Structured Machine Learning for Robotics

Machine Learning has become the essential tool for automating tasks that consist in predicting the output associated to a certain input. However many modern algorithms are mainly developed for the simple cases of classification and regression. Structured prediction is the field concerned with predicting outputs consisting of complex objects such as graphs, orientations or sequences. While these objects are often of practical interest, they do not have many of the mathematical properties that allow to design principled and computationally feasible algorithms with traditional techniques. In this thesis we investigate and develop algorithms for learning manifold-valued functions in the context of structured prediction. Differentiable manifolds are a mathematical abstraction used in many domains to describe sets with continuous constraints and non-Euclidean geometric properties. By taking a structured prediction approach we show how to define statistically consistent estimators for predicting elements of a manifold, in constrast to traditional structured predition algorithms that are restricted to output sets with finite cardinality. We introduce a wide range of applications that leverage manifolds structures. Above all, we study the case of the hyperbolic manifold, a space suited for representing hierarchical data. By representing supervised datasets within hyperbolic space we show how it is possible to invent new concepts in a previously known hierarchy and show promising results in hierarchical classification. We also study how modern structured approaches can help with practical robotics tasks, either improving performances in behavioural pipelines or showing more robust predictions for constrained tasks. Specifically, we show how structured prediction can be used to tackle inverse kinematics problems of redundant robots, accounting for the constraints of the robotic joints. We also consider the task of biological motion detection and show that by leveraging the sequence structure of video streams we significantly reduce the latency of the application. Our studies are complemented by empirical evaluations on both synthetic and real data

Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models

With the recent advances in machine learning, problems that traditionally would require accurate modeling to be solved analytically can now be successfully approached with data-driven strategies. Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinematics map. Moreover, most learning algorithms consider a completely data-driven approach, while often useful information on the structure of the robot is available and should be positively exploited. In this work, we present a simple, yet effective, approach for learning the inverse kinematics. We introduce a structured prediction algorithm that combines a data-driven strategy with the model provided by a forward kinematics function -- even when this function is misspecified -- to accurately solve the problem. The proposed approach ensures that predicted joint configurations are well within the robot's constraints. We also provide statistical guarantees on the generalization properties of our estimator as well as an empirical evaluation of its performance on trajectory reconstruction tasks.Comment: Accepted for publication in IEEE Robotics and Automation Letters (2021) and presentation at IEEE International Conference on Robotics and Automation (2021) Updated funding informatio

Soil selenium (Se) biofortification changes the physiological, biochemical and epigenetic responses to water stress in Zea mays L. by inducing a higher drought tolerance

Requiring water and minerals to grow and to develop its organs, Maize (Zea mays L.) production and distribution is highly rainfall-dependent. Current global climatic changes reveal irregular rainfall patterns and this could represent for maize a stressing condition resulting in yield and productivity loss around the world. It is well known that low water availability leads the plant to adopt a number of metabolic alterations to overcome stress or reduce its effects. In this regard, selenium (Se), a trace element, can help reduce water damage caused by the overproduction of reactive oxygen species (ROS). Here we report the effects of exogenous Se supply on physiological and biochemical processes that may influence yield and quality of maize under drought stress conditions. Plants were grown in soil fertilized by adding 150 mg of Se (sodium selenite). We verified the effects of drought stress and Se treatment. Selenium biofortification proved more beneficial for maize plants when supplied at higher Se concentrations. The increase in proline, K concentrations and nitrogen metabolism in aerial parts of plants grown in Se-rich substrates, seems to prove that Se-biofortification increased plant resistance to water shortage conditions. Moreover, the increase of SeMeSeCys and SeCys2 forms in roots and aerial parts of Se-treated plants suggest resistance strategies to Se similar to those existing in Se-hyperaccumulator species. In addition, epigenetic changes in DNA methylation due to water stress and Se treatment were also investigated using methylation sensitive amplified polymorphism (MSAP). Results suggest that Se may be an activator of particular classes of genes that are involved in tolerance to abiotic stresses. In particular, PSY (phytoene synthase) gene, essential for maintaining leaf carotenoid contents, SDH (sorbitol dehydrogenase), whose activity regulates the level of important osmolytes during drought stress and ADH (alcohol dehydrogenase), whose activity plays a central role in biochemical adaptation to environmental stress. In conclusion, Se-biofortification could help maize plants to cope with drought stress conditions, by inducing a higher drought tolerance

siRNA screen identifies QPCT as a druggable target for Huntington's disease.

Huntington's disease (HD) is a currently incurable neurodegenerative condition caused by an abnormally expanded polyglutamine tract in huntingtin (HTT). We identified new modifiers of mutant HTT toxicity by performing a large-scale 'druggable genome' siRNA screen in human cultured cells, followed by hit validation in Drosophila. We focused on glutaminyl cyclase (QPCT), which had one of the strongest effects on mutant HTT-induced toxicity and aggregation in the cell-based siRNA screen and also rescued these phenotypes in Drosophila. We found that QPCT inhibition induced the levels of the molecular chaperone αB-crystallin and reduced the aggregation of diverse proteins. We generated new QPCT inhibitors using in silico methods followed by in vitro screening, which rescued the HD-related phenotypes in cell, Drosophila and zebrafish HD models. Our data reveal a new HD druggable target affecting mutant HTT aggregation and provide proof of principle for a discovery pipeline from druggable genome screen to drug development

Quantum numbers of the X(3872)X(3872) state and orbital angular momentum in its ρ0Jψ\rho^0 J\psi decay

Angular correlations in $B^+\to X(3872) K^+$ decays, with $X(3872)\to \rho^0 J/\psi$, $\rho^0\to\pi^+\pi^-$ and $J/\psi \to\mu^+\mu^-$, are used to measure orbital angular momentum contributions and to determine the $J^{PC}$ value of the $X(3872)$ meson. The data correspond to an integrated luminosity of 3.0 fb$^{-1}$ of proton-proton collisions collected with the LHCb detector. This determination, for the first time performed without assuming a value for the orbital angular momentum, confirms the quantum numbers to be $J^{PC}=1^{++}$. The $X(3872)$ is found to decay predominantly through S wave and an upper limit of $4\%$ at $95\%$ C.L. is set on the fraction of D wave.Comment: 16 pages, 4 figure

Analysis of 339 pregnancies in 181 women with 13 different forms of inherited thrombocytopenia

65Pregnancy in women with inherited thrombocytopenias is a major matter of concern as both the mothers and the newborns are potentially at risk of bleeding. However, medical management of this condition cannot be based on evidence because of the lack of consistent information in the literature. To advance knowledge on this matter, we performed a multicentric, retrospective study evaluating 339 pregnancies in 181 women with 13 different forms of inherited thrombocytopenia. Neither the degree of thrombocytopenia nor the severity of bleeding tendency worsened during pregnancy and the course of pregnancy did not differ from that of healthy subjects in terms of miscarriages, fetal bleeding and pre-term births. The degree of thrombocytopenia in the babies was similar to that in the mother. Only 7 of 156 affected newborns had delivery-related bleeding, but 2 of them died of cerebral hemorrhage. The frequency of delivery-related maternal bleeding ranged from 6.8% to 14.2% depending on the definition of abnormal blood loss, suggesting that the risk of abnormal blood loss was increased with respect to the general population. However, no mother died or had to undergo hysterectomy to arrest bleeding. The search for parameters predicting delivery-related bleeding in the mother suggested that hemorrhages requiring blood transfusion were more frequent in women with history of severe bleedings before pregnancy and with platelet count at delivery below 50 × 10(9)/L.openopenPatrizia Noris; Nicole Schlegel; Catherine Klersy; Paula G. Heller; Elisa Civaschi; Nuria Pujol-Moix; Fabrizio Fabris; Remi Favier; Paolo Gresele; Véronique Latger-Cannard; Adam Cuker; Paquita Nurden; Andreas Greinacher; Marco Cattaneo; Erica De Candia; Alessandro Pecci; Marie-Françoise Hurtaud-Roux; Ana C. Glembotsky; Eduardo Muñiz-Diaz; Maria Luigia Randi; Nathalie Trillot; Loredana Bury; Thomas Lecompte; Caterina Marconi; Anna Savoia; Carlo L. Balduini; Sophie Bayart; Anne Bauters; Schéhérazade Benabdallah-Guedira; Françoise Boehlen; Jeanne-Yvonne Borg; Roberta Bottega; James Bussel; Daniela De Rocco; Emmanuel de Maistre; Michela Faleschini; Emanuela Falcinelli; Silvia Ferrari; Alina Ferster; Tiziana Fierro; Dominique Fleury; Pierre Fontana; Chloé James; Francois Lanza; Véronique Le Cam Duchez; Giuseppe Loffredo; Pamela Magini; Dominique Martin-Coignard; Fanny Menard; Sandra Mercier; Annamaria Mezzasoma; Pietro Minuz; Ilaria Nichele; Lucia D. Notarangelo; Tommaso Pippucci; Gian Marco Podda; Catherine Pouymayou; Agnes Rigouzzo; Bruno Royer; Pierre Sie; Virginie Siguret; Catherine Trichet; Alessandra Tucci; Béatrice Saposnik; Dino VeneriPatrizia, Noris; Nicole, Schlegel; Catherine, Klersy; Paula G., Heller; Elisa, Civaschi; Nuria Pujol, Moix; Fabrizio, Fabris; Remi, Favier; Paolo, Gresele; Véronique Latger, Cannard; Adam, Cuker; Paquita, Nurden; Andreas, Greinacher; Marco, Cattaneo; Erica De, Candia; Alessandro, Pecci; Marie Françoise Hurtaud, Roux; Ana C., Glembotsky; Eduardo Muñiz, Diaz; Maria Luigia, Randi; Nathalie, Trillot; Loredana, Bury; Thomas, Lecompte; Caterina, Marconi; Savoia, Anna; Carlo L., Balduini; Sophie, Bayart; Anne, Bauters; Schéhérazade Benabdallah, Guedira; Françoise, Boehlen; Jeanne Yvonne, Borg; Bottega, Roberta; James, Bussel; DE ROCCO, Daniela; Emmanuel de, Maistre; Faleschini, Michela; Emanuela, Falcinelli; Silvia, Ferrari; Alina, Ferster; Tiziana, Fierro; Dominique, Fleury; Pierre, Fontana; Chloé, James; Francois, Lanza; Véronique Le Cam, Duchez; Giuseppe, Loffredo; Pamela, Magini; Dominique Martin, Coignard; Fanny, Menard; Sandra, Mercier; Annamaria, Mezzasoma; Pietro, Minuz; Ilaria, Nichele; Lucia D., Notarangelo; Tommaso, Pippucci; Gian Marco, Podda; Catherine, Pouymayou; Agnes, Rigouzzo; Bruno, Royer; Pierre, Sie; Virginie, Siguret; Catherine, Trichet; Alessandra, Tucci; Béatrice, Saposnik; Dino, Vener

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe

Measurement of the top quark mass using charged particles in pp collisions at root s=8 TeV

Peer reviewe