arXiv subjects

Shashank Gupta

Publications and source records attributed to Shashank Gupta.

69 records · Page 4Linked to original sources

arXiv2021

Performance of Dense Coding and Teleportation for Random States --Augmentation via Pre-processing

In order to understand the resourcefulness of a natural quantum system in quantum communication tasks, we study the dense coding capacity (DCC) and teleportation fidelity (TF) of Haar uniformly generated random multipartite states of various ranks. We prove that when a rank-2 two-qubit state, a Werner state, and a pure state possess the same amount of entanglement, the DCC of a rank-2 state belongs to the envelope made by pure and Werner states. In a similar way, we obtain an upper bound via the generalized Greenberger-Horne-Zeilinger state for rank-2 three-qubit states when the dense coding with two senders and a single receiver is performed and entanglement is measured in the senders:receiver bipartition. The normalized frequency distribution of DCC for randomly generated two-, three- and four-qubit density matrices with global as well as local decodings at the receiver's end are reported. The estimation of mean DCC for two-qubit states is found to be in good agreement with the numerical simulations. Universally, we observe that the performance of random states for dense coding as well as teleportation decreases with the increase of the rank of states which we have shown to be surmounted by the local pre-processing operations performed on the shared states before starting the protocols, irrespective of the rank of the states. The local pre-processing employed here is based on positive operator valued measurements along with classical communication and we show that unlike dense coding with two-qubit random states, senders' operations are always helpful to probabilistically enhance the capabilities of implementing dense coding as well as teleportation.

quant-ph↗

arXiv2021

Genuine Einstein-Podolsky-Rosen steering of three-qubit states by multiple sequential observers

We investigate the possibility of multiple use of a single copy of three-qubit states for genuine tripartite Einstein-Podolsky-Rosen (EPR) steering. A pure three-qubit state of either the Greenberger-Horne-Zeilinger (GHZ)-type or W-type is shared between two fixed observers in two wings and a sequence of multiple observers in the third wing who perform unsharp or non-projective measurements. The choice of measurement settings for each of the multiple observers in the third wing is independent and uncorrelated with the measurement settings and outcomes of the previous observers. We investigate all possible types of (2->1) and (1->2) genuine tripartite steering in the above set-up. For each case, we obtain an upper limit on the number of observers on the third wing who can demonstrate genuine EPR steering through violation of a tripartite steering inequality. We show that the GHZ state allows for a higher number of observers compared to that for W state. Additionally, (1->2) steering is possible for a larger range of the sharpness parameter compared to that for the (2->1) steering cases.

quant-ph↗

arXiv2020

IITK-RSA at SemEval-2020 Task 5: Detecting Counterfactuals

This paper describes our efforts in tackling Task 5 of SemEval-2020. The task involved detecting a class of textual expressions known as counterfactuals and separating them into their constituent elements. Counterfactual statements describe events that have not or could not have occurred and the possible implications of such events. While counterfactual reasoning is natural for humans, understanding these expressions is difficult for artificial agents due to a variety of linguistic subtleties. Our final submitted approaches were an ensemble of various fine-tuned transformer-based and CNN-based models for the first subtask and a transformer model with dependency tree information for the second subtask. We ranked 4-th and 9-th in the overall leaderboard. We also explored various other approaches that involved the use of classical methods, other neural architectures and the incorporation of different linguistic features.

cs.CL↗

arXiv2018

CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection

Detecting whether a news article is fake or genuine is a crucial task in today's digital world where it's easy to create and spread a misleading news article. This is especially true of news stories shared on social media since they don't undergo any stringent journalistic checking associated with main stream media. Given the inherent human tendency to share information with their social connections at a mouse-click, fake news articles masquerading as real ones, tend to spread widely and virally. The presence of echo chambers (people sharing same beliefs) in social networks, only adds to this problem of wide-spread existence of fake news on social media. In this paper, we tackle the problem of fake news detection from social media by exploiting the very presence of echo chambers that exist within the social network of users to obtain an efficient and informative latent representation of the news article. By modeling the echo-chambers as closely-connected communities within the social network, we represent a news article as a 3-mode tensor of the structure - and propose a tensor factorization based method to encode the news article in a latent embedding space preserving the community structure. We also propose an extension of the above method, which jointly models the community and content information of the news article through a coupled matrix-tensor factorization framework. We empirically demonstrate the efficacy of our method for the task of Fake News Detection over two real-world datasets. Further, we validate the generalization of the resulting embeddings over two other auxiliary tasks, namely: \textbf{1)} News Cohort Analysis and \textbf{2)} Collaborative News Recommendation. Our proposed method outperforms appropriate baselines for both the tasks, establishing its generalization.

cs.IR↗

arXiv2018

Preservation of quantum non-bilocal correlations in noisy entanglement-swapping experiments using weak measurements

A tripartite quantum network is said to be bilocal if two independent sources produce a pair of bipartite entangled states. Quantum non-bilocal correlation emerges when the central party which possesses two particles from two different sources performs Bell-state measurement on them and nonlocality is generated between the other two uncorrelated systems in this entanglement-swapping protocol. The interaction of such systems with the environment reduces quantum non-bilocal correlations. Here we show that the diminishing effect modelled by the amplitude damping channel can be slowed down by employing the technique of weak measurements and reversals. It is demonstrated that for a large range of parameters the quantum non-bilocal correlations are preserved against decoherence by taking into account the average success rate of the post-selection governing weak measurements.

quant-ph↗

arXiv2018

Co-training for Extraction of Adverse Drug Reaction Mentions from Tweets

Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care. Current ADR surveillance systems are often associated with a substantial time lag before such events are officially published. On the other hand, online social media such as Twitter contain information about ADR events in real-time, much before any official reporting. Current state-of-the-art methods in ADR mention extraction use Recurrent Neural Networks (RNN), which typically need large labeled corpora. Towards this end, we propose a semi-supervised method based on co-training which can exploit a large pool of unlabeled tweets to augment the limited supervised training data, and as a result enhance the performance. Experiments with 0.1M tweets show that the proposed approach outperforms the state-of-the-art methods for the ADR mention extraction task by 5% in terms of F1 score.

cs.IR↗

arXiv2018

Multi-Task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets

Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care. Current ADR surveillance systems are often associated with a substantial time lag before such events are officially published. On the other hand, online social media such as Twitter contain information about ADR events in real-time, much before any official reporting. Current state-of-the-art in ADR mention extraction uses Recurrent Neural Networks (RNN), which typically need large labeled corpora. Towards this end, we propose a multi-task learning based method which can utilize a similar auxiliary task (adverse drug event detection) to enhance the performance of the main task, i.e., ADR extraction. Furthermore, in the absence of auxiliary task dataset, we propose a novel joint multi-task learning method to automatically generate weak supervision dataset for the auxiliary task when a large pool of unlabeled tweets is available. Experiments with 0.48M tweets show that the proposed approach outperforms the state-of-the-art methods for the ADR mention extraction task by 7.2% in terms of F1 score.

cs.IR↗

arXiv2017

Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction

Social media is an useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from twitter. Medical information extraction from social media is challenging, mainly due to short and highly information nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction relies on supervised learning methods, which suffers from labeled data scarcity problem. The State-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which are Long-Short-Term-Memory networks (LSTMs) \cite{hochreiter1997long}. Deep neural networks, due to their large number of free parameters relies heavily on large annotated corpora for learning the end task. But in real-world, it is hard to get large labeled data, mainly due to heavy cost associated with manual annotation. Towards this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. Through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction.

cs.IR↗

arXiv2017

Low-threshold optically pumped lasing in highly strained Ge nanowires

The integration of efficient, miniaturized group IV lasers into CMOS architecture holds the key to the realization of fully functional photonic-integrated circuits. Despite several years of progress, however, all group IV lasers reported to date exhibit impractically high thresholds owing to their unfavorable bandstructures. Highly strained germanium with its fundamentally altered bandstructure has emerged as a potential low-threshold gain medium, but there has yet to be any successful demonstration of lasing from this seemingly promising material system. Here, we demonstrate a low-threshold, compact group IV laser that employs germanium nanowire under a 1.6% uniaxial tensile strain as the gain medium. The amplified material gain in strained germanium can sufficiently surmount optical losses at 83 K, thus allowing the first observation of multimode lasing with an optical pumping threshold density of ~3.0 kW cm^-^2. Our demonstration opens up a new horizon of group IV lasers for photonic-integrated circuits.

physics.optics↗

arXiv2017

Simultaneous Inference of User Representations and Trust

Inferring trust relations between social media users is critical for a number of applications wherein users seek credible information. The fact that available trust relations are scarce and skewed makes trust prediction a challenging task. To the best of our knowledge, this is the first work on exploring representation learning for trust prediction. We propose an approach that uses only a small amount of binary user-user trust relations to simultaneously learn user embeddings and a model to predict trust between user pairs. We empirically demonstrate that for trust prediction, our approach outperforms classifier-based approaches which use state-of-the-art representation learning methods like DeepWalk and LINE as features. We also conduct experiments which use embeddings pre-trained with DeepWalk and LINE each as an input to our model, resulting in further performance improvement. Experiments with a dataset of $\sim$356K user pairs show that the proposed method can obtain an high F-score of 92.65%.

cs.IR↗

arXiv2017

Deep Learning for Hate Speech Detection in Tweets

Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ~18 F1 points.

cs.CL↗

arXiv2015

Ge Microdisk with Lithographically-Tunable Strain using CMOS-Compatible Process

We present germanium microdisk optical resonators under a large biaxial tensile strain using a CMOS-compatible fabrication process. Biaxial tensile strain of ~0.7% is achieved by means of a stress concentration technique that allows the strain level to be customized by carefully selecting certain lithographic dimensions. The partial strain relaxation at the edges of a patterned germanium microdisk is compensated by depositing compressively stressed silicon nitride layer. Two-dimensional Raman spectroscopy measurements along with finite-element method simulations confirm a relatively homogeneous strain distribution within the final microdisk structure. Photoluminescence results show clear optical resonances due to whispering gallery modes which are in good agreement with finite-difference time-domain optical simulations. Our bandgap-customizable microdisks present a new route towards an efficient germanium light source for on-chip optical interconnects.

physics.optics↗

arXiv2015

Direct Bandgap Light Emission from Strained Ge Nanowires Coupled with High-Q Optical Cavities

A silicon-compatible light source is the final missing piece for completing high-speed, low-power on-chip optical interconnects. In this paper, we present a germanium-based light emitter that encompasses all the aspects of potential low-threshold lasers: highly strained germanium gain medium, strain-induced pseudo-heterostructure, and high-Q optical cavity. Our light emitting structure presents greatly enhanced photoluminescence into cavity modes with measured quality factors of up to 2,000. The emission wavelength is tuned over more than 400 nm with a single lithography step. We find increased optical gain in optical cavities formed with germanium under high (>2.3%) tensile strain. Through quantitative analysis of gain/loss mechanisms, we find that free carrier absorption from the hole bands dominates the gain, resulting in no net gain even from highly strained, n-type doped germanium.

physics.optics↗

arXiv2015

Ultimate Limit of Biaxial Tensile Strain and N-Type Doping for Realizing an Efficient Low-Threshold Ge Laser

We theoretically investigate how the threshold of a Ge-on-Si laser can be minimized and how the slope efficiency can be maximized in presence of both biaxial tensile strain and n-type doping. Our finding shows that there exist ultimate limits beyond which point no further benefit can be realized through increased tensile strain or n-type doping. Here were quantify these limits, showing that the optimal design for minimizing threshold involves about 3.7% biaxial tensile strain and 2x1018 cm-3 n-type doping, whereas the optimal design for maximum slope efficiency involves about 2.3% biaxial tensile strain with 1x1019 cm-3 n-type doping. Increasing the strain and/or doping beyond these limits will degrade the threshold or slope efficiency, respectively.

physics.optics↗

arXiv2015

Anomalous Threshold Reduction from <100> Uniaxial Strain for a Low-Threshold Ge Laser

We theoretically investigate the effect of <100> uniaxial strain on a Ge-on-Si laser using deformation potentials. We predict a sudden and dramatic ~200x threshold reduction upon applying sufficient uniaxial tensile strain to the Ge gain medium. This anomalous reduction is accompanied by an abrupt jump in the emission wavelength and is explained by how the light-hole band raises relative to the heavy-hole band due to uniaxial strain. Approximately 3.2% uniaxial strain is required to achieve this anomalous threshold reduction for 1x1019 cm-3 n-type doping, and a complex interaction between uniaxial strain and n-type doping is observed. This anomalous threshold reduction represents a substantial performance advantage for uniaxially strained Ge lasers relative to other forms of Ge band engineering such as biaxial strain or tin alloying. Achieving this critical combination of uniaxial strain and doping for the anomalous threshold reduction is dramatically more relevant to practical devices than realizing a direct band gap.

physics.optics↗