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Controllable generation using StyleGANs is usually achieved by training the model using labeled data. For audio textures, however, there is currently a lack of large semantically labeled datasets. Therefore, to control generation, we develop a method for semantic control over an unconditionally trained StyleGAN in the absence of such labeled datasets. In this paper, we propose an example-based framework to determine guidance vectors for audio texture generation based on user-defined semantic attributes. Our approach leverages the semantically disentangled latent space of an unconditionally trained StyleGAN. By using a few synthetic examples to indicate the presence or absence of a semantic attribute, we infer the guidance vectors in the latent space of the StyleGAN to control that attribute during generation. Our results show that our framework can find user-defined and perceptually relevant guidance vectors for controllable generation for audio textures. Furthermore, we demonstrate an application of our framework to other tasks, such as selective semantic attribute transfer., Comment: Accepted for publication at IEEE Transactions on Audio, Speech and Language Processing

Detections of gravitational waves emitted from binary black hole coalescences allow us to probe the strong-field dynamics of general relativity (GR). One can compare the observed gravitational-wave signals with theoretical waveform models to constrain possible deviations from GR. Any physics that is not included in these waveform models might show up as apparent GR deviations. The waveform models used in current tests of GR describe binaries on quasicircular orbits, since most of the binaries detected by ground-based gravitational-wave detectors are expected to have negligible eccentricities. Thus, a signal from an eccentric binary in GR is likely to show up as a deviation from GR in the current implementation of these tests. We study the response of four standard tests of GR to eccentric binary black hole signals with the forecast O4 sensitivity of the LIGO-Virgo network. Specifically, we consider two parameterized tests (TIGER and FTI), the modified dispersion relation test, and the inspiral-merger-ringdown consistency test. To model eccentric signals, we use non-spinning numerical relativity simulations from the SXS catalog with three mass ratios $(1,2,3)$, which we scale to a redshifted total mass of $80M_\odot$ and luminosity distance of $400$ Mpc. For each of these mass ratios, we consider signals with eccentricities of $\sim0.05$ and $\sim 0.1$ at $17$ Hz. We find that signals with larger eccentricity lead to very significant false GR deviations in most tests while signals having smaller eccentricity lead to significant deviations in some tests. For the larger eccentricity cases, one would even get a deviation from GR with TIGER at $\sim 90\%$ credibility at a distance of $\gtrsim 1.5$ Gpc. Thus, it will be necessary to exclude the possibility of an eccentric binary in order to make any claim about detecting a deviation from GR., Comment: 16 pages, 6 figures, version accepted by PRD

In this paper, we propose a data-driven approach to train a Generative Adversarial Network (GAN) conditioned on "soft-labels" distilled from the penultimate layer of an audio classifier trained on a target set of audio texture classes. We demonstrate that interpolation between such conditions or control vectors provides smooth morphing between the generated audio textures, and shows similar or better audio texture morphing capability compared to the state-of-the-art methods. The proposed approach results in a well-organized latent space that generates novel audio outputs while remaining consistent with the semantics of the conditioning parameters. This is a step towards a general data-driven approach to designing generative audio models with customized controls capable of traversing out-of-distribution regions for novel sound synthesis., Comment: accepted to ICASSP 2023

This article deals with the complexity involved in fuzzy derivatives when both input and output are from nonempty, convex, and compact fuzzy space. Consider a fuzzy valued mapping, and for fuzzy differentiation of fuzzy valued function, we propose Modified Hukuhara derivative. To evaluate this derivative, we need to take the parametric form of, input and the mapping which is involved in it. Our definition gives a more realistic explanation of fuzzy derivatives, under this derivative, we also develop fuzzy Taylor series along with its convergence. Lastly, we solve a fully fuzzy differential equation with initial condition using Fuzzy Taylor series., Comment: 22 pages, 1 figure

Electric field control of the exchange bias effect across ferromagnet/antiferromagnet (FM/AF) interfaces has offered exciting potentials for low-energy-dissipation spintronics. In particular, the solid state magneto-ionic means is highly appealing as it may allow reconfigurable electronics by transforming the all-important FM/AF interfaces through ionic migration. In this work, we demonstrate an approach that combines the chemically induced magneto-ionic effect with the electric field driving of nitrogen in the Ta/Co$_{0.7}$Fe$_{0.3}$/MnN/Ta structure to electrically manipulate exchange bias. Upon field-cooling the heterostructure, ionic diffusion of nitrogen from MnN into the Ta layers occurs. A significant exchange bias of 618 Oe at 300 K and 1484 Oe at 10 K is observed, which can be further enhanced after a voltage conditioning by 5% and 19%, respectively. This enhancement can be reversed by voltage conditioning with an opposite polarity. Nitrogen migration within the MnN layer and into the Ta capping layer cause the enhancement in exchange bias, which is observed in polarized neutron reflectometry studies. These results demonstrate an effective nitrogen-ion based magneto-ionic manipulation of exchange bias in solid-state devices., Comment: 28 pages, 4 figures; supporting information: 17 pages, 11 figures

Standard evaluation metrics such as the Inception score and Fr\'echet Audio Distance provide a general audio quality distance metric between the synthesized audio and reference clean audio. However, the sensitivity of these metrics to variations in the statistical parameters that define an audio texture is not well studied. In this work, we provide a systematic study of the sensitivity of some of the existing audio quality evaluation metrics to parameter variations in audio textures. Furthermore, we also study three more potentially parameter-sensitive metrics for audio texture synthesis, (a) a Gram matrix based distance, (b) an Accumulated Gram metric using a summarized version of the Gram matrices, and (c) a cochlear-model based statistical features metric. These metrics use deep features that summarize the statistics of any given audio texture, thus being inherently sensitive to variations in the statistical parameters that define an audio texture. We study and evaluate the sensitivity of existing standard metrics as well as Gram matrix and cochlear-model based metrics to control-parameter variations in audio textures across a wide range of texture and parameter types, and validate with subjective evaluation. We find that each of the metrics is sensitive to different sets of texture-parameter types. This is the first step towards investigating objective metrics for assessing parameter sensitivity in audio textures., Comment: accepted for publication at ISMIR 2022

We introduce a new system for data-driven audio sound model design built around two different neural network architectures, a Generative Adversarial Network(GAN) and a Recurrent Neural Network (RNN), that takes advantage of the unique characteristics of each to achieve the system objectives that neither is capable of addressing alone. The objective of the system is to generate interactively controllable sound models given (a) a range of sounds the model should be able to synthesize, and (b) a specification of the parametric controls for navigating that space of sounds. The range of sounds is defined by a dataset provided by the designer, while the means of navigation is defined by a combination of data labels and the selection of a sub-manifold from the latent space learned by the GAN. Our proposed system takes advantage of the rich latent space of a GAN that consists of sounds that fill out the spaces ''between" real data-like sounds. This augmented data from the GAN is then used to train an RNN for its ability to respond immediately and continuously to parameter changes and to generate audio over unlimited periods of time. Furthermore, we develop a self-organizing map technique for ``smoothing" the latent space of GAN that results in perceptually smooth interpolation between audio timbres. We validate this process through user studies. The system contributes advances to the state of the art for generative sound model design that include system configuration and components for improving interpolation and the expansion of audio modeling capabilities beyond musical pitch and percussive instrument sounds into the more complex space of audio textures.

The quantum anomalous Hall (QAH) effect is characterized by a dissipationless chiral edge state with a quantized Hall resistance at zero magnetic field. Manipulating the QAH state is of great importance in both the understanding of topological quantum physics and the implementation of dissipationless electronics. Here, we realized the QAH effect in the magnetic topological insulator Cr-doped (Bi,Sb)2Te3 (CBST) grown on an uncompensated antiferromagnetic insulator Al-doped Cr2O3. Through polarized neutron reflectometry (PNR), we find a strong exchange coupling between CBST and Al-Cr2O3 surface spins fixing interfacial magnetic moments perpendicular to the film plane. The interfacial coupling results in an exchange-biased QAH effect. We further demonstrate that the magnitude and sign of the exchange bias can be effectively controlled using a field training process to set the magnetization of the Al-Cr2O3 layer. Our work demonstrates the use of the exchange bias effect to effectively manipulate the QAH state, opening new possibilities in QAH-based spintronics.

The intrinsic magnetic topological insulator MnBi2Te4 (MBT) has provided a platform for the successful realization of exotic quantum phenomena. To broaden the horizons of MBT-based material systems, we intercalate ferromagnetic MnTe layers to construct the [(MBT)(MnTe)m]N superlattices by molecular beam epitaxy. The effective incorporation of ferromagnetic spacers mediates the anti-ferromagnetic interlayer coupling among the MBT layers through the exchange spring effect at the MBT/MnTe hetero-interfaces. Moreover, the precise control of the MnTe thickness enables the modulation of relative strengths among the constituent magnetic orders, leading to tunable magnetoelectric responses, while the superlattice periodicity serves as an additional tuning parameter to tailor the spin configurations of the synthesized multi-layers. Our results demonstrate the advantages of superlattice engineering for optimizing the magnetic interactions in MBT-family systems, and the ferromagnet-intercalated strategy opens up new avenues in magnetic topological insulator structural design and spintronic applications., Comment: 20 pages, 5 figures

The anomalous Hall, Nernst and thermal Hall coefficients of Fe$_{3-x}$GeTe$_2$ display several features upon cooling, like a reversal in the Nernst signal below $T = 50$ K pointing to a topological transition (TT) associated to the development of magnetic spin textures. Since the anomalous transport variables are related to the Berry curvature, a possible TT might imply deviations from the Wiedemann-Franz (WF) law. However, the anomalous Hall and thermal Hall coefficients of Fe$_{3-x}$GeTe$_2$ are found, within our experimental accuracy, to satisfy the WF law for magnetic-fields $\mu_0H$ applied along its inter-layer direction. Surprisingly, large anomalous transport coefficients are also observed for $\mu_0H$ applied along the planar \emph{a}-axis as well as along the gradient of the chemical potential, a configuration that should not lead to their observation due to the absence of Lorentz force. However, as $\mu_0H$ $\|$ \emph{a}-axis is increased, magnetization and neutron scattering indicate just the progressive canting of the magnetic moments towards the planes followed by their saturation. These anomalous planar quantities are found to not scale with the component of the planar magnetization ($M_{\|}$), showing instead a sharp decrease beyond $\sim \mu_0 H_{\|} = $ 4 T which is the field required to align the magnetic moments along $\mu_0 H_{\|}$. We argue that locally chiral spin structures, such as skyrmions, and possibly skyrmion tubes, lead to a field dependent spin-chirality and hence to a novel type of topological anomalous transport. Locally chiral spin-structures are captured by our Monte-Carlo simulations incorporating small Dzyaloshinskii-Moriya and biquadratic exchange interactions., Comment: 34 pages, 10 figures, submitted to Applied Physics Reviews

THz pulses are generated from femtosecond pulse-excited ferromagnetic/nonmagnetic spintronic heterostructures via inverse spin Hall effect. The contribution from ultrafast demagnetization/remagnetization is extremely weak, in the comparison. The highest possible THz signal strength from spintronic THz emitters is limited by the optical damage threshold of the corresponding heterostructures. The THz generation efficiency does not saturate with the excitation fluence even up till the damage threshold. Bilayer (Fe, CoFeB)/(Pt, Ta) based FM/NM spintronic heterostructures have been studied for an optimized performance for THz generation when pumped by sub-50 fs amplified laser pulses at 800 nm. Among them, CoFeB/Pt is the best combination for an efficient THz source. The optimized FM/NM spintronic heterostructure on a quartz substrate, having alpha-phase Ta as the nonmagnetic layer, show the highest damage threshold as compared to those with Pt, irrespective of their generation efficiency. The damage threshold of the Fe/Ta heterostructure on quartz substrate is ~85 GW/cm2., Comment: Supporting information is included

Generative Adversarial Networks (GANs) currently achieve the state-of-the-art sound synthesis quality for pitched musical instruments using a 2-channel spectrogram representation consisting of log magnitude and instantaneous frequency (the "IFSpectrogram"). Many other synthesis systems use representations derived from the magnitude spectra, and then depend on a backend component to invert the output magnitude spectrograms that generally result in audible artefacts associated with the inversion process. However, for signals that have closely-spaced frequency components such as non-pitched and other noisy sounds, training the GAN on the 2-channel IFSpectrogram representation offers no advantage over the magnitude spectra based representations. In this paper, we propose that training GANs on single-channel magnitude spectra, and using the Phase Gradient Heap Integration (PGHI) inversion algorithm is a better comprehensive approach for audio synthesis modeling of diverse signals that include pitched, non-pitched, and dynamically complex sounds. We show that this method produces higher-quality output for wideband and noisy sounds, such as pops and chirps, compared to using the IFSpectrogram. Furthermore, the sound quality for pitched sounds is comparable to using the IFSpectrogram, even while using a simpler representation with half the memory requirements., Comment: Submitted to Sound and Music Computing Conference (SMC) 2021