arXiv subjects

Aditya Singh

Publications and source records attributed to Aditya Singh.

At least 73 records · Page 4Linked to original sources

arXiv2023

RASS-MCMF: A full-sky X-ray selected galaxy cluster catalog

We present the RASS-MCMF catalog of 8,465 X-ray selected galaxy clusters over 25,000 deg$^2$ of extragalactic sky. The accumulation of deep, multiband optical imaging data, the development of the Multi-Component Matched Filter cluster confirmation algorithm (MCMF), and the release of the DESI Legacy Survey DR10 catalog makes it possible -- for the first time, more than 30 years after the launch of the ROSAT X-ray satellite -- to identify the majority of the galaxy clusters detected in the second ROSAT All-Sky-Survey (RASS) source catalog (2RXS). The resulting 90\% pure RASS-MCMF catalog is the largest ICM-selected cluster sample to date. RASS-MCMF probes a large dynamic range in cluster mass spanning from galaxy groups to the most massive clusters. The cluster redshift distribution peaks at $z\sim0.1$ and extends to redshifts $z\sim1$. Out to $z\sim0.4$, the RASS-MCMF sample contains more clusters per redshift interval ($dN/dz$) than any other ICM-selected sample. In addition to the main sample, we present two subsamples with 6,924 and 5,516 clusters, exhibiting 95\% and 99\% purity, respectively.. We forecast the utility of the sample for a cluster cosmological study, using realistic mock catalogs that incorporate most observational effects, including the X-ray exposure time and background variations, the existence likelihood selection and the impact of the optical cleaning with the algorithm MCMF. Using realistic priors on the observable-mass relation parameters from a DES-based weak lensing analysis, we estimate the constraining power of the RASS-MCMF$\times$DES sample to be of 0.026, 0.033 and 0.15 ($1σ$) on the parameters $Ω_\mathrm{m}$, $σ_8$ and $w$, respectively.

astro-ph.CO↗

arXiv2023

Thermodynamic curvature of charged black holes with $AdS_2$ horizons

Sign and magnitude of the thermodynamic curvature provides empirical information about the nature of microstructures of a general thermodynamic system. For charged black holes in AdS, thermodynamic curvature is positive for large charge or chemical potential, and diverges for extremal black holes, indicating strongly repulsive nature. We compute the thermodynamic curvature at low temperatures, for charged black holes with AdS$_2$ near horizon geometry, and containing a zero temperature horizon radius $r_h$, in a spacetime which asymptotically approaches $AdS_D$ (for $D>3$). In the semi-classical analysis at low temperatures, the curvature shows a novel crossover from negative to positive side, indicating the shift from attraction to repulsion dominated regime near $T=0$, before diverging as $1/(γT)$, where $γ$ is the coefficient of leading low temperature correction to entropy. Accounting for quantum fluctuations, the curvature computed in the canonical ensemble is positive, whereas the one in the grand canonical ensemble, continues to show a crossover from negative to positive side. Moreover, the divergence of curvature at $T=0$ is cured irrespective of the ensemble used, resulting in a universal constant.

hep-th↗

arXiv2023

Understanding EEG signals for subject-wise Definition of Armoni Activities

In a growing world of technology, psychological disorders became a challenge to be solved. The methods used for cognitive stimulation are very conventional and based on one-way communication, which only relies on the material or method used for training of an individual. It doesn't use any kind of feedback from the individual to analyze the progress of the training process. We have proposed a closed-loop methodology to improve the cognitive state of a person with ID (Intellectual disability). We have used a platform named 'Armoni', for providing training to the intellectually disabled individuals. The learning is performed in a closed-loop by using feedback in the form of change in affective state. For feedback to the Armoni, an EEG (Electroencephalograph) headband is used. All the changes in EEG are observed and classified against the change in the mean and standard deviation value of all frequency bands of signal. This comparison is being helpful in defining every activity with respect to change in brain signals. In this paper, we have discussed the process of treatment of EEG signal and its definition against the different activities of Armoni. We have tested it on 6 different systems with different age groups and cognitive levels.

eess.SP↗

arXiv2023

Anisotropic Light-Matter Interactions in Single Crystal Topological Insulator Bismuth Selenide

Anisotropy of light-matter interactions in materials give remarkable information about the phonons and their interactions with electrons. We report the angle-resolved polarized Raman spectroscopy of single-crystal of Bi2Se3 to obtain the elements of Raman tensor for understanding the strength of polarization along different crystallographic orientations. Intensity variation in the polar plots corresponding to E_g^1 ~ 37 cm-1, A_1g^1 ~71 cm-1, E_g^2 ~ 130 cm-1, and A_1g^2 ~ 173 cm-1 suggests the higher differential polarizability along cross-plane (bc-plane). The polar patterns and the differences in elements of the Raman tensor provides the evidence of the fundamental electron-phonon and anisotropic light matter interactions in Bi2Se3.

cond-mat.mtrl-sci↗

arXiv2022

Machine Learning Framework: Competitive Intelligence and Key Drivers Identification of Market Share Trends Among Healthcare Facilities

The necessity of data driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a Healthcare Provider Facility or a Hospital (from here on termed as Facility) Market Share is of key importance. This pilot study aims at developing a data driven Machine Learning - Regression framework which aids strategists in formulating key decisions to improve the Facilitys Market Share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study; and the data spanning across 60 key Facilities in Washington State and about 3 years of historical data is considered. In the current analysis Market Share is termed as the ratio of facility encounters to the total encounters among the group of potential competitor facilities. The current study proposes a novel two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. The proposed method to identify pool of competitors in current analysis, develops Directed Acyclic Graphs (DAGs), feature level word vectors and evaluates the key connected components at facility level. This technique is robust since its data driven which minimizes the bias from empirical techniques. Post identifying the set of competitors among facilities, developed Regression model to predict the Market share. For relative quantification of features at a facility level, incorporated SHAP a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share.

cs.LG↗

arXiv2022

On Deep Neural Network Calibration by Regularization and its Impact on Refinement

Deep neural networks have been shown to be highly miscalibrated. often they tend to be overconfident in their predictions. It poses a significant challenge for safety-critical systems to utilise deep neural networks (DNNs), reliably. Many recently proposed approaches to mitigate this have demonstrated substantial progress in improving DNN calibration. However, they hardly touch upon refinement, which historically has been an essential aspect of calibration. Refinement indicates separability of a network's correct and incorrect predictions. This paper presents a theoretically and empirically supported exposition reviewing refinement of a calibrated model. Firstly, we show the breakdown of expected calibration error (ECE), into predicted confidence and refinement under the assumption of over-confident predictions. Secondly, linking with this result, we highlight that regularization based calibration only focuses on naively reducing a model's confidence. This logically has a severe downside to a model's refinement as correct and incorrect predictions become tightly coupled. Lastly, connecting refinement with ECE also provides support to existing refinement based approaches which improve calibration but do not explain the reasoning behind it. We support our claims through rigorous empirical evaluations of many state of the art calibration approaches on widely used datasets and neural networks. We find that many calibration approaches with the likes of label smoothing, mixup etc. lower the usefulness of a DNN by degrading its refinement. Even under natural data shift, this calibration-refinement trade-off holds for the majority of calibration methods.

cs.LG↗

arXiv2022

Cavity-Enhanced Raman Scattering from 2D Hybrid Perovskites

Two-dimensional (2D) hybrid organic-inorganic perovskites (HOIPs) are promising candidates for optoelectronic applications due to their efficient light emission properties, and strong dielectric confinement effects. Raman spectroscopy is a versatile, non-contact and often non-destructive technique, widely used to characterize crystalline materials. However, the inherently weak phonon scattering, strong background, and complex nature of the Raman signals from HOIPs present challenges in obtaining reliable signals. Further, the fragile nature of the 2D HOIP crystals results in rapid degradation upon exposure to heat, light and moisture, which presents further difficulty in enhancing Raman scattered photon signals. Herein, we report a novel approach to enhance the weak Raman scattering signals in Ruddlesden-Popper (RP) phase HOIPs by introducing an open-cavity comprising HOIP crystals on a gold substrate. We observe 15x enhancement of the Raman signals due to the Purcell effect inside the high-index HOIP crystals. Our simple approach can be extended to enhance the study of phonon scattering in other HOIP and van der Waals layered crystals.

cond-mat.mtrl-sci↗

arXiv2021

RandCrowns: A Quantitative Metric for Imprecisely Labeled Tree Crown Delineation

Supervised methods for object delineation in remote sensing require labeled ground-truth data. Gathering sufficient high quality ground-truth data is difficult, especially when targets are of irregular shape or difficult to distinguish from background or neighboring objects. Tree crown delineation provides key information from remote sensing images for forestry, ecology, and management. However, tree crowns in remote sensing imagery are often difficult to label and annotate due to irregular shape, overlapping canopies, shadowing, and indistinct edges. There are also multiple approaches to annotation in this field (e.g., rectangular boxes vs. convex polygons) that further contribute to annotation imprecision. However, current evaluation methods do not account for this uncertainty in annotations, and quantitative metrics for evaluation can vary across multiple annotators. In this paper, we address these limitations by developing an adaptation of the Rand index for weakly-labeled crown delineation that we call RandCrowns. Our new RandCrowns evaluation metric provides a method to appropriately evaluate delineated tree crowns while taking into account imprecision in the ground-truth delineations. The RandCrowns metric reformulates the Rand index by adjusting the areas over which each term of the index is computed to account for uncertain and imprecise object delineation labels. Quantitative comparisons to the commonly used intersection over union method shows a decrease in the variance generated by differences among multiple annotators. Combined with qualitative examples, our results suggest that the RandCrowns metric is more robust for scoring target delineations in the presence of uncertainty and imprecision in annotations that are inherent to tree crown delineation.

cs.CV↗

arXiv2021

Large-Area Transfer of 2D TMDCs Assisted by Water-soluble layer for Potential Device Applications

Layer transfer offers enormous potential for the industrial implementation of 2D material technology platforms. However, the transfer method used must retain as-grown uniformity and cleanliness in the transferred films for the fabrication of 2D material-based. Additionally, the method used must be capable of large-area transfer to maintain wafer-scale fabrication standards. Here, a facile route to transfer centimeter-scale synthesized 2D TMDCs (3L MoS2, 1L WS2) onto various substrates such as sapphire, SiO2/Si, and flexible substrates (mica, polyimide) has been developed using a water-soluble layer (Na2S/Na2SO4) underneath the as-grown film. The developed transfer process represents a fast, clean, generic, and scalable technique to transfer 2D atomic layers. The key strategy used in this process includes the dissolution of Na2S/Na2SO4 layer due to the penetration of NaOH solution between the growth substrate and hydrophobic 2D TMDC film. As a proof-of-concept device, a broadband photodetector has been fabricated onto transferred 3L MoS2, which shows photoresponse behavior for a wide range of wavelength ranging from NIR to UV. The enhancement in photocurrent was found to be 100 times and 10 times to dark current in the UV and visible region, respectively. This work opens up the pathway towards flexible electronics and optoelectronics.

physics.app-ph↗

arXiv2021

NaCl-assisted CVD growth of wafer scale high quality trilayer MoS$_2$ and the role of concentration boundary layer

Direct growth of wafer scale high quality 2D layered materials (2DLMs) on SiO$_2$/Si substrate is still a challenge. The chemical vapor deposition (CVD) technique has played a significant role in achieving a large area continuous film of 2DLMs. CVD growth requires the optimization of many growth parameters such as temperature, amount of precursors, pressure, carrier gas flow and distance between the reactants. However, the role of boundary layer of reactants concentration has not been explored yet. The amount of precursors which leads to the formation of reactants concentration boundary layer has a significant role in controlling the thickness of growing material. Here, we report the role of concentration boundary layer to achieve wafer-scale MoS$_2$ in NaCl-assisted CVD growth at low temperature. Control of boundary layer thickness has led to the synthesis monolayer, bilayer, trilayer, and bulk MoS$_2$ film and flakes in our single-zone CVD at atmospheric pressure. Most importantly, we have synthesized 7 $\times$ 2.5 cm$^2$ area continuous, high quality trilayer MoS$_2$ film with good repeatability. We believe that our approach may lead to synthesize other wafer-scale 2DLMs that will pave the way for nano- and optoelectronics.

cond-mat.mtrl-sci↗

arXiv2021

Single- and narrow-line photoluminescence in a boron nitride-supported MoSe$_2$/graphene heterostructure

Heterostructures made from van der Waals materials provide a template to investigate proximity effects at atomically sharp heterointerfaces. In particular, near-field charge and energy transfer in heterostructures made from semiconducting transition metal dichalcogenides (TMD) have attracted interest to design model 2D "donor-acceptor" systems and new optoelectronic components. Here, using of Raman scattering and photoluminescence spectroscopies, we report a comprehensive characterization of a molybedenum diselenide (MoSe$_2$) monolayer deposited onto hexagonal boron nitride (hBN) and capped by mono- and bilayer graphene. Along with the atomically flat hBN susbstrate, a single graphene epilayer is sufficient to passivate the MoSe$_2$ layer and provides a homogenous environment without the need for an extra capping layer. As a result, we do not observe photo-induced doping in our heterostructure and the MoSe$_2$ excitonic linewidth gets as narrow as 1.6~meV, hence approaching the homogeneous limit. The semi-metallic graphene layer neutralizes the 2D semiconductor and enables picosecond non-radiative energy transfer that quenches radiative recombination from long-lived states. Hence, emission from the neutral band edge exciton largely dominates the photoluminescence spectrum of the MoSe$_2$/graphene heterostructure. Since this exciton has a picosecond radiative lifetime at low temperature, comparable with the energy transfer time, its low-temperature photoluminescence is only quenched by a factor of $3.3 \pm 1$ and $4.4 \pm 1$ in the presence of mono- and bilayer graphene, respectively. Finally, while our bare MoSe$_2$ on hBN exhibits negligible valley polarization at low temperature and under near-resonant excitation, we show that interfacing MoSe$_2$ with graphene yields a single-line emitter with degrees of valley polarization and coherence up to $\sim 15\,\%$.

cond-mat.mes-hall↗

arXiv2021

Thermodynamic curvature of AdS black holes with dark energy

In this paper, we study the effect of dark energy on the extended thermodynamic structure and interacting microstructures of black holes in AdS, through an analysis of thermodynamic geometry. Considering various limiting cases of the novel equation of state obtained in charged rotating black holes with quintessence, and taking enthalpy $H$ as the key potential in the extended phase space, we scrutinize the behavior of the Ruppeiner curvature scalar $R$ in the entropy-pressure $(S,P)$-plane (or equivalently in the temperature-volume ($T,V$)-plane). Analysis of $R$ empirically reveals that dark energy parameterized by $α$, significantly alters the dominant interactions of neutral, charged and slowly rotating black hole microstructures. In the Schwarzschild-AdS case: black holes smaller than a certain size continue to have attractive interactions whereas larger black holes are completely dominated by repulsive interactions which arise to due dark energy. For charged or rotating AdS black holes with quintessence, $R$ can change sign at multiple points depending upon the relation between $α$ and charge $q$ or angular momentum $J$. In particular, above a threshold value of $α$, $R$ is never negative at all, suggesting heuristically that the repulsive interactions due to quintessence are long ranged as opposed to the previously known short ranged repulsion in charged AdS black holes. A mean field interaction potential is proposed whose extrema effectively capture the points where the curvature $R$ changes sign.

gr-qc↗

arXiv2020

Assessing The Importance Of Colours For CNNs In Object Recognition

Humans rely heavily on shapes as a primary cue for object recognition. As secondary cues, colours and textures are also beneficial in this regard. Convolutional neural networks (CNNs), an imitation of biological neural networks, have been shown to exhibit conflicting properties. Some studies indicate that CNNs are biased towards textures whereas, another set of studies suggests shape bias for a classification task. However, they do not discuss the role of colours, implying its possible humble role in the task of object recognition. In this paper, we empirically investigate the importance of colours in object recognition for CNNs. We are able to demonstrate that CNNs often rely heavily on colour information while making a prediction. Our results show that the degree of dependency on colours tend to vary from one dataset to another. Moreover, networks tend to rely more on colours if trained from scratch. Pre-training can allow the model to be less colour dependent. To facilitate these findings, we follow the framework often deployed in understanding role of colours in object recognition for humans. We evaluate a model trained with congruent images (images in original colours eg. red strawberries) on congruent, greyscale, and incongruent images (images in unnatural colours eg. blue strawberries). We measure and analyse network's predictive performance (top-1 accuracy) under these different stylisations. We utilise standard datasets of supervised image classification and fine-grained image classification in our experiments.

cs.CV↗

arXiv2020

Machine learning predicts early onset of fever from continuous physiological data of critically ill patients

Fever can provide valuable information for diagnosis and prognosis of various diseases such as pneumonia, dengue, sepsis, etc., therefore, predicting fever early can help in the effectiveness of treatment options and expediting the treatment process. This study aims to develop novel algorithms that can accurately predict fever onset in critically ill patients by applying machine learning technique on continuous physiological data. We analyzed continuous physiological data collected every 5-minute from a cohort of over 200,000 critically ill patients admitted to an Intensive Care Unit (ICU) over a 2-year period. Each episode of fever from the same patient were considered as an independent event, with separations of at least 24 hours. We extracted descriptive statistical features from six physiological data streams, including heart rate, respiration, systolic and diastolic blood pressure, mean arterial pressure, and oxygen saturation, and use these features to independently predict the onset of fever. Using a bootstrap aggregation method, we created a balanced dataset of 7,801 afebrile and febrile patients and analyzed features up to 4 hours before the fever onset. We found that supervised machine learning methods can predict fever up to 4 hours before onset in critically ill patients with high recall, precision, and F1-score. This study demonstrates the viability of using machine learning to predict fever among hospitalized adults. The discovery of salient physiomarkers through machine learning and deep learning techniques has the potential to further accelerate the development and implementation of innovative care delivery protocols and strategies for medically vulnerable patients.

q-bio.QM↗

arXiv2020

NaCl-Assisted Substrate Dependent 2D Planar Nucleated Growth of MoS2

Synthesis of large-scale, uniform, easily transferable, and highly crystalline monolayer (1L) molybdenum disulfide (MoS2) on different substrates is a challenge and could decide its suitability for opto-electronic device applications. Herein, we report a facile NaCl-assisted Chemical Vapor Deposition (CVD) synthesis of high-quality MoS2 on amorphous, crystalline and layered substrates. Optical microscopy and Raman spectroscopy show that sapphire and SiO2/Si are suitable substrates for large 1L-MoS2 flakes growth, while mica is excellent for large-area continuous films. Comparatively lesser full-width-at-half-maximum (FWHM) of predominant A exciton peak (which is associated with direct band gap at K point) in photoluminescence spectra of 1L-MoS2 on sapphire suggests its high crystalline quality. However, 1L-MoS2 on other substrates, especially on quartz and bare Si show poor crystalline quality. The study depicts that the NaCl assists in the formation of seeding promoter such as water-soluble layer of Na2S and/or Na2SO4 on the substrate that helps in 2D planar nucleation of MoS2. The formation of such intermediate seeding layers also helps in layer transfer owing to its easy water solubility. The study could be utilized for large-scale synthesis of 1L-MoS2 on different substrates for high-performance optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2020

Effect of different precursors on CVD growth of molybdenum disulfide

Control over thickness, size, and area of chemical vapor deposition (CVD) grown molybdenum disulfide (MoS2) flakes is crucial for device application. Herein, we report a quantitative comparison of CVD synthesis of MoS2 on SiO2/Si substrate using three different precursors viz., molybdenum trioxide (MoO3), ammonium heptamolybdate (AHM), and tellurium (Te). A three-step chemical reaction mechanism of evolution of MoS2 from MoO3 micro-crystals is proposed for MoO3 precursor. Furthermore, a strategy based on growth temperature and ratio of amount of precursors is developed to systematically control thickness and area of MoS2 flakes. Our findings show that for large-sized crystalline monolayer MoS2 flakes, MoO3 is a better choice than AHM and Te-assisted synthesis. Moreover, Te as growth promoter, can lower down growth temperature by 250C. This study can be further used to fabricate MoS2 based high-performance electronic devices such as photodetectors, thin film transistors, and sensors.

cond-mat.mtrl-sci↗

arXiv2017

Optimization of Ensemble Supervised Learning Algorithms for Increased Sensitivity, Specificity, and AUC of Population-Based Colorectal Cancer Screenings

Over 150,000 new people in the United States are diagnosed with colorectal cancer each year. Nearly a third die from it (American Cancer Society). The only approved noninvasive diagnosis tools currently involve fecal blood count tests (FOBTs) or stool DNA tests. Fecal blood count tests take only five minutes and are available over the counter for as low as \$15. They are highly specific, yet not nearly as sensitive, yielding a high percentage (25%) of false negatives (Colon Cancer Alliance). Moreover, FOBT results are far too generalized, meaning that a positive result could mean much more than just colorectal cancer, and could just as easily mean hemorrhoids, anal fissure, proctitis, Crohn's disease, diverticulosis, ulcerative colitis, rectal ulcer, rectal prolapse, ischemic colitis, angiodysplasia, rectal trauma, proctitis from radiation therapy, and others. Stool DNA tests, the modern benchmark for CRC screening, have a much higher sensitivity and specificity, but also cost \$600, take two weeks to process, and are not for high-risk individuals or people with a history of polyps. To yield a cheap and effective CRC screening alternative, a unique ensemble-based classification algorithm is put in place that considers the FIT result, BMI, smoking history, and diabetic status of patients. This method is tested under ten-fold cross validation to have a .95 AUC, 92% specificity, 89% sensitivity, .88 F1, and 90% precision. Once clinically validated, this test promises to be cheaper, faster, and potentially more accurate when compared to a stool DNA test.

stat.ML↗

arXiv2016

Learning to Hash-tag Videos with Tag2Vec

User-given tags or labels are valuable resources for semantic understanding of visual media such as images and videos. Recently, a new type of labeling mechanism known as hash-tags have become increasingly popular on social media sites. In this paper, we study the problem of generating relevant and useful hash-tags for short video clips. Traditional data-driven approaches for tag enrichment and recommendation use direct visual similarity for label transfer and propagation. We attempt to learn a direct low-cost mapping from video to hash-tags using a two step training process. We first employ a natural language processing (NLP) technique, skip-gram models with neural network training to learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a corpus of 10 million hash-tags. We then train an embedding function to map video features to the low-dimensional Tag2vec space. We learn this embedding for 29 categories of short video clips with hash-tags. A query video without any tag-information can then be directly mapped to the vector space of tags using the learned embedding and relevant tags can be found by performing a simple nearest-neighbor retrieval in the Tag2Vec space. We validate the relevance of the tags suggested by our system qualitatively and quantitatively with a user study.

cs.CV↗