Akademii Nauk Research Articles

Folklore Research in the USSR

This article reports on die study of folklore as a source of national culture and of formal art. Appearing in a journal issued by the USSR Academy of Sciences, it indicates the studies currently in progress and some organizational features of Soviet folklore research. (Urgent Problems of Folklore Research, Vestnik Akademii Nauk, 1960, No. 7, 1960—slightly abridged.)

Istoriia Akademii Nauk SSSR . [History of the Academy of Sciences of the U.S.S.R.]. vol. 1, 1724-1803 . K. V. Ostrovitianov, Ed. Academy of Sciences of the U.S.S.R. Press, Moscow, 1958 (in Russian). 483 pp.

<i>Istoriia Akademii Nauk SSSR</i> . [History of the Academy of Sciences of the U.S.S.R.]. vol. 1, <i>1724-1803</i> . K. V. Ostrovitianov, Ed. Academy of Sciences of the U.S.S.R. Press, Moscow, 1958 (in Russian). 483 pp.

Eleventh all-union conference on spectroscopy Papers presented at the Conference and published in the Journal Izvestiia Akademii Nauk SSSR Ser. Fiz. vol. 22 (1958)

Eleventh all-union conference on spectroscopy Papers presented at the Conference and published in the Journal Izvestiia Akademii Nauk SSSR Ser. Fiz. vol. 22 (1958)

Effect of surface-active lubricants upon the drawing of metal wire. (in Russian) : V. D. Chistota, S. Ya. Veiler, V. I. Likhtman, and P. A Rebinder. Doklady akademii nauk SSSR, vol. 110, no. 4, 1956, p. 562–565; 4 figures, 1600 wds. (Henry brutcher translation no. 3872, $ 3.90.)

Effect of surface-active lubricants upon the drawing of metal wire. (in Russian) : V. D. Chistota, S. Ya. Veiler, V. I. Likhtman, and P. A Rebinder. Doklady akademii nauk SSSR, vol. 110, no. 4, 1956, p. 562–565; 4 figures, 1600 wds. (Henry brutcher translation no. 3872, $ 3.90.)

Mechanism of action of lubricants in the working of metals. (in Russian) : S. Ya. Veiler, V. I. Tikhtman, and P.A. Rebinder. Doklady akademii nauk SSSR, vol. 110, no. 6, 1956, p. 985–988; 4 figures, 2100 words. (Henry Brutcher, Translation no. 3943, $ 4.90)

Mechanism of action of lubricants in the working of metals. (in Russian) : S. Ya. Veiler, V. I. Tikhtman, and P.A. Rebinder. Doklady akademii nauk SSSR, vol. 110, no. 6, 1956, p. 985–988; 4 figures, 2100 words. (Henry Brutcher, Translation no. 3943, $ 4.90)

Investigation of the interaction of lubricating oils with metals. (in Russian) : G. V. Vinogradov, M. M. Kusakov, 1u. S. Zaslavskii, and E. A. Razumovskaia. Vestnik akademii nauk SSSR, v. 25, no. 9, Sept. 1955, P. 35–40

The paper reviews and highlights distinctions between function-approximation (FA) and VC theory and methodology, mainly within the setting of regression problems and a squared-error loss function, and illustrates empirically the differences between the two when data is sparse and/or input distribution is non-uniform. In FA theory, the goal is to estimate an unknown true dependency (or ‘target’ function) in regression problems, or posterior probability P(y/x) in classification problems. In VC theory, the goal is to ‘imitate’ unknown target function, in the sense of minimization of prediction risk or good ‘generalization’. That is, the result of VC learning depends on (unknown) input distribution, while that of FA does not. This distinction is important because regularization theory originally introduced under clearly stated FA setting [Tikhonov, N. (1963). On solving ill-posed problem and method of regularization. Doklady Akademii Nauk USSR, 153, 501–504; Tikhonov, N., & V. Y. Arsenin (1977). Solution of ill-posed problems. Washington, DC: W. H. Winston], has been later used under risk-minimization or VC setting. More recently, several authors [Evgeniou, T., Pontil, M., & Poggio, T. (2000). Regularization networks and support vector machines. Advances in Computational Mathematics, 13, 1–50; Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference and prediction. Springer; Poggio, T. and Smale, S., (2003). The mathematics of learning: Dealing with data. Notices of the AMS, 50 (5), 537–544] applied constructive methodology based on regularization framework to learning dependencies from data (under VC-theoretical setting). However, such regularization-based learning is usually presented as a purely constructive methodology (with no clearly stated problem setting). This paper compares FA/regularization and VC/risk minimization methodologies in terms of underlying theoretical assumptions. The control of model complexity, using regularization and using the concept of margin in SVMs, is contrasted in the FA and VC formulations.

Solid lubricant for parts working under conditions of dry friction. (in Russian) : P. E. D'iachenko, O. E. Kestner, and L. A. Chatynian. Izvestiia akademii nauk SSSR, otdelenie tekhnicheshikh nauk, 1955, on. 12, Dec., p. 128–130

Solid lubricant for parts working under conditions of dry friction. (in Russian) : P. E. D'iachenko, O. E. Kestner, and L. A. Chatynian. Izvestiia akademii nauk SSSR, otdelenie tekhnicheshikh nauk, 1955, on. 12, Dec., p. 128–130

High-temperature bearing operation in the absence of liquid lubricants : S. S. Sorem and A. G. Cattaneo. LubricationEngineering, v. 12, July–Aug. 1956, p. 258–260

The paper reviews and highlights distinctions between function-approximation (FA) and VC theory and methodology, mainly within the setting of regression problems and a squared-error loss function, and illustrates empirically the differences between the two when data is sparse and/or input distribution is non-uniform. In FA theory, the goal is to estimate an unknown true dependency (or ‘target’ function) in regression problems, or posterior probability P(y/x) in classification problems. In VC theory, the goal is to ‘imitate’ unknown target function, in the sense of minimization of prediction risk or good ‘generalization’. That is, the result of VC learning depends on (unknown) input distribution, while that of FA does not. This distinction is important because regularization theory originally introduced under clearly stated FA setting [Tikhonov, N. (1963). On solving ill-posed problem and method of regularization. Doklady Akademii Nauk USSR, 153, 501–504; Tikhonov, N., & V. Y. Arsenin (1977). Solution of ill-posed problems. Washington, DC: W. H. Winston], has been later used under risk-minimization or VC setting. More recently, several authors [Evgeniou, T., Pontil, M., & Poggio, T. (2000). Regularization networks and support vector machines. Advances in Computational Mathematics, 13, 1–50; Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference and prediction. Springer; Poggio, T. and Smale, S., (2003). The mathematics of learning: Dealing with data. Notices of the AMS, 50 (5), 537–544] applied constructive methodology based on regularization framework to learning dependencies from data (under VC-theoretical setting). However, such regularization-based learning is usually presented as a purely constructive methodology (with no clearly stated problem setting). This paper compares FA/regularization and VC/risk minimization methodologies in terms of underlying theoretical assumptions. The control of model complexity, using regularization and using the concept of margin in SVMs, is contrasted in the FA and VC formulations.

Influence of the ratio of areas of friction upon wear : D.N. Garkunov. Henry Brutcher Translation no. 3764, 4 pp. (From Doklady Akademii Nauk SSSR, v. 104, no. 2, 1955, pp. 227–228.) Henry Brutcher, Altadena, Calif.

Influence of the ratio of areas of friction upon wear : D.N. Garkunov. Henry Brutcher Translation no. 3764, 4 pp. (From Doklady Akademii Nauk SSSR, v. 104, no. 2, 1955, pp. 227–228.) Henry Brutcher, Altadena, Calif.

Cermet hard alloys with unequal distribution of the carbide component, for the machining of abrasive materials. (in Russian) : G. A. Meerson and G. V. Samsonov. Izvestiia Akademii Nauk SSSR, Otdelenie Tekhnicheskikh Nauk, 1956, no. 4, Apr. 1956, no 4., Apr. 1956, p. 121–125

Cermet hard alloys with unequal distribution of the carbide component, for the machining of abrasive materials. (in Russian) : G. A. Meerson and G. V. Samsonov. Izvestiia Akademii Nauk SSSR, Otdelenie Tekhnicheskikh Nauk, 1956, no. 4, Apr. 1956, no 4., Apr. 1956, p. 121–125

Conditions of intensity of cavitation erosion. (in Russian) : K. K. Shal'nev. Izvestiia Akademii Nauk SSSR, Otdelenie Teknicheskikh Nauk, 1956, no. 1, Jan. 1956, p. 3–20 + 6 plates

Conditions of intensity of cavitation erosion. (in Russian) : K. K. Shal'nev. Izvestiia Akademii Nauk SSSR, Otdelenie Teknicheskikh Nauk, 1956, no. 1, Jan. 1956, p. 3–20 + 6 plates

Wear behavior of high temperature bearing materials : John T. Burwell. Precision metal molding, v. 14, Oct. 1956, p. 40 + 5 pages

The paper reviews and highlights distinctions between function-approximation (FA) and VC theory and methodology, mainly within the setting of regression problems and a squared-error loss function, and illustrates empirically the differences between the two when data is sparse and/or input distribution is non-uniform. In FA theory, the goal is to estimate an unknown true dependency (or ‘target’ function) in regression problems, or posterior probability P(y/x) in classification problems. In VC theory, the goal is to ‘imitate’ unknown target function, in the sense of minimization of prediction risk or good ‘generalization’. That is, the result of VC learning depends on (unknown) input distribution, while that of FA does not. This distinction is important because regularization theory originally introduced under clearly stated FA setting [Tikhonov, N. (1963). On solving ill-posed problem and method of regularization. Doklady Akademii Nauk USSR, 153, 501–504; Tikhonov, N., & V. Y. Arsenin (1977). Solution of ill-posed problems. Washington, DC: W. H. Winston], has been later used under risk-minimization or VC setting. More recently, several authors [Evgeniou, T., Pontil, M., & Poggio, T. (2000). Regularization networks and support vector machines. Advances in Computational Mathematics, 13, 1–50; Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference and prediction. Springer; Poggio, T. and Smale, S., (2003). The mathematics of learning: Dealing with data. Notices of the AMS, 50 (5), 537–544] applied constructive methodology based on regularization framework to learning dependencies from data (under VC-theoretical setting). However, such regularization-based learning is usually presented as a purely constructive methodology (with no clearly stated problem setting). This paper compares FA/regularization and VC/risk minimization methodologies in terms of underlying theoretical assumptions. The control of model complexity, using regularization and using the concept of margin in SVMs, is contrasted in the FA and VC formulations.

Hydraulic Method of Protecting Turbines From Cavitation Erosion. (in Russian) : K. K. Shal'nev. Vestnik akademii nauk SSSR, v. 25, no. 8, Aug. 1955, p. 50–52

Hydraulic Method of Protecting Turbines From Cavitation Erosion. (in Russian) : K. K. Shal'nev. Vestnik akademii nauk SSSR, v. 25, no. 8, Aug. 1955, p. 50–52

Investigation of the relation between the force of friction and “Elementary forces”. (in Russian) : A. V. Bulgadaev. Doklady akademii nauk SSSR, v. 97, no. 5, Aug. 11, 1954, p. 805–808

Investigation of the relation between the force of friction and “Elementary forces”. (in Russian) : A. V. Bulgadaev. Doklady akademii nauk SSSR, v. 97, no. 5, Aug. 11, 1954, p. 805–808

New method of determining the coefficient of sliding friction and its application to investigation of adsorption-lubricating action. (in Russian) : G. I. Epifanov, F. P. Soloshko, and P. A. Rebinder. Doklady akademii nauk SSSR, v. 99, no. 5, Dec. 11, 1954, p. 801–804

The paper reviews and highlights distinctions between function-approximation (FA) and VC theory and methodology, mainly within the setting of regression problems and a squared-error loss function, and illustrates empirically the differences between the two when data is sparse and/or input distribution is non-uniform. In FA theory, the goal is to estimate an unknown true dependency (or ‘target’ function) in regression problems, or posterior probability P(y/x) in classification problems. In VC theory, the goal is to ‘imitate’ unknown target function, in the sense of minimization of prediction risk or good ‘generalization’. That is, the result of VC learning depends on (unknown) input distribution, while that of FA does not. This distinction is important because regularization theory originally introduced under clearly stated FA setting [Tikhonov, N. (1963). On solving ill-posed problem and method of regularization. Doklady Akademii Nauk USSR, 153, 501–504; Tikhonov, N., & V. Y. Arsenin (1977). Solution of ill-posed problems. Washington, DC: W. H. Winston], has been later used under risk-minimization or VC setting. More recently, several authors [Evgeniou, T., Pontil, M., & Poggio, T. (2000). Regularization networks and support vector machines. Advances in Computational Mathematics, 13, 1–50; Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference and prediction. Springer; Poggio, T. and Smale, S., (2003). The mathematics of learning: Dealing with data. Notices of the AMS, 50 (5), 537–544] applied constructive methodology based on regularization framework to learning dependencies from data (under VC-theoretical setting). However, such regularization-based learning is usually presented as a purely constructive methodology (with no clearly stated problem setting). This paper compares FA/regularization and VC/risk minimization methodologies in terms of underlying theoretical assumptions. The control of model complexity, using regularization and using the concept of margin in SVMs, is contrasted in the FA and VC formulations.

Friction of rubber in lubricated bearings with high speed and heavy loads. (in Russian) : A. V. Kol'chenko and A. A. Silin. Doklady akademii nauk SSSR, v. 115, no. 4, Aug. 1, 1957, p. 714–716

Friction of rubber in lubricated bearings with high speed and heavy loads. (in Russian) : A. V. Kol'chenko and A. A. Silin. Doklady akademii nauk SSSR, v. 115, no. 4, Aug. 1, 1957, p. 714–716

Quantitative Determination of Machine Parts Wear With Radioactive Indicators. (in Russian) : Iu. S. Zaslavskii and G. I. Shor. Izvestiia akademii nauk SSSR, otdelenie tekhnicheskikh nauk, 1955, no. 4. Apr., p. 43–52

Quantitative Determination of Machine Parts Wear With Radioactive Indicators. (in Russian) : Iu. S. Zaslavskii and G. I. Shor. Izvestiia akademii nauk SSSR, otdelenie tekhnicheskikh nauk, 1955, no. 4. Apr., p. 43–52

An experimental study of the friction of rubber within a wide range of normal pressures. (in Russian) : V. V. Lavrent'ev. Doklady akademii nauk SSSR, v. 115, no. 4, Aug. 1, 1957, p. 717–720

An experimental study of the friction of rubber within a wide range of normal pressures. (in Russian) : V. V. Lavrent'ev. Doklady akademii nauk SSSR, v. 115, no. 4, Aug. 1, 1957, p. 717–720

The role of friction in metal-working processes : Hugh Ford. Institute of petroleum, journal, v. 40, Oct. 1954, p. 291–294

This paper is a review of the Soviet fundamental investigations on zirconium dioxide, ZrO2-based compounds and solid solutions as regards the tasks of high-temperature technics. The review covers a period of recent 20–30 years and is mainly based on publications in the Soviet magazines such as the “Proceedings of the Academy of Sciences of the USSR” (Doklady Academii Nauk SSSR), “Journal of Applied Chemistry” (Zhurnal Prikladnoi Khimii), “Inorganic Materials” (Izvestiya Akademii Nauk SSSR, Neorganicheskie Materialy) and “Refractories” (Ogneupory). The paper gives an account of research work including studies of the structure of zirconium dioxide, its polymorphic transformations, the influence of various impurities and other conditions on these transformations as well as investigations of the systems containing ZrO2 and refractory oxides of elements of groups II, III, IV and V of the periodic system. It is also concerned with problems of the mechanism and kinetics of interaction between oxides in ZrO2-containing systems. The second part will be devoted to applied investigations directed towards the elaboration of the synthesis processes of zirconia materials, the preparation of ceramic materials from them, the study of the relations between composition, structure and physicochemical properties. The third, concluding part, will contain information about the elaboration of several concrete technical materials for various purposes and their properties.

Influence of lubricants upon the surface flow of metal : S. Ya. Veiler. Henry Brutcher translation no. 3519, 6 pp. (From Doklady akademii nauk SSSR, v. 99, no. 6, 1954, p. 1025–1027.) Henry Brutcher, Altaden, Calif.

Influence of lubricants upon the surface flow of metal : S. Ya. Veiler. Henry Brutcher translation no. 3519, 6 pp. (From Doklady akademii nauk SSSR, v. 99, no. 6, 1954, p. 1025–1027.) Henry Brutcher, Altaden, Calif.