriate data security and exchange policy for lack of any compliance requirements to do so. (TLP) Another example of inconsistent metapolicies, out of numerous others, can be found in the recent rise of language generation systems and conversational AI agents. The thing is that not all conversational agents are ChatGPT-esque large neural networks. Most of these have been in deployment for decades as rules-based, task-specific language generation programs. Having a “common operating picture” by dialogue modeling and graph-based representation of context between such programs (as an organization operating in multiple domains/theaters could require) was an ongoing challenge before the world stumbled upon “attention is all you need”. So now basically we have a mammoth of legacy IT infrastructure in human-machine interface and a multi-modal AI automation paradigm that challenges it. Organizations undergoing “digital transformation” not only have to avoid inheriting legacy but also consider the resources and organizational for efficiently operating an AI-centric delivery model. Understandably, some organizations (including governments) may not want a complete transformation right away. Lacking standardized data and context exchange between the emerging and the legacy automated systems, many users are likely to continue with a paradigm they are most familiar with, not the one that is most revolutionary. technical debts requirements In fact much of cybersecurity today hinges on these timely data exchanges and automated orchestration, and thus these underlying information standards become absolutely critical to modern (post-industrial) societies and the governance of cyber-physical systems. Yet, instead of formulating or harmonizing the knowledge production metapolicies needed to govern AI security in a hyper-connected and transnational threat environment – we seem to be falling into the doomer traps of existential deliverance and unending uncanny valleys. That said, one of the primary reasons for the lack of compliance and a chaotic standards development scenario in security data production is the lack of a primary governance agent. The Governance of (Cyber) Security Information Present automation-centric cyber threat information-sharing generally follow a multistakeholder governance model. That means these follow a fundamentally bottom-up life-cycle approach, i.e. a cybersecurity information standard is developed and is then pushed “upward” for cross-standardisation with ITU and ISO. This upward mobility of technical standards is not easy. The Structured Threat Information Expression (STIX), which is perhaps the de facto industry standard now for transmitting machine-readable Cyber Threat Intelligence (CTI), is still awaiting approval from ITU.  Not that it’s really needed, because the way global governance in technology is structured, it is led by industry and not nations. The G7 has gone to the extent of this, and some members even any diplomatic efforts towards any different set of norms. standards formalizing blocking This works well for those nation-states that have the requisite structural and productive within their public-private technology partnerships. Consequently, the global governance of cyber technology standards becomes a reflection of the global order. Sans the naming of cyber threat actors, this still had been relatively objective by nature so far. But it is no longer true with the integration of online disinformation into offensive cyber operations and national cybersecurity policies – not only the conventional information standards can run into semantic , newer value-driven standards over information environment are also . Since the production and sharing of automation-driven social/political threat indicators can be shaped by and affect political preferences, as the threats of AI generated information and social botnets rises, the cybersecurity threat information standards also slide from a sufficiently objective to a more subjective posture. And states can do little to reconfigure this present system because the politics of cybersecurity standards has been deeply intertwined with their market-led multistakeholder development. capacities conflicts popping up Cyber threat attributions are a good case in point. MITRE began as a DARPA contractor, and today serves as an industry wide de-facto knowledge base for computer network threats and vulnerabilities. Of the Advanced Persistent Threat groups listed in MITRE ATT&CK, close to 1/3 of cyber threats are Chinese, another 1/3 come from Russia/Korea/Middle-East/India/South-America etc, and the remaining 1/3 (which contain the most sophisticated TTPs, the largest share of zero-day exploitations, and a geopolitically aligned targeting) remain unattributed. We’ll not speculate here but an abductive reasoning about the unattributed may leave readers with some ideas about the preferences and politics of global CTI production. threat cluster A fact of life is that in cyberspace power-seeking states have been playing the role of governance actors and sophisticated offenders at the same time, so this market led multistakeholderism has worked out well for their operational logic – promulgating a global . But it is bad for the production of cyber threat knowledge and security automation itself, which sometimes can get quite biased and politically over the internet. Society has walked this path long enough to not even think of it as a problem when moving into a world surrounded by increasingly autonomous systems. politics of interoperability motivated A Way Forward With the social AI risks looming larger, states intending to implement a defensible cybersecurity automation posture today might have to navigate a high signal-to-noise ratio in cybersecurity threat information, multiple CTI vendors and metapolicies, as well as constant pressures from industry and international organizations about “AI ethics” and “cyber norms” (we’ll not venture into a discussion of “whose ethics?” here). This chaos, as we noted, is an outcome of the design of bottom-up approaches. However, top-down approaches can lack the flexibility and agility of bottom-up approaches. For this reason, it is necessary to integrate the best of multistakeholderism with the best of multilateralism. That would mean rationalizing the present bottom-up setup of information standards under a multilateral vision and framework. While we do want to avoid partisan threats data production, we also want to make use of the disparate pool of industry expertise which requires coordination, resolution, and steering. While some UN organs, like the ITU and UNDIR, play an important role in global cybersecurity metapolicies – they do not have the sort of top-down regulatory effect needed to govern malicious social AI over the internet or implement any metapolicy controls over threat sharing for distributed autonomous platforms. Therefore, this integration of multistakeholderism with multilateralism needs to begin at the UNSC itself, or any other equivalent international security organization. Not that this was unforeseen. When the first UN resolution was made assessing Information Technologies in 1998, particularly the internet, some countries had explicitly that these technologies would end up at odds with international security and stability, hinting at the required reforms at the highest levels of international security. Indeed, the UNSC as an institution has not co-evolved well with digital technologies and the post-internet security reality. The unrestricted proliferation of state-affiliated APT operations is just but one example of its failure to regulate destabilizing state activities. Moreover, while the council seems still stuck in a 1945 vision of strategic security, there is and evidence to relocate the idea of “state violence” in light of strategically deployed offensive cyber and AI capabilities. pointed out enough reason While overcoming the resilience of global order and its entrenched bureaucracies is not going to be easy, if reformed in its charter and composition, the council (or its replacement) could serve as a valuable institution to fill the void that emerges from the lack of a primary agent in guiding the security and governance standards driving security automation and AI applications in cyberspace. It Is The Process At this point, it is necessary that we call out certain misunderstandings. It seems that regulators have some ideas about governing “AI products”, at least the EU’s AI Act the same. Here we must take a moment of silence and quiet to reflect on what is “AI” or “autonomous behavior” – and it will soon dawn upon most of us that the present methods of certifying products may not be adequate for addressing adaptive systems rooted in continuous learning and exchanging data with the real world. What we’re trying to say is that the regulators perhaps need to seriously consider the pros and cons of a product-centric vs a process-centric approach to regulating AI. suggests AI, at the end, is an outcome. It is the underlying processes and policies, from data engineering practices and model architectures to machine-to-machine information exchanges and optimization mechanisms, where the focus of governance and standards needs to be, not on the outcome itself. Further, as software shifts from an object-oriented to an agent-oriented engineering paradigm, regulators need to start thinking about policy in terms of code and code in terms of policy – anything else will always leave a giant gap between intent and implementation. If the aforementioned chaos of today’s multistakeholder cybersecurity governance is anything to go by, for AI security and governance we need an evidence-based (consider data that led to final CTI, and engaging with new types of technical evidence) threat data orchestration, runtime verification of AI-driven automation in cyber defense and security systems, clear non-partisan channels and standards for cyber threat information governance, and a multilateral consensus on the same. Focusing on the final AI product alone can leave much unaddressed and potentially partisan – as we see from the ecosystem of information metapolicies that drive security automation systems worldwide – hence we need to focus on better governing the underlying processes and policies that drive these systems and not the outcomes of those processes and policies. Also published here.

Stories, News & Mentions about REXCON

Rexcon’s Stories on HackerNoon

Rexcon's News & Mentions