Localization and transport in a strongly driven Anderson insulator

We study localization and charge dynamics in a monochromatically driven one-dimensional Anderson insulator focussing on the low-frequency, strong-driving regime. We study this problem using a mapping of the Floquet Hamiltonian to a hopping problem with correlated disorder in one higher harmonic-space dimension. We show that (i) resonances in this model correspond to \emph{adiabatic} Landau-Zener (LZ) transitions that occur due to level crossings between lattice sites over the course of dynamics; (ii) the proliferation of these resonances leads to dynamics that \emph{appear} diffusive over a single drive cycle, but the system always remains localized; (iii) actual charge transport occurs over many drive cycles due to slow dephasing between these LZ orbits and is logarithmic-in-time, with a crucial role being played by far-off Mott-like resonances; and (iv) applying a spatially-varying random phase to the drive tends to decrease localization, suggestive of weak-localization physics. We derive the conditions for the strong driving regime, determining the parametric dependencies of the size of Floquet eigenstates, and time-scales associated with the dynamics, and corroborate the findings using both numerical scaling collapses and analytical arguments.Comment: 7 pages + references, 6 figure

Improving Unsupervised Visual Program Inference with Code Rewriting Families

Programs offer compactness and structure that makes them an attractive representation for visual data. We explore how code rewriting can be used to improve systems for inferring programs from visual data. We first propose Sparse Intermittent Rewrite Injection (SIRI), a framework for unsupervised bootstrapped learning. SIRI sparsely applies code rewrite operations over a dataset of training programs, injecting the improved programs back into the training set. We design a family of rewriters for visual programming domains: parameter optimization, code pruning, and code grafting. For three shape programming languages in 2D and 3D, we show that using SIRI with our family of rewriters improves performance: better reconstructions and faster convergence rates, compared with bootstrapped learning methods that do not use rewriters or use them naively. Finally, we demonstrate that our family of rewriters can be effectively used at test time to improve the output of SIRI predictions. For 2D and 3D CSG, we outperform or match the reconstruction performance of recent domain-specific neural architectures, while producing more parsimonious programs that use significantly fewer primitives.Comment: Accepted at ICCV 23 (oral). Website: https://bardofcodes.github.io/coref

Breeding French Bean (Phaseolus vulgaris L.) for Resistance to Rust (Uromyces phaseoli Reben Wint.)

French bean is an important legume vegetable grown for its tender, green pods for both fresh consumption and processing. Rust, caused by Uromyces phaseoli, limits successful cultivation of this crop. Popular varieties like Contender, Pant Anupama, Pusa Parvathi, Arka Komal, Arka Suvidha, etc., are susceptible to this disease. The french bean variety, Arka Bold, having resistance to rust was used in hybridization with Arka Komal, a popular bush variety with high yield and slender, long green pods but susceptible to rust. Inheritance studies indicated that resistance to rust was controlled by a single, dominant gene. Pedigree method of breeding was followed for incorporating rust resistance in to commercially cultivated varieties. Breeding lines with resistance to rust were selected to F2 generation onwards. These were advanced up to F7, wherein, a promising line, (Arka Bold x Arka Komal) 99-17-2-1-4-12-3, with resistance to rust with high pod yield and good pod quality was selected and named Arka Anoop and released for commercial cultivation

Adaptive Extended Kalman Filter for Orbit Estimation of GEO Satellites

The aim of this paper is to develop Adaptive Extended Kalman Filter (AEKF) algorithm for the precise orbit estimation of GEO satellites (viz., GSAT-10 – Geostationary satellite and IRNSS-1A – Geosynchronous satellite) using two-way CDMA range measurements data from different ranging stations located in India. It brings forward the effectiveness of AEKF algorithm over Extended Kalman Filter (EKF) algorithm. EKF algorithm is adapted by updating process noise covariance (Q), measure of uncertainty in state dynamics during the time interval between measurement updates and measurement noise covariance (R), function of measurement update based on measurement residual. This paper addresses the modeling of all errors in measurement domain and the computation of measurement residual using observed and modeled measurement ranges for all stations. The filter incorporates non-linear model for measurement update, non-linear dynamic model for time update and estimation is carried out at every second. This paper also elaborates the development of indigenous full force propagation model considering all the perturbations during orbit prediction period for GEO Satellites. Adaptation of EKF algorithm in precise orbit estimation is done primarily for making the algorithm more robust by countering the uncertainties in process and measurement noises, resolving the problem of manual tuning of the filter and also by keeping the error covariance (P) consistent with real performance. Adaptation of Q is implemented based on the error in system states with respect to estimated states while Adaptation of R is implemented based on the error in observed measurements with respect to measurements obtained from estimated state vectors (aposteriori measurement expectation). Analysis of the estimated results using the above proposed method is carried out by comparison of Station-wise range residues for both the methods (AEKF and EKF). Consistency of obtained orbit for GEO Satellites are validated using overlapping technique for both AEKF and EKF methods, orbit estimated from these methods are also validated by comparing with batch least squares method and filter behavior is continuously monitored during data gaps by observing error covariance(P) for both the methods. Keywords: Kalman Filtering, Process Noise Covariance, Measurement Noise Covariance, Orbit Estimation, CDM