Department of Computer Science & Engineering

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IIT Jodhpur

In a communication channel, message transfer happens through a noisy medium which can introduce errors in the transmitted message. It is required to maintain an acceptable bit error rate for a reliable transmission. This can be handled by an efficient error control coding scheme. This paper discusses the hardware design of an error control decoder, namely turbo product code decoder. The iterative Chase-Pyndiah decoding algorithm has been used in the turbo decoder design. The decoder has been designed using both MATLAB and Verilog, and its performance has been analysed. To study the robustness of the decoder in dealing with different data types, performance analysis is done with both the received data and the encrypted version of the same. Advanced Encryption Standard (AES) has been used for the encryption process, and it is shown that there is only a negligible difference in the performance for both the data sets. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

World University root node

Lecture Notes in Electrical Engineering

Hardware Design of a Turbo Product Code Decoder

Despite their tremendous success in modelling high-dimensional data manifolds, deep neural networks suffer from the threat of adversarial attacks - Existence of perceptually valid input-like samples obtained through careful perturbation that lead to degradation in the performance of the underlying model. Major concerns with existing defense mechanisms include non-generalizability across different attacks, models and large inference time. In this paper, we propose a generalized defense mechanism capitalizing on the expressive power of regularized latent space based generative models. We design an adversarial filter, devoid of access to classifier and adversaries, which makes it usable in tandem with any classifier. The basic idea is to learn a Lipschitz constrained mapping from the data manifold, incorporating adversarial perturbations, to a quantized latent space and re-map it to the true data manifold. Specifically, we simultaneously auto-encode the data manifold and its perturbations implicitly through the perturbations of the regularized and quantized generative latent space, realized using variational inference. We demonstrate the efficacy of the proposed formulation in providing resilience against multiple attack types (black and white box) and methods, while being almost real-time. Our experiments show that the proposed method surpasses the state-of-the-art techniques in several cases. The implementation code is available at - https://github.com/mayank31398/lqvae.

Variational Inference with Latent Space Quantization for Adversarial Resilience

Understanding the origin of magnetic ordering in an undoped semiconductor with native defects is an open question, which is being explored actively in research. In this investigation, the interplay between magnetic ordering and excess induced native defects in undoped anatase TiO2 nanoparticles is explained using an experimental and theoretical approach. It is demonstrated that structurally disordered TiO2 nanoparticles with a high concentration of native defects such as titanium interstitials and oxygen vacancies are synthesized using controlled atmospheric rapid cooling (i.e. quenching) process. The structural disorders in the lattice have been examined using various spectroscopic and microscopic analyses revealed the existence of Ti deficiency in both pristine and quenched TiO2 nanoparticles. A possible origin of magnetic ordering in titanium deficient anatase TiO2 system is elucidated based on first-principle calculations. It was found that the overall magnetic moment of Ti deficient TiO2 system is determined by the distance between Ti interstitials and its neighboring vacancies (i.e. either V-Ti or V(O)s). However, quenched TiO2 nanoparticles possess excess Ti interstitials, Ti and O vacancies and therefore the net magnetic moment of the system is reduced due to anti-ferromagnetically coupled neighboring Ti-lattice ions.

NANOTECHNOLOGY

Unveiling the interplay between induced native defects and room temperature magnetic ordering in titanium deficient disordered-TiO2 nanoparticles

Frontiers in Artificial Intelligence

Kernelized Heterogeneity Aware Cross-View Face Recognition

We show that for each single crossing graph H, a polynomially bounded weight function for all H-minor free graphs G can be constructed in Logspace such that it gives nonzero weights to all the cycles in G. This class of graphs subsumes almost all classes of graphs for which such a weight function is known to be constructed in Logspace. As a consequence, we obtain that for the class of H-minor free graphs where H is a single crossing graph, reachability can be solved in UL, and bipartite maximum matching can be solved in SPL, which are small subclasses of the parallel complexity class NC. In the restrictive case of bipartite graphs, our maximum matching result improves upon the recent result of Eppstein and Vazirani, where they show an NC bound for constructing perfect matching in general single crossing minor free graphs.

arXiv preprint arXiv:2103.

Reachability and Matching in Single Crossing Minor Free Graphs

An odd cycle transversal (oct, for short) in a graph is a set of vertices whose deletion will leave a graph without any odd cycles. The Odd Cycle Transversal (OCT) problem takes an undirected graph G and a non-negative integer k as input, and the objective is to test if G has an oct of size at most k. The directed counterpart of the problem, Directed Odd Cycle Transversal (DOCT), where the input is a digraph and k, is defined analogously. When parameterized by k, OCT is known to be FPT [Reed et al., Oper. Res. Lett., 2004] whereas DOCT was recently shown to be W[1]-hard [Lokshtanov et al., SODA, 2020]. A mixed graph is a graph that contains both directed and undirected edges. In this paper, we study the Mixed Odd Cycle Transversal (MOCT) problem, i.e., OCT on mixed graphs. And we show that MOCT admits a fixed-parameter tractable algorithm when parameterized by k+ ℓ, where ℓ is the number of directed edges in the input mixed graph. In the course of designing our algorithm for MOCT, we also design a fixed-parameter tractable algorithm for a variant of the well-known Multiway Cut problem, which might be of independent interest. © 2021, Springer Nature Switzerland AG.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Odd Cycle Transversal in Mixed Graphs

Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We not only extract and encode visual and scene text cues but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images with scene text content to demonstrate its effectiveness. In the retrieval framework, we augment the contextual semantic representation with scene text cues to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous scene text recognition, we also apply query-based attention to the text channel. We show that our multi-channel approach, involving contextual semantics and scene text, improves upon the absolute accuracy of the current state-of-the-art methods on Advertisement Images Dataset by 8.9\% in the relevant statement retrieval task and by 5\% in the topic classification task. (c) 2021 Elsevier B.V. All rights reserved.

Fulltext

PATTERN RECOGNITION LETTERS

Beyond visual semantics: Exploring the role of scene text in image understanding

We consider a scenario where a party, say, Alice prepares a pure two-qubit (either maximally entangled or nonmaximally entangled) state and sends one half of this state to another distant party, say, Bob through a qubit (either unital or nonunital) channel. Finally, the shared state is used as a teleportation channel. In this scenario, we focus on characterizing the set of qubit channels with respect to the final state's efficacy as a resource of quantum teleportation (QT) in terms of maximal average fidelity and fidelity deviation (fluctuation in fidelity values over the input states). Importantly, we point out the existence of a subset of qubit channels for which the final state becomes useful for universal QT (having maximal average fidelity strictly greater than the classical bound and having zero fidelity deviation) when the initially prepared state is either useful for universal QT (i.e., for a maximally entangled state) or not useful for universal QT (i.e., for a subset of nonmaximally entangled pure states). Interestingly, in the latter case, we show that nonunital channels (dissipative interactions) are more effective than unital channels (nondissipative interactions) in producing useful states for universal QT from nonmaximally entangled pure states. © 2021 American Physical Society.

Department of Physics

Physical Review A

Characterizing qubit channels in the context of quantum teleportation

Dynamic Random Access Memory (DRAM) is the de-facto choice for main memories in modern day computing systems. It is based on capacitor technology, which is volatile in nature. Hence, these memories require periodic refreshing, usually at 64 ms, in order to ensure data persistence. Refreshing results in blocking of the memory device for performing normal read or write operations. However, it has been found that not all cells of the device requires uniform refreshing at 64 ms. Due to shrinking of technologies, deviations are observed in nominal parameters which causes variations in retention and restoration time. In this paper, we propose a retention aware DRAM refreshing model, which is operated in auto-refresh (AR) mode of a DRAM device. We call the proposed model Lightweight Retention Time Aware Refreshing, or simply LRAR, which can be operated either in a deterministic or an approximate mode while consuming a constant amount of hardware space. The former ensures consumption of least possible area in comparison to previously proposed works. While the latter is aimed to incorporate periodic refreshing for a newly emerged DRAM phenomenon called Variable Retention Time, or, VRT, which uses the basics of approximation. After extensive evaluation, we find that our proposed model reduces execution time of programs up to 11% (9.4% on average). The memory system's energy consumption is also reduced by an average of 11.5%, and refresh energy by an average of 73.6%. We achieve the aforementioned gains at a modest area overhead of 7$, 240 \mu \mathrm{m}^{2}\, (0.0018$% of a 400mm$^{2\, }$die) and storage overhead. © 2021 IEEE.

Proceedings - International Symposium on Quality Electronic Design, ISQED

Towards row sensitive dram refresh through retention awareness

Subject Recognition (SR) refers to the task of identifying persons performing activities in a smart environment using the data captured by the sensors installed in it. The existing literature mainly concentrates on supervised SR using the sensor data captured through multiple modalities. However, majority of the real-life sensor datasets are not annotated with the subjects performing the activities, which creates a scarcity of labeled data samples for this task. Issues of privacy and high manual annotation costs further complicate the problem of less labeled data. In addition to this problem, most of the datasets are of low modalities. Hence, the challenge lies in developing semi-supervised frameworks that are suitable for low-modal sensor data with sparse or no labels. Towards this, we initially perform benchmark experiments to analyze the factors of modality and amount of labeled data in the context of SR. Then, we propose semi-supervised frameworks for SR on the data collected by low-modal ubiquitous and visual sensors. In particular, we propose a clustering-based pseudo label generation algorithm to facilitate the training process in a semi-supervised domain for ubiquitous data. On the other hand, we propose Transfer Learning and Data Augmentation (TLDA) framework to perform SR on visual data in semi-supervised domain. To validate our proposed frameworks, we perform experiments on three real-world datasets, namely Smartphone, OPPORTUNITY, and UTD-MHAD dataset to achieve an accuracy of around 77%, 98%, and 91% respectively. Finally, we also provide an analysis on the aspect of merging modalities to propose a new research dimension for SR. © 2021