arXiv subjects

Rohit Gupta

Publications and source records attributed to Rohit Gupta.

72 records · Page 4Linked to original sources

arXiv2021

Towards a Systematic Engineering of Industrial Domain-Specific Language

Domain-Specific Languages (DSLs) help practitioners in contributing solutions to challenges of specific domains. The efficient development of user-friendly DSLs suitable for industrial practitioners with little expertise in modelling still is challenging. For such practitioners, who often do not model on a daily basis, there is a need to foster reduction of repetitive modelling tasks and providing simplified visual representations of DSL parts. For industrial language engineers, there is no methodical support for providing such guidelines or documentation as part of reusable language modules. Previous research either addresses the reuse of languages or guidelines for modelling. For the efficient industrial deployment of DSLs, their combination is essential: the efficient engineering of DSLs from reusable modules that feature integrated documentation and guidelines for industrial practitioners. To solve these challenges, we propose a systematic approach for the industrial engineering of DSLs based on the concept of reusable DSL Building Blocks, which rests on several years of experience in the industrial engineering of DSLs and their deployment to various organizations. We investigated our approach via focus group methods consisting of five participants from industry and research qualitatively. Ultimately, DSL Building Blocks support industrial language engineers in developing better usable DSLs and industrial practitioners in more efficiently achieving their modelling.

cs.SE↗

arXiv2020

A generalized approach to study low as well as high $p_T$ regime of transverse momentum spectra

A good understanding of the transverse momentum $(p_T)$ spectra is pivotal in the study of QCD matter created during the heavy-ion collision. Considering the difference in the underlying particle production mechanism, $p_T$ spectra can be divided into two distinct regions. Low-$p_T$ region corresponds to particle produced in soft-processes whereas particles produced in hard processes dominate the high-$p_T$ regime of the spectra. We will discuss a unified formalism to explain both low as well as high-$p_T$ region of the transverse momentum spectra in a consistent manner. This unified formalism is based on the generalisation of non-extensive statistical mechanics using the Pearson distribution. This generalised formalism also gives a strong insight into the study of elliptic flow in heavy-ion collision.

hep-ph↗

arXiv2020

Cassandra: Detecting Trojaned Networks from Adversarial Perturbations

Deep neural networks are being widely deployed for many critical tasks due to their high classification accuracy. In many cases, pre-trained models are sourced from vendors who may have disrupted the training pipeline to insert Trojan behaviors into the models. These malicious behaviors can be triggered at the adversary's will and hence, cause a serious threat to the widespread deployment of deep models. We propose a method to verify if a pre-trained model is Trojaned or benign. Our method captures fingerprints of neural networks in the form of adversarial perturbations learned from the network gradients. Inserting backdoors into a network alters its decision boundaries which are effectively encoded in their adversarial perturbations. We train a two stream network for Trojan detection from its global ($L_\infty$ and $L_2$ bounded) perturbations and the localized region of high energy within each perturbation. The former encodes decision boundaries of the network and latter encodes the unknown trigger shape. We also propose an anomaly detection method to identify the target class in a Trojaned network. Our methods are invariant to the trigger type, trigger size, training data and network architecture. We evaluate our methods on MNIST, NIST-Round0 and NIST-Round1 datasets, with up to 1,000 pre-trained models making this the largest study to date on Trojaned network detection, and achieve over 92\% detection accuracy to set the new state-of-the-art.

cs.CV↗

arXiv2020

Observation of geometric phase for unpolarized and partially polarized light fields

Geometric phase, owing to its topological nature and properties of fault tolerance, plays an important role in devising real world applications in both classical and quantum domain. For classical systems, geometric phase has been observed and studied so far for fully polarized light only. Using an interferometric experiment we demonstrate, for the first time, the existence of Pancharatnam-Berry phase for states covering all empty space inside the Poincaré sphere namely the unpolarized and partially polarized light fields. The observed geometric phase is found identical to its fully polarized counterpart in excellent agreement with the theoretical predictions.

physics.optics↗

arXiv2020

RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery

Accurate and fine-grained information about the extent of damage to buildings is essential for directing Humanitarian Aid and Disaster Response (HADR) operations in the immediate aftermath of any natural calamity. In recent years, satellite and UAV (drone) imagery has been used for this purpose, sometimes aided by computer vision algorithms. Existing Computer Vision approaches for building damage assessment typically rely on a two stage approach, consisting of building detection using an object detection model, followed by damage assessment through classification of the detected building tiles. These multi-stage methods are not end-to-end trainable, and suffer from poor overall results. We propose RescueNet, a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-toend. In order to to model the composite nature of this problem, we propose a novel localization aware loss function, which consists of a Binary Cross Entropy loss for building segmentation, and a foreground only selective Categorical Cross-Entropy loss for damage classification, and show significant improvement over the widely used Cross-Entropy loss. RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods and achieves generalization across varied geographical regions and disaster types.

cs.CV↗

arXiv2020

Block the blocker: Studying the effects of Anti Ad-blocking

Advertisements generate huge chunks of revenues for websites and online businesses. Ad-blocker and tracker blocking programs have gained momentum in the last few years with massive debates raging on privacy concerns and improving user experience online. Acceptable Ads programme and Anti Ad-blockers are primary elements emerging in recent years that combat ad-blockers. In this paper, we discuss at length data collection of top websites in the world, Germany, DACH region and news category. We generate feature based A/B testing metrics and employ classifier evaluations on them along with then analysing the result. Our paper also discusses how Anti Ad-blockers impact the economic, legal and ethical usage in Germany along with the recent changes in GDPR while taking a look at Acceptable ads programme and Whitelisting.

cs.CY↗

arXiv2019

Enhanced Plasmonic Photocatalysis through Cooperative Plasmonic--Photonic Hybridization

Plasmonic nanoparticles (NPs) hold tremendous promise for catalyzing light-driven chemical reactions. The conventionally assumed detrimental absorption loss from plasmon damping can now be harvested to drive chemical transformations of the NP adsorbent, through the excitation and transfer of energetic "hot" carriers. The rate and selectivity of plasmonic photocatalysis are dependent on the characteristics of the incident light. By engineering the strength and wavelength of the light harvesting of a NP, it is possible to achieve more efficient and predictive photocatalysts. We report a plasmonic--photonic resonance hybridization strategy to substantially enhance hot electron generation at tunable, narrow-band wavelengths. By coupling the plasmon resonance of silver NPs to the guided mode resonance in a photonic crystal (PC) slab, the reaction rate of a hot-electron-driven reduction conversion is greatly accelerated. The mechanism is broadly compatible with NPs with manifold materials and shapes optimized for the targeted chemistry. The novel enhancement platform sheds light on rational design of high-performance plasmonic photocatalysts.

physics.optics↗

arXiv2019

Character-based NMT with Transformer

Character-based translation has several appealing advantages, but its performance is in general worse than a carefully tuned BPE baseline. In this paper we study the impact of character-based input and output with the Transformer architecture. In particular, our experiments on EN-DE show that character-based Transformer models are more robust than their BPE counterpart, both when translating noisy text, and when translating text from a different domain. To obtain comparable BLEU scores in clean, in-domain data and close the gap with BPE-based models we use known techniques to train deeper Transformer models.

cs.CL↗

arXiv2019

Improving Robustness in Real-World Neural Machine Translation Engines

As a commercial provider of machine translation, we are constantly training engines for a variety of uses, languages, and content types. In each case, there can be many variables, such as the amount of training data available, and the quality requirements of the end user. These variables can have an impact on the robustness of Neural MT engines. On the whole, Neural MT cures many ills of other MT paradigms, but at the same time, it has introduced a new set of challenges to address. In this paper, we describe some of the specific issues with practical NMT and the approaches we take to improve model robustness in real-world scenarios.

cs.CL↗

arXiv2018

Uniqueness of Minimizers of Some Variational Problems Arising in Image Processing

We will study an open problem pertaining to the uniqueness of minimizers for a class of variational problems emanating from Meyer's model for the decomposition of an image into a geometric part and a texture part. Mainly, we are interested in the uniqueness of minimizers of the problem: \begin{equation} \label{eq:abs-1} (0.1)\qquad \inf \left\{J(u)+J^*\left(\frac{v}μ\right): (u,v)\in L^2(Ω)\times L^2( Ω){,}\; f=u+v \right\} \end{equation} where the image $f$ is a square integrable function on the domain $Ω$, the number $μ$ is a parameter, the functional $J$ stands for the total variation and the functional $J^*$ is its Legendre transform. We will consider Problem (0.1) as a special case of the problem: \begin{equation} \label{eq:abs-2} (0.2)\qquad \inf\{s(f-u)+s^*(u): u\in \mathcal{X}\} \end{equation} where $\mathcal X$ is a Hilbert space containing $f$ and $s$ is a continuous semi-norm on $\mathcal X$. In finite dimensions, we will prove that Problem (0.2) reduces to a projection problem onto the polar of the unit ball associated to a given norm on an appropriate Euclidean space. We will also provide a characterization for the uniqueness of minimizers for a more general projection problem defined by using any norm and any nonempty, closed, bounded and convex set of an Euclidean space. Finally, we will provide numerical evidence in favor of the uniqueness of minimizers for the decomposition problem.

math.OC↗

arXiv2018

ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers

Bibliographic reference parsers extract machine-readable metadata such as author names, title, journal, and year from bibliographic reference strings. To extract the metadata, the parsers apply heuristics or machine learning. However, no reference parser, and no algorithm, consistently gives the best results in every scenario. For instance, one tool may be best in extracting titles in ACM citation style, but only third best when APA is used. Another tool may be best in extracting English author names, while another one is best for noisy data (i.e. inconsistent citation styles). In this paper, which is an extended version of our recent RecSys poster, we address the problem of reference parsing from a recommender-systems and meta-learning perspective. We propose ParsRec, a meta-learning based recommender-system that recommends the potentially most effective parser for a given reference string. ParsRec recommends one out of 10 open-source parsers: Anystyle-Parser, Biblio, CERMINE, Citation, Citation-Parser, GROBID, ParsCit, PDFSSA4MET, Reference Tagger, and Science Parse. We evaluate ParsRec on 105k references from chemistry. We propose two approaches to meta-learning recommendations. The first approach learns the best parser for an entire reference string. The second approach learns the best parser for each metadata type in a reference string. The second approach achieved a 2.6% increase in F1 (0.909 vs. 0.886) over the best single parser (GROBID), reducing the false positive rate by 20.2% (0.075 vs. 0.094), and the false negative rate by 18.9% (0.107 vs. 0.132).

cs.IR↗

arXiv2017

The Discrete-Time Geometric Maximum Principle

We establish a variety of results extending the well-known Pontryagin maximum principle of optimal control to discrete-time optimal control problems posed on smooth manifolds. These results are organized around a new theorem on critical and approximate critical points for discrete-time geometric control systems. We show that this theorem can be used to derive Lie group variational integrators in Hamiltonian form; to establish a maximum principle for control problems in the absence of state constraints; and to provide sufficient conditions for exact penalization techniques in the presence of state or mixed constraints. Exact penalization techniques are used to study sensitivity of the optimal value function to constraint perturbations and to prove necessary optimality conditions, including in the form of a maximum principle, for discrete-time geometric control problems with state or mixed constraints.

math.OC↗

arXiv2017

DORE: An Experimental Framework to Enable Outband D2D Relay in Cellular Networks

Device-to-Device communications represent a paradigm shift in cellular networks. In particular, analytical results on D2D performance for offloading and relay are very promising, but no experimental evidence validates these results to date. This paper is the first to provide an experimental analysis of outband D2D relay schemes. Moreover, we design DORE, a complete framework for handling channel opportunities offered by outband D2D relay nodes. DORE consists of resource allocation optimization tools and protocols suitable to integrate QoS-aware opportunistic D2D communications within the architecture of 3GPP Proximity-based Services. We implement DORE using an SDR framework to profile cellular network dynamics in the presence of opportunistic outband D2D communication schemes. Our experiments reveal that outband D2D communications are suitable for relaying in a large variety of delay-sensitive cellular applications, and that DORE enables notable gains even with a few active D2D relay nodes.

cs.NI↗

arXiv2017

Linear Openness and Feedback Stabilization of Nonlinear Control Systems

It is well known from the seminal Brockett's theorem that the openness property of the mapping on the right-hand side of a given nonlinear ODE control system is a necessary condition for the existence of locally asymptotically stabilizing continuous stationary feedback laws. However, this condition fails to be sufficient for such a feedback stabilization. In this paper we develop an approach of variational analysis to continuous feedback stabilization of nonlinear control systems with replacing openness by the linear openness property, which has been well understood and characterized in variational theory. It allows us, in particular, to obtain efficient conditions via the system data supporting the sufficiency in Brockett's theorem and ensuring local exponential stabilization by means of continuous stationary feedback laws. Furthermore, we derive new necessary conditions for local exponential and asymptotic stabilization of continuous-time control systems by using both continuous and smooth stationary feedback laws and establish also some counterparts of the obtained sufficient conditions for local asymptotic stabilization by continuous stationary feedback laws in the case of nonlinear discrete-time control systems.

math.OC↗

arXiv2017

Optimal Control Problems with Symmetry Breaking Cost Functions

We investigate symmetry reduction of optimal control problems for left-invariant control systems on Lie groups, with partial symmetry breaking cost functions. Our approach emphasizes the role of variational principles and considers a discrete-time setting as well as the standard continuous-time formulation. Specifically, we recast the optimal control problem as a constrained variational problem with a partial symmetry breaking Lagrangian and obtain the Euler--Poincaré equations from a variational principle. By applying a Legendre transformation to it, we recover the Lie-Poisson equations obtained by A. D. Borum [Master's Thesis, University of Illinois at Urbana-Champaign, 2015] in the same context. We also discretize the variational principle in time and obtain the discrete-time Lie-Poisson equations. We illustrate the theory with some practical examples including a motion planning problem in the presence of an obstacle.

math.OC↗

arXiv2016

Reordering rules for English-Hindi SMT

Reordering is a preprocessing stage for Statistical Machine Translation (SMT) system where the words of the source sentence are reordered as per the syntax of the target language. We are proposing a rich set of rules for better reordering. The idea is to facilitate the training process by better alignments and parallel phrase extraction for a phrase-based SMT system. Reordering also helps the decoding process and hence improving the machine translation quality. We have observed significant improvements in the translation quality by using our approach over the baseline SMT. We have used BLEU, NIST, multi-reference word error rate, multi-reference position independent error rate for judging the improvements. We have exploited open source SMT toolkit MOSES to develop the system.

cs.CL↗

arXiv2016

MPC on manifolds with an application to the control of spacecraft attitude on SO(3)

We develop a model predictive control (MPC) design for systems with discrete-time dynamics evolving on smooth manifolds. We show that the properties of conventional MPC for dynamics evolving on $\mathbb R^n$ are preserved and we develop a design procedure for achieving similar properties. We also demonstrate that for discrete-time dynamics on manifolds with Euler characteristic not equal to 1, there do not exist globally stabilizing, continuous control laws. The MPC law is able to achieve global asymptotic stability on these manifolds, because the MPC law may be discontinuous. We apply the method to spacecraft attitude control, where the spacecraft attitude evolves on the Lie group SO(3) and for which a continuous globally stabilizing control law does not exist. In this case, the MPC law is discontinuous and achieves global stability.

math.OC↗

arXiv2014

A geometric approach to the optimal control of nonholnomic mechanical systems

In this paper, we describe a constrained Lagrangian and Hamiltonian formalism for the optimal control of nonholonomic mechanical systems. In particular, we aim to minimize a cost functional, given initial and final conditions where the controlled dynamics is given by nonholonomic mechanical system. In our paper, the controlled equations are derived using a basis of vector fields adapted to the nonholonomic distribution and the Riemannian metric determined by the kinetic energy. Given a cost function, the optimal control problem is understood as a constrained problem or equivalently, under some mild regularity conditions, as a Hamiltonian problem on the cotangent bundle of the nonholonomic distribution. A suitable Lagrangian submanifold is also shown to lead to the correct dynamics. We demonstrate our techniques in several examples including a continuously variable transmission problem and motion planning for obstacle avoidance problems.

math.OC↗