arXiv subjects

Rahul Singh

Publications and source records attributed to Rahul Singh.

At least 145 records · Page 8Linked to original sources

arXiv2020

Mather classes and conormal spaces of Schubert varieties in cominuscule spaces

Let $G/P$ be a complex cominuscule flag manifold. We prove a type independent formula for the torus equivariant Mather class of a Schubert variety in $G/P$, and for a Schubert variety pulled back via the natural projection $G/Q \to G/P$. We apply this to find formulae for the local Euler obstructions of Schubert varieties, and for the torus equivariant localizations of the conormal spaces of these Schubert varieties. We conjecture positivity properties for the local Euler obstructions and for the Schubert expansion of Mather classes. We check the conjectures in many cases, by utilizing results of Boe and Fu about the characteristic cycles of the intersection homology sheaves of Schubert varieties. We also conjecture that certain `Mather polynomials' are unimodal in general Lie type, and log concave in type A.

math.AG↗

arXiv2020

Improving Robustness via Risk Averse Distributional Reinforcement Learning

One major obstacle that precludes the success of reinforcement learning in real-world applications is the lack of robustness, either to model uncertainties or external disturbances, of the trained policies. Robustness is critical when the policies are trained in simulations instead of real world environment. In this work, we propose a risk-aware algorithm to learn robust policies in order to bridge the gap between simulation training and real-world implementation. Our algorithm is based on recently discovered distributional RL framework. We incorporate CVaR risk measure in sample based distributional policy gradients (SDPG) for learning risk-averse policies to achieve robustness against a range of system disturbances. We validate the robustness of risk-aware SDPG on multiple environments.

cs.LG↗

arXiv2020

Learning in Networked Control Systems

We design adaptive controller (learning rule) for a networked control system (NCS) in which data packets containing control information are transmitted across a lossy wireless channel. We propose Upper Confidence Bounds for Networked Control Systems (UCB-NCS), a learning rule that maintains confidence intervals for the estimates of plant parameters $(A_{(\star)},B_{(\star)})$, and channel reliability $p_{(\star)}$, and utilizes the principle of optimism in the face of uncertainty while making control decisions. We provide non-asymptotic performance guarantees for UCB-NCS by analyzing its "regret", i.e., performance gap from the scenario when $(A_{(\star)},B_{(\star)},p_{(\star)})$ are known to the controller. We show that with a high probability the regret can be upper-bounded as $\tilde{O}\left(C\sqrt{T}\right)$\footnote{Here $\tilde{O}$ hides logarithmic factors.}, where $T$ is the operating time horizon of the system, and $C$ is a problem dependent constant.

cs.LG↗

arXiv2020

Performance of closed orbit feedback systems with spatial model mismatch

Closed orbit feedback (COFB) systems used for the global orbit correction rely on the pseudo-inversion of the orbit response matrix (ORM). A mismatch between the model ORM used in the controller and the actual machine ORM can affect the performance of the feedback system. In this paper, the typical sources of such model mismatch such as acceleration ramp ORM variation, intensity-dependent tune shift and beta beating are considered in simulation studies. Their effect on the performance and the stability margins are investigated for both the slow and fast regimes of a COFB system operation. The spectral radius stability condition is utilized instead of the small gain theorem to arrive at the theoretical limits of COFB stability and comparisons with simulations for SIS18 of GSI and experiments at the Cooler synchrotron (COSY) in the Forschungzentrum Jülich (FZJ) are also presented.

physics.acc-ph↗

arXiv2020

Sample-based Distributional Policy Gradient

Distributional reinforcement learning (DRL) is a recent reinforcement learning framework whose success has been supported by various empirical studies. It relies on the key idea of replacing the expected return with the return distribution, which captures the intrinsic randomness of the long term rewards. Most of the existing literature on DRL focuses on problems with discrete action space and value based methods. In this work, motivated by applications in robotics with continuous action space control settings, we propose sample-based distributional policy gradient (SDPG) algorithm. It models the return distribution using samples via a reparameterization technique widely used in generative modeling and inference. We compare SDPG with the state-of-art policy gradient method in DRL, distributed distributional deterministic policy gradients (D4PG), which has demonstrated state-of-art performance. We apply SDPG and D4PG to multiple OpenAI Gym environments and observe that our algorithm shows better sample efficiency as well as higher reward for most tasks.

cs.LG↗

arXiv2019

Optimizing Information Freshness using Low-Power Status Updates via Sleep-Wake Scheduling

In this paper, we consider the problem of optimizing the freshness of status updates that are sent from a large number of low-power source nodes to a common access point. The source nodes utilize carrier sensing to reduce collisions and adopt an asychronized sleep-wake strategy to achieve an extended battery lifetime (e.g., 10-25 years). We use age of information (AoI) to measure the freshness of status updates, and design the sleep-wake parameters for minimizing the weighted-sum peak AoI of the sources, subject to per-source battery lifetime constraints. When the sensing time is zero, this sleep-wake design problem can be solved by resorting to nested convex optimization; however, for positive sensing times, the problem is non-convex. We devise a low-complexity solution to solve this problem and prove that, for practical sensing times, the solution is within a small gap from the optimum AoI performance. Our numerical and NS-3 simulation results show that our solution can indeed elongate the batteries lifetime of information sources, while providing a competitive AoI performance.

cs.IT↗

arXiv2019

Elasto-inertial Chains in a Two-dimensional Turbulent Flow

The interplay of inertia and elasticity is shown to have a significant impact on the transport of filamentary objects, modelled by bead-spring chains, in a two-dimensional turbulent flow. We show how elastic interactions amongst inertial beads result in a non-trivial sampling of the flow, ranging from entrapment within vortices to preferential sampling of straining regions. This behavior is quantified as a function of inertia and elasticity and is shown to be very different from free, non-interacting heavy particles, as well as inertialess chains [Picardo et al., Phys. Rev. Lett. 121, 244501 (2018)]. In addition, by considering two limiting cases, of a heavy-headed and a uniformly-inertial chain, we illustrate the critical role played by the mass distribution of such extended objects in their turbulent transport.

physics.flu-dyn↗

arXiv2019

Optimal Information Updating based on Value of Information

We address the problem of how to optimally schedule data packets over an unreliable channel in order to minimize the estimation error of a simple-to-implement remote linear estimator using a constant "Kalman'' gain to track the state of a Gauss Markov process. The remote estimator receives time-stamped data packets which contain noisy observations of the process. Additionally, they also contain the information about the "quality'' of the sensor\ source, i.e., the variance of the observation noise that was used to generate the packet. In order to minimize the estimation error, the scheduler needs to use both while prioritizing packet transmissions. It is shown that a simple index rule that calculates the value of information (VoI) of each packet, and then schedules the packet with the largest current value of VoI, is optimal. The VoI of a packet decreases with its age, and increases with the precision of the source. Thus, we conclude that, for constant filter gains, a policy which minimizes the age of information does not necessarily maximize the estimator performance.

cs.IT↗

arXiv2019

Smoothing of the slowly extracted coasting beam from a synchrotron

Slow extraction of beam from synchrotrons or storage rings as required by many fixed target experiments is performed by controlled excitation and feeding of a structural lattice resonance. Due to the sensitive nature of this resonant extraction process, the temporal structure of the extracted beam is modulated by the minuscule current fluctuations present on the quadrupole magnet power supplies. Such a modulation lead to pile-ups in detectors and a significant reduction in accumulated event statistics. This contribution proposes and experimentally demonstrates that by an introduction of further modulation on quadrupole currents with a specific amplitude and frequency, the inherent power supply fluctuations are mitigated leading to a smoothening of the beam temporal structure. The slow extraction beam dynamics associated with this method are explained along with the operational results.

physics.acc-ph↗

arXiv2019

Space-charge distortion of transverse profiles measured by electron-based Ionization Profile Monitors and correction methods

Measurements of transverse profiles using Ionization Profile Monitors (IPMs) for high brightness beams are affected by the electromagnetic field of the beam. This interaction may cause a distortion of the measured profile shape despite strong external magnetic field applied to impose limits on the transverse movement of electrons. The mechanisms leading to this distortion are discussed in detail. The distortion itself is described by means of analytic calculations for simplified beam distributions and a full simulation model for realistic distributions. Simple relation for minimum magnetic field scaling with beam parameters for avoiding profile distortions is presented. Further, application of machine learning algorithms to the problem of reconstructing the actual beam profile from distorted measured profile is presented. The obtained results show good agreement for tests on simulation data. The performance of these algorithms indicate that they could be very useful for operations of IPMs on high brightness beams or IPMs with weak magnetic field.

physics.acc-ph↗

arXiv2019

Optimal Routing for Delay-Sensitive Traffic in Overlay Networks

We design dynamic routing policies for an overlay network which meet delay requirements of real-time traffic being served on top of an underlying legacy network, where the overlay nodes do not know the underlay characteristics. We pose the problem as a constrained MDP, and show that when the underlay implements static policies such as FIFO with randomized routing, then a decentralized policy, that can be computed efficiently in a distributed fashion, is optimal. Our algorithm utilizes multi-timescale stochastic approximation techniques, and its convergence relies on the fact that the recursions asymptotically track a nonlinear differential equation, namely the replicator equation. Extensive simulations show that the proposed policy indeed outperforms the existing policies.

cs.NI↗

arXiv2019

Closed orbit correction at synchrotrons for symmetric and near-symmetric lattices

This contribution compiles the benefits of lattice symmetry in the context of closed orbit correction. A symmetric arrangement of BPMs and correctors results in structured orbit response matrices of Circulant or block Circulant type. These forms of matrices provide favorable properties in terms of computational complexity, information compression and interpretation of mathematical vector spaces of BPMs and correctors. For broken symmetries, a nearest-Circulant approximation is introduced and the practical advantages of symmetry exploitation are demonstrated with the help of simulations and experiments in the context of FAIR synchrotrons.

physics.acc-ph↗

arXiv2019

Optimal Decentralized Dynamic Policies for Video Streaming over Wireless Channels

The problem addressed is that of optimally controlling, in a decentralized fashion, the download of mobile video, which is expected to comprise 75 % of total mobile data traffic by 2020. The server can dynamically choose which packets to download to clients, from among several packets which encode their videos at different resolutions, as well as the power levels of their transmissions. This allows it to control packet delivery probabilities, and thereby, for example, avert imminent video outages at clients. It must however respect the access point's constraints on bandwidth and average transmission power. The goal is to maximize video "Quality of Experience" (QoE), which depends on several factors such as (i) outage duration when the video playback buffer is empty, (ii) number of outage periods, (iii) how many frames downloaded are of lower resolution, (iv) temporal variations in resolution, etc. It is shown that there exists an optimal decentralized solution where the AP announces the price of energy, and each client distributedly and dynamically maximizes its own QoE subject to the cost of energy. A distributed iterative algorithm to solve for optimal decentralized policy is also presented. Further, for the client-level QoE optimization, the optimal choice of video-resolution and power-level of packet transmissions has a simple monotonicity and threshold structure vis-a-vis video playback buffer level. When the number of orthogonal channels is less than the number of clients, there is an index policy for prioritizing packet transmissions. When the AP has to simply choose which clients' packets to transmit, the index policy is asymptotically optimal as the number of channels is scaled up with clients.

cs.NI↗

arXiv2019

Giant longitudinal negative magneto-resistance under perpendicular magnetic field in Bi$_{2-x}$Fe$_x$Se$_{3-x}$S$_x$ Topological insulators

The magnetic, magneto-transport and ARPES studies of Fe and S co-doped Bi2Se3 were investigated. With doping concentration magneto-resistance (MR) gradually decreases and for a certain doping concentration giant negative MR is observed which persists up to room temperature. Magnetic measurement indicates that the negative MR is observed when ferromagnetic ordering is induced with Fe doping. The magnetic ordering can be attributed with the RKKY interaction. Positive MR reappears with larger doping concentration which may be attributed to the decrease of FM ordering due to the turning off of the spin-orbit coupling leading to the destruction of non-trivial bulk state. This in-effect de-hybridizes the conduction band with the Fe spin. The ARPES data also indicates that above a critical doping concentration (x>0.09) the non-trivial bulk state is completely destroyed.

cond-mat.str-el↗

arXiv2019

Room temperature large spontaneous exchange bias in hard-soft antiferromagnetic composite BiFeO3-TbMnO3

We report the presence of giant spontaneous exchange bias (HSEB) in a hard and soft antiferromagnetic composite of BiFeO3-TbMnO3 (BFO-TMO in 7:3 and 8:2 ratio). The HSEB varies between 5-778Oe, but persists up to room temperature with a maximum near a spin reorientation transition temperature observed from magnetization vs. temperature measurement in Zero-field cooled (ZFC) and Field cooled (FC) modes. Isothermal remnant magnetization measurements at room temperature indicate the presence of an interfacial layer of a 2 dimensional dilute antiferromagnet in a field (2D DAFF). A stable value of the exchange bias has been observed via training effect measurements which signify the role of interfacial exchange coupling in the system. Based on the experimental results we explain the presence of the giant spontaneous exchange bias on the basis of a strong strain-mediated magnetoelectriccoupling induced exchange interaction and the creation of 2D DAFF layer at the interface. Theproperties of this layer are defined by canting and pinning of BFO spins at the interface with TMO due to Fe and Mn interaction. X-ray Magnetic Circular Dichroism (XMCD) confirms the presence of canted antiferromagnetic ordering of BiFeO3, charge transfer between Mn ions and different magnetically coupled layers which play vital role in getting the exchange bias.

cond-mat.str-el↗

arXiv2018

3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys

The need for advanced materials has led to the development of complex, multi-component alloys or solid-solution alloys. These materials have shown exceptional properties like strength, toughness, ductility, electrical and electronic properties. Current development of such material systems are hindered by expensive experiments and computationally demanding first-principles simulations. Atomistic simulations can provide reasonable insights on properties in such material systems. However, the issue of designing robust potentials still exists. In this paper, we explore a deep convolutional neural-network based approach to develop the atomistic potential for such complex alloys to investigate materials for insights into controlling properties. In the present work, we propose a voxel representation of the atomic configuration of a cell and design a 3D convolutional neural network to learn the interaction of the atoms. Our results highlight the performance of the 3D convolutional neural network and its efficacy in machine-learning the atomistic potential. We also explore the role of voxel resolution and provide insights into the two bounding box methodologies implemented for voxelization.

cond-mat.mtrl-sci↗

arXiv2018

Physics-aware Deep Generative Models for Creating Synthetic Microstructures

A key problem in computational material science deals with understanding the effect of material distribution (i.e., microstructure) on material performance. The challenge is to synthesize microstructures, given a finite number of microstructure images, and/or some physical invariances that the microstructure exhibits. Conventional approaches are based on stochastic optimization and are computationally intensive. We introduce three generative models for the fast synthesis of binary microstructure images. The first model is a WGAN model that uses a finite number of training images to synthesize new microstructures that weakly satisfy the physical invariances respected by the original data. The second model explicitly enforces known physical invariances by replacing the traditional discriminator in a GAN with an invariance checker. Our third model combines the first two models to reconstruct microstructures that respect both explicit physics invariances as well as implicit constraints learned from the image data. We illustrate these models by reconstructing two-phase microstructures that exhibit coarsening behavior. The trained models also exhibit interesting latent variable interpolation behavior, and the results indicate considerable promise for enforcing user-defined physics constraints during microstructure synthesis.

cond-mat.mtrl-sci↗

arXiv2018

Tuning Bandgap and Energy Stability of Organic-Inorganic Halide Perovskites through Surface Engineering

Organohalide perovskite with a variety of surface structures and morphologies have shown promising potential owing to the choice of the type of heterostructure dependent stability. We systematically investigate and discuss the impact of 2-dimensional molybdenum-disulphide (MoS2), molybdenum-diselenide (MoSe2), tungsten-disulphide (WS2), tungsten-diselenide (WSe2), boron- nitiride (BN) and graphene monolayers on band-gap and energy stability of organic-inorganic halide perovskites. We found that MAPbI3ML deposited on BN-ML shows room temperature stability (-25 meV~300K) with an optimal bandgap of ~1.6 eV. The calculated absorption coefficient also lies in the visible-light range with a maximum of 4.9 x 104 cm-1 achieved at 2.8 eV photon energy. On the basis of our calculations, we suggest that the encapsulation of an organic-inorganic halide perovskite monolayers by semiconducting monolayers potentially provides greater flexibility for tuning the energy stability and the bandgap.

physics.app-ph↗