Miracle-dz/UniFAR-FTUs huggingface dataset:
X-ray/H$\alpha$ scaling relationships in stellar flares

We report on the results of our simultaneous observations of three large stellar flares with soft X-rays (SXRs) and an H$\mathrm{\alpha}$ emission line from two binary systems of RS CVn type. The energies released in the X-ray and H$\mathrm{\alpha}$ emissions during the flares were $10^{36}$--$10^{38}$ and $10^{35}$--$10^{37}$ erg, respectively. It renders the set of the observations as the first successful simultaneous X-ray/H$\mathrm{\alpha}$ observations of the stellar flares with energies above $10^{35}$ erg; although the coverage of the H$\mathrm{\alpha}$ observations of the stellar flares with energies above $10^{35}$ erg; although the coverage of the H$\mathrm{\alpha}$ observations was limited, with $\sim$10\% of the $e$-folding time in the decay phase of the flares, that of the SXR ones was complete. Combining the obtained physical parameters and those in literature for solar and stellar flares, we obtained a good proportional relation between the emitted energies of X-ray and H$\mathrm{\alpha}$ emissions for a flare energy range of $10^{29}$--$10^{38}$ erg. The ratio of the H$\mathrm{\alpha}$-line to bolometric X-ray emissions was $\sim$0.1, where the latter was estimated by converting the observed SXR emission to that in the 0.1--100 keV band according to the best-fitting thin thermal model. We also found that the $e$-folding times of the SXR and H$\mathrm{\alpha}$ light curves in the decaying phase of a flare are in agreement for a time range of $1$--$10^4$~s. Even very large stellar flares with energies of six orders of magnitude larger than the most energetic solar flares follow the same scaling relationships with solar and much less energetic stellar flares. This fact suggests that their physical parameters can be estimated on the basis of the known physics of solar and stellar flares.
Equation for Aeroacoustics in a Quiescent Environment

The perturbation equation for aeroacoustics has been derived in a dissipative medium from the linearized compressible Navier-Stokes equation without any assumption, by expressing the same in spectral plane as in Continuum perturbation field in quiescent ambience: Common foundation of flows and acoustics Sengupta et al., Phys. Fluids,35, 056111 (2023). The governing partial differential equation (PDE) for the free-field propagation of the disturbances in the spectral plane provides the dispersion relation between wavenumber and circular frequency in the dissipative medium, as characterized by a nondimensional diffusion number. Here, the implications of the dispersion relation of the perturbation field in the quiescent medium are probed for different orders of magnitude of the generalized kinematic viscosity, across large ranges of the wavenumber and the circular frequency. The adopted global spectral analysis helps not only classify the PDE into parabolic and hyperbolic types, but also explain the existence of a critical wavenumber depending on space-time scales.
Sub-50 picosecond to microsecond carrier transport dynamics in pentacene thin films

Carrier transport dynamics from sub-50 ps to ∼μs over five temporal decades in pentacene films was studied by transient photoconductivity. The behavior of the temperature independent photocurrent peak suggests that the photogenerated carriers exhibit pre-trapping transport in extended states upon pulsed laser excitation. From 300 ps to ∼30 ns, the carriers thermalize and fall into shallow band tail states, and multiple-trapping and release transport dominates. From ∼30 ns to ∼μs, the weak temperature dependence of power-law photocurrent decay and the linear relation of logarithmic photocurrent with T−1/4 suggest a variable range hopping transport in deep trap states.Carrier transport dynamics from sub-50 ps to ∼μs over five temporal decades in pentacene films was studied by transient photoconductivity. The behavior of the temperature independent photocurrent peak suggests that the photogenerated carriers exhibit pre-trapping transport in extended states upon pulsed laser excitation. From 300 ps to ∼30 ns, the carriers thermalize and fall into shallow band tail states, and multiple-trapping and release transport dominates. From ∼30 ns to ∼μs, the weak temperature dependence of power-law photocurrent decay and the linear relation of logarithmic photocurrent with T−1/4 suggest a variable range hopping transport in deep trap states.
Phase diagram and specific heat of a nonequilibrium Curie-Weiss model

Adding activity or driving to a thermal system may modify its phase diagram and response functions. We study that effect for a Curie-Weiss model where the thermal bath switches rapidly between two temperatures. The critical temperature moves with the nonequilibrium driving, opening up a new region of stability for the paramagnetic phase (zero magnetization) at low temperatures. Furthermore, phase coexistence between the paramagnetic and ferromagnetic phases becomes possible at low temperatures. Following the excess heat formalism, we calculate the nonequilibrium thermal response and study its behaviour near phase transitions. Where the specific heat at the critical point makes a finite jump in equilibrium (discontinuity), it diverges once we add the second thermal bath. Finally, (also) the nonequilibrium specific heat goes to zero exponentially fast with vanishing temperature, realizing an extended Third Law.
Exciton Transport in a Germanium Quantum Dot Ladder

Quantum systems with engineered Hamiltonians can be used as simulators of many-body physics problems to provide insights beyond the capabilities of classical computers. Semiconductor gate-defined quantum dot arrays have emerged as a versatile platform for quantum simulation of generalized Fermi-Hubbard physics, one of the richest playgrounds in condensed matter physics. In this work, we employ a germanium 4$\times$2 quantum dot array and show that the naturally occurring long-range Coulomb interaction can lead to exciton formation and transport. We tune the quantum dot ladder into two capacitively-coupled channels and exploit Coulomb drag to probe the binding of electrons and holes. Specifically, we shuttle an electron through one leg of the ladder and observe that a hole is dragged along in the second leg under the right conditions. This corresponds to a transition from single-electron transport in one leg to exciton transport along the ladder. Our work paves the way for the study of excitonic states of matter in quantum dot arrays.
Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction

Internet of things (IoT) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT devices are highly vulnerable to cyber-attacks, which may result in security breaches and data leakages. To effectively prevent these attacks, a variety of machine learning-based network intrusion detection methods for IoT networks have been developed, which often rely on either feature extraction or feature selection techniques for reducing the dimension of input data before being fed into machine learning models. This aims to make the detection complexity low enough for real-time operations, which is particularly vital in any intrusion detection systems. This paper provides a comprehensive comparison between these two feature reduction methods of intrusion detection in terms of various performance metrics, namely, precision rate, recall rate, detection accuracy, as well as runtime complexity, in the presence of the modern UNSW-NB15 dataset as well as both binary and multiclass classification. For example, in general, the feature selection method not only provides better detection performance but also lower training and inference time compared to its feature extraction counterpart, especially when the number of reduced features K increases. However, the feature extraction method is much more reliable than its selection counterpart, particularly when K is very small, such as K = 4. Additionally, feature extraction is less sensitive to changing the number of reduced features K than feature selection, and this holds true for both binary and multiclass classifications. Based on this comparison, we provide a useful guideline for selecting a suitable intrusion detection type for each specific scenario, as detailed in Tab. 14 at the end of Section IV.
Dual symplectic classical circuits: An exactly solvable model of many-body chaos

We propose a general exact method of calculating dynamical correlation functions in dual symplectic brick-wall circuits in one dimension. These are deterministic classical many-body dynamical systems which can be interpreted in terms of symplectic dynamics in two orthogonal (time and space) directions. In close analogy with quantum dual-unitary circuits, we prove that two-point dynamical correlation functions are non-vanishing only along the edges of the light cones. The dynamical correlations are exactly computable in terms of a one-site Markov transfer operator, which is generally of infinite dimensionality. We test our theory in a specific family of dual-symplectic circuits, describing the dynamics of a classical Floquet spin chain. Remarkably, expressing these models in the form of a composition of rotations leads to a transfer operator with a block diagonal form in the basis of spherical harmonics. This allows us to obtain analytical predictions for simple local observables. We demonstrate the validity of our theory by comparison with Monte Carlo simulations, displaying excellent agreement with the latter for a choice of observables.
Mutual Information Analysis for Factor Graph-based MIMO Iterative Detections through Error Functions

The factor graph (FG) based iterative detection is considered an effective and practical method for multiple-input and multiple-out (MIMO), particularly massive MIMO (m-MIMO) systems. However, the convergence analysis for the FG-based iterative MIMO detection is insufficient, which is of great significance to the performance evaluation and algorithm design of detection methods. This paper investigates the mutual information update flow for the FG-based iterative MIMO detection and proposes a precise mutual information computation mechanism with the aid of Gaussian approximation and error functions, i.e., the error functions-aided analysis (EF-AA) mechanism. Numerical results indicate that the theoretical result calculated by the EF-AA mechanism is completely consistent with the bit error rate performance of the FG-based iterative MIMO detection. Furthermore, the proposed EF-AA mechanism can reveal the exact convergent iteration number and convergent signal-to-ratio value of the FG-based iterative MIMO detection, representing the performance bound of the MIMO detection.
Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones

Palm-sized nano-drones are an appealing class of edge nodes, but their limited computational resources prevent running large deep-learning models onboard. Adopting an edge-fog computational paradigm, we can offload part of the computation to the fog; however, this poses security concerns if the fog node, or the communication link, can not be trusted. To tackle this concern, we propose a novel distributed edge-fog execution scheme that validates fog computation by redundantly executing a random subnetwork aboard our nano-drone. Compared to a State-of-the-Art visual pose estimation network that entirely runs onboard, a larger network executed in a distributed way improves the $R^2$ score by +0.19; in case of attack, our approach detects it within 2s with 95% probability.
Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and their Impact

This chapter introduces the concept of adversarial attacks on image classification models built on convolutional neural networks (CNN). CNNs are very popular deep-learning models which are used in image classification tasks. However, very powerful and pre-trained CNN models working very accurately on image datasets for image classification tasks may perform disastrously when the networks are under adversarial attacks. In this work, two very well-known adversarial attacks are discussed and their impact on the performance of image classifiers is analyzed. These two adversarial attacks are the fast gradient sign method (FGSM) and adversarial patch attack. These attacks are launched on three powerful pre-trained image classifier architectures, ResNet-34, GoogleNet, and DenseNet-161. The classification accuracy of the models in the absence and presence of the two attacks are computed on images from the publicly accessible ImageNet dataset. The results are analyzed to evaluate the impact of the attacks on the image classification task.
Numerical Study of Turbulent Drag Reduction Over Riblet Surface

The paper focuses on numerical investigations of the characteristics of turbulent drag reduction over longitudinal riblet surface plate using the commercial software Fluent. The effects of riblet shape, depth and space, which are the main factors affecting drag reduction, are further analyzed after the numerical model is validated using the theoretical results and experimental data. Through numerical modeling, the distribution of velocity and vortices in near-wall fluid field are gotten, which are combined with the “Second Vortex” theory to analyze the mechanism of the turbulent drag reduction over riblet surface.
KDSTM: Neural Semi-supervised Topic Modeling with Knowledge Distillation

In text classification tasks, fine tuning pretrained language models like BERT and GPT-3 yields competitive accuracy; however, both methods require pretraining on large text datasets. In contrast, general topic modeling methods possess the advantage of analyzing documents to extract meaningful patterns of words without the need of pretraining. To leverage topic modeling's unsupervised insights extraction on text classification tasks, we develop the Knowledge Distillation Semi-supervised Topic Modeling (KDSTM). KDSTM requires no pretrained embeddings, few labeled documents and is efficient to train, making it ideal under resource constrained settings. Across a variety of datasets, our method outperforms existing supervised topic modeling methods in classification accuracy, robustness and efficiency and achieves similar performance compare to state of the art weakly supervised text classification methods.
Of Spiky SVDs and Music Recommendation

The truncated singular value decomposition is a widely used methodology in music recommendation for direct similar-item retrieval and downstream tasks embedding musical items. This paper investigates a curious effect that we show naturally occurring on many recommendation datasets: spiking formations in the embedding space. We first propose a metric to quantify this spiking organization’s strength, then mathematically prove its origin tied to underlying communities of items of varying internal popularity. With this new-found theoretical understanding, we finally open the topic with an industrial use case of estimating how music embeddings’ top-k similar items will change over time under the addition of data.
First results of the Laser-Interferometric Detector for Axions (LIDA)

We present the operating principle and the first observing run of a novel kind of direct detector for axions and axion-like particles in the galactic halo. Sensitive to the polarisation rotation of linearly polarised laser light induced by an axion field, our experiment is the first detector of its kind collecting scientific data. We discuss our current peak sensitivity of $1.51\times 10^{-10}$ $\text{GeV}^{-1}$ (95 % confidence level) to the axion-photon coupling strength in the axion mass range of $1.97$-$2.01$ $\text{neV}$ which is, for instance, motivated by supersymmetric grand-unified theories. We also report on effects that arise in our high-finesse in-vacuum cavity at unprecedented optical continuous-wave intensities. Our detector already belongs to the most sensitive direct searches within its measurement band, and our results pave the way towards surpassing the current sensitivity limits in the mass range from $10^{-8}$ $\text{eV}$ down to $10^{-16}$ $\text{eV}$ via quantum-enhanced laser interferometry.
MWPRanker: An Expression Similarity Based Math Word Problem Retriever

Math Word Problems (MWPs) in online assessments help test the ability of the learner to make critical inferences by interpreting the linguistic information in them. To test the mathematical reasoning capabilities of the learners, sometimes the problem is rephrased or the thematic setting of the original MWP is changed. Since manual identification of MWPs with similar problem models is cumbersome, we propose a tool in this work for MWP retrieval. We propose a hybrid approach to retrieve similar MWPs with the same problem model. In our work, the problem model refers to the sequence of operations to be performed to arrive at the solution. We demonstrate that our tool is useful for the mentioned tasks and better than semantic similarity-based approaches, which fail to capture the arithmetic and logical sequence of the MWPs. A demo of the tool can be found at https://www.youtube. com/watch? v=gSQWP3chFIs
Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction

Grammatical Error Correction (GEC) is the task of correcting errorful sentences into grammatically correct, semantically consistent, and coherent sentences. Popular GEC models either use large-scale synthetic corpora or use a large number of human-designed rules. The former is costly to train, while the latter requires quite a lot of human expertise. In recent years, AMR, a semantic representation framework, has been widely used by many natural language tasks due to its completeness and flexibility. A non-negligible concern is that AMRs of grammatically incorrect sentences may not be exactly reliable. In this paper, we propose the AMR-GEC, a seq-to-seq model that incorporates denoised AMR as additional knowledge. Specifically, We design a semantic aggregated GEC model and explore denoising methods to get AMRs more reliable. Experiments on the BEA-2019 shared task and the CoNLL-2014 shared task have shown that AMR-GEC performs comparably to a set of strong baselines with a large number of synthetic data. Compared with the T5 model with synthetic data, AMR-GEC can reduce the training time by 32\% while inference time is comparable. To the best of our knowledge, we are the first to incorporate AMR for grammatical error correction.
Introduction to Protein Structure

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation&dynamics. Within the living cell, protein molecules perform specific functions, typically by interacting with other proteins, DNA, RNA or small molecules. They take on a specific three dimensional structure, encoded by its amino acid sequence, which allows them to function within the cell. Hence, the understanding of a protein's function is tightly coupled to its sequence and its three dimensional structure. Before going into protein structure analysis and prediction, and protein folding and dynamics, here, we give a short and concise introduction into the basics of protein structures.
Causal Dependence Plots

Explaining artificial intelligence or machine learning models is increasingly important. To use such data-driven systems wisely we must understand how they interact with the world, including how they depend causally on data inputs. In this work we develop Causal Dependence Plots (CDPs) to visualize how one variable--an outcome--depends on changes in another variable--a predictor--$\textit{along with any consequent causal changes in other predictor variables}$. Crucially, CDPs differ from standard methods based on holding other predictors constant or assuming they are independent. CDPs make use of an auxiliary causal model because causal conclusions require causal assumptions. With simulations and real data experiments, we show CDPs can be combined in a modular way with methods for causal learning or sensitivity analysis. Since people often think causally about input-output dependence, CDPs can be powerful tools in the xAI or interpretable machine learning toolkit and contribute to applications like scientific machine learning and algorithmic fairness.
Optimal selection of laser modulation parameters in photothermal optical coherence tomography

Photothermal optical coherence tomography (PT-OCT) employs a secondary intensity-modulated photothermal laser to create modulated thermal strains that cause variations of the refractive index in the proximity of absorbing chromophores. These variations are directly detected with phase-sensitive OCT and offer insight to the molecular composition and thermo-elastic properties of the sample. Here, we define optimal PT laser modulation parameters by investigating the effect of PT laser power and modulation frequency on the ensuing thermal waves and thermal waves’ impact on the spatial resolution of PT-OCT imaging based on numerical simulations of PT-OCT and samples containing point absorbers.
Dynamic spectra of radio frequency bursts associated with edge-localized modes

Electromagnetic emissions in the radio frequency (RF) range are detected in the high-confinement-mode (H-mode) plasma using a fast RF spectrometer on the KSTAR tokamak. The emissions at the crash events of edge-localized modes (ELMs) are found to occur as strong RF bursts with dynamic features in intensity and spectrum. The RF burst spectra (obtained with frequency resolution better than 10 MHz) exhibit diverse spectral features and evolve in multiple steps before the onset and through the ELM crash: (1) a narrow-band spectral line around 200 MHz persistent for extended duration in the pre-ELM crash times, (2) harmonic spectral lines with spacing comparable to deuterium or hydrogen ion cyclotron frequency at the pedestal, (3) rapid onset (faster than ~1 μs) of intense RF burst with wide-band continuum in frequency which coincides with the onset of ELM crash, and (4) a few additional intense RF bursts with chirping-down narrow-band spectrum during the crash. These observations indicate plasma waves are excited in the pedestal region and strongly correlated with the ELM dynamics such as the onset of the explosive crash. Thus the investigation of RF burst occurrence and their dynamic spectral features potentially offers the possibility of exploring H-mode physics in great detail.