Kutt

kutt

Comparative Analysis of Option Pricing Methods: FDM, Monte Carlo Simulation, and Variance Reduction 

Improving Text Embeddings with
Large Language Models: Conclusion and References

Table of Links Abstract and 1 Introduction 1.1 The twincode platform 1.2 Related Work 2 Research Questions 3 Variables 3.1 Independent Variables 3.2 Dependent Variables 3.3 Confounding Variables 4 Participants 5 Execution Plan and 5.1 Recruitment 5.2 Training and 5.3 Experiment Execution 5.4 Data Analysis Acknowledgments and References ACKNOWLEDGMENTS We would like to thank the students who volunteered to participate in the pilot studies at the University of California, Berkeley and the University of Seville. We particularly acknowledge Vron Vance’s assistance regarding inclusive language around gender identity. This work has been partially supported by FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación under projects OPHELIA (RTI2018–101204–B–C22), HORATIO (RTI2018- 101204–B–C21), and Junta de Andalucía under project EKIPMENTPLUS (P18–FR–2895). REFERENCES [1] Ahmad Al-Jarrah and Enrico Pontelli. 2016. On the effectiveness of a collaborative virtual pair-programming environment. In International Conference on Learning and Collaboration Technologies. Springer, 583–595. [2] E. A. Chaparro, Aybala Yuksel, Pablo Romero, and Sallyann Bryant. 2005. Factors Affecting the Perceived Effectiveness of Pair Programming in Higher Education. In Proceedings of the 17th Workshop of the Psychology of Programming Interest Group. [3] Kyungsub S. Choi. 2013. Evaluating Gender Significance within a Pair Programming Context. In Proceedings of the 2013 46th Hawaii International Conference on System Sciences (HICSS ’13). IEEE Computer Society, USA, 4817–4825. https://doi.org/10.1109/HICSS.2013.209 [4] Kyungsub Stephen Choi. 2015. A comparative analysis of different gender pair combinations in pair programming. Behaviour & Information Technology 34, 8 (2015), 825–837. https://doi.org/10.1080/0144929X.2014.937460 arXiv:https://doi.org/10.1080/0144929X.2014.937460 [5] Louis Cohen, Lawrence Manion, and Keith Morrison. 2018. Research Methods in Education (8th ed.). Routledge. [6] Bernardo José da Silva Estácio and Rafael Prikladnicki. 2015. Distributed Pair Programming: A Systematic Literature Review. Information and Software Technology 63 (2015), 1–10. [7] N.K. Denzin. 2006. Sociological Methods: A Sourcebook (5th ed.). Aldine Transaction. [8] Dean Eckles, René F. Kizilcec, and Eytan Bakshy. 2016. Estimating peer effects in networks with peer encouragement designs. Proceedings of the National Academy of Sciences 113, 27 (2016), 7316–7322. https://doi.org/10.1073/pnas.1511201113 arXiv:https://www.pnas.org/content/113/27/7316.full.pdf [9] O. Gómez, M. Solari, C. Calvache, and A. Ledezma-Carrizalez. 2017. A Controlled Experiment on Productivity of Pair Programming Gender Combinations: Preliminary Results. In Proceedings of the XX Ibero–American Conference on Software Engineering. 197–210. [10] Brian Hanks, Sue Fitzgerald, Renée McCauley, Laurie Murphy, and Carol Zander. 2011. Pair programming in education: A literature review. Computer Science Education 21, 2 (2011), 135–173. [11] Jo E. Hannay, Erik Arisholm, Harald Engvik, and Dag I.K. Sjoberg. 2010. Effects of Personality on Pair Programming. IEEE Transactions on Software Engineering 36, 1 (2010), 61–80. https://doi.org/10.1109/TSE.2009.41 [12] Sarah I. Hofer. 2015. Studying Gender Bias in Physics Grading: The role of teaching experience and country. International Journal of Science Education 37, 17 (2015), 2879–2905. https://doi.org/10.1080/09500693.2015.1114190 arXiv:https://doi.org/10.1080/09500693.2015.1114190 [13] Lindsay Jarratt, Nicholas A. Bowman, K.C. Culver, and Alberto Maria Segre. 2019. A Large-Scale Experimental Study of Gender and Pair Composition in Pair Programming. In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (Aberdeen, Scotland Uk) (ITiCSE ’19). Association for Computing Machinery, New York, NY, USA, 176–181. https: //doi.org/10.1145/3304221.3319782 [14] N. Katira, L. Williams, and J. Osborne. 2005. Towards increasing the compatibility of student pair programmers. In Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005. 625–626. https://doi.org/10.1109/ICSE. 2005.1553618 [15] K. Kaur Chahal, A. Kaur, and M. Saini. 2021. Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts. Taylor & Francis Group, Chapter Empirical Studies on Using Pair Programming as a Pedagogical Tool in Higher Education Courses: A Systematic Literature Review, 251–287. [16] Sandeep Kaur Kuttal, Kevin Gerstner, and Alexandra Bejarano. 2019. Remote Pair Programming in Online CS Education: Investigating through a Gender Lens. In 2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). 75–85. https://doi.org/10.1109/VLHCC.2019.8818790 [17] J.R. Landis and G.G. Koch. 1977. The Measurement of Observer Agreement for Categorical Data. Biometrics 33, 1 (1977), 159–174. [18] Martell, D. M. Lane, and C. Emrich. 1996. Male-female differences: A computer simulation. American Psychologist 2, 51 (7 1996), 157–158. https://doi.org/10. 1037/0003-066X.51.2.157 [19] Fernando J. Rodríguez, Kimberly Michelle Price, and Kristy Elizabeth Boyer. 2017. Exploring the Pair Programming Process: Characteristics of Effective Collaboration. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (Seattle, Washington, USA) (SIGCSE ’17). Association for Computing Machinery, New York, NY, USA, 507–512. https://doi.org/10. 1145/3017680.3017748 [20] Norsaremah Salleh, Emilia Mendes, and John Grundy. 2014. Investigating the effects of personality traits on pair programming in a higher education setting through a family of experiments. Empirical Software Engineering 19, 3 (2014), 714–752. [21] Norsaremah Salleh, Emilia Mendes, John Grundy, and Giles St J Burch. 2010. The effects of neuroticism on pair programming: an empirical study in the higher education context. In Proceedings of the 2010 ACM-IEEE international symposium on empirical software engineering and measurement. 1–10. [22] David Stotts, Laurie Williams, Nachiappan Nagappan, Prashant Baheti, Dennis Jen, and Anne Jackson. 2003. Virtual teaming: Experiments and experiences with distributed pair programming. In Conference on Extreme Programming and Agile Methods. Springer, 129–141. [23] Lynda Thomas, Mark Ratcliffe, and Ann Robertson. 2003. Code Warriors and Code-a-Phobes: A Study in Attitude and Pair Programming. In Proceedings of the 34th SIGCSE Technical Symposium on Computer Science Education (Reno, Navada, USA) (SIGCSE ’03). Association for Computing Machinery, New York, NY, USA, 363–367. https://doi.org/10.1145/611892.612007 [24] Linda L. Werner, Brian Hanks, and Charlie McDowell. 2004. Pair-Programming Helps Female Computer Science Students. J. Educ. Resour. Comput. 4, 1 (March 2004), 4–es. https://doi.org/10.1145/1060071.1060075 [25] S. Xinogalos, M. Satratzemi, A. Chatzigeorgiou, and D. Tsompanoudi. 2017. Student perceptions on the benefits and shortcomings of distributed pair programming assignments. 2017 IEEE Global Engineering Education Conference (EDUCON) (2017), 1513–1521. Authors:
(1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es);
(2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es);
(3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es);
(4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu);
(5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu);
(6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 The twincode platform 1.1 The twincode platform 1.2 Related Work 1.2 Related Work 2 Research Questions 2 Research Questions 3 Variables 3 Variables 3.1 Independent Variables 3.1 Independent Variables 3.2 Dependent Variables 3.2 Dependent Variables 3.3 Confounding Variables 3.3 Confounding Variables 4 Participants 4 Participants 5 Execution Plan and 5.1 Recruitment 5 Execution Plan and 5.1 Recruitment 5.2 Training and 5.3 Experiment Execution 5.2 Training and 5.3 Experiment Execution 5.4 Data Analysis 5.4 Data Analysis Acknowledgments and References Acknowledgments and References ACKNOWLEDGMENTS We would like to thank the students who volunteered to participate in the pilot studies at the University of California, Berkeley and the University of Seville. We particularly acknowledge Vron Vance’s assistance regarding inclusive language around gender identity. This work has been partially supported by FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación under projects OPHELIA (RTI2018–101204–B–C22), HORATIO (RTI2018- 101204–B–C21), and Junta de Andalucía under project EKIPMENTPLUS (P18–FR–2895). REFERENCES [1] Ahmad Al-Jarrah and Enrico Pontelli. 2016. On the effectiveness of a collaborative virtual pair-programming environment. In International Conference on Learning and Collaboration Technologies. Springer, 583–595. [2] E. A. Chaparro, Aybala Yuksel, Pablo Romero, and Sallyann Bryant. 2005. Factors Affecting the Perceived Effectiveness of Pair Programming in Higher Education. In Proceedings of the 17th Workshop of the Psychology of Programming Interest Group. [3] Kyungsub S. Choi. 2013. Evaluating Gender Significance within a Pair Programming Context. In Proceedings of the 2013 46th Hawaii International Conference on System Sciences (HICSS ’13). IEEE Computer Society, USA, 4817–4825. https://doi.org/10.1109/HICSS.2013.209 [4] Kyungsub Stephen Choi. 2015. A comparative analysis of different gender pair combinations in pair programming. Behaviour & Information Technology 34, 8 (2015), 825–837. https://doi.org/10.1080/0144929X.2014.937460 arXiv:https://doi.org/10.1080/0144929X.2014.937460 [5] Louis Cohen, Lawrence Manion, and Keith Morrison. 2018. Research Methods in Education (8th ed.). Routledge. [6] Bernardo José da Silva Estácio and Rafael Prikladnicki. 2015. Distributed Pair Programming: A Systematic Literature Review. Information and Software Technology 63 (2015), 1–10. [7] N.K. Denzin. 2006. Sociological Methods: A Sourcebook (5th ed.). Aldine Transaction. [8] Dean Eckles, René F. Kizilcec, and Eytan Bakshy. 2016. Estimating peer effects in networks with peer encouragement designs. Proceedings of the National Academy of Sciences 113, 27 (2016), 7316–7322. https://doi.org/10.1073/pnas.1511201113 arXiv:https://www.pnas.org/content/113/27/7316.full.pdf [9] O. Gómez, M. Solari, C. Calvache, and A. Ledezma-Carrizalez. 2017. A Controlled Experiment on Productivity of Pair Programming Gender Combinations: Preliminary Results. In Proceedings of the XX Ibero–American Conference on Software Engineering. 197–210. [10] Brian Hanks, Sue Fitzgerald, Renée McCauley, Laurie Murphy, and Carol Zander. 2011. Pair programming in education: A literature review. Computer Science Education 21, 2 (2011), 135–173. [11] Jo E. Hannay, Erik Arisholm, Harald Engvik, and Dag I.K. Sjoberg. 2010. Effects of Personality on Pair Programming. IEEE Transactions on Software Engineering 36, 1 (2010), 61–80. https://doi.org/10.1109/TSE.2009.41 [12] Sarah I. Hofer. 2015. Studying Gender Bias in Physics Grading: The role of teaching experience and country. International Journal of Science Education 37, 17 (2015), 2879–2905. https://doi.org/10.1080/09500693.2015.1114190 arXiv:https://doi.org/10.1080/09500693.2015.1114190 [13] Lindsay Jarratt, Nicholas A. Bowman, K.C. Culver, and Alberto Maria Segre. 2019. A Large-Scale Experimental Study of Gender and Pair Composition in Pair Programming. In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (Aberdeen, Scotland Uk) (ITiCSE ’19). Association for Computing Machinery, New York, NY, USA, 176–181. https: //doi.org/10.1145/3304221.3319782 [14] N. Katira, L. Williams, and J. Osborne. 2005. Towards increasing the compatibility of student pair programmers. In Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005. 625–626. https://doi.org/10.1109/ICSE. 2005.1553618 [15] K. Kaur Chahal, A. Kaur, and M. Saini. 2021. Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts. Taylor & Francis Group, Chapter Empirical Studies on Using Pair Programming as a Pedagogical Tool in Higher Education Courses: A Systematic Literature Review, 251–287. [16] Sandeep Kaur Kuttal, Kevin Gerstner, and Alexandra Bejarano. 2019. Remote Pair Programming in Online CS Education: Investigating through a Gender Lens. In 2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). 75–85. https://doi.org/10.1109/VLHCC.2019.8818790 [17] J.R. Landis and G.G. Koch. 1977. The Measurement of Observer Agreement for Categorical Data. Biometrics 33, 1 (1977), 159–174. [18] Martell, D. M. Lane, and C. Emrich. 1996. Male-female differences: A computer simulation. American Psychologist 2, 51 (7 1996), 157–158. https://doi.org/10. 1037/0003-066X.51.2.157 [19] Fernando J. Rodríguez, Kimberly Michelle Price, and Kristy Elizabeth Boyer. 2017. Exploring the Pair Programming Process: Characteristics of Effective Collaboration. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (Seattle, Washington, USA) (SIGCSE ’17). Association for Computing Machinery, New York, NY, USA, 507–512. https://doi.org/10. 1145/3017680.3017748 [20] Norsaremah Salleh, Emilia Mendes, and John Grundy. 2014. Investigating the effects of personality traits on pair programming in a higher education setting through a family of experiments. Empirical Software Engineering 19, 3 (2014), 714–752. [21] Norsaremah Salleh, Emilia Mendes, John Grundy, and Giles St J Burch. 2010. The effects of neuroticism on pair programming: an empirical study in the higher education context. In Proceedings of the 2010 ACM-IEEE international symposium on empirical software engineering and measurement. 1–10. [22] David Stotts, Laurie Williams, Nachiappan Nagappan, Prashant Baheti, Dennis Jen, and Anne Jackson. 2003. Virtual teaming: Experiments and experiences with distributed pair programming. In Conference on Extreme Programming and Agile Methods. Springer, 129–141. [23] Lynda Thomas, Mark Ratcliffe, and Ann Robertson. 2003. Code Warriors and Code-a-Phobes: A Study in Attitude and Pair Programming. In Proceedings of the 34th SIGCSE Technical Symposium on Computer Science Education (Reno, Navada, USA) (SIGCSE ’03). Association for Computing Machinery, New York, NY, USA, 363–367. https://doi.org/10.1145/611892.612007 [24] Linda L. Werner, Brian Hanks, and Charlie McDowell. 2004. Pair-Programming Helps Female Computer Science Students. J. Educ. Resour. Comput. 4, 1 (March 2004), 4–es. https://doi.org/10.1145/1060071.1060075 [25] S. Xinogalos, M. Satratzemi, A. Chatzigeorgiou, and D. Tsompanoudi. 2017. Student perceptions on the benefits and shortcomings of distributed pair programming assignments. 2017 IEEE Global Engineering Education Conference (EDUCON) (2017), 1513–1521. Authors: (1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es); (2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es); (3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es); (4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu); (5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu); (6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). Authors: Authors: (1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es); (2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es); (3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es); (4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu); (5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu); (6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

Gender Bias in Remote Pair Programming: Acknowledgments and References

Fine-Tuning LLaMA for Multi-Stage Text Retrieval: Conclusion, Acknowledgements and References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) HARRISON WINCH, Department of Astronomy & Astrophysics, University of Toronto and Dunlap Institute for Astronomy and Astrophysics, University of Toronto;
(2) RENEE´ HLOZEK, Department of Astronomy & Astrophysics, University of Toronto and Dunlap Institute for Astronomy and Astrophysics, University of Toronto;
(3) DAVID J. E. MARSH, Theoretical Particle Physics and Cosmology, King’s College London;
(4) DANIEL GRIN, Haverford College;
(5) KEIR K. ROGERS, Dunlap Institute for Astronomy and Astrophysics, University of Toronto. Table of Links Abstract and Intro
Methods
Phenomenology
Discussion and Future Work
Conclusion
Acknowledgments and References 2. METHODS In order to model the behavior of extreme axions, we modified axionCAMB to include an arbitrary field potential shape (in our case, a cosine of the form given in Equation 1), and reconfigured the code to sample the extreme starting angles necessary to probe these potentials. We also modified the effective sound speed of the axions after the onset of oscillations to reflect the growth in structure resulting from the tachyonic field dynamics. Lastly, we implemented a computationally-efficient ‘lookup table’ of the axion background fluid evolution in order to speed up the computation of the perturbation equations of motion. The details of implementing extreme axions into axionCAMB are presented below [2]. 2.1. Review of axionCAMB The numerical treatment of axions in axionCAMB is described in detail in Hlozek et al. ˇ (2015), but we review the dynamics of axions here in a potential-agnostic way in order to set up our discussion of modeling extreme axions. In theory, the best way of modeling the dynamics of axion dark matter is to model the behaviour of the field throughout all of cosmic history, and derive all cosmological parameters from those primary variables. However, since this field evolution includes periods of extremely rapid oscillations at late times, simulating this is computationally prohibitive and numerically unstable. Instead, the axion field is modeled directly at early times, but the code switches to a simplified fluid approximation at late times (Hlozek et al. ˇ 2015). This piecewise background evolution could then be called when solving the equations of motion for the fluid perturbations (axion density perturbation δa and axion heat flux u), allowing for efficient and stable computation of the final axion power spectrum. This method is discussed here, building on the discussion in Hu (1998) and Hlozek et al. ˇ (2015). axionCAMB runs through this early pre-oscillatory phase several times in order to determine the proper initial value of the axion field required to produce the desired final axion density, and the time at which it can safely switch back to the free particle CDM solution at late times. It then dynamically evolves these initial conditions (integrating the equation of motion for a field using a Runge-Kutta integrator, Runge 1895) until the field starts to oscillate, at which point it switches over to the known free-particle solution for DM evolution (Hlozek et al. ˇ 2015). This results in a new set of equations of motion for the axion perturbations after the onset of oscillations: The perturbation equations of motion in these two regimes can be used to calculate the evolution of the axion perturbations, and make predictions for cosmological observables such as the MPS or CMB. 2.2. Finely tuned initial conditions Restructuring the initial shooting methods to specify the field starting angle allows us to probe the effects of extreme starting angles in new ways. We can specify starting angles arbitrarily close to π, in order to see the effects of these extremely finely tuned angles on other observables. In addition, when performing MCMC analysis, having the starting angle as a free parameter allows us to impose arbitrary priors on this starting angle. We can use these priors to test the dependence of any constraints on the level of fine tuning of the axion starting angle. 2.3. Modeling the early-oscillatory effective axion sound speed To get a sense of the effects of the anharmonic potential on the axion fluid sound speed, we first solve the axion field perturbation equations of motion, This approximation of the fluid sound speed is shown in red in the lower subplot of Figure 2. In order to approximate the boost in the axion sound speed shown in the field equations without changing the late-time evolution of the perturbations, we modified the vanilla axion fluid sound speed to include a large negative spike just after the onset of oscillations. This negative triangular spike is shown in green in the lower subplot of Figure 2. The width and height of this spike were fit to match the approximate sound speed computed from the field perturbation solution. The width (C1) was fit to the delay in scale factor a between the onset of axion oscillations and the asymptotic sign change in the field solution sound speed. This numerical width was then approximated as a power law function of the scale factor k of the perturbation, depends linearly on the scale factor at the onset of oscillations, which in turn depends on the axion mass, fraction, and starting angle, The power spectrum results for this method can be compared to the literature, where other groups have used the exact field perturbation equations of motion to compute the matter power spectrum for extreme axions, such as Leong et al. (2019). In Figure 3 we can see the comparison in the matter power spectrum for both a vanilla axion and an extreme axion with a starting angle deviating from π by 0.2 degrees, and we find that they are in remarkably close agreement with Leong et al. (2019). However, this close agreement seems to hold best at z = 0, when these power spectra are computed, while the higher redshifts comparison may be more nuanced. Figure 2 suggests that while the exact field solution and the new approximate fluid solution agree at very late times, their evolution at early times are not fully equivalent, so more work may need to be done on this approximation in order to perform comparisons to high-redshift observables. 2.4. Using lookup tables for efficient modeling of field Extending the full field evolution so much later past the onset of oscillations requires far more computational resources than ending the field evolution as soon as oscillations begin. Greater numerical resolution, in both time and possible field potential scale, is also required to integrate these rapidly oscillating variables. With these increases to computational time, the new version of axionCAMB takes around seventy seconds to complete. While this may be feasible when computing a single power spectrum result, this is computationally intensive with which to run an MCMC analysis, which may require tens to hundreds of thousands of separate calls to axionCAMB. 2.5. Summary of changes to axionCAMB In order to model axions with extreme starting angles in a cosine field potential using the computationally efficient field formalism used in axionCAMB, we have introduced a number of modifications to axionCAMB which are explained above, but summarized here. • We replaced the quadratic approximation of the field potential with an arbitrary potential function, currently set to the canonical cosine potential. • We modified the effective axion fluid sound speed to reproduce the growth in structure seen in the exact field perturbation equations of motion. • We precomputed a lookup table for the axion background evolution which significantly reduced the runtime. The result is an accurate modeling of extreme axion background and perturbation evolution for an arbitrary axion mass, density, and starting angle that only takes ∼ 7 seconds to run. This powerful tool can shed new light on the behaviour and detectability of these extreme axion models, as discussed below. 2.6. Data: Ly-α forest estimates of the MPS [2] axionCAMB is in turn based on the cosmological Boltzmann code, CAMB (Lewis & Bridle 2002). [4] A logarithmic dependence for the background field can be derived analytically for the anharmonic corrections to the relic density (Lyth 1992). This paper is available on arxiv under CC 4.0 license. Authors: (1) HARRISON WINCH, Department of Astronomy & Astrophysics, University of Toronto and Dunlap Institute for Astronomy and Astrophysics, University of Toronto; (2) RENEE´ HLOZEK, Department of Astronomy & Astrophysics, University of Toronto and Dunlap Institute for Astronomy and Astrophysics, University of Toronto; (3) DAVID J. E. MARSH, Theoretical Particle Physics and Cosmology, King’s College London; (4) DANIEL GRIN, Haverford College; (5) KEIR K. ROGERS, Dunlap Institute for Astronomy and Astrophysics, University of Toronto. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) HARRISON WINCH, Department of Astronomy & Astrophysics, University of Toronto and Dunlap Institute for Astronomy and Astrophysics, University of Toronto; (2) RENEE´ HLOZEK, Department of Astronomy & Astrophysics, University of Toronto and Dunlap Institute for Astronomy and Astrophysics, University of Toronto; (3) DAVID J. E. MARSH, Theoretical Particle Physics and Cosmology, King’s College London; (4) DANIEL GRIN, Haverford College; (5) KEIR K. ROGERS, Dunlap Institute for Astronomy and Astrophysics, University of Toronto. Table of Links Abstract and Intro Methods Phenomenology Discussion and Future Work Conclusion Acknowledgments and References Abstract and Intro Abstract and Intro Methods Methods Phenomenology Phenomenology Discussion and Future Work Discussion and Future Work Conclusion Conclusion Acknowledgments and References Acknowledgments and References 2. METHODS In order to model the behavior of extreme axions, we modified axionCAMB to include an arbitrary field potential shape (in our case, a cosine of the form given in Equation 1), and reconfigured the code to sample the extreme starting angles necessary to probe these potentials. We also modified the effective sound speed of the axions after the onset of oscillations to reflect the growth in structure resulting from the tachyonic field dynamics. Lastly, we implemented a computationally-efficient ‘lookup table’ of the axion background fluid evolution in order to speed up the computation of the perturbation equations of motion. The details of implementing extreme axions into axionCAMB are presented below [2]. 2.1. Review of axionCAMB The numerical treatment of axions in axionCAMB is described in detail in Hlozek et al. ˇ (2015), but we review the dynamics of axions here in a potential-agnostic way in order to set up our discussion of modeling extreme axions. In theory, the best way of modeling the dynamics of axion dark matter is to model the behaviour of the field throughout all of cosmic history, and derive all cosmological parameters from those primary variables. However, since this field evolution includes periods of extremely rapid oscillations at late times, simulating this is computationally prohibitive and numerically unstable. Instead, the axion field is modeled directly at early times, but the code switches to a simplified fluid approximation at late times (Hlozek et al. ˇ 2015). This piecewise background evolution could then be called when solving the equations of motion for the fluid perturbations (axion density perturbation δa and axion heat flux u), allowing for efficient and stable computation of the final axion power spectrum. This method is discussed here, building on the discussion in Hu (1998) and Hlozek et al. ˇ (2015). axionCAMB runs through this early pre-oscillatory phase several times in order to determine the proper initial value of the axion field required to produce the desired final axion density, and the time at which it can safely switch back to the free particle CDM solution at late times. It then dynamically evolves these initial conditions (integrating the equation of motion for a field using a Runge-Kutta integrator, Runge 1895) until the field starts to oscillate, at which point it switches over to the known free-particle solution for DM evolution (Hlozek et al. ˇ 2015). This results in a new set of equations of motion for the axion perturbations after the onset of oscillations: The perturbation equations of motion in these two regimes can be used to calculate the evolution of the axion perturbations, and make predictions for cosmological observables such as the MPS or CMB. 2.2. Finely tuned initial conditions Restructuring the initial shooting methods to specify the field starting angle allows us to probe the effects of extreme starting angles in new ways. We can specify starting angles arbitrarily close to π, in order to see the effects of these extremely finely tuned angles on other observables. In addition, when performing MCMC analysis, having the starting angle as a free parameter allows us to impose arbitrary priors on this starting angle. We can use these priors to test the dependence of any constraints on the level of fine tuning of the axion starting angle. 2.3. Modeling the early-oscillatory effective axion sound speed To get a sense of the effects of the anharmonic potential on the axion fluid sound speed, we first solve the axion field perturbation equations of motion, This approximation of the fluid sound speed is shown in red in the lower subplot of Figure 2. In order to approximate the boost in the axion sound speed shown in the field equations without changing the late-time evolution of the perturbations, we modified the vanilla axion fluid sound speed to include a large negative spike just after the onset of oscillations. This negative triangular spike is shown in green in the lower subplot of Figure 2. The width and height of this spike were fit to match the approximate sound speed computed from the field perturbation solution. The width (C1) was fit to the delay in scale factor a between the onset of axion oscillations and the asymptotic sign change in the field solution sound speed. This numerical width was then approximated as a power law function of the scale factor k of the perturbation, depends linearly on the scale factor at the onset of oscillations, which in turn depends on the axion mass, fraction, and starting angle, The power spectrum results for this method can be compared to the literature, where other groups have used the exact field perturbation equations of motion to compute the matter power spectrum for extreme axions, such as Leong et al. (2019). In Figure 3 we can see the comparison in the matter power spectrum for both a vanilla axion and an extreme axion with a starting angle deviating from π by 0.2 degrees, and we find that they are in remarkably close agreement with Leong et al. (2019). However, this close agreement seems to hold best at z = 0, when these power spectra are computed, while the higher redshifts comparison may be more nuanced. Figure 2 suggests that while the exact field solution and the new approximate fluid solution agree at very late times, their evolution at early times are not fully equivalent, so more work may need to be done on this approximation in order to perform comparisons to high-redshift observables. 2.4. Using lookup tables for efficient modeling of field Extending the full field evolution so much later past the onset of oscillations requires far more computational resources than ending the field evolution as soon as oscillations begin. Greater numerical resolution, in both time and possible field potential scale, is also required to integrate these rapidly oscillating variables. With these increases to computational time, the new version of axionCAMB takes around seventy seconds to complete. While this may be feasible when computing a single power spectrum result, this is computationally intensive with which to run an MCMC analysis, which may require tens to hundreds of thousands of separate calls to axionCAMB. 2.5. Summary of changes to axionCAMB In order to model axions with extreme starting angles in a cosine field potential using the computationally efficient field formalism used in axionCAMB, we have introduced a number of modifications to axionCAMB which are explained above, but summarized here. • We replaced the quadratic approximation of the field potential with an arbitrary potential function, currently set to the canonical cosine potential. • We modified the effective axion fluid sound speed to reproduce the growth in structure seen in the exact field perturbation equations of motion. • We precomputed a lookup table for the axion background evolution which significantly reduced the runtime. The result is an accurate modeling of extreme axion background and perturbation evolution for an arbitrary axion mass, density, and starting angle that only takes ∼ 7 seconds to run. This powerful tool can shed new light on the behaviour and detectability of these extreme axion models, as discussed below. 2.6. Data: Ly-α forest estimates of the MPS [2] axionCAMB is in turn based on the cosmological Boltzmann code, CAMB (Lewis & Bridle 2002). [4] A logarithmic dependence for the background field can be derived analytically for the anharmonic corrections to the relic density (Lyth 1992).

Extreme Axions Unveiled: A Novel Fluid Approach for Cosmological Modeling - Methods

চরম অক্ষরেখা উন্মোচন: মহাজাগতিক মডেলিংয়ের জন্য একটি অভিনব তরল পদ্ধতি - পদ্ধতি

Extreme Axionen enthüllt: Ein neuartiger Fluidansatz für die kosmologische Modellierung - Methoden

Se revelan los axiones extremos: un nuevo enfoque fluido para el modelado cosmológico - Métodos

Extreme Axions dévoilés : une nouvelle approche fluide pour la modélisation cosmologique - Méthodes

चरम एक्सियंस का अनावरण: ब्रह्माण्ड संबंधी मॉडलिंग के लिए एक नवीन द्रव दृष्टिकोण - विधियाँ

極限アクシオンの解明：宇宙論モデリングのための新しい流体アプローチ - 方法

Extreme Axions 공개: 우주론적 모델링을 위한 새로운 유체 접근 방식 - 방법

Extreme Axions revelados: uma nova abordagem de fluidos para modelagem cosmológica - métodos

Представлены экстремальные аксионы: новый жидкостный подход к космологическому моделированию - Методы

Extreme Axions Ortaya Çıktı: Kozmolojik Modelleme için Yeni Bir Akışkan Yaklaşımı - Yöntemler

Ra mắt các Axion cực đoan: Một phương pháp tiếp cận chất lỏng mới cho mô hình vũ trụ - Phương pháp

揭开极端轴子的面纱：一种用于宇宙学建模的新型流体方法 - 方法

Identifying Axion Conversion in Compact Star Magnetospheres With Radio-Wave Polarization Signatures

This paper is under CC 4.0 license. available on arxiv Authors: (1) Ryen W. White, Microsoft Research, Redmond, WA, USA. Table of Links Abstract and Taking Search to task AI Copilots Challenges Opportunities The Undiscovered Country and References 5 THE UNDISCOVERED COUNTRY AI copilots will transform how we search. Tasks are central to people’s lives and more support is needed for complex tasks in search settings. Some limited support for these tasks already exists in search engines, but copilots will expand the task frontier to make more tasks actionable and address the “last mile” in search interaction: task completion [58]. Moving forward, search providers should invest in “better together” experiences that utilize copilots plus traditional search, make these joint experiences more seamless for searchers, and add more support for their use in practice, e.g., help people to quickly understand copilot capabilities and potential and/or recommend the best modality for the current task or task stage. This includes experiences where both modalities are offered separately and can be selected by searchers and those where there is unification and the selection happens automatically based on the query and the conversation context. The foundation models that power copilots have other search-related applications, e.g., for generating and applying intent taxonomies [43] or for evaluation [19]. We must retain a continued focus on human-AI cooperation, where searchers stay in control while the degree of system support increases as needed [44], and on AI safety. Searchers need to be able to trust copilots in general but also be able to verify their answers with minimal effort. Overall, the future is bright for IR, and AI research in general, with the advent of generative AI and the copilots that build upon it. Copilots will help augment and empower searchers in their information seeking journeys. Computer science researchers and practitioners should embrace this new era of assistive agents and engage across the full spectrum of exciting practical and scientific opportunities, both within information seeking as we focused on here, and onwards into other important domains such as personal productivity [5] and scientific discovery [22]. REFERENCES [1] Eugene Agichtein, Ryen W White, Susan T Dumais, and Paul N Bennet. 2012. Search, interrupted: understanding and predicting search task continuation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 315-324. [2] Marcia J Bates. 1990. 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Navigating Complex Search Tasks with AI Copilots: The Undiscovered Country and References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Ayei E. Ibor, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (2) Mark Hooper, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (3) Carsten Maple, Trustworthy Digital Infrastructure for Identity Systems, The Alan Turing Institute, United Kingdom; (4) Gregory Epiphaniou, WMG, University of Warwick, United Kingdom. Table of Links Abstract & Introduction Background Review of Literature Methodology Discussion of Findings Conclusion and Future Work & References 6. Conclusion and Future Work Trustworthy interoperability is a key enabler for identity management and widens the dimensions of e-government in developing countries. This research provided insights into the challenges, opportunities, and requirements of interoperability and highlighted the limitations of current interoperability solutions such as the Estonian X-Road and the eIDAS of the European Union. We found that each existing interoperability solution has its benefits and drawbacks, although both are targeted at a common goal. To achieve seamless communication of identification data, we proposed an approach for scoping requirements by first identifying the key processes to identity management in an interoperability context. Furthermore, we addressed the limitations of the current solutions using an architecture that considers the flexibility of identity verification and validation through an interoperable layer that is based on four interacting layers of data representation, semantics, binding, and security and privacy. For future work, an analysis of country-specific use cases for achieving interoperability in the African identity ecosystem will be performed. References African Union, 2020. The Digital Transformation Strategy for Africa (2020-30), Available at https://openresearch-repository.anu.edu.au/bitstream/1885/278587/1/GhaEcon_24.pdf, Accessed 20 May 2023. Alamillo, I., Mouille, S., Rock, A., Soumelidis, N., Tabor, M., Gorniak, S., 2023. Digital identity standards: Analysis of standardisation requirements in support of cybersecurity policy, Available at file:///C:/Users/aIbor/Downloads/Digital_Identity_Standards%20(1).pdf, Accessed 30 July 2023. Aldosary, M. and Alqahtani, N., 2021. A Survey on Federated Identity Management Systems Limitation and Solutions. International Journal of Network Security & Its Applications (IJNSA) Vol, 13. ANSSI, 2023. The EIDAS Regulation, Available at https://www.ssi.gouv.fr/en/regulation/digital-confidence/the-eidas-regulation/, Accessed 09 April 2023. 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Kiourtis, A., Giannetsos, T., Menesidou, S.A., Mavrogiorgou, A., Symvoulidis, C., GRAZİANİ, A., KLEFTAKİS, S., MAVROGİORGOS, K., ZAFEİROPOULOS, N., GKOLİAS, C.A. and KYRİAZİS, D., 2023. Identity management standards: A literature review. Computers and Informatics, 3(1), pp.35-46. Krimmer, R., Dedovic, S., Schmidt, C. and Corici, A.A., 2021, August. Developing crossborder e-Governance: Exploring interoperability and cross-border integration. In International Conference on Electronic Participation (pp. 107-124). Cham: Springer International Publishing. Kotzé, P. and Alberts, R., 2017. Towards a Conceptual Model for an e-Government Interoperability Framework for South Africa. In ICEIS (3) (pp. 493-506). Lips, S., Bharosa, N. and Draheim, D., 2020. eIDAS implementation challenges: the case of Estonia and the Netherlands. In Electronic Governance and Open Society: Challenges in Eurasia: 7th International Conference, EGOSE 2020, St. Petersburg, Russia, November 18– 19, 2020, Proceedings 7 (pp. 75-89). Springer International Publishing. Luong, D.A. and Park, J.H., 2023. Privacy-Preserving Identity Management System on Blockchain Using Zk-SNARK. IEEE Access, 11, pp.1840-1853. Manda, M. I. and Backhouse, J., "Addressing trust, security and privacy concerns in egovernment integration, interoperability and information sharing through policy: a case of South Africa" (2016). CONF-IRM 2016 Proceedings. Paper 67. http://aisel.aisnet.org/confirm2016/6 Masiero, S., 2023. Digital identity as platform-mediated surveillance. Big Data & Society, 10(1), p.20539517221135176. McBride, K., Kütt, A., Ben Yahia, S. and Draheim, D., 2019, November. On positive feedback loops in digital government architecture. In Proceedings of the 11th International Conference on Management of Digital EcoSystems (pp. 174-180). Mocanu, S., Chiriac, A.M., Popa, C., Dobrescu, R. and Saru, D., 2019. Identification and trust techniques compatible with eIDAS regulation. In Security and Privacy in New Computing Environments: Second EAI International Conference, SPNCE 2019, Tianjin, China, April 13– 14, 2019, Proceedings 2 (pp. 656-665). Springer International Publishing. Pöhn, D. and Hommel, W., 2023. Towards an Improved Taxonomy of Attacks Related to Digital Identities and Identity Management Systems. Security and Communication Networks, 2023. Priisalu, J. and Ottis, R., 2017. Personal control of privacy and data: Estonian experience. Health and technology, 7(4), pp.441-451. Saputro, R., Pappel, I., Vainsalu, H., Lips, S. and Draheim, D., 2020, April. Prerequisites for the adoption of the X-Road interoperability and data exchange framework: a comparative study. In 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG) (pp. 216-222). IEEE. Schmidt, C. and Krimmer, R., 2022. How to implement the European digital single market: identifying the catalyst for digital transformation. Journal of European Integration, 44(1), pp.59-80. Sedlmeir, J., Smethurst, R., Rieger, A. and Fridgen, G., 2021. Digital identities and verifiable credentials. Business & Information Systems Engineering, 63(5), pp.603-613. Sharif, A., Ranzi, M., Carbone, R., Sciarretta, G. and Ranise, S., 2022, August. SoK: A Survey on Technological Trends for (pre) Notified eIDAS Electronic Identity Schemes. In Proceedings of the 17th International Conference on Availability, Reliability and Security (pp. 1-10). Sharma, R. and Panigrahi, P.K., 2015. Developing a roadmap for planning and implementation of interoperability capability in e-government. Transforming Government: People, Process and Policy, 9(4), pp.426-447. Solvak, M., Unt, T., Rozgonjuk, D., Võrk, A., Veskimäe, M. and Vassil, K., 2019. Egovernance diffusion: Population level e-service adoption rates and usage patterns. Telematics and Informatics, 36, pp.39-54. Spiliotopoulos, T., Sheik, A.T., Gottardello, D. and Dover, R., 2023. Onboarding Citizens to Digital Identity Systems. arXiv preprint arXiv:2306.13511. Srinivas, J., Das, A.K. and Kumar, N., 2019. Government regulations in cyber security: Framework, standards and recommendations. Future generation computer systems, 92, pp.178-188. Tang, F., Ma, C. and Cheng, K., 2023. Privacy-preserving authentication scheme based on zero trust architecture. Digital Communications and Networks. The World Bank, 2023a. ID4D Global Dataset, Available at https://id4d.worldbank.org/global-dataset, Accessed: 17 July 2023. The World Bank, 2023b. Practitioner’s Guide: Interoperability, Available at https://id4d.worldbank.org/guide/interoperability, Accessed: 22 April 2023. Walters, R., 2023. Theft [Identity]. In Cybersecurity and Data Laws of the Commonwealth: International Trade, Investment and Arbitration (pp. 297-306). Singapore: Springer Nature Singapore. 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Trustworthy Interoperability and Identity Management Systems: What Comes Next

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Scientific American, Volume XXIV., No. 12, March 18, 1871 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Official lists of patents here Official lists of patents ISSUED BY THE U. S. PATENT OFFICE. FOR THE WEEK ENDING MARCH 7, 1871. Reported Officially for the Scientific American. SCHEDULE OF PATENT FEES On each Caveat $10 On each Trade-Mark $25 On filing each application for a Patent, (seventeen years) $15 On issuing each original Patent $20 On appeal to Examiners-in-Chief $10 On appeal to Commissioner of Patents $20 On application for Reissue $30 On application for Extension of Patent $50 On granting the Extension $50 On filing a Disclaimer $10 On an application for Design (three and a half years) $10 On an application for Design (seven years) $15 On an application for Design (fourteen years) $30 For Copy of Claim of any Patent issued within 30 years $1 A sketch from the model or drawing, relating to such portion of a machine as the Claim covers, from upward, but usually at the price above-named. $1 The full Specification of any patent issued since Nov. 20, 1866 at which time the Patent Office commenced printing them $1.25 Official Copies of Drawings of any patent issued since 1836, we can supply at a reasonable cost, the price depending upon the amount of labor involved and the number of views. Full information, as to price of drawings, in each case, may be had by addressing MUNN & CO., Patent Solicitors, 37 Park Row, New York. 112,309.—Hose Sprinkler.—William Anderson, San Francisco, Cal. 112,310.—Locomotive Spark Arrester.—J. G. Armstrong, New Brunswick, N. J. 112,311.—Tool for Carriage Makers' Use.—George Atkinson, San Francisco, Cal. 112,312.—Potato Probe.—John A. Beal, Waterford, N. Y. 112,313.—Hinge for Carriage Doors.—George W. Beers, Bridgeport, Conn. 112,314.—Stove Leg.—James Birckhead, Jr., Baltimore, Md. 112,315.—Clothes Pin.—Orris A. Bishop, Chicago, Ill. 112,316.—Manufacture of Rochelle Salts and Borax.—V. G. Bloede, Brooklyn, N. Y. 112,317.—Beehive.—Felix Brewer, Waynesville, Mo. 112,318.—Thill Coupling.—Theodore Burr (assignor to Allen Muir and Henry Muir), Battle Creek, Mich. 112,319.—Evaporating Pan for Saccharine Liquids.—F. C. Butler, Bellows Falls, Vt., assignor to himself and James B. Williams, Glastonbury, Conn. 112,320.—Door Securer.—William H. Caldwell, Wheeling, W. Va. 112,321.—Toe-calk Bar.—R. B. Caswell, Springfield, Mass. Antedated March 2, 1871. 112,322.—Glass Flattening Furnace and Leer.—James Clabby, Lenox, Mass. 112,323.—Spring Bed Bottom.—Alex. Cole, Manamuskin, N. J. 112,324.—Water Wheel.—E. E. Coleman, West Cummington, Mass. 112,325.—Toy Horse and Carriage.—John B. Cuzner, Bridgeport, Conn. 112,326.—Mackerel-line Holder.—E. L. Decker, Southport, Me. 112,327.—Sewing Machine.—J. William Dufour, Stratford, Conn. 112,328.—Steam Boiler.—Edwards Evans, North Tonawanda, N. Y. 112,329.—Medical Compound for Cure of Catarrh and Asthma.—Erastus Field, Ostrander, Ohio. 112,330.—Machine for Grinding the Cutters of Mowers, etc.—H. C. Fisk, Wellsville, N. Y. 112,331.—Machine for Making Hooks and Eyes.—Jeremy T. Ford, San Francisco, Cal. 112,332.—Churn.—Thompson Freeman, Westfield, Ill. 112,333.—Attachment for Revolving Mold Boards for Plows.—J. S. Godfrey, Leslie, Mich., assignor to himself and S. M. Loveridge, Pittsburgh, Pa. 112,334.—Grain Cleaner and Fertilizer Sifter.—J. A. Green, Mill Dale, Va. 112,335.—Screw Propulsion.—E. C. Gregg (assignor to A. H. Gregg and C. P. Gregg), Trumansburg, N. Y. 112,336.—Seeding Machine.—P. M. Gundlach, Belleville, Ill. 112,337.—Compound for Kindling Fires.—J. L. Hannum and S. H. Stebbins, Berea, Ohio. 112,338.—Lawn Mower.—Benjamin Harnish, Lancaster, and D. H. Harnish, Pequea, Pa. 112,339.—Composition for Pavements.—C. B. Harris, New York city. Antedated February 25, 1870. 112,340.—Spring for Vehicles.—John R. Hiller, Woodland, Cal. 112,341.—Harvester Rake.—S. T. Holly, (assignor to John P. Manny), Rockford, Ill. 112,342.—Door Clamp.—Henry O. Hooper, Diamond Springs, Cal. 112,343.—Taper Holder.—Thomas W. Houchin, Morrisania, N. Y. 112,344.—Metallic Garter.—Henry A. House, Bridgeport, Conn. 112,345.—Bobbin Winder.—Henry A. House, Bridgeport, Conn. 112,346.—Method of Knitting Stockings, etc.—Henry A. House, Bridgeport, Conn. 112,347.—Apparatus for Evaporating and concentrating Liquids.—John Howarth, Salem, Mass. Antedated March 1, 1871. 112,348.—Apparatus for Evaporating and Concentrating Liquids.—John Howarth, Salem, Mass. Antedated March 1, 1871. 112,349.—Apparatus for Removing Oil from Vegetable and other Matters.—Elias S. Hutchinson, Baltimore, Md. 112,350.—Apparatus and Process for Removing Oil from Grain, Seeds, etc.—Elias S. Hutchinson, Baltimore, Md. 112,351.—Chandelier.—Charles F. Jacobsen, New York city. 112,352.—Culinary Vessel.—Carrie Jessup, New Haven, Conn. 112,353.—Machine for Cutting Leather.—Aberdeen Keith, North Bridgewater, Mass. 112,354.—Attaching Knobs to their Spindles.—John F. Keller and Nathaniel Sehner, Hagerstown, Md. 112,355.—Miter Machine.—T. E. King, Boston, Mass. 112,356.—Take-up for Corset Looms.—Julius Kuttner, New York city. 112,357.—Elevator and Carrier.—T. W. Lackore, Worth, Ill. 112,358.—Apparatus for Burning Hydrocarbon Oils.—James R. Lee, Grass Valley, Cal. 112,359.—Burglar Alarm.—Robert Lee, Cincinnati, Ohio. 112,360.—Telegraph Apparatus.—L. T. Lindsey, Jackson, Tenn. 112,361.—Harvester.—J. P. Manny, Rockford, Ill. 112,362.—Harvester.—J. P. Manny, Rockford, Ill. 112,363.—Harvester Rake.—J. P. Manny, Rockford, Ill. 112,364.—Cheese Curd Sink.—H. C. Markham, Collinsville, N. Y. 112,365.—Mowing Machine.—H. C. Markham and Dewitt C. Markham, Collinsville, N. Y. 112,366.—Propeller.—Alex. J. Marshall, Warrenton, Va. Antedated March 3, 1871. 112,367.—Oiler.—Edward McDuff and E. D. Forrow, Warwick, R. I. 112,368.—Wash Boiler.—John McInnes, Oxford, Pa. 112,369.—Propelling Canal Boats.—H. B. Meech, Fort Edward, N. Y. Antedated February 25, 1871. 112,370.—Water-proof Compound for Coating Cloth Wood, Metals, etc.—Peter E. Minor, Schenectady, N. Y. 112,371.—Cooking Stove.—W. N. Moore, Neenah, Wis. 112,372.—Boring Machine.—J. H. Pardieck (assignor to himself and S. M. Brown), Acton, Ind. 112,373.—Vapor Burner.—R. W. Park, Philadelphia, Pa. 112,374.—Machine for Pointing Blanks for Cultivator Teeth.—John Pedder and George Abel, West Pittsburgh, Pa. 112,375.—Bale Tie.—J. E. Perkins, San Francisco, Cal. 112,376.—Lining Walls with Felt, etc.—James Phillips, Chicago, Ill. 112,377.—Cooking Stove.—Samuel Pierce, Boston, Mass. 112,378.—Tack.—A. A. Porter, New Haven, Conn. Antedated Feb. 25, 1871. 112,379.—Machine for Shaping and Cutting Gear Cutters.—F. A. Pratt (assignor to the Pratt & Whitney Company), Hartford, Conn. 112,380.—Combination Camera and Developing Box.—E. C. Ratzell, Philadelphia, Pa. 112,381.—Punching Machine.—J. C. Rhodes, South Abington, Mass. 112,382.—Washing Machine.—J. W. Ricker, Chelsea, Mass. 112,383.—Curtain Fixture.—Charles Robin. Chester, Conn. 112,384.—Machine for Making Printers' Leads.—Isaac Schoenberg, New York city. 112,385.—Slide Valve for Steam Riveting Machines.—Coleman Sellers (assignor to William Sellers & Co.), Philadelphia, Pa. 112,386.—Machine for Polishing Thread.—Samuel Semple, Sr., John Semple, Samuel Semple, Jr., and R. A. Semple, Mount Holly, N. J. 112,387.—Paint Brush.—F. S. Shearer, Washington, Ill. 112,388.—Bee Hive.—S. A. Short, F. J. Short, J. B. Short, and Jasper Kile, Decatur, Ala. 112,389.—Apparatus for Removing Oil from Vegetable and other Matter.—Thomas Sim, Baltimore, Md. 112,390.—Retort for Producing Bisulphide of Carbon.—Thomas Sim, Baltimore, Md. 112,391.—Utilizing the Silky Down of the Wild Cotton. —M. H. Simpson, Boston, Mass. 112,392.—Pruning Shears.—Frank Smiley, Batavia, N. Y. 112,393.—Water-closet Valve.—A. J. Smith, San Francisco, Cal. 112,394.—Gang Plow.—J. W. Sursa, San Leandro, Cal. 112,395.—Grinding Pan and Amalgamator.—W. H. Thoss, West Point, Cal. 112,396.—Street Lantern.—Augustus Tufts, Malden, Mass. 112,397.—Cooking Stove.—Alvin Warren, Swanton, Ohio. 112,398.—Safety Bridle.—James Weatherhead, San José, Cal. 112,399.—Fire Grate.—George Wellhouse, Akron, Ohio. 112,400.—Hay Knife.—G. F. Weymouth, Dresden, Me. 112,401.—Claw Bar.—Charles Winter, Chillicothe, Ohio. 112,402.—Steam Generator.—J. C. Woodhead, Pittsburgh, Pa. 112,403.—Animal Trap.—W. D. Wrightson, Queenstown England. 112,404.—Brush.—John Ames, Lansingburg, N. Y. 112,405.—Clod Fender.—F. L. Bailey, Freeport, Ind. 112,406.—Ruler.—H. S. Ball, Spartanburg, S. C. 112,407.—Fanning Mill.—Benjamin Barney, Time, Ill. 112,408.—Ice-cutting Machine.—Lafayett Barnum (assignor to himself and A. R. Hale), Bridgeport, Conn. 112,409.—Manufacture of Ice.—T. J. Bigger, Kansas City, Mo. 112,410.—Machine for Heading Bolts and Spikes.—Reinhold Boeklen, Brooklyn, N. Y., assignor to himself and Henry Torstrick New York city. Antedated Feb. 28, 1871. 112,411.—Washing Machine.—Joseph Boswell, L. M. Boswell, Jonathan Palmer, and J. H. James (assignors to themselves and Thomas Starbuck), Wilmington, Ohio. 112,412.—Water Wheel.—E. C. Boyles, New York city. 112,413.—Cotton Press.—R. M. Brooks, Pike county, Ga. 112,414.—Paper-cutting Machine.—Samuel Brown (assignor to himself and C. R. Carver), Philadelphia, Pa. 112,415.—Governor for Direct-acting Engines.—A. S. Cameron, New York city. 112,416.—Governor for Direct-acting Engines.—A. S. Cameron, New York city. 112,417.—Butt Hinge.—J. W. Carleton (assignor to the Union Manufacturing Co.), New Britain, Conn. 112,418.—Machine for Cutting Sheet Metal.—C. R. Choate, East Saginaw, Mich. 112,419.—Bit Brace.—William Cleveland, Lawrence, Mass., assignor to himself and James Swan, Seymour, Conn. 112,420.—Steam Engine Governor.—C. A. Condé, Indianapolis, Ind. 112,421.—Carpet-cleaning Machine.—J. C. Craft (assignor to himself and Antonio Rosello), Baltimore, Md. 112,422.—Steam Regulator for Paper Dryers.—Daniel Crosby, Hampden, Me. 112 423.—Metallic Piston and Valve Rod Packing.—G. M. Cruickshank, Providence, R. I. 112,424.—Grain-thrashing and Separating Machine.—John Culham, Grand Rapids, Mich. Antedated Feb. 25, 1871. 112,425.—Cooking Stove.—David Curtis, Mishawaka, assignor to himself and C. B. Graham, South Bend, Ind. 112,426.—Lightning Rod.—S. D. Cushman, New Lisbon, Ohio. 112,427.—Hose Bridge.—Patrick Daily (assignor to himself and J. J. Kehoe), New York city. 112,428.—Cover for Openings in Sidewalks.—William Dale, New York city. 112,429.—Rotary Pump.—F. O. Deschamps, Philadelphia, Pa. 112,430.—Machine for Cutting Files.—James Dodge, Manchester, England, assignor to David Blake, Spencertown, N. Y. 112,431.—Coupling for Railway Cars.—Henry Dubs and S. G. Goodall-Copestake, Glasgow, Great Britain. 112,432.—Tobacco Pipe.—P. J. Dwyer, Elizabethport, N. J. 112,433.—Basket for House Plants.—Albert P. Eastman, Washington, D. C. 112,434.—Sulky Plow.—Milo A. Elliott, Stratford Hollow, N. H. 112,435.—Stretcher for Paintings.—James Fairman, New York city. 112,436.—Body Lantern Holder.—Samuel C. Fessenden, Stamford, Conn. 112,437.—Stove Leg.—Amon L. Finch, Sing Sing, N. Y. 112,438.—Pump Piston.—John S. Follansbee and George Doolittle (assignors to the Forrester Manufacturing Company), Bridgeport, Conn. 112,439.—Shoe.—Samuel W. Francis (assignor to himself and W. H. Newton), Newport, R. I. 112,440.—Guard-Finger for Harvesters.—George Fyfe and Chester Hard, Ottawa, Ill. 112,441.—Dining Table.—S. R. Gardner (assignor to himself and S. M. Marquette), Independence, Iowa. 112,442.—Step Ladder.—M. Boland Geary, New York City. 112,443.—Oilcloth Printing Machinery.—Ebenezer A. Goodes (assignor to Philadelphia Patent and Novelty Company), Philadelphia, Pa. 112,444.—Tenoning Machine.—Lyman Gould, Norwich, Conn. 112,445.—Printer's Case.—Wm. H. A. Gresham, Atlanta, Ga. 112,446.—Lamp Chimney.—Geo. W. Griswold, Factoryville, Pa. 112,447.—Grain Separator.—Philander Griswold, Hudson, Mich. 112,448.—Clamp for Thill Couplings.—John W. Guider (assignor to himself and John Kiefer), St. Joseph, Mo. 112,449.—Bird Cage.—Gottlob Gunther, New York city. 112,450.—Stop Cock and Valve.—William Haas, New York city. 112,451.—Valve for Steam Engines.—Joseph L. Harley, Baltimore, Md., and Xaver Fendrich, Georgetown, D. C. 112,452.—Metallic Hub.—John H. Harper, Pittsburgh, Pa. 112,453.—Composition for Lubricating Machinery.—E. Q. Henderson (assignor to John C. Burroughs and Richard A. Springs) Charlotte, N. C. 112,454.—Post-hole Digger.—Bryant B. Herrick, Decatur, Mich. 112,455.—Door Check.—Levi S. Hicks (assignor to himself, J. Perrin Johnson, and John Buell), Peoria, Ill. 112,456.—Railway-car Brake.—Luther Hill, Stoneham, and Seth D. Tripp, Lynn, Mass. 112,457.—Truss.—Adam Hinoult, Montgomery, N. Y. 112,458.—Feed Bag for Horses.—Walter A. Hough, South Butler, N. Y. 112,459.—Shade Holder for Lamps—Mark W. House, Cleveland, Ohio. 112,460.—Lamp Chimney.—Mark Wiggins House (assignor to the Cleveland Non-Explosive Lamp Company), Cleveland, Ohio. Antedated March 1, 1871. 112,461.—Horse Hay Rake.—James Howard and E. T. Bousfield, Bedford, England. 112,462.—Tongs for Rolling Barrels.—Mark W. Ingle, Indianapolis, Ind. 112,463.—Pitman.—George W. Jayson, Lodi, Ohio. 112,464.—Paste for Paper Hangings.—John Jones (assignor to himself and Henry A. Smith), New York city. 112,465.—Twine Holder.—Edward M. Judd, New Haven, Ct. 112,466.—Clothes Pin or Clasp.—Amos L. Keeports and William Yount, Littletown, Pa. 112,467.—Putting up Hams.—Samuel Edward Kelly, Philadelphia, Pa. 112,468.—Limn Kiln.—Thomas A. Kirk, Kansas City, Mo. 112,469.—Fastening for Seats for Wagons or Sleighs.—John G. Knapp and John F. Robertson (assignors of one third their right to James H. Holly), Warwick, N. Y. 112,470.—Potato Planter.—George Knowlton (assignor for one-half his right to N. Haynes), Johnstown, Pa. 112,471.—Revolving Firearm.—Edwin S. Leaycroft, Brooklyn, N. Y., assignor by mesne assignment, to "Colt's Patent Firearms Manufacturing Company," Hartford, Conn. 112,472.—Revolving Firearm.—Edwin S. Leaycroft, Brooklyn, N. Y., assignor, by mesne assignment, to "Colt's Patent Firearms Manufacturing Company," Hartford, Conn. 112,473.—Railroad Cattle-Guard Gate.—J. H. Mallory, La Porte, Ind. 112,474.—Back-reflecting Mirror.—Richard Mason (assignor to himself and Matthew Ely), Newark, N. J. 112,475.—Ventilator and Chimney Top.—James McGowan (assignor to himself and Daniel H. Waring), New York city. 112,476.—Apparatus for Rectifying and Refining Spirits.—Frederick Measey (assignor to himself and Henry D. Fling), Philadelphia, Pa. 112,477.—Tin Can.—John F. Merrill (assignor to himself and Alexander Stewart), Cincinnati, Ohio. 112,478.—Take-up Mechanism for Looms.—John Michna and Joseph Fischer, New York city. 112,479.—Combined Baker and Broiler.—Wm. H. Miller, Brandenburg, Ky. 112,480.—Shuttle for Sewing Machines.—James D. Moore, Grinnell, Iowa. 112,481.—Cotton Chopper and Grain Cultivator.—Daniel Mosely, Osark, Arkansas. 112,482.—Sad and Fluting Iron.—Frederick Myers, New York city. 112,483.—Reed for Organs and Melodeons.—Augustus Newell, Chicago, Ill. 112,484.—Straw Cutter.—Amon Park, Germanville, Iowa. 112,485.—Apparatus for Aging Whisky and Other Spirits.—Josiah Peiffer and Samuel Richards, Valonia, Pa. 112,486.—Combined Cotton and Corn Planter.—Louis A. Perrault (assignor to himself and Joseph Huber), Natchez, Miss. 112,487.—Faucet.—Solomon Pfleger, Reading, assignor to himself and J. S. Pfleger, Tamaqua, Pa. 112,488.—Treadle.—George K. Proctor, Salem, Mass. 112,489.—Lubricating Compound.—Victory Purdy, Poughkeepsie, N. Y. 112,490.—Fertilizer and Seeding Machine.—Archibald Putnam (assignor to Elizabeth Putnam), Owego, N. Y. 112,491.—Rotary Pump.—George W. Putnam, South Glens Falls, N. Y. 112,492.—Hat Brush.—Robert Dunbar Radcliffe, Palmyra, N. Y. 112,493.—Refrigerating Show Case.—Thomas L. Rankin, Lyndon, Kansas, assignor to himself and D. W. Rockwell, Elyria, Ohio. 112,494.—Device for Starting and Stopping Cars.—Philip Rhoads, Carlisle, Pa. 112,495.—Pipe-molding Machine.—George Richardson, Milwaukee, Wis. 112,496.—Sulky Cultivator.—Richard B. Robbins, Adrian, Mich. 112,497.—Hand Plow.—Nelson Rue, Harrodsburg, Ky. 112,498.—Mechanical Movement.—Edward G. Russell, Ravenna, Ohio. 112,499.—Railway Car Brake.—Lyman Alphonzo Russell, Shrewsbury, Vt. 112,500.—Stovepipe Cleaner.—David Sanford, Ashton, Ill. 112,501.—Twine Holder.—Joseph B. Sargent and Purmont Bradford (assignors to Sargent & Co.), New Haven, Conn. 112,502.—Dovetailing Machine.—James M. Seymour, Newark, N. J. 112,503.—Wooden Pavement.—Eaton Shaw, Portland, Me. 112,504.—Guard for Circular Saws.—George W. Shipman, Ischua, N. Y. 112,505.—Breech-loading Firearm.—Dexter Smith and Martin J. Chamberlin, Springfield, Mass. 112,506.—Spark Arrester.—James Smith, Altoona, Pa. 112,507.—Horse Hay Rake.—Solomon P. Smith, Waterford, N. Y. 112,508.—Plow.—S. M. Stewart, New Harrisburg, Ohio. 112,509.—Medical Compound for Treating Fever and Ague.—George E. Swan, Mount Vernon, Ohio. 112,510.—Device for Cooling Journals of Car Axles.—Henry G. Thompson, Milford, Conn. 112,511.—Cooling Journal of Car Axles.—Henry G. Thompson, Milford, Conn. 112,512.—Cooling Journal of Car-wheel Axles.—Henry G. Thompson, Milford, Conn. 112,513.—Device for Cooling Journals of Railway Cars.—Henry G. Thompson, Milford, Conn. 112,514.—Non-heating Handle for Sad Irons, etc.—William H. Towers, Boston, Mass. 112,515.—Lubricator.—John Erst Uhl, Renovo, Pa. 112,516.—Combined Corn Planter and Cultivator.—Franklin Underwood, South Rutland, N. Y. 112,517.—King Bolt.—Wendel Vondersaar, Indianapolis, Ind. 112,518.—Wheat Roaster.—George W. Waitt (assignor to himself and Robert B. Fitts), Philadelphia, Pa. 112,519.—Plaster Sower.—Thomas J. West, Alfred Center, N. Y. 112,520.—Ticket Holder.—Henry Wexel, Providence, R. I. 112,521.—Tobacco Press.—Abraham N. Zell, Lancaster, Pa. 112,522.—Combined Bag Holder and Scales.—William Zimmerman, Lebanon, Pa. Antedated February 25, 1871. 112,523.—Breech-loading Firearm.—James M. Mason, Washington, D. C. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2006). Scientific American, Volume XXIV., No. 12, March 18, 1871. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/19180/pg19180-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

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