arXiv subjects

Ayush Singh

Publications and source records attributed to Ayush Singh.

29 records · Page 2Linked to original sources

arXiv2023

Latent Graph Attention for Enhanced Spatial Context

Global contexts in images are quite valuable in image-to-image translation problems. Conventional attention-based and graph-based models capture the global context to a large extent, however, these are computationally expensive. Moreover, the existing approaches are limited to only learning the pairwise semantic relation between any two points on the image. In this paper, we present Latent Graph Attention (LGA) a computationally inexpensive (linear to the number of nodes) and stable, modular framework for incorporating the global context in the existing architectures, especially empowering small-scale architectures to give performance closer to large size architectures, thus making the light-weight architectures more useful for edge devices with lower compute power and lower energy needs. LGA propagates information spatially using a network of locally connected graphs, thereby facilitating to construct a semantically coherent relation between any two spatially distant points that also takes into account the influence of the intermediate pixels. Moreover, the depth of the graph network can be used to adapt the extent of contextual spread to the target dataset, thereby being able to explicitly control the added computational cost. To enhance the learning mechanism of LGA, we also introduce a novel contrastive loss term that helps our LGA module to couple well with the original architecture at the expense of minimal additional computational load. We show that incorporating LGA improves the performance on three challenging applications, namely transparent object segmentation, image restoration for dehazing and optical flow estimation.

cs.CV↗

arXiv2023

Ultrasound based Gas Detection: Analyzing Acoustic Impedance for High-Performance and Low-Cost Solutions

Gas leakage is a serious hazard in a variety of sectors, including industrial, domestic, and gas-powered vehicles. The installation of gas leak detection systems has emerged as a critical preventative measure to solve this problem. Traditional gas sensors, such as electrochemical, infrared point, and Metal Oxide Semiconductor sensors, have been widely used for gas leak detection. However, these sensors have limitations in terms of their adaptation to various gases, as well as their high cost and difficulties in scaling. In this paper, a novel non-contact gas detection technique based on a 40 kHz ultrasonic signal is described. The proposed approach employs the reflections of the emitted ultrasonic wave to detect the gas leaks and also able to identify the exact gas in real-time. To confirm the method's effectiveness, trials were carried out using Hydrogen, Helium, Argon, and Butane gas. The system identified gas flow breaches in 0.01 seconds, whereas the gas identification procedure took 0.8 seconds. An interesting extension of the proposed approach is real-time visualisation of gas flow employing an array of transducers.

eess.SP↗

arXiv2023

Ultrafast Ultraviolet C and Visible Laser Light with Broadly Tunable Spectrum

We demonstrate a versatile technique for generating continuously wavelength-tunable laser waveforms, with mJ pulse energies and ultrashort pulse durations down to few-cycle in the ultraviolet C and visible spectral ranges. Using the processes of self-phase modulation or Raman-induced spectral broadening, we substantially expand the spectrum of a femtosecond 1030 nm Yb:CaF$_{2}$ laser, allowing for an extensive wavelength-tunability of the second and fourth harmonics of the laser within the visible and ultraviolet C spectral regions at 460-580 nm and 230-290 nm. In addition, our approach exploits nonlinearly assisted self-compression in the second harmonic upconversion of the spectrally broadened infrared pulses with high-order dispersion. This results in 8-30 femtosecond pulses in the visible with an intensity enhancement of up to 30 times. Such an ultrashort visible and ultraviolet C source is ideal for investigating ultrafast dynamics in molecules, solids, and bio and nano systems. Furthermore, this tunable light source enables the generation of bright, narrow-bandwidth, continuously wavelength-tunable, coherent light in the extreme ultraviolet to soft X-ray spectral region. Theoretically, the X-ray pulse structure can consist of sub-200 as pulse trains, making such a source highly suitable for dynamic multidimensional imaging. This includes coherent diffractive imaging of ferromagnetic nanostructures where resonant X-ray scattering is essential, as well as X-ray absorption spectroscopies of advanced quantum materials at pico-nanometer spatial and atto-femtosecond temporal scales.

physics.optics↗

arXiv2023

A Prototype System for High Frame Rate Ultrasound Imaging based Prosthetic Arm Control

The creation of unique control methods for a hand prosthesis is still a problem that has to be addressed. The best choice of a human-machine interface (HMI) that should be used to enable natural control is still a challenge. Surface electromyography (sEMG), the most popular option, has a variety of difficult-to-fix issues (electrode displacement, sweat, fatigue). The ultrasound imaging-based methodology offers a means of recognising complex muscle activity and configuration with a greater SNR and less hardware requirements as compared to sEMG. In this study, a prototype system for high frame rate ultrasound imaging for prosthetic arm control is proposed. Using the proposed framework, a virtual robotic hand simulation is developed that can mimic a human hand as illustrated in the link [10]. The proposed classification model simulating four hand gestures has a classification accuracy of more than 90%.

cs.RO↗

arXiv2022

Addressing Distribution Shift at Test Time in Pre-trained Language Models

State-of-the-art pre-trained language models (PLMs) outperform other models when applied to the majority of language processing tasks. However, PLMs have been found to degrade in performance under distribution shift, a phenomenon that occurs when data at test-time does not come from the same distribution as the source training set. Equally as challenging is the task of obtaining labels in real-time due to issues like long-labeling feedback loops. The lack of adequate methods that address the aforementioned challenges constitutes the need for approaches that continuously adapt the PLM to a distinct distribution. Unsupervised domain adaptation adapts a source model to an unseen as well as unlabeled target domain. While some techniques such as data augmentation can adapt models in several scenarios, they have only been sparsely studied for addressing the distribution shift problem. In this work, we present an approach (MEMO-CL) that improves the performance of PLMs at test-time under distribution shift. Our approach takes advantage of the latest unsupervised techniques in data augmentation and adaptation to minimize the entropy of the PLM's output distribution. MEMO-CL operates on a batch of augmented samples from a single observation in the test set. The technique introduced is unsupervised, domain-agnostic, easy to implement, and requires no additional data. Our experiments result in a 3% improvement over current test-time adaptation baselines.

cs.CL↗

arXiv2022

BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing

Training and evaluating language models increasingly requires the construction of meta-datasets --diverse collections of curated data with clear provenance. Natural language prompting has recently lead to improved zero-shot generalization by transforming existing, supervised datasets into a diversity of novel pretraining tasks, highlighting the benefits of meta-dataset curation. While successful in general-domain text, translating these data-centric approaches to biomedical language modeling remains challenging, as labeled biomedical datasets are significantly underrepresented in popular data hubs. To address this challenge, we introduce BigBIO a community library of 126+ biomedical NLP datasets, currently covering 12 task categories and 10+ languages. BigBIO facilitates reproducible meta-dataset curation via programmatic access to datasets and their metadata, and is compatible with current platforms for prompt engineering and end-to-end few/zero shot language model evaluation. We discuss our process for task schema harmonization, data auditing, contribution guidelines, and outline two illustrative use cases: zero-shot evaluation of biomedical prompts and large-scale, multi-task learning. BigBIO is an ongoing community effort and is available at https://github.com/bigscience-workshop/biomedical

cs.CL↗

arXiv2021

Magic angle twisted bilayer graphene as a highly efficient quantum Otto engine

At a discrete set of magic angles, twisted bilayer graphene has been shown to host extraordinarily flat bands, correlated insulating states, unconventional superconductivity, and distinct Landau level degeneracies. In this work, we design a highly efficient quantum Otto engine using a twisted bilayer graphene sample. Flat bands, which occur at magic angles, make the prospect of extracting useful work from our Otto engine lucrative. We use an eight-band continuum model of twisted bilayer graphene to compute efficiencies and work outputs for magic and non-magic angle twists, and compare the results with an $AB$ stacked bilayer and a monolayer. It is observed that the efficiency varies smoothly with the twist angle and the maximum is attained at the magic angle.

cond-mat.mes-hall↗

arXiv2021

Semantic Parsing Natural Language into Relational Algebra

Natural interface to database (NLIDB) has been researched a lot during the past decades. In the core of NLIDB, is a semantic parser used to convert natural language into SQL. Solutions from traditional NLP methodology focuses on grammar rule pattern learning and pairing via intermediate logic forms. Although those methods give an acceptable performance on certain specific database and parsing tasks, they are hard to generalize and scale. On the other hand, recent progress in neural deep learning seems to provide a promising direction towards building a general NLIDB system. Unlike the traditional approach, those neural methodologies treat the parsing problem as a sequence-to-sequence learning problem. In this paper, we experimented on several sequence-to-sequence learning models and evaluate their performance on general database parsing task.

cs.CL↗

arXiv2020

Single image dehazing for a variety of haze scenarios using back projected pyramid network

Learning to dehaze single hazy images, especially using a small training dataset is quite challenging. We propose a novel generative adversarial network architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze. Our architecture incorporates learning of multiple levels of complexities while retaining spatial context through iterative blocks of UNets and structural information of multiple scales through a novel pyramidal convolution block. These blocks together for the generator and are amenable to learning through back projection. We have shown that our network can be trained without over-fitting using as few as 20 image pairs of hazy and non-hazy images. We report the state of the art performances on NTIRE 2018 homogeneous haze datasets for indoor and outdoor images, NTIRE 2019 denseHaze dataset, and NTIRE 2020 non-homogeneous haze dataset.

eess.IV↗

arXiv2020

Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging

Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, large, and irregular fetal movements. It is, therefore, performed through visual monitoring of fetal motion and repeated acquisitions to ensure diagnostic-quality images are acquired. Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient. The current process is highly operator-dependent, increases scanner usage and cost, and significantly increases the length of fetal MRI scans which makes them hard to tolerate for pregnant women. To help build automatic MRI motion tracking and navigation systems to overcome the limitations of the current process and improve fetal imaging, we have developed a new real time image-based motion tracking method based on deep learning that learns to predict fetal motion directly from acquired images. Our method is based on a recurrent neural network, composed of spatial and temporal encoder-decoders, that infers motion parameters from anatomical features extracted from sequences of acquired slices. We compared our trained network on held out test sets (including data with different characteristics, e.g. different fetuses scanned at different ages, and motion trajectories recorded from volunteer subjects) with networks designed for estimation as well as methods adopted to make predictions. The results show that our method outperformed alternative techniques, and achieved real-time performance with average errors of 3.5 and 8 degrees for the estimation and prediction tasks, respectively. Our real-time deep predictive motion tracking technique can be used to assess fetal movements, to guide slice acquisitions, and to build navigation systems for fetal MRI.

eess.IV↗

arXiv2018

Sentiment Transfer using Seq2Seq Adversarial Autoencoders

Expressing in language is subjective. Everyone has a different style of reading and writing, apparently it all boil downs to the way their mind understands things (in a specific format). Language style transfer is a way to preserve the meaning of a text and change the way it is expressed. Progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data, use cases, and reliable evaluation metrics. In response to the challenge of lacking parallel data, we explore learning style transfer from non-parallel data. We propose a model combining seq2seq, autoencoders, and adversarial loss to achieve this goal. The key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks. Considering the problem of evaluating style transfer tasks, we frame the problem as sentiment transfer and evaluation using a sentiment classifier to calculate how many sentiments was the model able to transfer. We report our results on several kinds of models.

cs.CL↗