Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Investigation of Airfoil Trailing Edge Noise Reduction by using TE Serrations

The growing prominence of aerodynamic noise from wind turbine blades at high wind speeds has made it the primary source of noise for wind turbines, with adverse effects on nearby residents' living conditions. This study focuses on experimental research conducted in an anechoic wind tunnel to investigate the noise reduction mechanism of wind turbine blade airfoils using serrated trailing edges, aiming to contribute to the development of low-noise wind turbine blades. Three models, including two types of NACA series airfoils and one reference plate with attachable serrated trailing edges, were tested. The findings reveal that airfoils with serrated trailing edges exhibit a 3 to 6 dB reduction in the mid-high frequency wideband noise, with the width of the frequency band of noise reduction slightly increasing as the Reynolds number rises. The presence of serrations also eliminates multiple tones of high amplitude exceeding 10 dB. The study highlights serration height as the most influential factor for noise reduction, surpassing the significance of serration width and the ratio of width to height. Moreover, investigations into the noise reduction mechanism indicate varying degrees of reduction in streamwise fluctuating velocity spectra near the serrated trailing edge, even aligning with changes in the sound power spectra. Serrations were found to alter the turbulence length scale in the downstream flow field, potentially impacting noise generation. This study suggests that the reduction in streamwise fluctuating velocity near the serrated trailing edge plays a crucial role in noise reduction, highlighting the importance of detailed flow field measurements and analysis for a comprehensive understanding of the mechanistic relationship between flow changes and serration-induced noise reduction.
Electrical Control of Spin and Valley in Spin-Orbit Coupled Graphene Multilayers

Electrical control of magnetism has been a major techonogical pursuit of the spintronics community, owing to its far-reaching implications for data storage and transmission. Here, we propose and analyze a new mechanism for electrical switching of isospin, using chiral-stacked graphene multilayers, such as bernal bilayer graphene or rhombohedral trilayer graphene, encapsulated by transition metal dichalcogenide (TMD) substrates. Leveraging the proximity-induced spin-orbit coupling from the TMD, we demonstrate electrical switching of correlation-induced spin and/or valley polarization, by reversing a perpendicular displacement field or the chemical potential. We substantiate our proposal with both analytical arguments and self-consistent Hartree-Fock numerics. Finally, we illustrate how the relative alignment of the TMDs, together with the top and bottom gate voltages, can be used to selectively switch distinct isospin flavors, putting forward correlated van der Waals heterostructures as a promising platform for spintronics and valleytronics.
Code-Switched Urdu ASR for Noisy Telephonic Environment using Data Centric Approach with Hybrid HMM and CNN-TDNN

Call Centers have huge amount of audio data which can be used for achieving valuable business insights and transcription of phone calls is manually tedious task. An effective Automated Speech Recognition system can accurately transcribe these calls for easy search through call history for specific context and content allowing automatic call monitoring, improving QoS through keyword search and sentiment analysis. ASR for Call Center requires more robustness as telephonic environment are generally noisy. Moreover, there are many low-resourced languages that are on verge of extinction which can be preserved with help of Automatic Speech Recognition Technology. Urdu is the $10^{th}$ most widely spoken language in the world, with 231,295,440 worldwide still remains a resource constrained language in ASR. Regional call-center conversations operate in local language, with a mix of English numbers and technical terms generally causing a"code-switching"problem. Hence, this paper describes an implementation framework of a resource efficient Automatic Speech Recognition/ Speech to Text System in a noisy call-center environment using Chain Hybrid HMM and CNN-TDNN for Code-Switched Urdu Language. Using Hybrid HMM-DNN approach allowed us to utilize the advantages of Neural Network with less labelled data. Adding CNN with TDNN has shown to work better in noisy environment due to CNN's additional frequency dimension which captures extra information from noisy speech, thus improving accuracy. We collected data from various open sources and labelled some of the unlabelled data after analysing its general context and content from Urdu language as well as from commonly used words from other languages, primarily English and were able to achieve WER of 5.2% with noisy as well as clean environment in isolated words or numbers as well as in continuous spontaneous speech.
Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning

Oftentimes, environments for sequential decision-making problems can be quite sparse in the provision of evaluative feedback to guide reinforcement-learning agents. In the extreme case, long trajectories of behavior are merely punctuated with a single terminal feedback signal, leading to a significant temporal delay between the observation of a non-trivial reward and the individual steps of behavior culpable for achieving said reward. Coping with such a credit assignment challenge is one of the hallmark characteristics of reinforcement learning. While prior work has introduced the concept of hindsight policies to develop a theoretically moxtivated method for reweighting on-policy data by impact on achieving the observed trajectory return, we show that these methods experience instabilities which lead to inefficient learning in complex environments. In this work, we adapt existing importance-sampling ratio estimation techniques for off-policy evaluation to drastically improve the stability and efficiency of these so-called hindsight policy methods. Our hindsight distribution correction facilitates stable, efficient learning across a broad range of environments where credit assignment plagues baseline methods.
HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness

Convolutional Neural Networks (CNN) are known to exhibit poor generalization performance under distribution shifts. Their generalization have been studied extensively, and one line of work approaches the problem from a frequency-centric perspective. These studies highlight the fact that humans and CNNs might focus on different frequency components of an image. First, inspired by these observations, we propose a simple yet effective data augmentation method HybridAugment that reduces the reliance of CNNs on high-frequency components, and thus improves their robustness while keeping their clean accuracy high. Second, we propose HybridAugment++, which is a hierarchical augmentation method that attempts to unify various frequency-spectrum augmentations. HybridAugment++ builds on HybridAugment, and also reduces the reliance of CNNs on the amplitude component of images, and promotes phase information instead. This unification results in competitive to or better than state-of-the-art results on clean accuracy (CIFAR-10/100 and ImageNet), corruption benchmarks (ImageNet-C, CIFAR-10-C and CIFAR-100-C), adversarial robustness on CIFAR-10 and out-of-distribution detection on various datasets. HybridAugment and HybridAugment++ are implemented in a few lines of code, does not require extra data, ensemble models or additional networks 1.
A Theoretically Guaranteed Quaternion Weighted Schatten p-norm Minimization Method for Color Image Restoration

Inspired by the fact that the matrix formulated by nonlocal similar patches in a natural image is of low rank, the rank approximation issue have been extensively investigated over the past decades, among which weighted nuclear norm minimization (WNNM) and weighted Schatten $p$-norm minimization (WSNM) are two prevailing methods have shown great superiority in various image restoration (IR) problems. Due to the physical characteristic of color images, color image restoration (CIR) is often a much more difficult task than its grayscale image counterpart. However, when applied to CIR, the traditional WNNM/WSNM method only processes three color channels individually and fails to consider their cross-channel correlations. Very recently, a quaternion-based WNNM approach (QWNNM) has been developed to mitigate this issue, which is capable of representing the color image as a whole in the quaternion domain and preserving the inherent correlation among the three color channels. Despite its empirical success, unfortunately, the convergence behavior of QWNNM has not been strictly studied yet. In this paper, on the one side, we extend the WSNM into quaternion domain and correspondingly propose a novel quaternion-based WSNM model (QWSNM) for tackling the CIR problems. Extensive experiments on two representative CIR tasks, including color image denoising and deblurring, demonstrate that the proposed QWSNM method performs favorably against many state-of-the-art alternatives, in both quantitative and qualitative evaluations. On the other side, more importantly, we preliminarily provide a theoretical convergence analysis, that is, by modifying the quaternion alternating direction method of multipliers (QADMM) through a simple continuation strategy, we theoretically prove that both the solution sequences generated by the QWNNM and QWSNM have fixed-point convergence guarantees.
Automatic Data Augmentation Learning using Bilevel Optimization for Histopathological Images

Training a deep learning model to classify histopathological images is challenging, because of the color and shape variability of the cells and tissues, and the reduced amount of available data, which does not allow proper learning of those variations. Variations can come from the image acquisition process, for example, due to different cell staining protocols or tissue deformation. To tackle this challenge, Data Augmentation (DA) can be used during training to generate additional samples by applying transformations to existing ones, to help the model become invariant to those color and shape transformations. The problem with DA is that it is not only dataset-specific but it also requires domain knowledge, which is not always available. Without this knowledge, selecting the right transformations can only be done using heuristics or through a computationally demanding search. To address this, we propose an automatic DA learning method. In this method, the DA parameters, i.e. the transformation parameters needed to improve the model training, are considered learnable and are learned automatically using a bilevel optimization approach in a quick and efficient way using truncated backpropagation. We validated the method on six different datasets. Experimental results show that our model can learn color and affine transformations that are more helpful to train an image classifier than predefined DA transformations, which are also more expensive as they need to be selected before the training by grid search on a validation set. We also show that similarly to a model trained with RandAugment, our model has also only a few method-specific hyperparameters to tune but is performing better. This makes our model a good solution for learning the best DA parameters, especially in the context of histopathological images, where defining potentially useful transformation heuristically is not trivial.
First-principles calculations of MoSeTe/WSeTe bilayers: Stability, phonons, electronic band offsets, and Rashba splitting

Janus materials have attracted much interest due to their intrinsic electric dipole moment and Rashba band splitting. We show that, by building bilayers of MoSeTe and WSeTe with different chalcogen atom sequences and different stacking patterns, one can modulate the net dipole moment strength and the Rashba effect, as well as the band alignment of the MoSeTe/WSeTe bilayer. Type-II band alignment is found which can be exploited to create long-lived interlayer excitons. Moreover, it is shown that the atomic sequence and stacking play pivotal roles in the interlayer distance of MoSeTe/WSeTe and thus its electronic structure and vibrational, especially low-frequency, characteristics. The long-range dispersion forces between atoms are treated with a conventional additive pairwise, as well as a many-body-dispersion method. It is shown that under the many-body dispersion method, more clear and rational thermodynamic trends of bilayer stacking are realized and interface distances are estimated more accurately. Vibrational spectra of the bilayers are calculated using first-principles phonon calculations and the fingerprints of monolayer attraction and repulsion are identified. An anti-correlation between distance and the shearing mode frequency of the rigid monolayers is demonstrated which agrees well with experimental findings. The results suggest that the judicious selection of the atomic sequence and stacking helps to widen the scope of the low-dimensional materials by adding or enhancing properties for specific applications, e.g. for spintronics or valleytronics devices.
EnTri: Ensemble Learning with Tri-level Representations for Explainable Scene Recognition

Scene recognition based on deep-learning has made significant progress, but there are still limitations in its performance due to challenges posed by inter-class similarities and intra-class dissimilarities. Furthermore, prior research has primarily focused on improving classification accuracy, yet it has given less attention to achieving interpretable, precise scene classification. Therefore, we are motivated to propose EnTri, an ensemble scene recognition framework that employs ensemble learning using a hierarchy of visual features. EnTri represents features at three distinct levels of detail: pixel-level, semantic segmentation-level, and object class and frequency level. By incorporating distinct feature encoding schemes of differing complexity and leveraging ensemble strategies, our approach aims to improve classification accuracy while enhancing transparency and interpretability via visual and textual explanations. To achieve interpretability, we devised an extension algorithm that generates both visual and textual explanations highlighting various properties of a given scene that contribute to the final prediction of its category. This includes information about objects, statistics, spatial layout, and textural details. Through experiments on benchmark scene classification datasets, EnTri has demonstrated superiority in terms of recognition accuracy, achieving competitive performance compared to state-of-the-art approaches, with an accuracy of 87.69%, 75.56%, and 99.17% on the MIT67, SUN397, and UIUC8 datasets, respectively.
Characterization of relativistic electron bunch duration and travelling wave structure phase velocity based on momentum spectra measurements on the ARES linac at DESY

The ARES linac at DESY aims to generate and characterize ultrashort electron bunches (fs to sub-fs duration) with high momentum and arrival time stability for the purpose of applications related to accelerator R&D, e.g. development of advanced and compact diagnostics and accelerating structures, test of new accelerator components, medical applications studies, machine learning, etc. During its commissioning phase, the bunch duration characterization of the electron bunches generated at ARES has been performed with an RF-phasing technique relying on momentum spectra measurements, using only common accelerator elements (RF accelerating structures and magnetic spectrometers). The sensitivity of the method allowed highlighting different response times for Mo and Cs2Te cathodes. The measured electron bunch duration in a wide range of machine parameters shows excellent agreement overall with the simulation predictions, thus demonstrating a very good understanding of the ARES operation on the bunch duration aspect. The importance of a precise in-situ experimental determination of the phase velocity of the first travelling wave accelerating structure after the electron source, for which we propose a simple new beam-based method precise down to sub-permille variation respective to the speed of light in vacuum, is emphasized for this purpose. A minimum bunch duration of 20 fs rms, resolution-limited by the space charge forces, is reported. This is, to the best of our knowledge, around 4 times shorter than what has been previously experimentally demonstrated based on RF-phasing techniques with a single RF structure. The present study constitutes a strong basis for future time characterization down to the sub-fs level at ARES, using dedicated X-band transverse deflecting structures.
Maximal information leakage from quantum encoding of classical data

A new measure of information leakage for quantum encoding of classical data is defined. An adversary can access a single copy of the state of a quantum system that encodes some classical data and is interested in correctly guessing a general randomized or deterministic function of the data (e.g., a specific feature or attribute of the data in quantum machine learning) that is unknown to the security analyst. The resulting measure of information leakage, referred to as maximal quantum leakage, is the multiplicative increase of the probability of correctly guessing any function of the classical data upon observing measurements of the quantum state. Maximal quantum leakage is shown to satisfy post-processing inequality (i.e., applying a quantum channel reduces information leakage) and independence property (i.e., leakage is zero if the quantum state is independent of the classical data), which are fundamental properties required for privacy and security analysis. It also bounds accessible information. Effects of global and local depolarizing noise models on the maximal quantum leakage are established.
Data-free Black-box Attack based on Diffusion Model

Since the training data for the target model in a data-free black-box attack is not available, most recent schemes utilize GANs to generate data for training substitute model. However, these GANs-based schemes suffer from low training efficiency as the generator needs to be retrained for each target model during the substitute training process, as well as low generation quality. To overcome these limitations, we consider utilizing the diffusion model to generate data, and propose a data-free black-box attack scheme based on diffusion model to improve the efficiency and accuracy of substitute training. Despite the data generated by the diffusion model exhibits high quality, it presents diverse domain distributions and contains many samples that do not meet the discriminative criteria of the target model. To further facilitate the diffusion model to generate data suitable for the target model, we propose a Latent Code Augmentation (LCA) method to guide the diffusion model in generating data. With the guidance of LCA, the data generated by the diffusion model not only meets the discriminative criteria of the target model but also exhibits high diversity. By utilizing this data, it is possible to train substitute model that closely resemble the target model more efficiently. Extensive experiments demonstrate that our LCA achieves higher attack success rates and requires fewer query budgets compared to GANs-based schemes for different target models.
Exponential speedup of quantum algorithms for the pathfinding problem

Given $s, t$ in an unweighted undirected graph $G$, the goal of the pathfinding problem is to find an $s$-$t$ path. In this work, we first construct a graph $G$ based on welded trees and define a pathfinding problem in the adjacency list oracle $O$. Then we provide an efficient quantum algorithm to find an $s$-$t$ path in the graph $G$. Finally, we prove that no classical algorithm can find an $s$-$t$ path in subexponential time with high probability. The pathfinding problem is one of the fundamental graph-related problems. Our findings suggest that quantum algorithms may potentially offer advantages in more types of graphs to solve the pathfinding problem and open up new possibilities for practical applications of quantum computations in various fields.
Quantum geometric-induced third-order nonlinear transport in antiferromagnetic topological insulator MnBi2Te4

Discovering the nonlinear transport features in antiferromagnets is of fundamental interest in condensed matter physics as it offers a new frontier of the understanding deep connections between multiple degrees of freedom, including magnetic orders, symmetries, and band geometric properties. Antiferromagnetic topological insulator MnBi${_2}$Te${_4}$ has provided a highly tunable platform for experimental explorations due to its rich magnetic structures and striking topological band structures. Here, we experimentally investigate the third-order nonlinear transport properties in bulk MnBi${_2}$Te${_4}$ flakes. The measured third-harmonic longitudinal ($V_{xx}^{3{\omega}}$) and transverse ($V_{xy}^{3{\omega}}$) voltages show intimate connection with magnetic transitions of MnBi${_2}$Te${_4}$ flakes and their magnitudes change abruptly as MnBi${_2}$Te${_4}$ flakes go through magnetic transitions with varying temperature and magnetic fields. In addition, the measured $V_{xx}^{3{\omega}}$ exhibits an even-symmetric feature with changing magnetic field direction and the $V_{xy}^{3{\omega}}$ shows an odd-symmetric property, which are believed to be related to the quantum metric and the emergency of non-zero Berry curvature quadrupole with broken ${PT}$ symmetry and non-degenerate band structures under external magnetic fields, respectively. Our work shows great advances in the understanding of the underlying interactions between multiple geometric quantities.
Discrete time optimal investment under model uncertainty

We study a robust utility maximization problem in a general discrete-time frictionless market under quasi-sure no-arbitrage. The investor is assumed to have a random and concave utility function defined on the whole real-line. She also faces model ambiguity on her beliefs about the market, which is modeled through a set of priors. We prove the existence of an optimal investment strategy using only primal methods. For that we assume classical assumptions on the market and on the random utility function as asymptotic elasticity constraints. Most of our other assumptions are stated on a prior-by-prior basis and correspond to generally accepted assumptions in the literature on markets without ambiguity. We also propose a general setting including utility functions with benchmark for which our assumptions are easily checked.