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259 results on '"Malinowski, S."'

2. Randomized Controlled Field Trials of Predictive Policing

Author

Mohler, GO, Short, MB, Malinowski, S, Johnson, M, Tita, GE, Bertozzi, AL, and Brantingham, PJ

Subjects
Crime, Experimental methods, Machine learning, Point processes, Policing dosage, Statistics & Probability, Statistics, Econometrics, Demography
Abstract

The concentration of police resources in stable crime hotspots has proven effective in reducing crime, but the extent to which police can disrupt dynamically changing crime hotspots is unknown. Police must be able to anticipate the future location of dynamic hotspots to disrupt them. Here we report results of two randomized controlled trials of near real-time epidemic-type aftershock sequence (ETAS) crime forecasting, one trial within three divisions of the Los Angeles Police Department and the other trial within two divisions of the Kent Police Department (United Kingdom). We investigate the extent to which (i) ETAS models of short-term crime risk outperform existing best practice of hotspot maps produced by dedicated crime analysts, (ii) police officers in the field can dynamically patrol predicted hotspots given limited resources, and (iii) crime can be reduced by predictive policing algorithms under realistic law enforcement resource constraints. While previous hotspot policing experiments fix treatment and control hotspots throughout the experimental period, we use a novel experimental design to allow treatment and control hotspots to change dynamically over the course of the experiment. Our results show that ETAS models predict 1.4–2.2 times as much crime compared to a dedicated crime analyst using existing criminal intelligence and hotspot mapping practice. Police patrols using ETAS forecasts led to an average 7.4% reduction in crime volume as a function of patrol time, whereas patrols based upon analyst predictions showed no significant effect. Dynamic police patrol in response to ETAS crime forecasts can disrupt opportunities for crime and lead to real crime reductions.

Published
2015

3. Physics of Stratocumulus Top (POST): turbulent mixing across capping inversion

Author

Malinowski, S. P, Gerber, H., Jen-La Plante, I., Kopec, M. K, Kumala, W., Nurowska, K., Chuang, P. Y, Khelif, D., and Haman, K. E

Subjects
Critical Richardson-Number, Large-Eddy Simulation, Mixed-Layer Closure, Marine Stratocumulus, Boundary-Layer, Entrainment, Clouds, Convection, Dynamics, Microphysics
Published
2013

5. The H-Boltzmann's Theorem for Magnetic Systems

Author

Malinowski, S.

Subjects
Condensed Matter
Abstract

In a time-reversal non-invariant system the entropy production, dS/dt, may be negative. Close to the critical point of medium it is positive: larger or smaller as compared with one above critical point depending on whether the transitions from lower states to higher states of the system are more (less) probable than the reverse transitions. PACS numbers: 05.60.+w, 05.70.Fh, 05.70.Ln, Comment: LaTeX 2.09, 5 pp, no figures

Published
1995

7. Studying non-equilibrium turbulence inside and around cumulus clouds

Author

Krol, S., Wacławczyk, M., and Malinowski, S.

Abstract

Estimating the characteristics of turbulence in a changing environment, where assumptions about stationarity and homogenity do not necessarily hold true, is burdened with error. Clouds can be thought of as an example of such environment in which many non-stationarities, inhomogenities and sudden changes are present. A number of recent research works address the problem of the equilibrium Taylor’s law and its failure in the presence of rapid changes of the system.The main idea of this work is to calculate C-epsilon from in-situ airborne measurements of atmospheric turbulence, where the flow conditions in the investigated region are unknown a priori and only limited information, that is 1D intersections of the flow field along the flight track, are available. By studying the dependence of C-epsilon on Reλ, we can identify whether turbulence is in- or out of equilibrium. We can also recover information about temporal tendencies in the investigated region by comparing C-epsilon with its equilibrium value C0. We focus on the data from the EUREC4A (Elucidating the role of clouds-circulation coupling in climate) campaign, collected near Barbados, and most importantly, the data from the inside and outside clouds. We treat the cloud as a disturbance in the flow field, but we also analyse its surroundings.Preliminary results show that indeed C-epsilon value inside a cloud is different than its equilibrium value, indicating active production of turbulence. We compare the results calculated for clouds at varying heights and different days., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Published
2023
Full Text
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9. EUREC4A

Author

Stevens, B, Bony, S, Farrell, D, Ament, F, Blyth, A, Fairall, C, Karstensen, J, Quinn, P, Speich, S, Acquistapace, C, Aemisegger, F, Albright, A, Bellenger, H, Bodenschatz, E, Caesar, K, Chewitt-Lucas, R, De Boer, G, Delanoe, J, Denby, L, Ewald, F, Fildier, B, Forde, M, George, G, Gross, S, Hagen, M, Hausold, A, Heywood, K, Hirsch, L, Jacob, M, Jansen, F, Kinne, S, Klocke, D, Kolling, T, Konow, H, Lothon, M, Mohr, W, Naumann, A, Nuijens, L, Olivier, L, Pincus, R, Pohlker, M, Reverdin, G, Roberts, G, Schnitt, S, Schulz, H, Pier Siebesma, A, Stephan, C, Sullivan, P, Touze-Peiffer, L, Vial, J, Vogel, R, Zuidema, P, Alexander, N, Alves, L, Arixi, S, Asmath, H, Bagheri, G, Baier, K, Bailey, A, Baranowski, D, Baron, A, Barrau, S, Barrett, P, Batier, F, Behrendt, A, Bendinger, A, Beucher, F, Bigorre, S, Blades, E, Blossey, P, Bock, O, Boing, S, Bosser, P, Bourras, D, Bouruet-Aubertot, P, Bower, K, Branellec, P, Branger, H, Brennek, M, Brewer, A, Brilouet, P, Brugmann, B, Buehler, S, Burke, E, Burton, R, Calmer, R, Canonici, J, Carton, X, Cato, G, Charles, J, Chazette, P, Chen, Y, Chilinski, M, Choularton, T, Chuang, P, Clarke, S, Coe, H, Cornet, C, Coutris, P, Couvreux, F, Crewell, S, Cronin, T, Cui, Z, Cuypers, Y, Daley, A, Damerell, G, Dauhut, T, Deneke, H, Desbios, J, Dorner, S, Donner, S, Douet, V, Drushka, K, Dutsch, M, Ehrlich, A, Emanuel, K, Emmanouilidis, A, Etienne, J, Etienne-Leblanc, S, Faure, G, Feingold, G, Ferrero, L, Fix, A, Flamant, C, Flatau, P, Foltz, G, Forster, L, Furtuna, I, Gadian, A, Galewsky, J, Gallagher, M, Gallimore, P, Gaston, C, Gentemann, C, Geyskens, N, Giez, A, Gollop, J, Gouirand, I, Gourbeyre, C, De Graaf, D, De Groot, G, Grosz, R, Guttler, J, Gutleben, M, Hall, K, Harris, G, Helfer, K, Henze, D, Herbert, C, Holanda, B, Ibanez-Landeta, A, Intrieri, J, Iyer, S, Julien, F, Kalesse, H, Kazil, J, Kellman, A, Kidane, A, Kirchner, U, Klingebiel, M, Korner, M, Kremper, L, Kretzschmar, J, Kruger, O, Kumala, W, Kurz, A, L'Hegaret, P, Labaste, M, Lachlan-Cope, T, Laing, A, Landschutzer, P, Lang, T, Lange, D, Lange, I, Laplace, C, Lavik, G, Laxenaire, R, Lebihan, C, Leandro, M, Lefevre, N, Lena, M, Lenschow, D, Li, Q, Lloyd, G, Los, S, Losi, N, Lovell, O, Luneau, C, Makuch, P, Malinowski, S, Manta, G, Marinou, E, Marsden, N, Masson, S, Maury, N, Mayer, B, Mayers-Als, M, Mazel, C, Mcgeary, W, Mcwilliams, J, Mech, M, Mehlmann, M, Meroni, A, Mieslinger, T, Minikin, A, Minnett, P, Moller, G, Avalos, Y, Muller, C, Musat, I, Napoli, A, Neuberger, A, Noisel, C, Noone, D, Nordsiek, F, Nowak, J, Oswald, L, Parker, D, Peck, C, Person, R, Philippi, M, Plueddemann, A, Pohlker, C, Portge, V, Poschl, U, Pologne, L, Posyniak, M, Prange, M, Melendez, E, Radtke, J, Ramage, K, Reimann, J, Renault, L, Reus, K, Reyes, A, Ribbe, J, Ringel, M, Ritschel, M, Rocha, C, Rochetin, N, Rottenbacher, J, Rollo, C, Royer, H, Sadoulet, P, Saffin, L, Sandiford, S, Sandu, I, Schafer, M, Schemann, V, Schirmacher, I, Schlenczek, O, Schmidt, J, Schroder, M, Schwarzenboeck, A, Sealy, A, Senff, C, Serikov, I, Shohan, S, Siddle, E, Smirnov, A, Spath, F, Spooner, B, Katharina Stolla, M, Szkolka, W, De Szoeke, S, Tarot, S, Tetoni, E, Thompson, E, Thomson, J, Tomassini, L, Totems, J, Ubele, A, Villiger, L, Von Arx, J, Wagner, T, Walther, A, Webber, B, Wendisch, M, Whitehall, S, Wiltshire, A, Wing, A, Wirth, M, Wiskandt, J, Wolf, K, Worbes, L, Wright, E, Wulfmeyer, V, Young, S, Zhang, C, Zhang, D, Ziemen, F, Zinner, T, Zoger, M, Stevens B., Bony S., Farrell D., Ament F., Blyth A., Fairall C., Karstensen J., Quinn P. K., Speich S., Acquistapace C., Aemisegger F., Albright A. L., Bellenger H., Bodenschatz E., Caesar K. -A., Chewitt-Lucas R., De Boer G., Delanoe J., Denby L., Ewald F., Fildier B., Forde M., George G., Gross S., Hagen M., Hausold A., Heywood K. J., Hirsch L., Jacob M., Jansen F., Kinne S., Klocke D., Kolling T., Konow H., Lothon M., Mohr W., Naumann A. K., Nuijens L., Olivier L., Pincus R., Pohlker M., Reverdin G., Roberts G., Schnitt S., Schulz H., Pier Siebesma A., Stephan C. C., Sullivan P., Touze-Peiffer L., Vial J., Vogel R., Zuidema P., Alexander N., Alves L., Arixi S., Asmath H., Bagheri G., Baier K., Bailey A., Baranowski D., Baron A., Barrau S., Barrett P. A., Batier F., Behrendt A., Bendinger A., Beucher F., Bigorre S., Blades E., Blossey P., Bock O., Boing S., Bosser P., Bourras D., Bouruet-Aubertot P., Bower K., Branellec P., Branger H., Brennek M., Brewer A., Brilouet P. -E., Brugmann B., Buehler S. A., Burke E., Burton R., Calmer R., Canonici J. -C., Carton X., Cato G., Charles J. A., Chazette P., Chen Y., Chilinski M. T., Choularton T., Chuang P., Clarke S., Coe H., Cornet C., Coutris P., Couvreux F., Crewell S., Cronin T., Cui Z., Cuypers Y., Daley A., Damerell G. M., Dauhut T., Deneke H., Desbios J. -P., Dorner S., Donner S., Douet V., Drushka K., Dutsch M., Ehrlich A., Emanuel K., Emmanouilidis A., Etienne J. -C., Etienne-Leblanc S., Faure G., Feingold G., Ferrero L., Fix A., Flamant C., Flatau P. J., Foltz G. R., Forster L., Furtuna I., Gadian A., Galewsky J., Gallagher M., Gallimore P., Gaston C., Gentemann C., Geyskens N., Giez A., Gollop J., Gouirand I., Gourbeyre C., De Graaf D., De Groot G. E., Grosz R., Guttler J., Gutleben M., Hall K., Harris G., Helfer K. C., Henze D., Herbert C., Holanda B., Ibanez-Landeta A., Intrieri J., Iyer S., Julien F., Kalesse H., Kazil J., Kellman A., Kidane A. T., Kirchner U., Klingebiel M., Korner M., Kremper L. A., Kretzschmar J., Kruger O., Kumala W., Kurz A., L'Hegaret P., Labaste M., Lachlan-Cope T., Laing A., Landschutzer P., Lang T., Lange D., Lange I., Laplace C., Lavik G., Laxenaire R., LeBihan C., Leandro M., Lefevre N., Lena M., Lenschow D., Li Q., Lloyd G., Los S., Losi N., Lovell O., Luneau C., Makuch P., Malinowski S., Manta G., Marinou E., Marsden N., Masson S., Maury N., Mayer B., Mayers-Als M., Mazel C., McGeary W., McWilliams J. C., Mech M., Mehlmann M., Meroni A. N., Mieslinger T., Minikin A., Minnett P., Moller G., Avalos Y. M., Muller C., Musat I., Napoli A., Neuberger A., Noisel C., Noone D., Nordsiek F., Nowak J. L., Oswald L., Parker D. J., Peck C., Person R., Philippi M., Plueddemann A., Pohlker C., Portge V., Poschl U., Pologne L., Posyniak M., Prange M., Melendez E. Q., Radtke J., Ramage K., Reimann J., Renault L., Reus K., Reyes A., Ribbe J., Ringel M., Ritschel M., Rocha C. B., Rochetin N., Rottenbacher J., Rollo C., Royer H., Sadoulet P., Saffin L., Sandiford S., Sandu I., Schafer M., Schemann V., Schirmacher I., Schlenczek O., Schmidt J., Schroder M., Schwarzenboeck A., Sealy A., Senff C. J., Serikov I., Shohan S., Siddle E., Smirnov A., Spath F., Spooner B., Katharina Stolla M., Szkolka W., De Szoeke S. P., Tarot S., Tetoni E., Thompson E., Thomson J., Tomassini L., Totems J., Ubele A. A., Villiger L., Von Arx J., Wagner T., Walther A., Webber B., Wendisch M., Whitehall S., Wiltshire A., Wing A. A., Wirth M., Wiskandt J., Wolf K., Worbes L., Wright E., Wulfmeyer V., Young S., Zhang C., Zhang D., Ziemen F., Zinner T., Zoger M., Stevens, B, Bony, S, Farrell, D, Ament, F, Blyth, A, Fairall, C, Karstensen, J, Quinn, P, Speich, S, Acquistapace, C, Aemisegger, F, Albright, A, Bellenger, H, Bodenschatz, E, Caesar, K, Chewitt-Lucas, R, De Boer, G, Delanoe, J, Denby, L, Ewald, F, Fildier, B, Forde, M, George, G, Gross, S, Hagen, M, Hausold, A, Heywood, K, Hirsch, L, Jacob, M, Jansen, F, Kinne, S, Klocke, D, Kolling, T, Konow, H, Lothon, M, Mohr, W, Naumann, A, Nuijens, L, Olivier, L, Pincus, R, Pohlker, M, Reverdin, G, Roberts, G, Schnitt, S, Schulz, H, Pier Siebesma, A, Stephan, C, Sullivan, P, Touze-Peiffer, L, Vial, J, Vogel, R, Zuidema, P, Alexander, N, Alves, L, Arixi, S, Asmath, H, Bagheri, G, Baier, K, Bailey, A, Baranowski, D, Baron, A, Barrau, S, Barrett, P, Batier, F, Behrendt, A, Bendinger, A, Beucher, F, Bigorre, S, Blades, E, Blossey, P, Bock, O, Boing, S, Bosser, P, Bourras, D, Bouruet-Aubertot, P, Bower, K, Branellec, P, Branger, H, Brennek, M, Brewer, A, Brilouet, P, Brugmann, B, Buehler, S, Burke, E, Burton, R, Calmer, R, Canonici, J, Carton, X, Cato, G, Charles, J, Chazette, P, Chen, Y, Chilinski, M, Choularton, T, Chuang, P, Clarke, S, Coe, H, Cornet, C, Coutris, P, Couvreux, F, Crewell, S, Cronin, T, Cui, Z, Cuypers, Y, Daley, A, Damerell, G, Dauhut, T, Deneke, H, Desbios, J, Dorner, S, Donner, S, Douet, V, Drushka, K, Dutsch, M, Ehrlich, A, Emanuel, K, Emmanouilidis, A, Etienne, J, Etienne-Leblanc, S, Faure, G, Feingold, G, Ferrero, L, Fix, A, Flamant, C, Flatau, P, Foltz, G, Forster, L, Furtuna, I, Gadian, A, Galewsky, J, Gallagher, M, Gallimore, P, Gaston, C, Gentemann, C, Geyskens, N, Giez, A, Gollop, J, Gouirand, I, Gourbeyre, C, De Graaf, D, De Groot, G, Grosz, R, Guttler, J, Gutleben, M, Hall, K, Harris, G, Helfer, K, Henze, D, Herbert, C, Holanda, B, Ibanez-Landeta, A, Intrieri, J, Iyer, S, Julien, F, Kalesse, H, Kazil, J, Kellman, A, Kidane, A, Kirchner, U, Klingebiel, M, Korner, M, Kremper, L, Kretzschmar, J, Kruger, O, Kumala, W, Kurz, A, L'Hegaret, P, Labaste, M, Lachlan-Cope, T, Laing, A, Landschutzer, P, Lang, T, Lange, D, Lange, I, Laplace, C, Lavik, G, Laxenaire, R, Lebihan, C, Leandro, M, Lefevre, N, Lena, M, Lenschow, D, Li, Q, Lloyd, G, Los, S, Losi, N, Lovell, O, Luneau, C, Makuch, P, Malinowski, S, Manta, G, Marinou, E, Marsden, N, Masson, S, Maury, N, Mayer, B, Mayers-Als, M, Mazel, C, Mcgeary, W, Mcwilliams, J, Mech, M, Mehlmann, M, Meroni, A, Mieslinger, T, Minikin, A, Minnett, P, Moller, G, Avalos, Y, Muller, C, Musat, I, Napoli, A, Neuberger, A, Noisel, C, Noone, D, Nordsiek, F, Nowak, J, Oswald, L, Parker, D, Peck, C, Person, R, Philippi, M, Plueddemann, A, Pohlker, C, Portge, V, Poschl, U, Pologne, L, Posyniak, M, Prange, M, Melendez, E, Radtke, J, Ramage, K, Reimann, J, Renault, L, Reus, K, Reyes, A, Ribbe, J, Ringel, M, Ritschel, M, Rocha, C, Rochetin, N, Rottenbacher, J, Rollo, C, Royer, H, Sadoulet, P, Saffin, L, Sandiford, S, Sandu, I, Schafer, M, Schemann, V, Schirmacher, I, Schlenczek, O, Schmidt, J, Schroder, M, Schwarzenboeck, A, Sealy, A, Senff, C, Serikov, I, Shohan, S, Siddle, E, Smirnov, A, Spath, F, Spooner, B, Katharina Stolla, M, Szkolka, W, De Szoeke, S, Tarot, S, Tetoni, E, Thompson, E, Thomson, J, Tomassini, L, Totems, J, Ubele, A, Villiger, L, Von Arx, J, Wagner, T, Walther, A, Webber, B, Wendisch, M, Whitehall, S, Wiltshire, A, Wing, A, Wirth, M, Wiskandt, J, Wolf, K, Worbes, L, Wright, E, Wulfmeyer, V, Young, S, Zhang, C, Zhang, D, Ziemen, F, Zinner, T, Zoger, M, Stevens B., Bony S., Farrell D., Ament F., Blyth A., Fairall C., Karstensen J., Quinn P. K., Speich S., Acquistapace C., Aemisegger F., Albright A. L., Bellenger H., Bodenschatz E., Caesar K. -A., Chewitt-Lucas R., De Boer G., Delanoe J., Denby L., Ewald F., Fildier B., Forde M., George G., Gross S., Hagen M., Hausold A., Heywood K. J., Hirsch L., Jacob M., Jansen F., Kinne S., Klocke D., Kolling T., Konow H., Lothon M., Mohr W., Naumann A. K., Nuijens L., Olivier L., Pincus R., Pohlker M., Reverdin G., Roberts G., Schnitt S., Schulz H., Pier Siebesma A., Stephan C. C., Sullivan P., Touze-Peiffer L., Vial J., Vogel R., Zuidema P., Alexander N., Alves L., Arixi S., Asmath H., Bagheri G., Baier K., Bailey A., Baranowski D., Baron A., Barrau S., Barrett P. A., Batier F., Behrendt A., Bendinger A., Beucher F., Bigorre S., Blades E., Blossey P., Bock O., Boing S., Bosser P., Bourras D., Bouruet-Aubertot P., Bower K., Branellec P., Branger H., Brennek M., Brewer A., Brilouet P. -E., Brugmann B., Buehler S. A., Burke E., Burton R., Calmer R., Canonici J. -C., Carton X., Cato G., Charles J. A., Chazette P., Chen Y., Chilinski M. T., Choularton T., Chuang P., Clarke S., Coe H., Cornet C., Coutris P., Couvreux F., Crewell S., Cronin T., Cui Z., Cuypers Y., Daley A., Damerell G. M., Dauhut T., Deneke H., Desbios J. -P., Dorner S., Donner S., Douet V., Drushka K., Dutsch M., Ehrlich A., Emanuel K., Emmanouilidis A., Etienne J. -C., Etienne-Leblanc S., Faure G., Feingold G., Ferrero L., Fix A., Flamant C., Flatau P. J., Foltz G. R., Forster L., Furtuna I., Gadian A., Galewsky J., Gallagher M., Gallimore P., Gaston C., Gentemann C., Geyskens N., Giez A., Gollop J., Gouirand I., Gourbeyre C., De Graaf D., De Groot G. E., Grosz R., Guttler J., Gutleben M., Hall K., Harris G., Helfer K. C., Henze D., Herbert C., Holanda B., Ibanez-Landeta A., Intrieri J., Iyer S., Julien F., Kalesse H., Kazil J., Kellman A., Kidane A. T., Kirchner U., Klingebiel M., Korner M., Kremper L. A., Kretzschmar J., Kruger O., Kumala W., Kurz A., L'Hegaret P., Labaste M., Lachlan-Cope T., Laing A., Landschutzer P., Lang T., Lange D., Lange I., Laplace C., Lavik G., Laxenaire R., LeBihan C., Leandro M., Lefevre N., Lena M., Lenschow D., Li Q., Lloyd G., Los S., Losi N., Lovell O., Luneau C., Makuch P., Malinowski S., Manta G., Marinou E., Marsden N., Masson S., Maury N., Mayer B., Mayers-Als M., Mazel C., McGeary W., McWilliams J. C., Mech M., Mehlmann M., Meroni A. N., Mieslinger T., Minikin A., Minnett P., Moller G., Avalos Y. M., Muller C., Musat I., Napoli A., Neuberger A., Noisel C., Noone D., Nordsiek F., Nowak J. L., Oswald L., Parker D. J., Peck C., Person R., Philippi M., Plueddemann A., Pohlker C., Portge V., Poschl U., Pologne L., Posyniak M., Prange M., Melendez E. Q., Radtke J., Ramage K., Reimann J., Renault L., Reus K., Reyes A., Ribbe J., Ringel M., Ritschel M., Rocha C. B., Rochetin N., Rottenbacher J., Rollo C., Royer H., Sadoulet P., Saffin L., Sandiford S., Sandu I., Schafer M., Schemann V., Schirmacher I., Schlenczek O., Schmidt J., Schroder M., Schwarzenboeck A., Sealy A., Senff C. J., Serikov I., Shohan S., Siddle E., Smirnov A., Spath F., Spooner B., Katharina Stolla M., Szkolka W., De Szoeke S. P., Tarot S., Tetoni E., Thompson E., Thomson J., Tomassini L., Totems J., Ubele A. A., Villiger L., Von Arx J., Wagner T., Walther A., Webber B., Wendisch M., Whitehall S., Wiltshire A., Wing A. A., Wirth M., Wiskandt J., Wolf K., Worbes L., Wright E., Wulfmeyer V., Young S., Zhang C., Zhang D., Ziemen F., Zinner T., and Zoger M.

Abstract

The science guiding the EUREC4A campaign and its measurements is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic - eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC4A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air-sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC4A explored - from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation - are presented along with an overview of EUREC4A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at 10.25326/165 , and a film documenting the campaign is provided as a video supplement.

Published
2021

12. Temporal Exceptional Model Mining Using Dynamic Bayesian Networks

Author

Bueno, Marcos L. P., Hommersom, A.J., Lucas, Peter J. F., Lemaire, V., Malinowski, S., Bagnall, A., Guyet, Th., Tavenard, R., Ifrim, G., Department of Computer Science, RS-Research Line Resilience (part of LIRS program), Digital Society Institute, Datamanagement & Biometrics, Lemaire, V., Malinowski, S., Bagnall, A., Guyet, Th., Tavenard, R., and Ifrim, G.

Subjects
Measure (data warehouse), Computer science, Process (engineering), 22/2 OA procedure, Bayesian network, Exceptional model mining, computer.software_genre, Task (project management), Temporal database, Bayesian networks, Machine learning, Subgroup discovery, Relevance (information retrieval), Graphical model, Data mining, Graphical models, computer, Dynamic Bayesian network, Temporal data
Abstract

The discovery of subsets of data that are characterized by models that differ significantly from the entire dataset, is the goal of exceptional model mining. With the increasing availability of temporal data, this task has clear relevance in discovering deviating temporal subprocesses that can bring insight into industrial processes, medical treatments, etc. As temporal data is often noisy, high-dimensional and has complex statistical dependencies, discovering such temporal subprocesses is challenging for current exceptional model mining methods. In this paper, we introduce Temporal Exceptional Model Mining to capture multiple and complex relationships among temporal variables of a dataset in a principled way. Our contributions are as follows: (i) we define the new task of temporal exceptional model mining; (ii) we characterize the discovery of exceptional temporal submodels using dynamic Bayesian networks by means of a new distance measure, (iii) we introduce a search procedure for exceptional dynamic Bayesian networks optimized by properties of the proposed distance, and (iv) the practical value of the proposed method is demonstrated based on simulated data and process data of funding applications and by comparisons with other exceptional model mining methods.

Published
2020
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15. Distributed coding using punctured quasi-arithmetic codes for memory and memoryless sources

Author

Malinowski, S., Artigas, X., Guillemot, C., and Torres, L.

Subjects
Distributed processing (Computers) -- Analysis, Entropy (Information theory) -- Analysis, Maximum likelihood estimates (Statistics) -- Usage, Signal processing -- Analysis, Distributed processing (Computers), Digital signal processor, Business, Computers, Electronics, Electronics and electrical industries
Published
2009

17. EUREC 4 A

Author

Stevens, Bjorn, Bony, Sandrine, Farrell, David, Ament, Felix, Blyth, Alan M., Fairall, Chiristopher W., Karstensen, Johannes, Quinn, Patricia K., Speich, Sabrina, Acquistapace, Claudia, Aemisegger, Franziska, Albright, Anna, Bellenger, Hugo, Bodenschatz, Eberhard, Caesar, Kathy-Ann, Chewitt-Lucas, Rebecca, De Boer, Gijs, Delanoë, Julien, Denby, Leif Christopher, Ewald, Florian, Fildier, Benjamin, Forde, Marvin, George, Geet, Gross, Silke, Hagen, Martin, Hausold, Andrea, Heywood, Karen J., Hirsch, Lutz, Jacob, Marek, Jansen, Friedhelm, Kinne, Stefan, Klocke, Daniel, Kölling, Tobias, Konow, Heike, Lothon, Marie, Mohr, Wiebke, Naumann, Ann Kristin, Nuijens, Louise, Olivier, Léa, Pincus, Robert, Pöhlker, Mira L., Reverdin, Gilles, Roberts, Gregory, Schnitt, Sabrina, Sullivan, Peter P., Touzé-Peiffer, Ludovic, Vial, Jessica, Vogel, Raphaela, Alexander, Nicola, Alves, Lyndon, Arixi, Sophian, Asmath, Hamish, Bagheri, Gholamhossein, Bailey, Adriana, Baranowski, Dariusz, Baron, Alexandre, Barrau, Sébastien, Barrett, Paul Alan, Behrendt, Andreas, Bendinger, Arne, Beucher, Florent, Bigorre, Sebastien, Bosser, Pierre, Blossey, P., Bock, Olivier, Bourras, Denis, Bouruet-Aubertot, Pascale, Bower, K, Branger, H, Brennek, M, Brewer, A, Brilouet, P.-E, Brügmann, B, Buehler, S, Burke, E, Burton, R, Böing, S, Calmer, R, Canonici, J.-C, Carton, X, Cato, G, Charles, J, Chazette, Patrick, Chen, Y, Choularton, T, Chuang, P, Clarke, S, Coe, H, Cornet, C, Coutris, P, Couvreux, F, Crewell, S, Cronin, T, Cui, Z, Cuypers, Y, Daley, A, Damerell, G, Dauhut, T, De Graaf, D, De Groot, G, De Szoeke, S, Deneke, H, Desbios, J.-P, Douet, V, Drushka, K, Dütsch, M, Ehrlich, A, Emanuel, K, Emmanouilidis, A, Etienne, J.-C, Etienne-Leblanc, S, Faure, G, Feingold, G, Ferrero, L, Fix, A, Flamant, Cyrille, Flatau, P, Foltz, G, Gadian, A, Galewsky, J, Gallagher, M, Gallimore, P, Gaston, C, Gentemann, C, Geyskens, N, Giez, A, Gourbeyre, C, Grosz, R, Gutleben, M, Güttler, J, Hall, K, Harris, G, Helfer, K, Henze, D, Herbert, C, Holanda, B, Ibanez-Landeta, A, Intrieri, J, Iyer, S, Julien, F, Kalesse, H, Kazil, J, Kellman, A, Kirchner, U, Klingebiel, M, Kremper, L, Kretzschmar, J, Krüger, O, Kurz, A, Körner, M, L'hégaret, P, Lachlan-Cope, T, Laing, A, Landschützer, P, Lang, T, Lange, D, Lange, I, Laplace, C, Laxenaire, R, Le Bihan, C, Leandro, M, Lefevre, N, Lenschow, D, Li, Q, Lloyd, G, Los, S, Losi, N, Lovell, O, Luneau, C, Makuch, P, Malinowski, S, Manta, G, Marinou, E, Marsden, N, Matthieu, L, Maury, N, Mayer, B, Mayers-Als, M, Mazel, Christophe, Mcgeary, W, Mcwilliams, J, Mech, M, Mehlmann, M, Meroni, A, Mieslinger, T, Minikin, A, Avalos, Y, Muller, Caroline, Musat, I, Möller, G, Napoli, A, Neuberger, A, Noone, D, Nordsiek, F, Nowak, J, Oswald, L, Parker, D, Peck, C, Person, R, Plueddemann, A, Pologne, L, Posyniak, M, Prange, M, Pöhlker, C, Pörtge, V, Pöschl, U, Quiñones Meléndez, E, Radtke, J, Ramage, K, Reimann, J, Renault, L, Reus, K, Reyes, A, Ribbe, J, Ringel, M, Ritschel, M, Rocha, C, Rochetin, N, Rollo, C, Röttenbacher, J, Saffin, L, Sandiford, S, Sandu, I, Schemann, V, Schirmacher, I, Schlenczek, O, Schmidt, J, Schröder, M, Schulz, H, Schwarzenboeck, A, Schäfer, M, Sealy, A, Serikov, I, Shohan, S, Siddle, E, Siebesma, A, Späth, F, Stephan, C, Stolla, M, Szkółka, W, Tarot, S, Tetoni, E, Thompson, E, Thomson, J, Tomassini, L, Totems, J, Villiger, L, Walther, A, Webber, B, Wendisch, M, Whitehall, S, Wiltshire, A, Wing, A, Wirth, M, Wiskandt, J, Wolf, K, Worbes, L, Wright, E, Wulfmeyer, V, Young, S, Zhang, D, Zhang, C, Ziemen, F, Zinner, T, Zuidema, P, Zöger, M, Max Planck Institute for Meteorology (MPI-M), Max-Planck-Gesellschaft, Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Caribbean Institute for Meteorology and Hydrology (CIMH), Meteorological Institute [Hamburg], University of Hamburg, National Centre for Atmospheric Science [Leeds] (NCAS), Natural Environment Research Council (NERC), NOAA Earth System Research Laboratory (ESRL), National Oceanic and Atmospheric Administration (NOAA), Helmholtz Centre for Ocean Research [Kiel] (GEOMAR), NOAA Pacific Marine Environmental Laboratory [Seattle] (PMEL), Institute for Geophysics and Meteorology [Köln] (IGM), University of Cologne, Institute for Atmospheric and Climate Science [Zürich] (IAC), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich] (ETH Zürich), Max Planck Institute for Dynamics and Self-Organization (MPIDS), NOAA Physical Sciences Laboratory (PSL), SPACE - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), University of Leeds, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Deutscher Wetterdienst [Offenbach] (DWD), Ludwig-Maximilians-Universität München (LMU), Laboratoire d'aérologie (LA), Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Universität Hamburg (UHH), Max Planck Institute for Marine Microbiology, Delft University of Technology (TU Delft), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Universität zu Köln, École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Université Gustave Eiffel, Institut de Physique du Globe de Paris (IPGP), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), National Center for Atmospheric Research [Boulder] (NCAR), Institut de Recherche sur les Phénomènes Hors Equilibre (IRPHE), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), TROPO - LATMOS, Institut Pythéas (OSU PYTHEAS), and Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDE]Environmental Sciences, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

18. Demand Forecasting in the Presence of Privileged Information

Author

Ariannezhad, M., Schelter, S., de Rijke, M., Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., Ifrim, G., and Information and Language Processing Syst (IVI, FNWI)

Subjects
Artificial neural network, Computer science, Process (engineering), business.industry, Demand forecasting, Machine learning, computer.software_genre, Dual (category theory), Component (UML), Leverage (statistics), Artificial intelligence, Architecture, Symmetric mean absolute percentage error, business, computer
Abstract

Predicting the amount of sales in the future is a fundamental problem in the replenishment process of retail companies. Models for forecasting the demand of an item typically rely on influential features and historical sales of the item. However, the values of some influential features (to which we refer as non-plannable features) are only known during model training (for the past), and not for the future at prediction time. Examples of such features include sales in other channels, such as other stores in chain supermarkets. Existing forecasting methods ignore such non-plannable features or wrongly assume that they are also known at prediction time. We identify non-plannable features as privileged information, i.e., information that is available at training time but not at prediction time, and design a neural network to leverage this source of data accordingly. We present a dual branch neural network architecture that incorporates non-plannable features at training time, with a first branch to embed the historical information, and a second branch, the privileged information (PI) branch, to predict demand based on privileged information. Next, we leverage a single branch network at prediction time, which applies a simulation component to mimic the behavior of the PI branch, whose inputs are not available at prediction time. We evaluate our approach on two real-world forecasting datasets, and find that it outperforms state-of-the-art competitors in terms of mean absolute error and symmetric mean absolute percentage error metrics. We further provide visualizations and conduct experiments to validate the contribution of different components in our proposed architecture.

Published
2020
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24. Event and Anomaly Detection Using Tucker3 Decomposition

Author

Tork, H. F., Oliveira, M., Gama, J., Malinowski, S., Ricardo Morla, Faculdade de Economia, and Faculdade de Engenharia

Subjects
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence
Abstract

Failure detection in telecommunication networks is a vital task. So far, several supervised and unsupervised solutions have been provided for discovering failures in such networks. Among them unsupervised approaches has attracted more attention since no label data is required. Often, network devices are not able to provide information about the type of failure. In such cases the type of failure is not known in advance and the unsupervised setting is more appropriate for diagnosis. Among unsupervised approaches, Principal Component Analysis (PCA) is a well-known solution which has been widely used in the anomaly detection literature and can be applied to matrix data (e.g. Users-Features). However, one of the important properties of network data is their temporal sequential nature. So considering the interaction of dimensions over a third dimension, such as time, may provide us better insights into the nature of network failures. In this paper we demonstrate the power of three-way analysis to detect events and anomalies in time-evolving network data.

Published
2014

32. Discovering temporal patterns in water quality time series, focusing on floods with the LDA method

Author

Aubert A., Tavenard R., Emonet R., Malinowski S., Guyet T., Quiniou R., Odobez J-M., and Gascuel-Odoux C.

Abstract

Studying floods has been a major issue in hydrological research for years. It is often done in terms of water quantity but it is also of interest in terms of water quality. Stream chemistry is a mix of solutes. They originate from various sources in the catchment reach the stream by various flow pathways and are transformed by biogeochemical reactions at different locations. Therefore we hypothesized that reaction of the stream chemistry to a rainfall event is not unique but varies according to the season (1) and the global meteorological conditions of the year (2). In this paper we propose a topic modeling approach to identify a typology of temporal chemical patterns of reaction to a rainfall event as a way to better understand catchment processes at the flood time scale.

Published
2013

42. Dynamics and chemistry of marine stratocumulus - DYCOMS II

Author

Stevens, B., https://orcid.org/0000-0003-3795-0475, Lenschow, D., Vali, G., Gerber, H., Bandy, A., Blomquist, B., Brenguier, J., Bretherton, C., Burnet, F., Campos, T., Chai, S., Faloona, I., Friesen, D., Haimov, S., Laursen, K., Lilly, D., Loehrer, S., Malinowski, S., Morley, B., Petters, M., Rogers, D., Russell, L., Savic-Jovac, V., Snider, J., Straub, D., Szumowski, M., Takagi, H., Thornton, D., Tschudi, M., Twohy, C., Wetzel, M., and van Zanten, C.

Published
2003

43. POST - A New Look at Stratocumulus

Author

NAVAL RESEARCH LAB WASHINGTON DC, Gerber, H., Frick, G., Malinowski, S. P., Kumula, W., Krueger, S., NAVAL RESEARCH LAB WASHINGTON DC, Gerber, H., Frick, G., Malinowski, S. P., Kumula, W., and Krueger, S.

Abstract

POST (Physics of Stratocumulus Top) is an aircraft field study conducted off the California Coast in July and August of 2008. A single aircraft, investigate unbroken stratocumulus clouds (Sc) near cloud top. The aircraft was instrumented with a full suite of probes including probes for measuring state parameters of the atmosphere, drop spectra, CCN concentrations and spectra, radiation irradiances, and wind velocity and turbulence. A motivation to conduct another Sc field study in that region was the desire to apply lessons learned from the earlier DYCOMS II study of CA Sc of which the measured scale of entrained parcels were smaller than the critical probe separation, and the lengthy distance of the gust probe from these probes was too large. The Twin Otter (TO) aircraft permitted the close co-location of these probes to the gust probe on the aircraft. The Ultra-Fast Temperature (UFT) probe provided by the U. of Warsaw (Kumula et al., 2010) and the PVM (liquid water content and effective radius; Gerber et al., 1994) provided high rate data to take advantage of the close separation (approximately 1 m) to the gust probe. Both probes produced 1000-hz data, and their averaged 50-hz data is consistent with that separation at the TO aircraft speed of approximately 50 m/s. The UFT and PVM as well as the whole suite of the other probes provided the first opportunity to look in detail at small scales that DYCOMS II taught us play a role in the Sc cloud-top physical processes. Unresolved questions include the role played by cooling at cloud-top caused by evaporation of cloud water due to the entrainment process. Is this cooling significant or negligible as proposed by Gerber et al. (2005)? What is the role of the Entrainment Interface Layer (EIL) in affecting entrainment? What affect does shear have near Sc top? How does the entrainment process affect microphysics? And, are such details required to understand the behavior and evolution of the Sc?, American Meteorological Society (AMS) Conference on Cloud Physics (13th), Conference on Atmospheric Radiation (13th), 28 June-2 July 2010, Portland, Oregon. Prepared in collaboration with Gerber Scientific, Inc, Reston, VA; Institute of Geophysics, University of Warsaw, Warsaw, Poland; and the Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT. The original document contains color images.

Published
2010

48. Droplet growth in warm turbulent clouds

Author

Devenish, B. J., primary, Bartello, P., additional, Brenguier, J.‐L., additional, Collins, L. R., additional, Grabowski, W. W., additional, IJzermans, R. H. A., additional, Malinowski, S. P., additional, Reeks, M. W., additional, Vassilicos, J. C., additional, Wang, L.‐P., additional, and Warhaft, Z., additional

Published
2012
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