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In this paper, we study the asymptotic behavior of solutions to the compressible Navier-Stokes system considered on a sequence of spatial domains, whose boundaries exhibit fast oscillations with amplitude and characteristic wave length proportional to a small parameter. Imposing the full-slip boundary conditions we show that in the asymptotic limit the fluid sticks completely to the boundary, provided the oscillations are non-degenerate, meaning not oriented in a single direction.

This paper presents a simulation approach to enhance the power quality of a PV-micro hydro grid supplying both linear consumer load and non-linear industrial load by integrating Shunt Active Power Filter (SAPF), utilizing instantaneous PQ theory and hysteresis current control band logic. The non-linear load draws reactive power and harmonic current from the source thereby affecting the power quality. The integration of the SAPF at the point of common coupling (PCC) offers reactive power and harmonic current compensation, ensuring that the current supply to the grid remains nearly sinusoidal and proportional to the active power. By injecting equal and opposite harmonic components, the SAPF effectively reduces Total Harmonic Distortion (THD) from 7% to 2.96%, thereby enhancing the overall power quality of the PV-micro hydro grid system., Comment: 5 pages, 21 figures, submitted on IEEE powercon 2024 conference

A major challenge in defending against adversarial attacks is the enormous space of possible attacks that even a simple adversary might perform. To address this, prior work has proposed a variety of defenses that effectively reduce the size of this space. These include randomized smoothing methods that add noise to the input to take away some of the adversary's impact. Another approach is input discretization which limits the adversary's possible number of actions. Motivated by these two approaches, we introduce a new notion of adversarial loss which we call distributional adversarial loss, to unify these two forms of effectively weakening an adversary. In this notion, we assume for each original example, the allowed adversarial perturbation set is a family of distributions (e.g., induced by a smoothing procedure), and the adversarial loss over each example is the maximum loss over all the associated distributions. The goal is to minimize the overall adversarial loss. We show generalization guarantees for our notion of adversarial loss in terms of the VC-dimension of the hypothesis class and the size of the set of allowed adversarial distributions associated with each input. We also investigate the role of randomness in achieving robustness against adversarial attacks in the methods described above. We show a general derandomization technique that preserves the extent of a randomized classifier's robustness against adversarial attacks. We corroborate the procedure experimentally via derandomizing the Random Projection Filters framework of \cite{dong2023adversarial}. Our procedure also improves the robustness of the model against various adversarial attacks.

We study the difference between the number of facets of the order polytope and the chain polytope of a poset. Hibi and Li classified posets where the gap is exactly zero. We describe the bounds on this gap using the new notion of crossing numbers, and then use this result to classify the posets where the gap is exactly one., Comment: 12 pages, 5 figures

This paper explores the question of creating and maintaining terrain maps in environments where the terrain changes. The specific example explored is the construction of terrain maps from 3D LiDAR measurements on an electric rope shovel. The approach extends the height grid representation of terrain to include a Hidden Markov Model in each cell, enabling confidence-based mapping of constantly changing terrain. There are inherent difficulties in this problem, including semantic labelling of the LiDAR measurements associated with machinery and determining the pose of the sensor. Solutions to both of these problems are explored. The significance of this work lies in the need for accurate terrain mapping to support autonomous machine operation., Comment: 6 pages, 5 figures

Kerr-cat qubits are bosonic qubits with autonomous protection against bit-flips. They have been studied widely using driven Superconducting Nonlinear Asymmetric Inductive eLement (SNAIL) oscillators. We theoretically investigate an alternate circuit for the Kerr-cat qubit, namely Symmetrically Threaded SQUIDs (STS). We perform the circuit analysis and derive the Gorini-Kossakowski-Sudarshan-Lindblad (GKLS) master equation for the Kerr-cat qubit attached to a thermal environment. We find that the lifetime time of the coherent states ($T_\alpha$) of the Kerr-cat qubit is the same in both the STS and SNAIL circuits for weak Kerr nonlinearity. However, the STS Kerr-cat qubits have the additional benefit of being resistant against higher order photon dissipation effects, resulting in significantly longer $T_\alpha$ even with stronger Kerr nonlinearity on the order of $10{~\rm MHz}$. We also examine the effects of strong flux driving and asymmetric Josephson junctions on $T_\alpha$. Unlike the SNAIL design, we find a dip in $T_\alpha$ of the STS Kerr-cat qubit for weak two-photon drive. However, we show that the dip can be mitigated by applying a suitable drive-dependent detuning. With the proposed design and considering a cat size of 10 photons, we predict $T_\alpha$ of the order of tens of milliseconds even in the presence of multi-photon heating and dephasing effects. The robustness of the STS Kerr-cat qubit makes it a promising component for fault-tolerant quantum processors., Comment: 26 pages, 12 figures

Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for evaluating medical imaging technologies. Computational phantoms, which mimic real patient anatomy and physiology, play a central role in VITs. However, the current libraries of computational phantoms face limitations, particularly in terms of sample size and diversity. Insufficient representation of the population hampers accurate assessment of imaging technologies across different patient groups. Traditionally, the more realistic computational phantoms were created by manual segmentation, which is a laborious and time-consuming task, impeding the expansion of phantom libraries. This study presents a framework for creating realistic computational phantoms using a suite of automatic segmentation models and performing three forms of automated quality control on the segmented organ masks. The result is the release of over 2500 new computational phantoms, so-named XCAT3.0 after the ubiquitous XCAT computational construct. This new formation embodies 140 structures and represents a comprehensive approach to detailed anatomical modeling. The developed computational phantoms are formatted in both voxelized and surface mesh formats. The framework is combined with an in-house CT scanner simulator to produce realistic CT images. The framework has the potential to advance virtual imaging trials, facilitating comprehensive and reliable evaluations of medical imaging technologies. Phantoms may be requested at https://cvit.duke.edu/resources/. Code, model weights, and sample CT images are available at https://xcat-3.github.io/.

In this paper, we invoke a generalized uncertainty principle (GUP) in the symmetry-reduced cosmological Hamiltonian for a universe driven by a quintessence scalar field with potential. Our study focuses on semi-classical regime. In particular, we derive the GUP-distorted Friedmann, Raychaudhuri, and the Klein-Gordon equation. This is followed by a systematic analysis of the qualitative dynamics for the choice of potential $V(\phi)= V_0 \sinh^{-n}{(\mu \phi)}$. This involves constructing an autonomous dynamical system of equations by choosing appropriate dynamical variables, followed by a qualitative study using linear stability theory. Our analysis shows that incorporating GUP significantly changes the existing fixed points compared to the limiting case without quantum effects by switching off the GUP., Comment: 12. arXiv admin note: text overlap with arXiv:2404.09049

Thermoelectric devices that operate on quantum principles have been under extensive investigation in the past decades. These devices are at the fundamental limits of miniaturized heat engines and refrigerators, advancing the field of quantum thermodynamics. Most research in this area concerns the use of conduction electrons and holes as charge and heat carriers, and only very recently have superconductors been considered as thermal engines and thermoelectric devices. Here, we investigate the thermoelectric response of an Abrikosov vortex in type-II superconductors in the deep quantum limit. We consider two thermoelectric geometries, a type-II SIN junction and a local Scanning Tunneling Microscope (STM)-tip normal metal probe over the superconductor. We exploit the strong breaking of particle-hole symmetry in bound states at sub-gap energies within the superconducting vortex to realize a giant thermoelectric response in the presence of fluxons. We predict a thermovoltage of a few mV/K at sub-Kelvin temperatures using both semi-analytic and numerical self-consistent solutions of the Bogoliubov-de Gennes equations. Relevant thermoelectric coefficients and figures of merit are found within our model, both in linear and nonlinear regimes. The ZT of the SIN junction is around 1, rising to above 3 for the STM junction centered at the vortex core. We also discuss how this system can be used as a sensitive thermocouple, diode, or localized bolometer to detect low-energy single photons., Comment: 11 pages, 4 figures

We present 1 - 12 GHz Karl G. Jansky Very Large Array observations of 9 off-nuclear persistent radio sources (PRSs) in nearby (z < 0.055) dwarf galaxies, along with high-resolution European very-long baseline interferometry (VLBI) Network (EVN) observations for one of them at 1.7GHz. We explore the plausibility that these PRSs are associated with fast radio burst (FRB) sources by examining their properties, physical sizes, host-normalized offsets, spectral energy distributions (SEDs), radio luminosities, and light curves, and compare them to those of the PRSs associated with FRBs 20121102A and 20190520B, two known active galactic nuclei (AGN), and one likely AGN in our sample with comparable data, as well as other radio transients exhibiting characteristics analogous to FRB-PRSs. We identify a single source in our sample, J1136+2643, as the most promising FRB- PRS, based on its compact physical size and host-normalized offset. We further identify two sources, J0019+1507 and J0909+5955, with physical sizes comparable to FRB-PRSs, but which exhibit large offsets and flat spectral indices potentially indicative of a background AGN origin. We test the viability of neutron star wind nebulae and hypernebulae models for J1136+2643, and find that the physical size, luminosity, and SED of J1136+2643 are broadly consistent with these models. Finally, we discuss the alternative interpretation that the radio sources are instead powered by accreting massive black holes and outline future prospects and follow-up observations for differentiating between these scenarios., Comment: 25 pages, 7 figures, 3 tables

Sampling theory in fractional Fourier Transform (FrFT) domain has been studied extensively in the last decades. This interest stems from the ability of the FrFT to generalize the traditional Fourier Transform, broadening the traditional concept of bandwidth and accommodating a wider range of functions that may not be bandlimited in the Fourier sense. Beyond bandlimited functions, sampling and recovery of sparse signals has also been studied in the FrFT domain. Existing methods for sparse recovery typically operate in the transform domain, capitalizing on the spectral features of spikes in the FrFT domain. Our paper contributes two new theoretical advancements in this area. First, we introduce a novel time-domain sparse recovery method that avoids the typical bottlenecks of transform domain methods, such as spectral leakage. This method is backed by a sparse sampling theorem applicable to arbitrary FrFT-bandlimited kernels and is validated through a hardware experiment. Second, we present Cram\'er-Rao Bounds for the sparse sampling problem, addressing a gap in existing literature., Comment: Accepted with minor revisions, IEEE SPL

The Kerr-cat qubit is a bosonic qubit in which multi-photon Schrodinger cat states are stabilized by applying a two-photon drive to an oscillator with a Kerr nonlinearity. The suppressed bit-flip rate with increasing cat size makes this qubit a promising candidate to implement quantum error correction codes tailored for noise-biased qubits. However, achieving strong light-matter interactions necessary for stabilizing and controlling this qubit has traditionally required strong microwave drives that heat the qubit and degrade its performance. In contrast, increasing the coupling to the drive port removes the need for strong drives at the expense of large Purcell decay. By integrating an effective band-block filter on-chip, we overcome this trade-off and realize a Kerr-cat qubit in a scalable 2D superconducting circuit with high coherence. This filter provides 30 dB of isolation at the qubit frequency with negligible attenuation at the frequencies required for stabilization and readout. We experimentally demonstrate quantum non-demolition readout fidelity of 99.6% for a cat with 8 photons. Also, to have high-fidelity universal control over this qubit, we combine fast Rabi oscillations with a new demonstration of the X(90) gate through phase modulation of the stabilization drive. Finally, the lifetime in this architecture is examined as a function of the cat size of up to 10 photons in the oscillator achieving a bit-flip time higher than 1 ms and only a linear decrease in the phase-flip time, in good agreement with the theoretical analysis of the circuit. Our qubit shows promise as a building block for fault-tolerant quantum processors with a small footprint.

Quantum gravity has been baffling the theoretical physicist for decades now: both for its mathematical obscurity and phenomenological testing. Nevertheless, the new era of precision cosmology presents a promising avenue to test the effects of quantum gravity. In this study, we consider a bottom-up approach. Without resorting to any candidate quantum gravity, we invoke a generalized uncertainty principle (GUP) directly into the cosmological Hamiltonian for a universe sourced by a phantom scalar field with potential to study the early epoch of the evolution. This is followed by a systematic analysis of the dynamics, both qualitatively and quantitatively. Our qualitative analysis shows that the introduction of GUP significantly alters the existence of fixed points for the potential considered in this contribution. In addition, we confirm the existence of an inflationary epoch and analyze the behavior of relevant cosmological parameters with respect to the strength of GUP distortion.

The success of full-stack full-duplex communication systems depends on how effectively one can achieve digital self-interference cancellation (SIC). Towards this end, in this paper, we consider unlimited sensing framework (USF) enabled full-duplex system. We show that by injecting folding non-linearities in the sensing pipeline, one can not only suppress self-interference but also recover the signal of interest (SoI). This approach leads to novel design of the receiver architecture that is complemented by a modulo-domain channel estimation method. We then demonstrate the advantages of modulo ADC by analyzing the relationship between quantization noise, quantization bits, and dynamic range. Numerical experiments show that the USF enabled receiver structure can achieve up to 40 dB digital SIC by using as few as 4-bits per sample. Our method outperforms the previous approach based on adaptive filters when it comes to SoI reconstruction, detection, and digital SIC performance., Comment: Accepted to IEEE Communications Letters

The binary manganese aluminium (MnAl) alloy with L$1_0$ crystal structure is a promising rare earth element-free permanent magnetic material because of its exceptional magnetic properties. However, experimentally synthesizing it in a stable bulk form is extremely challenging. Here, an alternative method of stabilizing the material is proposed and theoretically verified by partially substituting Mn and Al sites with Fe and Ni and identifying its enhanced phase stability, magnetic anisotropy, and coercivity from density functional theory (DFT), machine learning (ML) crystal graph convolution neural network (CGCNN), and micro-magnetic modeling. When considering a fixed ($50\%$)-Ni, the magnetic anisotropy increases with the increasing Fe content but decreases the formation energy. The calculated formation energies, elastic constants, and phonon frequencies demonstrate that all the binary and quaternary compositions are stable. Most importantly, the magnetic moment and magnetic anisotropy constants in $50\%$-Fe substituted composition (equiatomic phase) increase significantly compared to the MnAl. The predicted coercivity of the equiatomic phase is larger than the parent compound calculated by combining DFT computed parameters with micromagnetic simulations., Comment: 12 pages, 8 figures

Recent advancements in morpheme segmentation primarily emphasize word-level segmentation, often neglecting the contextual relevance within the sentence. In this study, we redefine the morpheme segmentation task as a sequence-to-sequence problem, treating the entire sentence as input rather than isolating individual words. Our findings reveal that the multilingual model consistently exhibits superior performance compared to monolingual counterparts. While our model did not surpass the performance of the current state-of-the-art, it demonstrated comparable efficacy with high-resource languages while revealing limitations in low-resource language scenarios., Comment: 5 pages, 3 tables

Average Treatment Effect (ATE) estimation is a well-studied problem in causal inference. However, it does not necessarily capture the heterogeneity in the data, and several approaches have been proposed to tackle the issue, including estimating the Quantile Treatment Effects. In the finite population setting containing $n$ individuals, with treatment and control values denoted by the potential outcome vectors $\mathbf{a}, \mathbf{b}$, much of the prior work focused on estimating median$(\mathbf{a}) -$ median$(\mathbf{b})$, where median($\mathbf x$) denotes the median value in the sorted ordering of all the values in vector $\mathbf x$. It is known that estimating the difference of medians is easier than the desired estimand of median$(\mathbf{a-b})$, called the Median Treatment Effect (MTE). The fundamental problem of causal inference -- for every individual $i$, we can only observe one of the potential outcome values, i.e., either the value $a_i$ or $b_i$, but not both, makes estimating MTE particularly challenging. In this work, we argue that MTE is not estimable and detail a novel notion of approximation that relies on the sorted order of the values in $\mathbf{a-b}$. Next, we identify a quantity called variability that exactly captures the complexity of MTE estimation. By drawing connections to instance-optimality studied in theoretical computer science, we show that every algorithm for estimating the MTE obtains an approximation error that is no better than the error of an algorithm that computes variability. Finally, we provide a simple linear time algorithm for computing the variability exactly. Unlike much prior work, a particular highlight of our work is that we make no assumptions about how the potential outcome vectors are generated or how they are correlated, except that the potential outcome values are $k$-ary, i.e., take one of $k$ discrete values.

Modelling musical structure is vital yet challenging for artificial intelligence systems that generate symbolic music compositions. This literature review dissects the evolution of techniques for incorporating coherent structure, from symbolic approaches to foundational and transformative deep learning methods that harness the power of computation and data across a wide variety of training paradigms. In the later stages, we review an emerging technique which we refer to as "sub-task decomposition" that involves decomposing music generation into separate high-level structural planning and content creation stages. Such systems incorporate some form of musical knowledge or neuro-symbolic methods by extracting melodic skeletons or structural templates to guide the generation. Progress is evident in capturing motifs and repetitions across all three eras reviewed, yet modelling the nuanced development of themes across extended compositions in the style of human composers remains difficult. We outline several key future directions to realize the synergistic benefits of combining approaches from all eras examined., Comment: Accepted to 13th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) 2024