arXiv subjects

Vikas Yadav

Publications and source records attributed to Vikas Yadav.

51 records · Page 3Linked to original sources

arXiv2021

String/${\cal M}$-theory Dual of Large-$N$ Thermal QCD-Like Theories at Intermediate Gauge/'t Hooft Coupling and Holographic Phenomenology

Considering the setup of arXiv:0902.1540 [hep-th] involving UV-complete top-down type IIB holographic dual of large-N thermal QCD with a fluxed resolved warped deformed conifold, in arXiv:1306.4339 [hep-th] delocalized type IIA S(trominger)-Y(au)-Z(aslow)mirror of the type IIB background of arXiv:0902.1540 [hep-th] was constructed via three T dualities along a special Lagrangian $T^{3}$ fibered over a large base and then uplifted, locally, to the 11-dimensional ${\cal M}$-theory. Considering the aforementioned setup arXiv:1306.4339 [hep-th] in the `MQGP' limit, in arXiv:1703.01306 [hep-th] we obtained the masses of the $0^{++}, 0^{-+},0^{--}, 1^{++}, 2^{++}$ (`glueball') states. We also obtained analytical expressions for the vector and scalar meson spectra in arXiv:1707.02818 [hep-th]. We used WKB quantization conditions and Neumann/Dirichlet boundary conditions at an IR cut-off (`$r_0$')/horizon radius (`$r_h$') on the solutions to the equations of motion. We also discussed the $r_h=0$-limits of all calculations which correspond to the thermal background. Subsequently, in arXiv:1808.01182 [hep-th] we obtained the interaction Lagrangian corresponding to exotic scalar glueball $\left( G_{E}\right)-ρ/π$- meson. Assuming $M_G>2M_ρ$, we then computed $ρ\rightarrow2π, G_E\rightarrow2π, 2ρ, ρ+2π$ decay widths as well as the direct and indirect (mediated via $ρ$ mesons) $G_E\rightarrow4π$ decays. In arXiv:2004.07259 [hep-th] we obtained ${\cal O}\left(l_p^6\right)$ corrections to the MQGP background of arXiv:1306.4339 [hep-th] to study a top-down holographic dual of the thermal QCD-like theories at intermediate 't Hooft coupling and in arXiv:2011.04660 [hep-th] we obtained the values of the coupling constants of the ${\cal O}(p^4)$ $χ$PT Lagrangian in the chiral limit, inclusive of the ${\cal O}(R^4)$ corrections.

hep-th↗

arXiv2021

(Phenomenology/Lattice-Compatible) $SU(3)$ M$χ$PT HD up to ${\cal O}(p^4)$ and the ${\cal O}\left(R^4\right)$-Large-$N$ Connection

Obtaining the values of the coupling constants of the low energy effective theory corresponding to QCD, compatible with experimental data, even in the (vector) mesonic sector from (the ${\cal M}$-theory uplift of) a UV-complete string theory dual, has thus far been missing in the literature. We take the first step in this direction by obtaining the values of the coupling constants of the ${\cal O}(p^4)$ $χ$PT Lagrangian in the chiral limit involving the NGBs and $ρ$ meson (and its flavor partners) from the ${\cal M}$-theory /type IIA dual of large-$N$ thermal QCD, inclusive of the ${\cal O}(R^4)$ corrections. We observe that ensuring compatibility with phenomenological/lattice results (the values ) as given in arXiv:1510.01634[hep-ph], requires a relationship relating the ${\cal O}(R^4)$ corrections and large-$N$ suppression. In other words, QCD demands that the higher derivative corrections and the large-$N$ suppressed corrections in its M/string theory dual are related. As a bonus, we explicitly show that the ${\cal O}(R^4)$ corrections in the UV to the ${\cal M}$-theory uplift of the type IIB dual of large-$N$ thermal QCD, can be consistently set to be vanishingly small.

hep-th↗

arXiv2021

Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Reading

We propose a simple and effective strategy for data augmentation for low-resource machine reading comprehension (MRC). Our approach first pretrains the answer extraction components of a MRC system on the augmented data that contains approximate context of the correct answers, before training it on the exact answer spans. The approximate context helps the QA method components in narrowing the location of the answers. We demonstrate that our simple strategy substantially improves both document retrieval and answer extraction performance by providing larger context of the answers and additional training data. In particular, our method significantly improves the performance of BERT based retriever (15.12\%), and answer extractor (4.33\% F1) on TechQA, a complex, low-resource MRC task. Further, our data augmentation strategy yields significant improvements of up to 3.9\% exact match (EM) and 2.7\% F1 for answer extraction on PolicyQA, another practical but moderate sized QA dataset that also contains long answer spans.

cs.CL↗

arXiv2021

Towards Robust Neural Retrieval Models with Synthetic Pre-Training

Recent work has shown that commonly available machine reading comprehension (MRC) datasets can be used to train high-performance neural information retrieval (IR) systems. However, the evaluation of neural IR has so far been limited to standard supervised learning settings, where they have outperformed traditional term matching baselines. We conduct in-domain and out-of-domain evaluations of neural IR, and seek to improve its robustness across different scenarios, including zero-shot settings. We show that synthetic training examples generated using a sequence-to-sequence generator can be effective towards this goal: in our experiments, pre-training with synthetic examples improves retrieval performance in both in-domain and out-of-domain evaluation on five different test sets.

cs.CL↗

arXiv2020

Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering

Evidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of the corresponding QA method. We introduce a simple, fast, and unsupervised iterative evidence retrieval method, which relies on three ideas: (a) an unsupervised alignment approach to soft-align questions and answers with justification sentences using only GloVe embeddings, (b) an iterative process that reformulates queries focusing on terms that are not covered by existing justifications, which (c) a stopping criterion that terminates retrieval when the terms in the given question and candidate answers are covered by the retrieved justifications. Despite its simplicity, our approach outperforms all the previous methods (including supervised methods) on the evidence selection task on two datasets: MultiRC and QASC. When these evidence sentences are fed into a RoBERTa answer classification component, we achieve state-of-the-art QA performance on these two datasets.

cs.CL↗

arXiv2020

Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering

We propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) that (a) maximizes the relevance of the selected sentences, (b) minimizes the overlap between the selected facts, and (c) maximizes the coverage of both question and answer. This unsupervised sentence selection method can be coupled with any supervised QA approach. We show that the sentences selected by our method improve the performance of a state-of-the-art supervised QA model on two multi-hop QA datasets: AI2's Reasoning Challenge (ARC) and Multi-Sentence Reading Comprehension (MultiRC). We obtain new state-of-the-art performance on both datasets among approaches that do not use external resources for training the QA system: 56.82% F1 on ARC (41.24% on Challenge and 64.49% on Easy) and 26.1% EM0 on MultiRC. Our justification sentences have higher quality than the justifications selected by a strong information retrieval baseline, e.g., by 5.4% F1 in MultiRC. We also show that our unsupervised selection of justification sentences is more stable across domains than a state-of-the-art supervised sentence selection method.

cs.CL↗

arXiv2019

SCOUT: Signal Correction and Uncertainty Quantification Toolbox in MATLAB

This manuscript describes the software package SCOUT, which analyzes, characterizes, and corrects one-dimensional signals. Specifically, it allows to check and correct for stationarity, detect spurious samples, check for normality, check for periodicity, filter, perform spectral analysis, determine the integral time scale, and perform uncertainty analysis on individual and on propagated signals through a data reduction equation. The novelty of SCOUT lies in combining these various methods into one compact and easy-to-use toolbox, which enables students and professionals alike to analyze, characterize, and correct for signals without expert knowledge. The program is oriented towards time traces, but an easy adaptation to spatial distributions can be performed by the user. SCOUT is available in two variants: a graphical user interface (GUI) and a script-based version. A key motivation of having two variants is to offer maximum flexibility to adaptively and visually adjust the analysis settings using the GUI version and to enable large batch processing capabilities and own code-integration using the script-based version. The package includes both variants as well as three example scripts with their corresponding signals.

physics.data-an↗

arXiv2019

A Survey on Recent Advances in Named Entity Recognition from Deep Learning models

Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.

cs.CL↗

arXiv2019

Multi-class Hierarchical Question Classification for Multiple Choice Science Exams

Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately. However, developing strong QC algorithms has been hindered by the limited size and complexity of annotated data available. To address this, we present the largest challenge dataset for QC, containing 7,787 science exam questions paired with detailed classification labels from a fine-grained hierarchical taxonomy of 406 problem domains. We then show that a BERT-based model trained on this dataset achieves a large (+0.12 MAP) gain compared with previous methods, while also achieving state-of-the-art performance on benchmark open-domain and biomedical QC datasets. Finally, we show that using this model's predictions of question topic significantly improves the accuracy of a question answering system by +1.7% P@1, with substantial future gains possible as QC performance improves.

cs.CL↗

arXiv2018

M-Theory Exotic Scalar Glueball Decays to Mesons at Finite Coupling

Using the pull-back of the perturbed type IIA metric corresponding to the perturbation of arXiv:hep-th/1306.4339's M-theory uplift of arXiv:hep-th/0902.1540's UV-complete top-down type IIB holographic dual of large-$N$ thermal QCD, at finite coupling, we obtain the interaction Lagrangian corresponding to exotic scalar glueball($G_E$)-$ρ/π$-meson interaction, linear in the exotic scalar glueball and up to quartic order in the $π$ mesons. In the Lagrangian, the coupling constants are determined as (radial integrals of) arXiv:hep-th/1306.4339's M-theory uplift's metric components and six radial functions appearing in the M-theory metric perturbations. Assuming $M_G>2M_ρ$, we then compute $ρ\rightarrow2π, G_E\rightarrow2π, 2ρ, ρ+2π$ decay widths as well as the direct and indirect (mediated via $ρ$ mesons) $G_E\rightarrow4π$ decays. For numerics, we choose $f0[1710]$ and compare with previous calculations. We emphasize that our results can be made to match PDG data (and improvements thereof) exactly by appropriate tuning of some constants of integration appearing in the solution of the M-theory metric perturbations and the $ρ$ and $π$ meson radial profile functions - a flexibility that our calculations permits.

hep-th↗

arXiv2018

Sanity Check: A Strong Alignment and Information Retrieval Baseline for Question Answering

While increasingly complex approaches to question answering (QA) have been proposed, the true gain of these systems, particularly with respect to their expensive training requirements, can be inflated when they are not compared to adequate baselines. Here we propose an unsupervised, simple, and fast alignment and information retrieval baseline that incorporates two novel contributions: a \textit{one-to-many alignment} between query and document terms and \textit{negative alignment} as a proxy for discriminative information. Our approach not only outperforms all conventional baselines as well as many supervised recurrent neural networks, but also approaches the state of the art for supervised systems on three QA datasets. With only three hyperparameters, we achieve 47\% P@1 on an 8th grade Science QA dataset, 32.9\% P@1 on a Yahoo! answers QA dataset and 64\% MAP on WikiQA. We also achieve 26.56\% and 58.36\% on ARC challenge and easy dataset respectively. In addition to including the additional ARC results in this version of the paper, for the ARC easy set only we also experimented with one additional parameter -- number of justifications retrieved.

cs.IR↗

arXiv2017

Delocalized SYZ Mirrors and Confronting Top-Down $SU(3)$-Structure Holographic Meson Masses at Finite $g$ and $N_c$ with P(article) D(ata) G(roup) Values

Meson spectroscopy at finite gauge coupling - whereat any perturbative QCD computation would break down - and finite number of colors, from a top-down holographic string model, has thus far been entirely missing in the literature. This paper fills in this gap. Using the delocalized type IIA SYZ mirror (with $SU(3)$ structure) of the holographic type IIB dual of large-$N$ thermal QCD of arXiv:hep-th/0902.1540 as constructed in arXiv:hep-th/1306.4339 at finite coupling and number of colors ($N_c$ = Number of $D5(\overline{D5}$)-branes wrapping a vanishing two-cycle in the top-down holographic construct of arXiv:hep-th/0902.1540 = ${\cal O}(1)$ in the IR in the MQGP limit of arXiv:hep-th/1306.4339 at the end of a Seiberg duality cascade), we obtain analytical (not just numerical) expressions for the vector and scalar meson spectra and compare our results with previous calculations of hep-th/0412141, arXiv:1409.0559 [hep-th], and obtain a closer match with the Particle Data Group (PDG) results (Chin. Phys. C, 38, 090001 (2014) and 2015 update). Through explicit computations, we verify that the vector and scalar meson spectra obtained by the gravity dual with a black hole for all temperatures (small and large) are nearly isospectral with the spectra obtained by a thermal gravity dual valid for only low temperatures; the isospectrality is much closer for vector mesons than scalar mesons. The black hole gravity dual (with a horizon radius smaller than the deconfinement scale) also provides the expected large-$N$ suppressed decrease in vector meson mass with an increase of temperature.

hep-th↗

arXiv2017

Distributed Finite Time Termination of Consensus in the Presence of Delays

Linear consensus iterations guarantee asymptotic convergence, thereby, limiting their applicability in applications where consensus value needs to be used in real time to perform a system level task. It also leads to wastage of power and communication resources. In this article, an algorithm is proposed which enables each node to detect in a distributed manner and in finite number of iterations, when every agent in the network is within a user specified threshold of the consensus value (approximate consensus) and hence terminate further communications and computations associated with consensus iterations. This article develops a distributed algorithm for achieving this approximate consensus in presence of random time-varying bounded communication delays. Moreover, the article instantiates the algorithm developed to distributively determine the average of the initial values held by agents in finite number of iterations. Specifically, this algorithm relies on distributively determining the maximum and minimum of values held by the agents. The approach presented here offers several advantages, including reduced computational complexity, and hence, is suited for hardware implementation. An experimental test bed of Raspberry-Pi agents that communicate wirelessly over neighborhoods is employed as a platform to demonstrate the effectiveness of the developed algorithm.

math.OC↗

arXiv2017

Top-Down Holographic $G$-Structure Glueball Spectroscopy at (N)LO in $N$ and Finite Gauge Coupling

The top-down type IIB holographic dual of large-$N$ thermal QCD as constructed in arXiv:0902.1540 involving a fluxed resolved warped deformed conifold, its delocalized type IIA SYZ mirror as well as its M-theory uplift constructed in arXiv:1306.4339 - both in the finite gauge coupling ($g_s\stackrel{<}{\sim}1$)/`MQGP' limit of arXiv:1306.4339 - were shown explicitly to possess a local $SU(3)/G_2$-structure in arXiv:1507.02692 Glueball spectroscopy at finite gauge coupling has thus far been missing in the literature. In this paper, we fill this gap by calculating the masses of the $0^{++}, 0^{-+},0^{--}, 1^{++}, 2^{++}$ (`glueball') states (which correspond to fluctuations in the dilaton or complexified two-forms or appropriate metric components) in the aforementioned backgrounds of $G$-structure in the `MQGP' limit of arXiv:1306.4339, using WKB quantization conditions on one hand and imposing Neumann/Dirichlet boundary conditions at an IR cut-off/horizon radius $r_h$ on the solutions to the equations of motion on the other. We also discuss $r_h=0$-limits of all calculations; in this context we also calculate the $0^{++}, 0^{--},1^{++}, 2^{++}$ glueball masses up to NLO in $N$ and find a $\frac{g_sM^2}{N}(g_sN_f)$-suppression similar to and further validating a similar semi-universality of NLO corrections to transport coefficients, observed in arXiv:1606.04949.

hep-th↗

arXiv2008

Phase Transitions on Fixed Connected Graphs and Random Graphs in the Presence of Noise

In this paper, we study the phase transition behavior emerging from the interactions among multiple agents in the presence of noise. We propose a simple discrete-time model in which a group of non-mobile agents form either a fixed connected graph or a random graph process, and each agent, taking bipolar value either +1 or -1, updates its value according to its previous value and the noisy measurements of the values of the agents connected to it. We present proofs for the occurrence of the following phase transition behavior: At a noise level higher than some threshold, the system generates symmetric behavior (vapor or melt of magnetization) or disagreement; whereas at a noise level lower than the threshold, the system exhibits spontaneous symmetry breaking (solid or magnetization) or consensus. The threshold is found analytically. The phase transition occurs for any dimension. Finally, we demonstrate the phase transition behavior and all analytic results using simulations. This result may be found useful in the study of the collective behavior of complex systems under communication constraints.

math.OC↗