arXiv subjects

Nitesh Kumar

Publications and source records attributed to Nitesh Kumar.

At least 55 records · Page 3Linked to original sources

arXiv2023

Adaptive Gravity Compensation Control of a Cable-Driven Upper-Arm Soft Exosuit

This paper proposes an adaptive gravity compensation (AGC) control strategy for a cable-driven upper-limb exosuit intended to assist the wearer with lifting tasks. Unlike most model-based control techniques used for this human-robot interaction task, the proposed control design does not assume knowledge of the anthropometric parameters of the wearer's arm and the payload. Instead, the uncertainties in human arm parameters, such as mass, length, and payload, are estimated online using an indirect adaptive control law that compensates for the gravity moment about the elbow joint. Additionally, the AGC controller is agnostic to the desired joint trajectory followed by the human arm. For the purpose of controller design, the human arm is modeled using a 1-DOF manipulator model. Further, a cable-driven actuator model is proposed that maps the assistive elbow torque to the actuator torque. The performance of the proposed method is verified through a co-simulation, wherein the control input realized in MATLAB is applied to the human bio-mechanical model in OpenSim under varying payload conditions. Significant reductions in human effort in terms of human muscle torque and metabolic cost are observed with the proposed control strategy. Further, simulation results show that the performance of the AGC controller converges to that of the gravity compensation (GC) controller, demonstrating the efficacy of AGC-based online parameter learning.

cs.RO↗

arXiv2023

Predicting light curves of RR Lyrae variables using artificial neural network based interpolation of a grid of pulsation models

We present a new technique to generate the light curves of RRab stars in different photometric bands ($I$ and $V$ bands) using Artificial Neural Networks (ANN). A pre-computed grid of models was used to train the ANN, and the architecture was tuned using the $I$ band light curves. The best-performing network was adopted to make the final interpolators in the $I$ and $V$ bands. The trained interpolators were used to predict the light curve of RRab stars in the Magellanic Clouds, and the distances to the LMC and SMC were determined based on the reddening independent Wesenheit index. The estimated distances are in good agreement with the literature. The comparison of the predicted and observed amplitudes, and Fourier amplitude ratios showed good agreement, but the Fourier phase parameters displayed a few discrepancies. To showcase the utility of the interpolators, the light curve of the RRab star EZ Cnc was generated and compared with the observed light curve from the Kepler mission. The reported distance to EZ Cnc was found to be in excellent agreement with the updated parallax measurement from Gaia EDR3. Our ANN interpolator provides a fast and efficient technique to generate a smooth grid of model light curves for a wide range of physical parameters, which is computationally expensive and time-consuming using stellar pulsation codes.

astro-ph.SR↗

arXiv2023

Reply to: Low-frequency quantum oscillations in LaRhIn$_5$: Dirac point or nodal line?

We thank G.P. Mikitik and Yu.V. Sharlai for contributing this note and the cordial exchange about it. First and foremost, we note that the aim of our paper is to report a methodology to diagnose topological (semi)metals using magnetic quantum oscillations. Thus far, such diagnosis has been based on the phase offset of quantum oscillations, which is extracted from a "Landau fan plot". A thorough analysis of the Onsager-Lifshitz-Roth quantization rules has shown that the famous $π$-phase shift can equally well arise from orbital- or spin magnetic moments in topologically trivial systems with strong spin-orbit coupling or small effective masses. Therefore, the "Landau fan plot" does not by itself constitute a proof of a topologically nontrivial Fermi surface. In the paper at hand, we report an improved analysis method that exploits the strong energy-dependence of the effective mass in linearly dispersing bands. This leads to a characteristic temperature dependence of the oscillation frequency which is a strong indicator of nontrivial topology, even for multi-band metals with complex Fermi surfaces. Three materials, Cd$_3$As$_2$, Bi$_2$O$_2$Se and LaRhIn$_5$ served as test cases for this method. Linear band dispersions were detected for Cd$_3$As$_2$, as well as the $F$ $\approx$ 7 T pocket in LaRhIn$_5$.

cond-mat.str-el↗

arXiv2023

Hybrid Probabilistic Logic Programming: Inference and Learning

This thesis focuses on advancing probabilistic logic programming (PLP), which combines probability theory for uncertainty and logic programming for relations. The thesis aims to extend PLP to support both discrete and continuous random variables, which is necessary for applications with numeric data. The first contribution is the introduction of context-specific likelihood weighting (CS-LW), a new sampling algorithm that exploits context-specific independencies for computational gains. Next, a new hybrid PLP, DC#, is introduced, which integrates the syntax of Distributional Clauses with Bayesian logic programs and represents three types of independencies: i) conditional independencies (CIs) modeled in Bayesian networks; ii) context-specific independencies (CSIs) represented by logical rules, and iii) independencies amongst attributes of related objects in relational models expressed by combining rules. The scalable inference algorithm FO-CS-LW is introduced for DC#. Finally, the thesis addresses the lack of approaches for learning hybrid PLP from relational data with missing values and (probabilistic) background knowledge with the introduction of DiceML, which learns the structure and parameters of hybrid PLP and tackles the relational autocompletion problem. The conclusion discusses future directions and open challenges for hybrid PLP.

cs.AI↗

arXiv2023

First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs

Statistical relational AI and probabilistic logic programming have so far mostly focused on discrete probabilistic models. The reasons for this is that one needs to provide constructs to succinctly model the independencies in such models, and also provide efficient inference. Three types of independencies are important to represent and exploit for scalable inference in hybrid models: conditional independencies elegantly modeled in Bayesian networks, context-specific independencies naturally represented by logical rules, and independencies amongst attributes of related objects in relational models succinctly expressed by combining rules. This paper introduces a hybrid probabilistic logic programming language, DC#, which integrates distributional clauses' syntax and semantics principles of Bayesian logic programs. It represents the three types of independencies qualitatively. More importantly, we also introduce the scalable inference algorithm FO-CS-LW for DC#. FO-CS-LW is a first-order extension of the context-specific likelihood weighting algorithm (CS-LW), a novel sampling method that exploits conditional independencies and context-specific independencies in ground models. The FO-CS-LW algorithm upgrades CS-LW with unification and combining rules to the first-order case.

cs.AI↗

arXiv2022

Multimodal Neural Network For Demand Forecasting

Demand forecasting applications have immensely benefited from the state-of-the-art Deep Learning methods used for time series forecasting. Traditional uni-modal models are predominantly seasonality driven which attempt to model the demand as a function of historic sales along with information on holidays and promotional events. However, accurate and robust sales forecasting calls for accommodating multiple other factors, such as natural calamities, pandemics, elections, etc., impacting the demand for products and product categories in general. We propose a multi-modal sales forecasting network that combines real-life events from news articles with traditional data such as historical sales and holiday information. Further, we fuse information from general product trends published by Google trends. Empirical results show statistically significant improvements in the SMAPE error metric with an average improvement of 7.37% against the existing state-of-the-art sales forecasting techniques on a real-world supermarket dataset.

cs.LG↗

arXiv2022

Magnetic phase crossover in strongly correlated EuMn2P2

Strong electron correlations underlie a plethora of electronic and magnetic components and devices and are often used to identify and probe novel ground states in quantum materials. Herein we report a magnetic phase crossover in EuMn2P2, an insulator which shows Eu antiferromagnetism at TN=17K, but no phase transition attributed to Mn magnetism. The absence of a Mn magnetic phase transition contrasts with the formation of long-range Mn order at T=130K in isoelectronic EuMn2Sb2 and EuMn2As2. Temperature-dependent specific heat and 31P NMR measurements provide evidence for the development of Mn magnetic correlations from T=250-100 K. Density functional theory calculations demonstrate an unusual sensitivity of the band structure to the details of the imposed Mn and Eu magnetic order, with antiferromagnetic Mn order required to recapitulate an insulating state. Our results imply a picture in which long range Mn magnetic order is suppressed by chemical pressure, but that magnetic correlations persist, narrowing bands and producing an insulating state.

cond-mat.str-el↗

arXiv2022

Anomalous transport in itinerant van der Waals ferromagnets Fe$_n$GeTe$_2$ (\emph{n}=3, 4, 5)

Ferromagnetic (FM) semimetals Fe$_n$GeTe$_2$(n=3, 4, 5), exhibit several symmetry-protected band-crossing points or lines near the Fermi energy (E$_F$) and these topological properties of energy bands lead to interesting transport properties. We study these materials employing the first-principle calculations and the tight-binding Hamiltonian constructed by fitting the parameters of the first principles calculation. In the presence of spin-orbit coupling (SOC) for n=3,5 a large Berry curvature (BC) concentrated on the nodal lines is observed. The consequence of the correlation of the topological nodal line and magnetic moments on anomalous Hall conductivity (AHC) $σ_{xy}$ and anomalous Nernst conductivity (ANC) $α_{xy}$ have been investigated. We find $σ_{xy}=150$ S/cm for n=3, 295 S/cm for n=4, and 90 S/cm for n=5 at 0 K, while the ANC is observed as $α_{xy}=0.55$ A/Km for n=3, 0.10 A/Km for n=5, and 0.80 A/Km for n=4, at the E$_F$ at room temperature. Our calculated AHC values at 0 K, i.e., 150 S/cm for Fe$_3$GeTe$_2$ and 90 S/cm Fe$_5$GeTe$_2$, are consistent with the experimentally reported values. Also the experimentally reported value of ANC for Fe$_5$GeTe$_2$ is close to our calculated value at room temperature, i.e., 0.10 A/Km.

cond-mat.str-el↗

arXiv2022

Temperature dependence of quantum oscillations from non-parabolic dispersions

The phase offset of quantum oscillations is commonly used to experimentally diagnose topologically non-trivial Fermi surfaces. This methodology, however, is inconclusive for spin-orbit-coupled metals where $π$-phase-shifts can also arise from non-topological origins. Here, we show that the linear dispersion in topological metals leads to a $T^2$-temperature correction to the oscillation frequency that is absent for parabolic dispersions. We confirm this effect experimentally in the Dirac semi-metal Cd$_3$As$_2$ and the multiband Dirac metal LaRhIn$_5$. Both materials match a tuning-parameter-free theoretical prediction, emphasizing their unified origin. For topologically trivial Bi$_2$O$_2$Se, no frequency shift associated to linear bands is observed as expected. However, the $π$-phase shift in Bi$_2$O$_2$Se would lead to a false positive in a Landau-fan plot analysis. Our frequency-focused methodology does not require any input from ab-initio calculations, and hence is promising for identifying correlated topological materials.

cond-mat.str-el↗

arXiv2022

Application of Data Collected by Endpoint Detection and Response Systems for Implementation of a Network Security System based on Zero Trust Principles and the EigenTrust Algorithm

Traditionally, security systems for enterprises have implicit access based on strong cryptography, authentication and key sharing, wherein access control is based on Role Based Access Control (RBAC), in which roles such as manager, accountant and so on provide a way of deciding a subject's authority. However, years of post-attack analysis on enterprise networks has shown that a majority of times, security breaches occur intentionally or accidently due to implicitly trusted people of an enterprise itself. Zero Trust Architecture works on the principle of never granting trust implicitly, but rather continuously evaluating the trust parameters for each resource access request and has a strict, but not rigid, set of protocols for access control of a subject to resources. Endpoint Detection and Response (EDR) systems are tools that collect a large number of attributes in and around machines within an enterprise network to have close visibility into sophisticated intrusion. In our work, we seek to deploy EDR systems and build trust algorithms using tactical provenance analysis, threshold cryptography and reputation management to continuously record data, evaluate trust of a subject, and simultaneously analyze them against a database of known threat vectors to provide conditional access control. However, EDR tools generate a high volume of data that leads to false alarms, misdetections and correspondingly a high backlog of tasks that makes it infeasible, which is addressed using tactical provenance analysis and information theory.

cs.CR↗

arXiv2021

Imaging phonon-mediated hydrodynamic flow in WTe2

In the presence of interactions, electrons in condensed-matter systems can behave hydrodynamically, exhibiting phenomena associated with classical fluids, such as vortices and Poiseuille flow. In most conductors, electron-electron interactions are minimized by screening effects, hindering the search for hydrodynamic materials; however, recently, a class of semimetals has been reported to exhibit prominent interactions. Here we study the current flow in the layered semimetal tungsten ditelluride by imaging the local magnetic field using a nitrogen-vacancy defect in a diamond. We image the spatial current profile within three-dimensional tungsten ditelluride and find that it exhibits non-uniform current density, indicating hydrodynamic flow. Our temperature-resolve current profile measurements reveal a non-monotonic temperature dependence, with the strongest hydrodynamic effects at approximately 20 K. We also report ab initio calculations showing that electron-electron interactions are not explained by the Coulomb interaction alone, but are predominantly mediated by phonons. This provides a promising avenue in the search for hydrodynamic flow and prominent electron interactions in high-carrier-density materials.

cond-mat.mes-hall↗

arXiv2021

Anisotropic Nodal-Line-Derived Large Anomalous Hall Conductivity in ZrMnP and HfMnP

The nontrivial band structure of semimetals has attracted substantial research attention in condensed matter physics and materials science in recent years owing to its intriguing physical properties. Within this class, a group of non-trivial materials known as nodal-line semimetals is particularly important. Nodal-line semimetals exhibit the potential effects of electronic correlation in nonmagnetic materials, whereas they enhance the contribution of the Berry curvature in magnetic materials, resulting in high anomalous Hall conductivity (AHC). In this study, two ferromagnetic compounds, namely ZrMnP and HfMnP, are selected, wherein the abundance of mirror planes in the crystal structure ensures gapped nodal lines at the Fermi energy. These nodal lines result in one of the largest AHC values of 2840 ohm^-1cm^-1, with a high anomalous Hall angle of 13.6 % in these compounds. First-principles calculations provide a clear and detailed understanding of nodal line-enhanced AHC. Our finding suggests a guideline for searching large AHC compounds.

cond-mat.mtrl-sci↗

arXiv2021

Three-dimensional quasi-quantized Hall insulator phase in SrSi2

In insulators, the longitudinal resistivity becomes infinitely large at zero temperature. For classic insulators, the Hall conductivity becomes zero at the same time. However, there are special systems, such as two-dimensional quantum Hall isolators, in which a more complex scenario is observed at high magnetic fields. Here, we report experimental evidence for a quasi-quantized Hall insulator in the quantum limit of the three-dimensional semimetal SrSi2. Our measurements reveal a magnetic field-range, in which the longitudinal resistivity diverges with decreasing temperature, while the Hall conductivity approaches a quasi-quantized value that is given only by the conductance quantum and the Fermi wave vector in the field-direction. The quasi-quantized Hall insulator appears in a magnetic-field induced insulating ground state of three-dimensional materials and is deeply rooted in quantum Hall physics.

cond-mat.mtrl-sci↗

arXiv2021

Giant anomalous Hall conductivity in the itinerant ferromagnet LaCrSb3 and the effect of f-electrons

Itinerant ferromagnets constitute an important class of materials wherein spin-polarization can affect the electric transport properties in nontrivial ways. One such phenomenon is anomalous Hall effect which depends on the details of the band structure such as the amount of band crossings in the valence band of the ferromagnet. Here, we have found extraordinary anomalous Hall effect in an itinerant ferromagnetic metal LaCrSb3. The rather two-dimensional nature of the magnetic subunit imparts large anisotropic anomalous Hall conductivity of 1250 S/cm at 2K. Our investigations suggest that a strong Berry curvature by abundant momentum-space crossings and narrow energy-gap openings are the primary sources of the anomalous Hall conductivity. An important observation is the existence of quasi-dispersionless bands in LaCrSb3 which is now known to increase the anomalous Hall conductivity. After introducing f-electrons, anomalous Hall conductivity experiences more than two-fold increase and reaches 2900 S/cm in NdCrSb3.

cond-mat.mtrl-sci↗

arXiv2021

Topological quantum materials from the viewpoint of chemistry

Topology, a mathematical concept, has recently become a popular and truly transdisciplinary topic encompassing condensed matter physics, solid state chemistry, and materials science. Since there is a direct connection between real space, namely atoms, valence electrons, bonds and orbitals, and reciprocal space, namely bands and Fermi surfaces, via symmetry and topology, classifying topological materials within a single-particle picture is possible. Currently, most materials are classified as trivial insulators, semimetals and metals, or as topological insulators, Dirac and Weyl nodal-line semimetals, and topological metals. The key ingredients for topology are: certain symmetries, the inert pair effect of the outer electrons leading to inversion of the conduction and valence bands, and spin-orbit coupling. This review presents the topological concepts related to solids from the viewpoint of a solid-state chemist, summarizes techniques for growing single crystals, and describes basic physical property measurement techniques to characterize topological materials beyond their structure and provide examples of such materials. Finally, a brief outlook on the impact of topology in other areas of chemistry is provided at the end of the article.

cond-mat.mtrl-sci↗

arXiv2021

Context-Specific Likelihood Weighting

Sampling is a popular method for approximate inference when exact inference is impractical. Generally, sampling algorithms do not exploit context-specific independence (CSI) properties of probability distributions. We introduce context-specific likelihood weighting (CS-LW), a new sampling methodology, which besides exploiting the classical conditional independence properties, also exploits CSI properties. Unlike the standard likelihood weighting, CS-LW is based on partial assignments of random variables and requires fewer samples for convergence due to the sampling variance reduction. Furthermore, the speed of generating samples increases. Our novel notion of contextual assignments theoretically justifies CS-LW. We empirically show that CS-LW is competitive with state-of-the-art algorithms for approximate inference in the presence of a significant amount of CSIs.

cs.AI↗

arXiv2021

A Case Study of Space-time Performance Comparison of Wind Turbines on a Wind Farm

This paper presents an academia-industry joint case study, which was conducted to quantify and compare multi-year changes in power production performance of multiple turbines scattered over a mid-size wind farm. This analysis is referred to as a space-time performance comparison. One key aspect in power performance analysis is to have the wind and environmental inputs controlled for. This research employs, in a sequential fashion, two principal modeling components to exercise tight control of multiple input conditions -- a covariate matching method, followed by a Gaussian process model-based functional comparison. The analysis method is applied to a wind farm that houses 66 turbines on a moderately complex terrain. The power production and environmental data span nearly four years, during which period the turbines have gone through multiple technical upgrades. The space-time analysis presents a quantitative and global picture showing how turbines differ relative to each other as well as how each of them changes over time.

stat.AP↗

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↗