Abstract

The accuracy of information retrieval systems is often measured using average precision (AP). Given a set of positive (relevant) and negative (non-relevant) samples, the parameters of a retrieval system can be estimated using the AP-SVM framework, which minimizes a regularized convex upper bound on the empirical AP loss. However, the high computational complexity of loss-augmented inference, which is required for learning an AP-SVM, prohibits its use with large training datasets. To alleviate this deficiency, we propose three complementary approaches. The first approach guarantees an asymptotic decrease in the computational complexity of loss-augmented inference by exploiting the problem structure. The second approach takes advantage of the fact that we do not require a full ranking during loss-augmented inference. This helps us to avoid the expensive step of sorting the negative samples according to their individual scores. The third approach approximates the AP loss over all samples by the AP loss over difficult samples (for example, those that are incorrectly classified by a binary SVM), while ensuring the correct classification of the remaining samples. Using the PASCAL VOC action classification and object detection datasets, we show that our approaches provide significant speed-ups during training without degrading the test accuracy of AP-SVM.

Abstract

The performance of binary classification tasks, such as action classification and object detection, is often measured in terms of the average precision (AP). Yet it is common practice in computer vision to employ the support vector machine (SVM) classifier, which optimizes a surrogate 0-1 loss. The popularity of SVM can be attributed to its empirical performance. Specifically, in fully supervised settings, SVM tends to provide similar accuracy to the AP-SVM classifier, which directly optimizes an AP-based loss. However, we hypothesize that in the significantly more challenging and practically useful setting of weakly supervised learning, it becomes crucial to optimize the right accuracy measure. In order to test this hypothesis, we propose a novel latent AP-SVM that minimizes a carefully designed upper bound on the AP-based loss function over weakly supervised samples. Using publicly available datasets, we demonstrate the advantage of our approach over standard loss-based binary classifiers on two challenging problems: action classification and character recognition.

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

Abstract

Formant frequencies represent resonances of vocal tract system during the production of speech signals. Bandwidths associated with the formant frequencies are important parameters in analysis and synthesis of speech signals. In this paper, a method is proposed to extract the bandwidths associated with formant frequencies, by analysing short segments (2–3 ms) of speech signal. The method is based on two important properties of group delay function (GDF): (a) The GDF exhibits prominent peaks at resonant frequencies and (b) the influence of one resonant frequency on other resonances is negligible in GDF. The accuracy of the method is demonstrated for synthetic signals generated using all-pole filters. The method is evaluated by extracting bandwidths of synthetic signals in closed phase and open phase regions within a pitch period. The accuracy of the proposed method is also compared with that of two other methods, one based on linear prediction analysis of speech signals, and another based on filterbank arrays for obtaining amplitude envelopes and instantaneous frequency signals. Results indicate that the method based on the properties of GDF is suitable for accurate extraction of formant bandwidths, even from short segments of speech signal within a pitch period.

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

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

Even though, a lot of research has already been done on Machine Translation, translating complex sentences has been a stumbling block in the process. To improve the performance of machine translation on complex sentences, simplifying the sentences becomes imperative. In this paper, we present a rule based approach to address this problem by simplifying complex sentences in Hindi into multiple simple sentences. The sentence is split using clause boundaries and dependency parsing which identifies different arguments of verbs, thus changing the grammatical structure in a way that the semantic information of the original sentence stay preserved.

Abstract

A novel CMOS current bleeding mixer based on inductive degeneration has been proposed in this paper. The noise figure of a radio frequency (RF) mixer is considerably affected by the flicker noise. This flicker noise can be tuned down by employing inductive degeneration at source and gate as the degenerative inductors improve the linearity and reduce the noise figure. A conventional current bleeding Gilbert mixer is also designed for analysis and comparison with proposed design. These mixers are designed using 0.18 micron technology. The simulated results show that the proposed mixer design has conversion gain of 12.47 dB, 1.7 dB decrease of noise figure and 3.72 dBm improvement of IIP3 at a maximum LO power of -3 dBm as compared to the conventional bleeding mixer.

Abstract

In this paper, we present a noise cancellation technique for common gate ultra wide band (UWB) low noise amplifiers (LNAs) which not only reduces the noise but also increases the gain. On implementing this technique in one of the existing UWB LNAs significant reduction of noise figure (NF) compared to same LNA without noise cancellation technique was observed. Noise cancellation of the most dominating thermal noise source viz. the input matching device with theoretical analysis is presented in this paper. Simulation results with TSMC 0.18 (m CMOSRF technology shows a peak gain of 28 dB and base NF of 3.16 dB for a 6.6 GHz (2.4-9 GHz) band LNA with noise cancellation when compared to peak gain of 21 dB and base NF of 4.09 dB without noise cancellation. Both theoretical analysis and simulation results are in good agreement.

Abstract

The Doda Earthquake of 1st May 2013 at 12:27 Hrs. (IST) hit border region of two states of India (Jammu & Kashmir and Himachal Pradesh) (Mw= 5.8). This paper describes the findings of the 5-day post-earthquake reconnaissance survey undertaken in the affected areas with particular emphasis onbuilding typology, construction materials and construction practices. Post-earthquake damage assessment of the built environment suggests that recently built structures do not have adequate earthquake resistant featuresto resist severe earthquake shaking (of about IX on MSK scale) expected in the region. On the other hand, traditional constructions with good earthquake resistant features performed much better.The maximum intensity of shaking during the said event was only about VI on MSK scale, and the damage was less. But, the type of damage suggests that huge losses can be incurred in the region during a future earthquake of higher intensity, if the built environment is not retrofitted.