arXiv subjects

Amit Kumar

Publications and source records attributed to Amit Kumar.

At least 235 records · Page 13Linked to original sources

arXiv2018

Onboard Automated CME Detection Algorithm for Visible Emission Line Coronagraph on ADITYA-L1

ADITYA-L1 is India's first space mission to study the Sun from Lagrangian 1 position. { \textit{Visible Emission Line Coronagraph}} (VELC) is one of the seven payloads in ADITYA-L1 mission scheduled to be launched around 2020. One of the primary objectives of the VELC is to study the dynamics of coronal mass ejections (CMEs) in the inner corona. This will be accomplished by taking high resolution ($\approx$ 2.51 arcsec pixel$^{-1}$) images of corona from 1.05 R$_{\odot}$ -- 3 R$_{\odot}$ at high cadence of 1 s in 10 Å passband centered at 5000 Å. Due to limited telemetry at Lagrangian 1 position we plan to implement an onboard automated CME detection algorithm. The detection algorithm is based on the intensity thresholding followed by the area thresholding in successive difference images spatially re-binned to improve signal to noise ratio. We present the results of the application of this algorithm on the data from existing space- and ground-based coronagraph. Since, no existing space-based coronagraph has FOV similar to VELC, we have created synthetic coronal images for VELC FOV after including photon noise and injected different type of CMEs. The performance of CME detection algorithm is tested on these images. We found that for VELC images, the telemetry can be reduced by a factor of 85\% or more keeping CME detection rate of 70\% or above at the same time. Finally, we discuss the advantages and disadvantages of this algorithm. The application of such onboard algorithm in future will enable us to take higher resolution images with improved cadence from space and also reduce the load on limited telemetry at the same time. This will help in better understanding of CMEs by studying their characteristics with improved spatial and temporal resolutions.

astro-ph.SR↗

arXiv2018

Non-Preemptive Flow-Time Minimization via Rejections

We consider the online problem of minimizing weighted flow-time on unrelated machines. Although much is known about this problem in the resource-augmentation setting, these results assume that jobs can be preempted. We give the first constant-competitive algorithm for the non-preemptive setting in the rejection model. In this rejection model, we are allowed to reject an $\varepsilon$-fraction of the total weight of jobs, and compare the resulting flow-time to that of the offline optimum which is required to schedule all jobs. This is arguably the weakest assumption in which such a result is known for weighted flow-time on unrelated machines. While our algorithms are simple, we need a delicate dual-fitting argument to bound the flow-time while only a small fraction of elements are rejected.

cs.DS↗

arXiv2018

Fully-Dynamic Bin Packing with Limited Repacking

We study the classic Bin Packing problem in a fully-dynamic setting, where new items can arrive and old items may depart. We want algorithms with low asymptotic competitive ratio \emph{while repacking items sparingly} between updates. Formally, each item $i$ has a \emph{movement cost} $c_i\geq 0$, and we want to use $α\cdot OPT$ bins and incur a movement cost $γ\cdot c_i$, either in the worst case, or in an amortized sense, for $α, γ$ as small as possible. We call $γ$ the \emph{recourse} of the algorithm. This is motivated by cloud storage applications, where fully-dynamic Bin Packing models the problem of data backup to minimize the number of disks used, as well as communication incurred in moving file backups between disks. Since the set of files changes over time, we could recompute a solution periodically from scratch, but this would give a high number of disk rewrites, incurring a high energy cost and possible wear and tear of the disks. In this work, we present optimal tradeoffs between number of bins used and number of items repacked, as well as natural extensions of the latter measure.

cs.DS↗

arXiv2018

Disentangling 3D Pose in A Dendritic CNN for Unconstrained 2D Face Alignment

Heatmap regression has been used for landmark localization for quite a while now. Most of the methods use a very deep stack of bottleneck modules for heatmap classification stage, followed by heatmap regression to extract the keypoints. In this paper, we present a single dendritic CNN, termed as Pose Conditioned Dendritic Convolution Neural Network (PCD-CNN), where a classification network is followed by a second and modular classification network, trained in an end to end fashion to obtain accurate landmark points. Following a Bayesian formulation, we disentangle the 3D pose of a face image explicitly by conditioning the landmark estimation on pose, making it different from multi-tasking approaches. Extensive experimentation shows that conditioning on pose reduces the localization error by making it agnostic to face pose. The proposed model can be extended to yield variable number of landmark points and hence broadening its applicability to other datasets. Instead of increasing depth or width of the network, we train the CNN efficiently with Mask-Softmax Loss and hard sample mining to achieve upto $15\%$ reduction in error compared to state-of-the-art methods for extreme and medium pose face images from challenging datasets including AFLW, AFW, COFW and IBUG.

cs.CV↗

arXiv2017

Approximate Clustering with Same-Cluster Queries

Ashtiani et al. proposed a Semi-Supervised Active Clustering framework (SSAC), where the learner is allowed to make adaptive queries to a domain expert. The queries are of the kind "do two given points belong to the same optimal cluster?" There are many clustering contexts where such same-cluster queries are feasible. Ashtiani et al. exhibited the power of such queries by showing that any instance of the $k$-means clustering problem, with additional margin assumption, can be solved efficiently if one is allowed $O(k^2 \log{k} + k \log{n})$ same-cluster queries. This is interesting since the $k$-means problem, even with the margin assumption, is $\mathsf{NP}$-hard. In this paper, we extend the work of Ashtiani et al. to the approximation setting showing that a few of such same-cluster queries enables one to get a polynomial-time $(1 + \varepsilon)$-approximation algorithm for the $k$-means problem without any margin assumption on the input dataset. Again, this is interesting since the $k$-means problem is $\mathsf{NP}$-hard to approximate within a factor $(1 + c)$ for a fixed constant $0 < c < 1$. The number of same-cluster queries used is $\textrm{poly}(k/\varepsilon)$ which is independent of the size $n$ of the dataset. Our algorithm is based on the $D^2$-sampling technique. We also give a conditional lower bound on the number of same-cluster queries showing that if the Exponential Time Hypothesis (ETH) holds, then any such efficient query algorithm needs to make $Ω\left(\frac{k}{poly \log k} \right)$ same-cluster queries. Our algorithm can be extended for the case when the oracle is faulty. Another result we show with respect to the $k$-means++ seeding algorithm is that a small modification to the $k$-means++ seeding algorithm within the SSAC framework converts it to a constant factor approximation algorithm instead of the well known $O(\log{k})$-approximation algorithm.

cs.DS↗

arXiv2017

Magnetic field driven enhanced ferroelectric switching in self-grown ferroelectric-ferromagnetic composite in the BiFeO3-BaTiO3 multiferroic alloy system

Over the years attempts have been made to compensate for the inherent weaknesses in the bulk state of the multiferroic BiFeO3, such as high leakage current and the absence of ferromagnetic correlation, and exploit its magnetoelectric potential by forming solid solutions with other perovskites. Studies in the recent few years have shown that alloying of BiFeO3 with BaTiO3, both with and without additives, can induce both ferroelectric and ferromagnetic switching. While the coexistence of both the ferroic orders is encouraging from the view point of technological applications, the origin of ferromagnetism in this system remains elusive. Here, we synthesized such compositions and carried out a detailed structural analysis employing magnetic separation of the powder particles. We found that the origin of ferromagnetism lies in the spontaneous precipitation of a minor ( ~ 1 wt %) barium hexaferrite (BaFe9O19) phase, often undetected in routine x-ray diffraction studies of powders sampled from the entire specimen. We also demonstrate that inspite of the insignificant fraction the ferrimagnetic phase, this self-grown composite exhibit noticeably enhanced ferroelectric switching in the presence of external magnetic field. We obtained a dc magnetoelectric coupling of ~ 9 x 10-8 s/m, a value which is comparable to what has been reported for layered ferroelectric/ferromagnetic laminates and bilayer thin film ferroelectric-ferromagnetic hetrostructures. Our study suggests that reasonably large magnetoelectric coupling is realizable in simple 0-3 ferroelectric-ferromagnetic bulk composites provided synthesis strategies are developed which induces spontaneous precipitation of the ferromagnetic phase in small volume fraction to ensure good insulating behaviour of the composite thus developed.

cond-mat.mtrl-sci↗

arXiv2017

Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks

Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems. This has been made possible due to the availability of large annotated datasets, a better understanding of the non-linear mapping between input images and class labels as well as the affordability of GPUs. In this paper, we present the design details of a deep learning system for unconstrained face recognition, including modules for face detection, association, alignment and face verification. The quantitative performance evaluation is conducted using the IARPA Janus Benchmark A (IJB-A), the JANUS Challenge Set 2 (JANUS CS2), and the LFW dataset. The IJB-A dataset includes real-world unconstrained faces of 500 subjects with significant pose and illumination variations which are much harder than the Labeled Faces in the Wild (LFW) and Youtube Face (YTF) datasets. JANUS CS2 is the extended version of IJB-A which contains not only all the images/frames of IJB-A but also includes the original videos for evaluating the video-based face verification system. Some open issues regarding DCNNs for face verification problems are then discussed.

cs.CV↗

arXiv2017

A Local-Search Algorithm for Steiner Forest

In the Steiner Forest problem, we are given a graph and a collection of source-sink pairs, and the goal is to find a subgraph of minimum total length such that all pairs are connected. The problem is APX-Hard and can be 2-approximated by, e.g., the elegant primal-dual algorithm of Agrawal, Klein, and Ravi from 1995. We give a local-search-based constant-factor approximation for the problem. Local search brings in new techniques to an area that has for long not seen any improvements and might be a step towards a combinatorial algorithm for the more general survivable network design problem. Moreover, local search was an essential tool to tackle the dynamic MST/Steiner Tree problem, whereas dynamic Steiner Forest is still wide open. It is easy to see that any constant factor local search algorithm requires steps that add/drop many edges together. We propose natural local moves which, at each step, either (a) add a shortest path in the current graph and then drop a bunch of inessential edges, or (b) add a set of edges to the current solution. This second type of moves is motivated by the potential function we use to measure progress, combining the cost of the solution with a penalty for each connected component. Our carefully-chosen local moves and potential function work in tandem to eliminate bad local minima that arise when using more traditional local moves.

cs.DS↗

arXiv2017

Solving the $\hat{q}$ puzzle with $x$ and scale dependence

We present here a possible resolution of the jet quenching parameter $\hat{q}$ puzzle by exploring the momentum fraction $x$ and scale $Q^2$ dependence of $\hat{q}$, in addition to temperature dependence. We explore the momentum broadening of the jet due to Glauber gluon exchange with the quark-gluon plasma (QGP). This has led us to define the momentum fraction $x$ for the partons in the QGP. We also show here, for the first time, possible forms of the parton distribution function (PDF) of the QGP. The QGP-PDF as input in $\hat{q}$ has been used to fit the PHENIX and CMS data simultaneously. We observe that the scale evolution of the QGP-PDF and the energy of the jet are the missing ingredients responsible for the enhancement of $\hat{q}$ at the same temperature in RHIC collisions compared to LHC collisions.

nucl-th↗

arXiv2017

Multistage Monte-Carlo simulation of jet modification in a static medium

The modification of hard jets in an extended static medium held at a fixed temperature is studied using three different Monte-Carlo event generators (LBT, MATTER, MARTINI). Each event generator contains a different set of assumptions regarding the energy and virtuality of the partons within a jet versus the energy scale of the medium, and hence, applies to a different epoch in the space-time history of the jet evolution. For the first time, modeling is developed where a jet may sequentially transition from one generator to the next, on a parton-by-parton level, providing a detailed simulation of the space-time evolution of medium modified jets over a much broader dynamic range than has been attempted previously in a single calculation. Comparisons are carried out for different observables sensitive to jet quenching, including the parton fragmentation function and the azimuthal distribution of jet energy around the jet axis. The effect of varying the boundary between different generators is studied and a theoretically motivated criterion for the location of this boundary is proposed. The importance of such an approach with coupled generators to the modeling of jet quenching is discussed.

nucl-th↗

arXiv2017

Intelligent Personal Assistant with Knowledge Navigation

An Intelligent Personal Agent (IPA) is an agent that has the purpose of helping the user to gain information through reliable resources with the help of knowledge navigation techniques and saving time to search the best content. The agent is also responsible for responding to the chat-based queries with the help of Conversation Corpus. We will be testing different methods for optimal query generation. To felicitate the ease of usage of the application, the agent will be able to accept the input through Text (Keyboard), Voice (Speech Recognition) and Server (Facebook) and output responses using the same method. Existing chat bots reply by making changes in the input, but we will give responses based on multiple SRT files. The model will learn using the human dialogs dataset and will be able respond human-like. Responses to queries about famous things (places, people, and words) can be provided using web scraping which will enable the bot to have knowledge navigation features. The agent will even learn from its past experiences supporting semi-supervised learning.

cs.AI↗

arXiv2017

A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection

Recently, Deep Convolution Networks (DCNNs) have been applied to the task of face alignment and have shown potential for learning improved feature representations. Although deeper layers can capture abstract concepts like pose, it is difficult to capture the geometric relationships among the keypoints in DCNNs. In this paper, we propose a novel convolution-deconvolution network for facial keypoint detection. Our model predicts the 2D locations of the keypoints and their individual visibility along with 3D head pose, while exploiting the spatial relationships among different keypoints. Different from existing approaches of modeling these relationships, we propose learnable transform functions which captures the relationships between keypoints at feature level. However, due to extensive variations in pose, not all of these relationships act at once, and hence we propose, a pose-based routing function which implicitly models the active relationships. Both transform functions and the routing function are implemented through convolutions in a multi-task framework. Our approach presents a single-shot keypoint detection method, making it different from many existing cascade regression-based methods. We also show that learning these relationships significantly improve the accuracy of keypoint detections for in-the-wild face images from challenging datasets such as AFW and AFLW.

cs.CV↗

arXiv2017

KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors

Keypoint detection is one of the most important pre-processing steps in tasks such as face modeling, recognition and verification. In this paper, we present an iterative method for Keypoint Estimation and Pose prediction of unconstrained faces by Learning Efficient H-CNN Regressors (KEPLER) for addressing the face alignment problem. Recent state of the art methods have shown improvements in face keypoint detection by employing Convolution Neural Networks (CNNs). Although a simple feed forward neural network can learn the mapping between input and output spaces, it cannot learn the inherent structural dependencies. We present a novel architecture called H-CNN (Heatmap-CNN) which captures structured global and local features and thus favors accurate keypoint detecion. HCNN is jointly trained on the visibility, fiducials and 3D-pose of the face. As the iterations proceed, the error decreases making the gradients small and thus requiring efficient training of DCNNs to mitigate this. KEPLER performs global corrections in pose and fiducials for the first four iterations followed by local corrections in the subsequent stage. As a by-product, KEPLER also provides 3D pose (pitch, yaw and roll) of the face accurately. In this paper, we show that without using any 3D information, KEPLER outperforms state of the art methods for alignment on challenging datasets such as AFW and AFLW.

cs.CV↗

arXiv2017

The $x$ and $Q^2$ dependence of $\hat{q}$, quasi-particles and the JET puzzle

We present the first attempt to extract the "$x$" dependence of the parton distribution function (PDF) of the quark gluon plasma (QGP). In the absence of knowledge regarding the mass of a QGP constituent, we define the new variable $x_N$, the nucleonic momentum fraction, which represents the ratio of the momentum of the parton to that of a self-contained section of the plasma that has the mass of a nucleon. Calculations are compared to data for single hadron suppression in terms of the nuclear modification factor $R_{AA}$ and the azimuthal anisotropy parameter $v_{2}$, as a function of transverse momentum $p_{T}$, centrality and energy of the collision. It is demonstrated that the scale evolution of the QGP-PDF is responsible for the reduction in normalization of the jet transport coefficient $\hat{q}$, between fits to Relativistic Heavy-Ion Collider (RHIC) and Large Hadron Collider (LHC) data; a puzzle, first discovered by the JET collaboration. Best fits to data are suggestive of the presence of quasi-particles with wide dispersion relations in the QGP.

nucl-th↗

arXiv2016

Mapping Distortion of Detectors in UVIT Onboard AstroSat Observatory

Ultraviolet Imaging Telescope (UVIT) is one of the payloads onboard AstroSat, India's first multi-wavelength Astronomy mission. UVIT is primarily designed to make high resolution images in wide field, in three wavelength channels simultaneously: FUV (130 - 180 nm), NUV (200 - 300 nm) and VIS (320 - 550 nm). The intensified imagers used in UVIT suffer from distortions, and a correction is necessary for these to achieve good astrometry. In this article we describe the methodology and calculations used to estimate the distortions in ground calibrations.

astro-ph.IM↗

arXiv2016

The Heterogeneous Capacitated $k$-Center Problem

In this paper we initiate the study of the heterogeneous capacitated $k$-center problem: given a metric space $X = (F \cup C, d)$, and a collection of capacities. The goal is to open each capacity at a unique facility location in $F$, and also to assign clients to facilities so that the number of clients assigned to any facility is at most the capacity installed; the objective is then to minimize the maximum distance between a client and its assigned facility. If all the capacities $c_i$'s are identical, the problem becomes the well-studied uniform capacitated $k$-center problem for which constant-factor approximations are known. The additional choice of determining which capacity should be installed in which location makes our problem considerably different from this problem, as well the non-uniform generalizations studied thus far in literature. In fact, one of our contributions is in relating the heterogeneous problem to special-cases of the classical Santa Claus problem. Using this connection, and by designing new algorithms for these special cases, we get the following results: (a)A quasi-polynomial time $O(\log n/ε)$-approximation where every capacity is violated by $1+\varepsilon$, (b) A polynomial time $O(1)$-approximation where every capacity is violated by an $O(\log n)$ factor. We get improved results for the {\em soft-capacities} version where we can place multiple facilities in the same location.

cs.DS↗

arXiv2016

Online and Dynamic Algorithms for Set Cover

In this paper, we study the set cover problem in the fully dynamic model. In this model, the set of active elements, i.e., those that must be covered at any given time, can change due to element arrivals and departures. The goal is to maintain an algorithmic solution that is competitive with respect to the current optimal solution. This model is popular in both the dynamic algorithms and online algorithms communities. The difference is in the restriction placed on the algorithm: in dynamic algorithms, the running time of the algorithm making updates (called update time) is bounded, while in online algorithms, the number of updates made to the solution (called recourse) is limited. In this paper we show the following results: In the update time setting, we obtain O(log n)-competitiveness with O(f log n) amortized update time, and O(f^3)-competitiveness with O(f^2) update time. The O(log n)-competitive algorithm is the first one to achieve a competitive ratio independent of f in this setting. In the recourse setting, we show a competitive ratio of O(min{log n,f}) with constant amortized recourse. Note that this matches the best offline bounds with just constant recourse, something that is impossible in the classical online model. Our results are based on two algorithmic frameworks in the fully-dynamic model that are inspired by the classic greedy and primal-dual algorithms for offline set cover. We show that both frameworks can be used for obtaining both recourse and update time bounds, thereby demonstrating algorithmic techniques common to these strands of research.

cs.DS↗

arXiv2016

Zigzag antiferromagnetic ground state with anisotropic correlation lengths in the quasi-2D honeycomb lattice compound Na2Co2TeO6

The crystal structure, magnetic ground state, and the temperature dependent microscopic spin-spin correlations of the frustrated honeycomb lattice antiferromagnet Na2Co2TeO6 have been investigated by powder neutron diffraction. A three dimensional (3D) long-range antiferromagnetic ordering has been found below TN ~ 24.8 K. The magnetic ground state, determined to be zigzag antiferromagnet [k = (1/2 0 0)], occurs due to the competing exchange interactions up to third nearest neighbors within the honeycomb lattice. The exceptional existence of a limited magnetic correlation length along the c axis (perpendicular to the honeycomb layers in the ab planes) has been found within the 3D long-range ordered state, even at 1.8 K, well below TN. The observed limited correlation along the c axis is explained by the disorder distribution of the Na ions within the intermediate layers. The reduced ordered moments mCo(1) = 2.77(3) muB/Co2+ and mCo(2) = 2.45(2) muB/Co2+ at 1.8 K reflect the persistence of spin fluctuations in the ordered state. Above TN, the presence of short-range magnetic correlations, manifested by broad diffuse magnetic peaks, has been found. Reverse Monte Carlo analysis of the experimental diffuse magnetic scattering data reveals that the spin correlations are mainly confined within the 2D honeycomb layers (ab plane) with a correlation length of ~ 12 Å at 25 K. The nature of spin arrangements is found to be similar in both the short-range and long-range ordered magnetic states. This implies that the short-range correlation grows with decreasing temperature and leads to the zigzag AFM ordering at T < TN. The present study provides a comprehensive picture of the magnetic correlations over the temperature range above and below TN and their relation to the crystal structure. The role of intermediate soft Na-layers on the magnetic coupling between honeycomb planes is discussed.

cond-mat.str-el↗