arXiv subjects

Vivek Kumar

Publications and source records attributed to Vivek Kumar.

At least 73 records · Page 4Linked to original sources

arXiv2022

Rashba-like spin-orbit interaction and spin texture at the KTaO$_\text{3}$ (001) surface from DFT calculations

Rashba-like spin-orbit interaction at oxide heterostructures emerges as a much sought-after feature in the context of oxide spintronics and spin-orbitronics. KTaO$_3$ (KTO) is one of the best substrates available for the purpose, owing to its strong spin-orbit interaction and alternating $+1|-1$ charged layers along the (001) direction. Employing first-principles calculations within density functional theory (DFT) and proposing a possible electrostatic model for charge transfer to the surfaces of KTO slabs, we comprehensively analyze Rashba-like spin-orbit interaction with the help of three-dimensional band dispersion, isoenergetic contours, and projected spin textures $-$ all directly obtained from our DFT results $-$ in a thin insulating slab and a conducting thick slab of KTO. Our results reveal reasonably strong linear Rashba interaction with no signature of Dresselhaus or higher order Rashba interactions in the systems considered here. The rigorous analysis presented here may be crucial for future developments in oxide spintronics.

cond-mat.mtrl-sci↗

arXiv2022

Fluctuating Fractionalized Spins in Quasi Two-dimensional Magnetic V0.85PS3

Quantum spin liquid (QSL), a state characterized by exotic low energy fractionalized excitations and statistics is still elusive experimentally and may be gauged via indirect experimental signatures. Remnant of QSL phase may reflect in the spin dynamics as well as quanta of lattice vibrations, i.e., phonons, via the strong coupling of phonons with the underlying fractionalized excitations i.e., Majorana fermions. Inelastic light scattering (Raman) studies on V1-xPS3 single crystals evidences the spin fractionalization into Majorana fermions deep into the paramagnetic phase reflected in the emergence of a low frequency quasielastic response along with a broad magnetic continuum marked by a crossover temperature T* ~ 200 K from a pure paramagnetic state to fractionalized spins regime qualitatively gauged via dynamic Raman susceptibility. We further evidenced anomalies in the phonons self-energy parameters in particular phonon line broadening and line asymmetry evolution at this crossover temperature, attributed to the decaying of phonons into itinerant Majorana fermions. This anomalous scattering response is thus indicative of fluctuating fractionalized spins suggesting a phase proximate to the quantum spin liquid state in this quasi two-dimensional (2D) magnetic system.

cond-mat.str-el↗

arXiv2022

Optimization of rule-based energy management strategies for hybrid vehicles using dynamic programming

Reducing energy consumption is a key focus for hybrid electric vehicle (HEV) development. The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle measurement system does. However, feedback from the measurement system is what the vehicle controller actually uses to manage energy consumption. Therefore, the optimization solely using the model does not represent what the vehicle controller sees in the vehicle. This paper reports the utility factor-weighted energy consumption using a rule-based strategy under a real-world representative drive cycle. In addition, the vehicle test data was used to perform the optimization approach. By comparing results from both rule-based and optimization-based strategies, the areas for further improving rule-based strategy are discussed. Furthermore, recent development of OBD raises a concern about the increase of energy consumption. This paper investigates the energy consumption increase with extensive OBD usage.

eess.SY↗

arXiv2022

Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers

Existing Math Word Problem (MWP) solvers have achieved high accuracy on benchmark datasets. However, prior works have shown that such solvers do not generalize well and rely on superficial cues to achieve high performance. In this paper, we first conduct experiments to showcase that this behaviour is mainly associated with the limited size and diversity present in existing MWP datasets. Next, we propose several data augmentation techniques broadly categorized into Substitution and Paraphrasing based methods. By deploying these methods we increase the size of existing datasets by five folds. Extensive experiments on two benchmark datasets across three state-of-the-art MWP solvers show that proposed methods increase the generalization and robustness of existing solvers. On average, proposed methods significantly increase the state-of-the-art results by over five percentage points on benchmark datasets. Further, the solvers trained on the augmented dataset perform comparatively better on the challenge test set. We also show the effectiveness of proposed techniques through ablation studies and verify the quality of augmented samples through human evaluation.

cs.CL↗

arXiv2022

Pneumonia Detection in Chest X-Rays using Neural Networks

With the advancement in AI, deep learning techniques are widely used to design robust classification models in several areas such as medical diagnosis tasks in which it achieves good performance. In this paper, we have proposed the CNN model (Convolutional Neural Network) for the classification of Chest X-ray images for Radiological Society of North America Pneumonia (RSNA) datasets. The study also tries to achieve the same RSNA benchmark results using the limited computational resources by trying out various approaches to the methodologies that have been implemented in recent years. The proposed method is based on a non-complex CNN and the use of transfer learning algorithms like Xception, InceptionV3/V4, EfficientNetB7. Along with this, the study also tries to achieve the same RSNA benchmark results using the limited computational resources by trying out various approaches to the methodologies that have been implemented in recent years. The RSNA benchmark MAP score is 0.25, but using the Mask RCNN model on a stratified sample of 3017 along with image augmentation gave a MAP score of 0.15. Meanwhile, the YoloV3 without any hyperparameter tuning gave the MAP score of 0.32 but still, the loss keeps decreasing. Running the model for a greater number of iterations can give better results.

eess.IV↗

arXiv2022

Deterministic Terahertz wave control in scattering media

Scattering-assisted synthesis of broadband optical pulses is recognized to have a cross-disciplinary funda-mental and application importance. Achieving full-waveform synthesis generally requires means for assessing the instantaneous electric field, i.e. the absolute electromagnetic phase. These are generally not accessible to established methodologies for scattering-assisted pulse envelope and phase shaping. The lack of field sensitivity also results in complex indirect approaches to evaluate the scattering space-time properties. The terahertz frequency domain potentially offers some distinctive new possibilities thanks to the availability of methods to perform absolute measurements of the scattered electric field, as opposed to optical intensity-based diagnostics. An interesting conceptual question is whether this additional degree of freedom can lead to different types of methodologies toward wave shaping and to a direct field-waveform control. In this work, we theoretically investigate a deterministic scheme to achieve broadband, spatio-temporal waveform control of terahertz fields mediated by a scattering medium. The direct field access via Time-Domain Spectroscopy enables a process in which the field and scattering matrix of the medium are assessed with minimal experimental efforts. Then, the illumination conditions for an arbitrary targeted output field waveform are deterministically determined. In addition, the complete field knowledge enables reconstructing field distributions with complex phase profiles, as in the case of phase-only masks and optical vortices, a significantly challenging task for traditional implementations at optical frequencies based on intensity measurements aided with interferometric techniques.

physics.optics↗

arXiv2021

Weighted composition operators between weighted Hardy spaces on rooted trees

In this paper, we introduce a discrete analogue of weighted Hardy spaces on rooted trees and study weighted composition operators between them in detail. In particular, we characterize bounded and compact weighted composition operators between discrete Hardy spaces. We also consider isometric weighted composition operators between these spaces.

math.FA↗

arXiv2021

Cuttlefish: Library for Achieving Energy Efficiency in Multicore Parallel Programs

A low-cap power budget is challenging for exascale computing. Dynamic Voltage and Frequency Scaling (DVFS) and Uncore Frequency Scaling (UFS) are the two widely used techniques for limiting the HPC application's energy footprint. However, existing approaches fail to provide a unified solution that can work with different types of parallel programming models and applications. This paper proposes Cuttlefish, a programming model oblivious C/C++ library for achieving energy efficiency in multicore parallel programs running over Intel processors. An online profiler periodically profiles model-specific registers to discover a running application's memory access pattern. Using a combination of DVFS and UFS, Cuttlefish then dynamically adapts the processor's core and uncore frequencies, thereby improving its energy efficiency. The evaluation on a 20-core Intel Xeon processor using a set of widely used OpenMP benchmarks, consisting of several irregular-tasking and work-sharing pragmas, achieves geometric mean energy savings of 19.4% with a 3.6% slowdown.

cs.DC↗

arXiv2021

Adversarial Examples for Evaluating Math Word Problem Solvers

Standard accuracy metrics have shown that Math Word Problem (MWP) solvers have achieved high performance on benchmark datasets. However, the extent to which existing MWP solvers truly understand language and its relation with numbers is still unclear. In this paper, we generate adversarial attacks to evaluate the robustness of state-of-the-art MWP solvers. We propose two methods Question Reordering and Sentence Paraphrasing to generate adversarial attacks. We conduct experiments across three neural MWP solvers over two benchmark datasets. On average, our attack method is able to reduce the accuracy of MWP solvers by over 40 percentage points on these datasets. Our results demonstrate that existing MWP solvers are sensitive to linguistic variations in the problem text. We verify the validity and quality of generated adversarial examples through human evaluation.

cs.CL↗

arXiv2021

Effect of recirculation zones on the ventilation of a public washroom

Air-borne transmission can pose a major risk of infection spread in enclosed spaces. Venting the air out using exhaust fans and ducts is a common approach to mitigate the risk. In this work, we study the air flow set up by an exhaust fan in a typical shared washroom that can be a potential hot spot for COVID-19 transmission. The primary focus is on the regions of recirculating flow that can harbor infectious aerosol for much longer than the well-ventilated parts of the room. Computational fluid dynamics is used to obtain the steady state air flow field, and Lagrangian tracking of particles give the spatial and temporal distribution of infectious aerosol in the domain. It is found that the washbasin located next to the door is in a prominent recirculation zone, and particles injected in this region take much longer to be evacuated. The ventilation rate is found to be governed by the air residence time in the recirculation zone, and it is much higher than the time scale based on fully-mixed reactor model of the room. Increasing the fan flow rate can reduce the ventilation time, but cannot eliminate the recirculation zones in the washroom.

physics.flu-dyn↗

arXiv2021

Electron-Phonon Coupling, Thermal Expansion Coefficient, Resonance Effect and Phonon Dynamics in High Quality CVD Grown Mono and Bilayer MoSe2

Probing phonons, quasi-particle excitations and their coupling has enriched our understanding of these 2D materials and proved to be crucial for developing their potential applications. Here, we report comprehensive temperature, 4-330 K, and polarization-dependent Raman measurements on mono and bilayer MoSe2. Phonon's modes up to fourth-order are observed including forbidden Raman and IR modes, understood considering Frohlich mechanism of exciton-phonon coupling. Most notably, anomalous variations in the phonon linewidths with temperature pointed at the significant role of electron-phonon coupling in these systems, especially for the out-of-plane (A1g) and shear mode (E22g), which is found to be more prominent in the narrow-gaped bilayer than the large gapped monolayer. Via polarization-dependent measurements, we deciphered the ambiguity in symmetry assignments, especially to the peaks around ~ 170 cm-1 and ~ 350 cm-1. Temperature-dependent thermal expansion coefficient, an important parameter for the device performance, is carefully extracted for both mono and bilayer by monitoring the temperature-dependence of the real-part of the phonon self-energy parameter. Our temperature-dependent in-depth Raman studies provide a pave for uncovering the deeper role of phonons in these 2D layered materials from a fundamental as well as application point of view.

cond-mat.mtrl-sci↗

arXiv2021

TF-IDF vs Word Embeddings for Morbidity Identification in Clinical Notes: An Initial Study

Today, we are seeing an ever-increasing number of clinical notes that contain clinical results, images, and textual descriptions of patient's health state. All these data can be analyzed and employed to cater novel services that can help people and domain experts with their common healthcare tasks. However, many technologies such as Deep Learning and tools like Word Embeddings have started to be investigated only recently, and many challenges remain open when it comes to healthcare domain applications. To address these challenges, we propose the use of Deep Learning and Word Embeddings for identifying sixteen morbidity types within textual descriptions of clinical records. For this purpose, we have used a Deep Learning model based on Bidirectional Long-Short Term Memory (LSTM) layers which can exploit state-of-the-art vector representations of data such as Word Embeddings. We have employed pre-trained Word Embeddings namely GloVe and Word2Vec, and our own Word Embeddings trained on the target domain. Furthermore, we have compared the performances of the deep learning approaches against the traditional tf-idf using Support Vector Machine and Multilayer perceptron (our baselines). From the obtained results it seems that the latter outperforms the combination of Deep Learning approaches using any word embeddings. Our preliminary results indicate that there are specific features that make the dataset biased in favour of traditional machine learning approaches.

cs.CL↗

arXiv2021

Towards Detecting Need for Empathetic Response in Motivational Interviewing

Empathetic response from the therapist is key to the success of clinical psychotherapy, especially motivational interviewing. Previous work on computational modelling of empathy in motivational interviewing has focused on offline, session-level assessment of therapist empathy, where empathy captures all efforts that the therapist makes to understand the client's perspective and convey that understanding to the client. In this position paper, we propose a novel task of turn-level detection of client need for empathy. Concretely, we propose to leverage pre-trained language models and empathy-related general conversation corpora in a unique labeller-detector framework, where the labeller automatically annotates a motivational interviewing conversation corpus with empathy labels to train the detector that determines the need for therapist empathy. We also lay out our strategies of extending the detector with additional-input and multi-task setups to improve its detection and explainability.

cs.CL↗

arXiv2021

Fractional Spin fluctuations and quantum liquid signature in Gd2ZnIrO6

Hitherto, the discrete identification of quantum spin liquid phase, holy grail of condensed matter physics, remains a challenging task experimentally. However, the precursor of quantum spin liquid state may reflect in the spin dynamics even in the paramagnetic phase over a wide temperature range as conjectured theoretically. Here we report comprehensive inelastic light (Raman) scattering measurements on the Ir based double perovskite, Gd2ZnIrO6, as a function of different incident photon energies and polarization in a broad temperature range. Our results evidenced the spin fractionalization within the paramagnetic phase reflected in the emergence of a polarization independent quasi-elastic peak at low energies with lowering temperature. Also, the fluctuating scattering amplitude measured via dynamic Raman susceptibility increases with lowering temperature and decreases mildly upon entering into long-range magnetic ordering phase, below 23 K, suggesting the magnetic origin of these fluctuations. This anomalous scattering response is thus indicative of fluctuating fractional spin evincing the quantum spin liquid phase in a three-dimensional double perovskite system.

cond-mat.str-el↗

arXiv2020

OneStopTuner: An End to End Architecture for JVM Tuning of Spark Applications

Java is the backbone of widely used big data frameworks, such as Apache Spark, due to its productivity, portability from JVM-based execution, and support for a rich set of libraries. However, the performance of these applications can widely vary depending on the runtime flags chosen out of all existing JVM flags. Manually tuning these flags is both cumbersome and error-prone. Automated tuning approaches can ease the task, but current solutions either require considerable processing time or target a subset of flags to avoid time and space requirements. In this paper, we present OneStopTuner, a Machine Learning based novel framework for autotuning JVM flags. OneStopTuner controls the amount of data generation by leveraging batch mode active learning to characterize the user application. Based on the user-selected optimization metric, OneStopTuner then discards the irrelevant JVM flags by applying feature selection algorithms on the generated data. Finally, it employs sample efficient methods such as Bayesian optimization and regression guided Bayesian optimization on the shortlisted JVM flags to find the optimal values for the chosen set of flags. We evaluated OneStopTuner on widely used Spark benchmarks and compare its performance with the traditional simulated annealing based autotuning approach. We demonstrate that for optimizing execution time, the flags chosen by OneStopTuner provides a speedup of up to 1.35x over default Spark execution, as compared to 1.15x speedup by using the flag configurations proposed by simulated annealing. OneStopTuner was able to reduce the number of executions for data-generation by 70% and was able to suggest the optimal flag configuration 2.4x faster than the standard simulated annealing based approach, excluding the time for data-generation.

cs.DC↗

arXiv2020

Detection of antiskyrmions by topological Hall effect in Heusler compounds

Heusler compounds having $\textit{D}$${}_{2d}$ crystal symmetry gained much attention recently due to the stabilization of a vortex-like spin texture called antiskyrmions in thin lamellae of Mn${}_{1.4}$Pt${}_{0.9}$Pd${}_{0.1}$Sn as reported in the work of Nayak $\textit{et al.}$ [Nature (London) 548, 561 (2017)]. Here we show that bulk Mn${}_{1.4}$Pt${}_{0.9}$Pd${}_{0.1}$Sn undergoes a spin-reorientation transition from a collinear ferromagnetic to a noncollinear configuration of Mn moments below 135 K, which is accompanied by the emergence of a topological Hall effect. We tune the topological Hall effect in Pd and Rh substituted Mn${}_{1.4}$PtSn Heusler compounds by changing the intrinsic magnetic properties and spin textures. A unique feature of the present system is the observation of a zero-field topological Hall resistivity with a sign change which indicates the robust formation of antiskyrmions.

cond-mat.mtrl-sci↗

arXiv2020

On the utility of cloth facemasks for controlling ejecta during respiratory events

The utility of wearing simple cloth face masks is analyzed using computational fluid dynamics simulations. We simulate the aerodynamic flow through the mask and the spatial spread of droplet ejecta resulting from respiratory events such as coughing or sneezing. Without a mask, a turbulent jet forms, and droplets with a broad size distribution are ejected. Large droplets (greater than about 125 μm in diameter) fall to the ground within about 2 m, while turbulent clouds transport a mist of small aerosolized droplets over significant distances (~ 5 m), consistent with reported experimental findings. A loosely fitted simple cotton cloth mask (with a pore size ~ 4 microns) qualitatively changes the propagation of the high velocity jet, and largely eliminates the turbulent cloud downstream of the mask. About 12\% of the airflow leaks around the sides of a mask, considering a uniform gap of only 1 mm all around, between the face and the mask. The spread of ejecta is also changed, with most large droplets trapped at the mask surface. We present the viral load in the air and deposited around the person, and show that wearing even a simple cloth mask substantially decreases the extent of spatial spread of virus particles when an infected person coughs or sneezes.

physics.med-ph↗

arXiv2020

Crystal and magnetic structure of antiferromagnetic Mn$_{2}$PtPd

We have investigated the crystal and magnetic structure of Mn${}_{2}$PtPd alloy using powder x-ray and neutron diffraction experiments. This compound is believed to belong to the Heusler family having crystal symmetry $\mathit{I}$4/$\mathit{mmm}$ (TiAl${}_{3}$-type). However, in this work we found that the Pd and Pt atoms are disordered and thus Mn${}_{2}$PtPd crystallizes in the $\mathit{L}$1${}_{0}$ structure having $\mathit{P}$4/$\mathit{mmm}$ symmetry (CuAu-I type) like MnPt and MnPd binary alloys. The lattice constants are $\mathit{a}$ = 2.86 Å and $\mathit{c}$ = 3.62 Å at room temperature. Mn${}_{2}$PtPd has a collinear antiferromagnetic spin structure below the Néel temperature $\mathit{T}$${}_{N}$ = 866 K, where Mn moments of $\mathrm{\sim}$4 $μ$${}_{B}$ lie in the $\mathit{ab}$-plane. We observed a strong change in the lattice parameters near $\mathit{T}$${}_{N}$. The sample exhibits metallic behaviour, where electrical resistivity and carrier concentration are of the order of 10${}^{-5}$ $Ω$ cm and 10${}^{21}$ cm${}^{-3}$, respectively.

cond-mat.mtrl-sci↗