Miracle-dz/UniFAR-FTUs huggingface dataset:
Raman Scattering with infrared excitation resonant with MoSe$_2$ indirect band gap

Resonance Raman scattering, which probes electrons, phonons and their interplay in crystals, is extensively used in two-dimensional materials. Here we investigate Raman modes in MoSe$_2$ at different laser excitation energies from 2.33 eV down to the near infrared 1.16 eV. The Raman spectrum at 1.16 eV excitation energy shows that the intensity of high-order modes is strongly enhanced if compared to the first-order phonon modes intensity due to resonance effects with the MoSe$_2$ indirect band gap. By comparing the experimental results with the two-phonon density of states calculated with density functional theory, we show that the high-order modes originate mostly from two-phonon modes with opposite momenta. In particular, we identify the momenta of the phonon modes that couple strongly with the electrons to produce the resonance process at 1.16 eV, while we verify that at 2.33 eV the two-phonon modes lineshape compares well with the two-phonon density of state calculated over the entire Brillouin Zone. We also show that, by lowering the crystal temperature, we actively suppress the intensity of the resonant two-phonon modes and we interpret this as the result of the increase of the indirect band gap at low temperature that moves our excitation energy out of the resonance condition.
Magnetic Flux Transport in Radiatively Inefficient Accretion Flows and the Pathway toward a Magnetically Arrested Disk

Large-scale magnetic fields play a vital role in determining the angular momentum transport and generating jets/outflows in accreting systems, yet their origins remain poorly understood. We focus on radiatively inefficient accretion flows (RIAFs) around black holes (BHs), and conduct 3D general-relativistic magnetohydrodynamic simulations using the Athena++ code. We first reconfirm that the magnetorotational instability driven dynamo in the RIAF alone does not spontaneously form a magnetically arrested disk (MAD), conducive for strong-jet formation. We next investigate the other possibility, where the large-scale magnetic fields are advected inward from external sources (e.g., the companion star in X-ray binaries and the magnetized ambient medium in active galactic nuclei). Although the actual configurations of the external fields could be complex and uncertain, they are likely to be closed. As a first study, we treat them as closed field loops of different sizes, shapes, and field strengths. Unlike earlier studies of flux transport, where the magnetic flux is injected into the initial laminar flow, we inject the magnetic field loops into the quasi-stationary turbulent RIAF in inflow equilibrium, then follow their evolution. We find that a substantial fraction (∼15%–40%) of the flux injected at large radii reaches the BH, with a weak dependence on the loop parameters, except when the loops are injected at high latitudes, away from the midplane. The relatively high efficiency of the flux transport observed in our study hints that a MAD might easily be formed relatively close to the BH, provided that a source of the large-scale field exists at larger radii.
Design of secure and robust cognitive system for malware detection

The computer systems for decades have been threatened by various types of hardware and software attacks of which Malware have been one of the pivotal issues. This malware has the ability to steal, destroy, contaminate, gain unintended access, or even disrupt the entire system. There have been techniques to detect malware by performing static and dynamic analysis of malware ﬁles, but, stealthy malware has circumvented the static analysis method and for dynamic analysis, there have been previous works that propose different methods to detect malware. However, these techniques do not perform well on stealthy malware. Moreover, the rising trend and advancements in machine learning has resulted into its numerous applications in the ﬁeld of computer vision, pattern recognition to providing security to hardware devices. Machine learning based malware detection techniques rely on grayscale images of malware and tends to classify malware based on the distribution of textures in graycale images. Albeit the advancement and promising results shown by machine learning techniques, attackers can exploit the vulnerabilities by generating adversarial samples. Adversarial samples are generated by intelligently crafting and adding perturbations to the input samples. There exists majority of the software based adversarial attacks and defenses. To defend against the adversaries, the existing malware detection based on machine learning and grayscale images needs a preprocessing for the adversarial data. This can cause an additional overhead and can prolong the real-time malware detection. So, as an alternative to this, we explore RRAM (Resistive Random Access Memory) based defense against adversaries. Therefore, the aim of this thesis is to address the above mentioned critical system security issues. The above mentioned challenges are addressed by demonstrating proposed techniques to design a secure and robust cognitive system. First, a novel technique to detect stealthy malware is proposed. The technique uses malware binary images and then extract different features from the same and then employ different ML-classiﬁers on the dataset thus obtained. Results demonstrate that this technique is successful in differentiating classes
Domestic Activity Clustering from Audio via Depthwise Separable Convolutional Autoencoder Network

Automatic estimation of domestic activities from audio can be used to solve many problems, such as reducing the labor cost for nursing the elderly people. This study focuses on solving the problem of domestic activity clustering from audio. The target of domestic activity clustering is to cluster audio clips which belong to the same category of domestic activity into one cluster in an unsupervised way. In this paper, we propose a method of domestic activity clustering using a depthwise separable convolutional autoencoder network. In the proposed method, initial embeddings are learned by the depthwise separable convolutional autoencoder, and a clustering-oriented loss is designed to jointly optimize embedding refinement and cluster assignment. Different methods are evaluated on a public dataset (a derivative of the SINS dataset) used in the challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) in 2018. Our method obtains the normalized mutual information (NMI) score of 54.46%, and the clustering accuracy (CA) score of 63.64%, and outperforms state-of-the-art methods in terms of NMI and CA. In addition, both computational complexity and memory requirement of our method is lower than that of previous deep-model-based methods. Codes: https://github.com/vinceasvp/domestic-activity-clustering-from-audio
Spin and charge density waves in the quasi-one-dimensional KMn6Bi5

A Mn 6 Bi 5 materials ( A = Na, K, Rb and Cs) consisting of unique Mn-cluster chains emerge as a new family of superconductors with the suppression of their antiferromagnetic (AFM) order under high pressures. Here, we report transverse incommensurate spin density waves (SDWs) for the Mn atoms with a propagating direction along the chain axes as a ground state for KMn 6 Bi 5 by single crystal neutron diffraction. The SDWs have a refined amplitude of ~2.46 µ B for the Mn atoms in the pentagons and ~0.29 µ B with a large standard deviation for Mn atoms in the center between the pentagons. AFM dominate both the nearest-neighbor Mn-Mn interactions within the pentagon and next-nearest-neighbor Mn-Mn interactions out of the pentagon (along the propagating wave). The SDWs exhibit both local and itinerant characteristics probably formed by a cooperative interaction between local magnetic exchange and conduction electrons. A significant magnetoelastic effect during the AFM transition, especially along the chain direction, has been demonstrated by temperature-dependent x-ray powder diffraction. Single crystal x-ray diffraction below the AFM transition revealed satellite peaks originating from charge density waves along the chain direction with a q -vector twice as large as the SDW one, pointing to a strong real space coupling between them. Our work not only manifests a fascinating interplay among spin, charge, lattice and one dimensionality to trigger intertwined orders in KMn 6 Bi 5 but also provides important piece of information for the magnetic structure of the parent compound to understand the mechanism of superconductivity in this new family.
VFTS 243 as predicted by the BPASS fiducial models

The recent discovery of an unambiguous quiescent BH and main sequence O star companion in VFTS 243 opens the door to new constraints on theoretical stellar evolution and population models looking to reproduce the progenitors of black hole - black hole binaries. Here we show that the Binary Population and Spectral Synthesis fiducial models (BPASSv2.2.1) natively predict VFTS 243-like systems: We find that VFTS 243 likely originated from a binary system in a ∼15 day orbit with primary mass ranging from 40 to 50 M⊙ and secondary star with initial mass 24–25 M⊙. Additionally we find that the death of the primary star must have resulted in a low energy explosion E < 1050 ergs. With a uniform prior we find that the kick velocity of the new-born black hole was ≤10 km s−1. The very low eccentricity reported for VFTS 243 and the subsequent conclusion by the authors that the SN kick must have been very small is in line with the peak in the posterior distribution between 0 and 5 km s−1 found from our numerical simulations performed with a uniform prior. Finally, the reduced Hobbs kick distribution commonly used in black hole population synthesis is strongly disfavoured, whereas the Bray kick with the most recent parameter calibration predicts 4.2 ± 3.4 km s−1, which is very consistent with the posterior velocity distributions obtained for our matching VFTS 243-like models using a uniform kick prior.
GRB 171205A: Hypernova and Newborn Neutron Star

GRB 171205A is a low-luminosity, long-duration gamma-ray burst (GRB) associated with SN 2017iuk, a broad-line type Ic supernova (SN). It is consistent with having been formed in the core collapse of a widely separated binary, which we have called the binary-driven hypernova of type III. The core collapse of the CO star forms a newborn NS (νNS) and the SN explosion. Fallback accretion transfers mass and angular momentum to the νNS, here assumed to be born non-rotating. The accretion energy injected into the expanding stellar layers powers the prompt emission. The multiwavelength power-law afterglow is explained by the synchrotron radiation of electrons in the SN ejecta, powered by energy injected by the spinning νNS. We calculate the amount of mass and angular momentum gained by the νNS, as well as the νNS rotational evolution. The νNS spins up to a period of 47 ms, then releases its rotational energy powering the synchrotron emission of the afterglow. The paucity of the νNS spin explains the low-luminosity characteristic and that the optical emission of the SN from the nickel radioactive decay outshines the optical emission from the synchrotron radiation. From the νNS evolution, we infer that the SN explosion had to occur at most 7.36 h before the GRB trigger. Therefore, for the first time, the analysis of the GRB data leads to the time of occurrence of the CO core collapse leading to the SN explosion and the electromagnetic emission of the GRB event.
Efficiency of optically pumping a quantum battery and a two-stroke heat engine

In this work, we study the eﬃciency of charging a quantum battery through optical pumping. The battery consists of a qutrit and it is connected to a natural thermal reservoir and an external coherent drive in the limit where its upper energy level can be adiabatically eliminated from the dynamics. In this scenario, the drive plus spontaneous emission optically pumps the intermediate energy level of the qutrit and the battery can be understood as being charged by an eﬀective higher temperature reservoir that takes it out of equilibrium with the natural reservoir and stores useful energy in it. We also analyse the eﬃciency of using this battery and charging scheme as the work ﬂuid of a two-stroke thermal machine. The thermal machine includes a fourth level through which work is extracted from the battery via a unitary transformation, therefore setting the limit of maximum eﬃciency of the machine.
N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models

Avoiding the generation of responses that contradict the preceding context is a significant challenge in dialogue response generation. One feasible method is post-processing, such as filtering out contradicting responses from a resulting n-best response list. In this scenario, the quality of the n-best list considerably affects the occurrence of contradictions because the final response is chosen from this n-best list. This study quantitatively analyzes the contextual contradiction-awareness of neural response generation models using the consistency of the n-best lists. Particularly, we used polar questions as stimulus inputs for concise and quantitative analyses. Our tests illustrate the contradiction-awareness of recent neural response generation models and methodologies, followed by a discussion of their properties and limitations.
Multi-modal volumetric concept activation to explain detection and classification of metastatic prostate cancer on PSMA-PET/CT

Explainable artificial intelligence (XAI) is increasingly used to analyze the behavior of neural networks. Concept activation uses human-interpretable concepts to explain neural network behavior. This study aimed at assessing the feasibility of regression concept activation to explain detection and classification of multi-modal volumetric data. Proof-of-concept was demonstrated in metastatic prostate cancer patients imaged with positron emission tomography/computed tomography (PET/CT). Multi-modal volumetric concept activation was used to provide global and local explanations. Sensitivity was 80% at 1.78 false positive per patient. Global explanations showed that detection focused on CT for anatomical location and on PET for its confidence in the detection. Local explanations showed promise to aid in distinguishing true positives from false positives. Hence, this study demonstrated feasibility to explain detection and classification of multi-modal volumetric data using regression concept activation.
Low Resistance Ohmic Contact on Epitaxial MOVPE Grown β-Ga2O3 and β-(AlxGa1−x)2O3 Films

We report on the realization of record low resistance Ohmic contacts to MOVPE-grown heavily Si-doped <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga2O3 and <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-(AlxGa1-x)2O3 epitaxial films. Transfer length measurement (TLM) patterns were fabricated on the heavily Si-doped homoepitaxial <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga2O3 films with electron concentration (n) ranging from 1.77 to <inline-formula> <tex-math notation="LaTeX">$3.23\times 10^{20}$ </tex-math></inline-formula> cm−3. Record low specific contact resistance (<inline-formula> <tex-math notation="LaTeX">$\rho _{c}$ </tex-math></inline-formula>) and total contact resistance (<inline-formula> <tex-math notation="LaTeX">${\text{R}_{c}}$ </tex-math></inline-formula>) of <inline-formula> <tex-math notation="LaTeX">$1.62\times 10^{-7}\,\,\Omega $ </tex-math></inline-formula>. cm2 and <inline-formula> <tex-math notation="LaTeX">$0.023 \Omega $ </tex-math></inline-formula>. mm were realized for <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga2O3: Si films with n <inline-formula> <tex-math notation="LaTeX">$ > 3\times 10^{20}$ </tex-math></inline-formula> cm−3. TLM structures were also fabricated on heavily Si doped coherently strained <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-(AlxGa1-x)2O3/<inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga2O3 (x = 12%, 17% and 22%) films. The film with 12% Al composition (n <inline-formula> <tex-math notation="LaTeX">$=1.23\times 10^{20}$ </tex-math></inline-formula> cm−3) showed <inline-formula> <tex-math notation="LaTeX">$\rho _{c}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$5.86\times 10^{-6}\,\,\Omega $ </tex-math></inline-formula>. cm2, but it increased to <inline-formula> <tex-math notation="LaTeX">$2.19\times 10^{-4}\,\,\Omega $ </tex-math></inline-formula>. cm2 for a layer with a 22% Al composition. Annealing the samples post metal deposition has generally led to a decrease in contact resistance, but for high Al content <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-(AlxGa1-x)2O3, the contact resistance did not change significantly after the annealing process. The low contact resistance values measured in this work are the lowest for ultrawide bandgap semiconductors and are very promising for the fabrication of high frequency power devices.
Multi-scale Sampling and Aggregation Network For High Dynamic Range Imaging

—High dynamic range (HDR) imaging is a fundamental problem in image processing, which aims to generate well-exposed images, even in the presence of varying illumination in the scenes. In recent years, multi-exposure fusion methods have achieved remarkable results, which merge multiple low dynamic range (LDR) images, captured with different exposures, to generate corresponding HDR images. However, synthesizing HDR images in dynamic scenes is still challenging and in high demand. There are two challenges in producing HDR images: 1). Object motion between LDR images can easily cause undesirable ghosting artifacts in the generated results. 2). Under and overexposed regions often contain distorted image content, because of insufﬁcient compensation for these regions in the merging stage. In this paper, we propose a multi-scale sampling and aggregation network for HDR imaging in dynamic scenes. To effectively alleviate the problems caused by small and large motions, our method implicitly aligns LDR images by sampling and aggregating high-correspondence features in a coarse-to-ﬁne manner. Furthermore, we propose a densely connected network based on discrete wavelet transform for performance improvement, which decomposes the input into several non-overlapping frequency subbands and adaptively performs compensation in the wavelet domain. Experiments show that our proposed method can achieve state-of-the-art performances under diverse scenes, compared to other promising HDR imaging methods. In addition, the HDR images generated by our method contain cleaner and more detailed content, with fewer distortions, leading to better visual quality.
Numerical and modeling error assessment of large-eddy simulation using direct-numerical-simulation-aided large-eddy simulation

We study the numerical errors of large-eddy simulation (LES) in isotropic and wall-bounded turbulence. A direct-numerical-simulation (DNS)-aided LES formulation, where the subgrid-scale (SGS) term of the LES is computed by using filtered DNS data is introduced. We first verify that this formulation has zero error in the absence of commutation error between the filter and the differentiation operator of the numerical algorithm. This method allows the evaluation of the time evolution of numerical errors for various numerical schemes at grid resolutions relevant to LES. The analysis shows that the numerical errors are of the same order of magnitude as the modeling errors and often cancel each other. This supports the idea that supervised machine learning algorithms trained on filtered DNS data might not be suitable for robust SGS model development, as this approach disregards the existence of numerical errors in the system that accumulates over time. The assessment of errors in turbulent channel flow also identifies that numerical errors close to the wall dominate, which has implications for the development of wall models.
Improving Meta-Learning Generalization with Activation-Based Early-Stopping

Meta-Learning algorithms for few-shot learning aim to train neural networks capable of generalizing to novel tasks using only a few examples. Early-stopping is critical for performance, halting model training when it reaches optimal generalization to the new task distribution. Early-stopping mechanisms in Meta-Learning typically rely on measuring the model performance on labeled examples from a meta-validation set drawn from the training (source) dataset. This is problematic in few-shot transfer learning settings, where the meta-test set comes from a different target dataset (OOD) and can potentially have a large distributional shift with the meta-validation set. In this work, we propose Activation Based Early-stopping (ABE), an alternative to using validation-based early-stopping for meta-learning. Specifically, we analyze the evolution, during meta-training, of the neural activations at each hidden layer, on a small set of unlabelled support examples from a single task of the target tasks distribution, as this constitutes a minimal and justifiably accessible information from the target problem. Our experiments show that simple, label agnostic statistics on the activations offer an effective way to estimate how the target generalization evolves over time. At each hidden layer, we characterize the activation distributions, from their first and second order moments, then further summarized along the feature dimensions, resulting in a compact yet intuitive characterization in a four-dimensional space. Detecting when, throughout training time, and at which layer, the target activation trajectory diverges from the activation trajectory of the source data, allows us to perform early-stopping and improve generalization in a large array of few-shot transfer learning settings, across different algorithms, source and target datasets.
Concurrent estimation of noise and compact-binary signal parameters in gravitational-wave data

Gravitational-wave parameter estimation for compact binary signals typically relies on sequential estimation of the properties of the detector Gaussian noise and of the binary parameters. This procedure assumes that the noise variance, expressed through its power spectral density, is perfectly known in advance. We assess the impact of this approximation on the estimated parameters by means of an analysis that simultaneously estimates the noise and compact binary parameters, thus allowing us to marginalize over uncertainty in the noise properties. We compare the traditional sequential estimation method and the new full marginalization method using events from the GWTC-3 catalog. We ﬁnd that the recovered signals and inferred parameters agree to within their statistical measurement uncertainty. At current detector sensitivities, uncertainty about the noise power spectral density is a subdominant eﬀect compared to other sources of uncertainty.