Abstract

In this paper, we present an approach to integrate unlexicalised grammatical features into Malt dependency parser. Malt parser is a lexicalised parser, and like every lexicalised parser, it is prone to data sparseness. We aim to address this problem by providing features from an unlexicalised parser. Contrary to lexicalised parsers, unlexicalised parsers are known for their robustness. We build a simple unlexicalised grammatical parser with POS tag sequences as grammar rules. We use the features from the grammatical parser as additional features to Malt. We achieved improvements of about 0.17-0.30% (UAS) on both English and Hindi state-of-the-art Malt results. Keywords-Malt Parser; unlexicalised grammar; syntactic parsing;

Abstract

Word sketches are one-page automatic, corpus-based summaries of a word’s grammatical and collocational behaviour. These are widely used for studying a language and in lexicography. Sketch Engine is a leading corpus tool which takes as input a corpus and generates word sketches for the words of that language. It also generates a thesaurus and ‘sketch differences’, which specify similarities and differences between near-synonyms. In this paper, we present the functionalities of Sketch Engine for Hindi. We collected HindiWaC, a web crawled corpus for Hindi with 240 million words. We lemmatized, POS tagged the corpus and then loaded it into Sketch Engine.

Abstract

Light field rendering is a widely used technique to generate novel views of a scene from novel viewpoints. Interpolative methods for light field rendering require a dense description of the scene in the form of closely spaced images. In this work, we present a simple method for dense view interpolation over general static scenes, using commonly available mobile devices. We capture an approximate focal stack of the scene from adjacent camera locations and interpolate intermediate images by shifting each focal region according to appropriate disparities. We do not rely on focus distance control to capture focal stacks and describe an automatic method of estimating the focal textures and the blur and disparity parameters required for view interpolation.

Abstract

In this paper, we present an all-core implementation of Burrows Wheeler Compression algorithm that exploits all computing resources on a system. Our focus is to provide significant benefit to everyday users on common end-to-end applications by exploiting the parallelism of multiple CPU cores and many-core GPU on their machines. The all-core framework is suitable for problems that process large files or buffers in blocks. We consider a system to be made up of compute stations and use a work-queue to dynamically divide the tasks among them. Each compute station uses an implementation that optimally exploits its architecture. We develop a fast GPU BWC algorithm by extending the state-of-the-art GPU string sort to efficiently perform BWT step of BWC. Our hybrid BWC implementation achieves a 2.9× speedup over the best CPU implementation. Our all-core framework allows concurrent processing of blocks by both GPU and all available CPU cores. We achieve a 3.06× speedup by using all CPU cores and a 4.87× speedup using the GPU also in the all-core framework. Our approach will scale to the number and type of computing resources on a system.

Abstract

In this paper, we present a new multistage approach for SfM reconstruction of a single component. Our method begins with building a coarse 3D reconstruction using highscale features of given images. This step uses only a fraction of features and is fast. We enrich the model in stages by localizing remaining images to it and matching and triangulating remaining features. Unlike traditional incremental SfM, localization and triangulation steps in our approach are made efficient and embarrassingly parallel using geometry of the coarse model. The coarse model allows us to use 3D-2D correspondences based direct localization techniques to register remaining images. We further utilize the geometry of the coarse model to reduce the pair-wise image matching effort as well as to perform fast guided feature matching for majority of features. Our method produces similar quality models as compared to incremental SfM methods while being notably fast and parallel. Our algorithm can reconstruct a 1000 images dataset in 15 hours using a single core, in about 2 hours using 8 cores and in a few minutes by utilizing full parallelism of about 200 cores.

Abstract

We present a method to interactively simulate and visualise Generalised Newtonian Fluids (GNF) using GPUs. GNFs include regular constant viscosity fluids as well as other fluids such as blood, which display variable viscosity due to variable shear rate. We use a statistical approach called Lattice Boltzmann Method (LBM) for the simulation. LBM is easy to understand and implement and does not include discretisation of differential equations. We exploit the inherent parallelism of LBM coupled with its memory access pattern to create a fast GPU implementation that gives scientifically accurate and fast results such as interactive real time simulations for reasonable domain size. MultiGPU implementations provide the potential to scale to larger problem size

Abstract

Optimally controlled initiation of intramolecular H-transfer in malonaldehyde is accomplished by designing a sequence of ultrashort (∼80 fs) down-chirped pump-dump ultra violet (UV)-laser pulses through an optically bright electronic excited [S2 (ππ∗)] state as a mediator. The sequence of such laser pulses is theoretically synthesized within the framework of optimal control theory (OCT) and employing the well-known pump-dump scheme of Tannor and Rice [D.J. Tannor, S.A. Rice, J. Chem. Phys. 83, 5013 (1985)]. In the OCT, the control task is framed as the maximization of cost functional defined in terms of an objective function along with the constraints on the field intensity and system dynamics. The latter is monitored by solving the time-dependent Schr¨odinger equation. The initial guess, laser driven dynamics and the optimized pulse structure (i.e., the spectral content and temporal profile) followed by associated mechanism involved in fulfilling the control task are examined in detail and discussed. A comparative account of the dynamical outcomes within the Condon approximation for the transition dipole moment versus its more realistic value calculated ab initio is also presented.

Abstract

: Preference for a cis/trans peptide bond between residues of dipeptides formed by substituted β2,3 (I) and β3 (II) homoproline is investigated using density functional theory (DFT). Potential energy surfaces for monomer and linear dimers are explored at the B3LYP/6-31G(d,p) level of theory. Minimum energy conformations of the dipeptides are optimized using B3LYP, PBE1PBE, B97D, and M06-2X functionals at the 6-31G(d,p) level of basis set in both the gas phase and solvent phase. The relative free energy difference between the selected conformations is marginal. Results obtained using the functionals M06-2X and B97D on dimers of I and II, respectively, agree with experimental results. The lowest energy conformations predicted by B97D/6-31G(d,p) and M06-2X/6-31G(d,p) levels of theory show greater relative MP2 correlation energy. Dipeptides of I with hydrophilic substituents show preference for a trans peptide bond. Support for cis/trans isomerism in dimers of I with hydrophobic substituents comes from potential energy surfaces and free energy data. Although dipeptides of II with hydrophilic substituents show preference for cis peptide bond, the dipeptides with hydrophobic substituent prefer trans bond.

Abstract

The notion of relative attributes as introduced by Parikh and Grauman (ICCV, 2011) provides an appealing way of comparing two images based on their visual properties (or attributes) such as “smiling” for face images, “naturalness” for outdoor images, etc. For learning such attributes, a Ranking SVM based formulation was proposed that uses globally represented pairs of annotated images. In this paper, we extend this idea towards learning relative attributes using local parts that are shared across categories. First, instead of using a global representation, we introduce a part-based representation combining a pair of images that specifically compares corresponding parts. Then, with each part we associate a locally adaptive “significancecoefficient” that represents its discriminative ability with respect to a particular attribute. For each attribute, the significance-coefficients are learned simultaneously with a max-margin ranking model in an iterative manner. Compared to the baseline method, the new method is shown to achieve significant improvement in relative attribute prediction accuracy. Additionally, it is also shown to improve relative feedback based interactive image search.

Abstract

We propose an approach for segmenting the individual buildings in typical skyline images. Our approach is based on a Markov Random Field (MRF) formulation that exploits the fact that such images contain overlapping objects of similar shapes exhibiting a “tiered” structure. Our contributions are the following: (1) A dataset of 120 highresolution skyline images from twelve different cities with over 4,000 individually labeled buildings that allows us to quantitatively evaluate the performance of various segmentation methods, (2) An analysis of low-level features that are useful for segmentation of buildings, and (3) A shapeconstrained MRF formulation that enforces shape priors over the regions. For simple shapes such as rectangles, our formulation is significantly faster to optimize than a standard MRF approach, while also being more accurate. We experimentally evaluate various MRF formulations and demonstrate the effectiveness of our approach in segmenting skyline images