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This work aims to develop an integrated control strategy for Brushless Direct Current Motors for a wide range of applications in robotics systems. The controller is suited for both high torque - low speed and high-speed control of the motors. Hardware validation is done by developing a custom BLDC drive system, and the circuit elements are optimised for power efficiency., Comment: 6 Pages, 8 figures, 1 table

The sense of touch is fundamental to how we interact with the physical and digital world. Conventional interactive surfaces and tactile interfaces use electronic sensors embedded into objects, however this approach poses serious challenges both for environmental sustainability and a future of truly ubiquitous interaction systems where information is encoded into everyday objects. In this work, we present Biodegradable Interactive Materials: backyard-compostable interactive interfaces that leverage information encoded in material properties. Inspired by natural systems, we propose an architecture that programmatically encodes multidimensional information into materials themselves and combines them with wearable devices that extend human senses to perceive the embedded data. We combine unrefined biological matter from plants and algae like chlorella with natural minerals like graphite and magnetite to produce materials with varying electrical, magnetic, and surface properties. We perform in-depth analysis using physics models, computational simulations, and real-world experiments to characterize their information density and develop decoding methods. Our passive, chip-less materials can robustly encode 12 bits of information, equivalent to 4096 unique classes. We further develop wearable device prototypes that can decode this information during touch interactions using off-the-shelf sensors. We demonstrate sample applications such as customized buttons, tactile maps, and interactive surfaces. We further demonstrate the natural degradation of these interactive materials in degrade outdoors within 21 days and perform a comparative environmental analysis of the benefits of this approach.

Magnetic field emerging from the geometric curvature of quantum structure of electrons in a crystal bends electronic trajectory causing anomalous linear and nonlinear electrical Hall effects that have been observed in low symmetry crystals with narrow electronic band gap. We present first-principles theoretical analysis to show that dynamical lowering of crystal symmetry by lattice vibrations results in oscillations in the quantum geometry of electrons which have observable nonlinear Hall signatures. Using these, we introduce a vibrational spectroscopy based on Geometry of Quantum Electronic Structure (GQuES) making specific predictions for the transport and radiative GQuES spectra of 2D materials. As the crystal symmetry is commonly lowered by dynamical fields, GQuES is applicable to a wide range of materials and excitations spanning sub-GHz, THz and infrared frequencies.

Ferroelectric domain walls, recognized as distinct from the bulk in terms of symmetry, structure, and electronic properties, host exotic phenomena including conductive walls, ferroelectric vortices, novel topologies, and negative capacitance. Contrary to conventional understanding, our study reveals that the structure of domain walls in HfO2 closely resembles its bulk. First, our first-principles simulations unveil that the robust ferroelectricity is supported by bosonic pairing of all the anionic phonons in bulk HfO2. Strikingly, the paired phonons strongly bond with each other and successfully reach the center of the domain wall without losing their integrity and produce bulk-like domain walls. We then confirmed preservation of the bulk phonon displacements and consequently full revival of the bulk structure at domain walls via aberration-corrected STEM. The newly found duality between the bulk and the domain wall sheds light on previously enigmatic properties such as zero-energy domain walls, perfect Ising-type polar ordering, and exceptionally robust ferroelectricity at the sub-nm scales. The phonon-pairing discovered here is robust against physical boundaries such as domain walls and enables zero momentum and zero-energy cost local ferroelectric switching. This phenomenon demonstrated in Si-compatible ferroelectrics provides a novel technological platform where data storage on domain walls is as feasible as that within the domains, thereby expanding the potential for high-density data storage and advanced ferroelectric applications., Comment: 24 pages, 4 figures

In our study, we explored the properties of a spatially homogeneous and anisotropic Bianchi type V I0 Universe. Our investigation centered on integrating cosmic domain walls into the f(R, T) theory of gravitation, initially proposed by Harko et al. in 2011. To tackle the field equations, we employed the relationship between the expansion scalar ({\theta}) and the shear scalar ({\sigma}). Our analysis encompassed both the dynamic and cosmological aspects of the Universe. By comparing our findings to the {\Lambda}CDM model, specifically focusing on the evolution of the jerk parameter, we found a striking agreement between the two models. A noteworthy discovery was the verification of accelerated expansion in our described model, consistent with the prevailing observational data. Finally, we examine the energy condition criteria and determine that the violation of the Strong Energy Condition (SEC), while the Null Energy Condition (NEC), Weak Energy Condition (WEC) and Dominant Energy Condition (DEC) continue to meet the requirements for positivity., Comment: 19 pages

X-ray binaries hosting a compact object have been among the main targets of the Imaging X-ray Polarimetry Explorer (IXPE) since its launch, due to their high brightness in the 2-8 keV energy band. The spectropolarimetric analysis performed so far has proved to be of great importance in providing constraints on the accretion geometry of these systems. However, the data statistics is not enough to unambiguously disentangle the contribution of the single components to the net observed polarimetric signal. In this work, we aim to present a model for computing the polarization degree and polarization angle of the boundary layer around weakly magnetized neutron stars in low-mass X-ray binaries in the soft state. The main motivation is to provide strong theoretical support to data interpretation of observations performed by IXPE or future satellites for X-ray polarimetry. The results were obtained by modeling the boundary layer as an equatorial belt around the compact object and locally approximating it as a plane-parallel scattering atmosphere, for which the associated radiative transfer equation for polarized radiation in the Thomson limit was solved. The polarimetric quantities were then transformed from the comoving frame to the observer frame using the numerical methods formerly developed for X-ray pulsars. For typical values of the optical depth and electron temperature of the boundary layer of these systems in a soft state, the polarization degree was less then 0.5\%, while the polarization angle was rotated by $\protect \la 5^{\circ}$ with respect to the neutron star spin axis due to special and general relativistic effects for fast rotation, the amount progressively decreasing for lower spin frequencies. The derived quantities can be used to remove degeneracy when multicomponent spectropolarimetry is performed., Comment: 10 pages, 8 figures, accepted for publication in A&A

Context. In September 2022, the transient neutron star low-mass X-ray binary XTE J1701-462 went into a new outburst. Aims. The objective of this work is to examine the evolution of the accretion geometry of XTE J1701-462 by studying the spectro-polarimetric properties along the Z track of this source. The simultaneous observations archived by the Insight-Hard X-ray Modulation Telescope (HXMT) and the Imaging X-ray Polarimetry Explorer (IXPE) give us the opportunity. Methods. We present a comprehensive X-ray spectro-polarimetric analysis of XTE J1701-462, using simultaneous observations from IXPE, Insight-HXMT and NuSTAR. For IXPE observations, two methods are employed to measure the polarization: a model-independent measurement with PCUBE and a model-dependent polarization-spectral analysis with XSPEC. The corresponding spectra from Insight-HXMT and NuSTAR are studied with two configurations that correspond to a slab-like corona and a spherical shell-like corona, respectively. Results. Significant polarization characteristics are detected in XTE J1701-462. The polarization degree shows a decreasing trend along the Z track, reducing from (4.84 $\pm$ 0.37)% to (3.76 $\pm$ 0.43)% on the horizontal branch and jumping to less than 1% on the normal branch. The simultaneous spectral analysis from Insight-HXMT and NuSTAR suggests that the redistribution between the thermal and Comptonized emission could be the reason for the PD evolution along the Z track. Based on the correlated spectro-polarimetric properties, we propose that this source likely has a slab coronal geometry and the size/thickness of the corona decreases along the Z track.

We introduce SANPO, a large-scale egocentric video dataset focused on dense prediction in outdoor environments. It contains stereo video sessions collected across diverse outdoor environments, as well as rendered synthetic video sessions. (Synthetic data was provided by Parallel Domain.) All sessions have (dense) depth and odometry labels. All synthetic sessions and a subset of real sessions have temporally consistent dense panoptic segmentation labels. To our knowledge, this is the first human egocentric video dataset with both large scale dense panoptic segmentation and depth annotations. In addition to the dataset we also provide zero-shot baselines and SANPO benchmarks for future research. We hope that the challenging nature of SANPO will help advance the state-of-the-art in video segmentation, depth estimation, multi-task visual modeling, and synthetic-to-real domain adaptation, while enabling human navigation systems. SANPO is available here: https://google-research-datasets.github.io/sanpo_dataset/, Comment: 10 pages plus additional references. 13 figures

We study the positive-definite completion problem for kernels on a variety of domains and prove results concerning the existence, uniqueness, and characterization of solutions. In particular, we study a special solution called the canonical completion which is the reproducing kernel analogue of the determinant-maximizing completion known to exist for matrices. We establish several results concerning its existence and uniqueness, which include algebraic and variational characterizations. Notably, we prove the existence of a canonical completion for domains which are equivalent to the band containing the diagonal. This corresponds to the existence of a canonical extension in the context of the classical extension problem of positive-definite functions, which can be understood as the solution to an abstract Cauchy problem in a certain reproducing kernel Hilbert space.

We consider the problem of recovering conditional independence relationships between $p$ jointly distributed Hilbertian random elements given $n$ realizations thereof. We operate in the sparse high-dimensional regime, where $n \ll p$ and no element is related to more than $d \ll p$ other elements. In this context, we propose an infinite-dimensional generalization of the graphical lasso. We prove model selection consistency under natural assumptions and extend many classical results to infinite dimensions. In particular, we do not require finite truncation or additional structural restrictions. The plug-in nature of our method makes it applicable to any observational regime, whether sparse or dense, and indifferent to serial dependence. Importantly, our method can be understood as naturally arising from a coherent maximum likelihood philosophy.

With first-principles theoretical analysis of the local structure using Bond Orientational Order parameters and Voronoi partitioning, we establish (a) HCP$\rightarrow$BCC structural transformation in high-entropy alloys (HEAs) Nb$_x$(HfZrTi)$_y$ at 16% Nb-concentration, and (b) that the internal lattice distortions (ILDs) peak at the transition. We demonstrate that the relative stability of HCP and BCC structures is driven by energetics, while the overall stability is achieved with contribution from the vibrational entropy that exceeds the configurational entropy of mixing. We show that along with atomic size mismatch, low average number ($< $5) of valence electrons and disparity in the crystal structures of constituent elements are responsible for larger ILDs in Nb$_x$(HfZrTi)$_y$ than in HEAs like Nb$_a$Mo$_b$W$_c$Ta$_d$., Comment: 8 pages, 4 figures

We develop a generalisation of Mercer's theorem to operator-valued kernels in infinite dimensional Hilbert spaces. We then apply our result to deduce a Karhunen-Lo\`eve theorem, valid for mean-square continuous Hilbertian functional data, i.e. flows in Hilbert spaces. That is, we prove a series expansion with uncorrelated coefficients for square-integrable random flows in a Hilbert space, that holds uniformly over time.

Let $X = \{X_{u}\}_{u \in U}$ be a real-valued Gaussian process indexed by a set $U$. It can be thought of as an undirected graphical model with every random variable $X_{u}$ serving as a vertex. We characterize this graph in terms of the covariance of $X$ through its reproducing kernel property. Unlike other characterizations in the literature, our characterization does not restrict the index set $U$ to be finite or countable, and hence can be used to model the intrinsic dependence structure of stochastic processes in continuous time/space. Consequently, this characterization is not in terms of the zero entries of an inverse covariance. This poses novel challenges for the problem of recovery of the dependence structure from a sample of independent realizations of $X$, also known as structure estimation. We propose a methodology that circumvents these issues, by targeting the recovery of the underlying graph up to a finite resolution, which can be arbitrarily fine and is limited only by the available sample size. The recovery is shown to be consistent so long as the graph is sufficiently regular in an appropriate sense. We derive corresponding convergence rates and finite sample guarantees. Our methodology is illustrated by means of a simulation study and two data analyses.

The process of design and discovery of new materials can be significantly expedited and simplified if we can learn effectively from available data. Deep learning (DL) approaches have recently received a lot of interest for their ability to speed up the design of novel materials by predicting material properties with precision close to experiments and ab-initio calculations. The application of deep learning to predict materials properties measured by experiments are valuable yet challenging due to the limited amount of experimental data. Most of the existing approaches to predict properties from computational data have also been directed towards specific material properties. In this work, we extend this approach, by proposing Landscape Crystal Graph Convolution Network(LCGCN), an accurate and transferable deep learning framework based on graph convolutional networks. LCGCN directly learns the potential energy surface (PES) from atomic configurations. This approach can enable transferable models that can predict different material properties. We apply this framework to bulk crystals (i.e. Al2O3), and test it by calculating potential energy surfaces at different temperatures and across different phases of crystal.