Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Kerr effect in vacuum

From an effective field theory of electromagnetism in vacuum including all lowest-order nonlinear terms consistent with Lorentz invariance and locality of photon-photon interactions, we derive an effective-medium description of strong background fields as regards their influence on a weak probe. We mainly consider as background a pump beam with well-defined wave vector and polarization. This leads us to define a nonlinear index of vacuum which, in the Euler-Heisenberg model derived from QED, has an optimal value of $1.555\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}33}\phantom{\rule{0.28em}{0ex}}{\mathrm{cm}}^{2}/\mathrm{W}$ for a linearly polarized pump as seen by a counterpropagating, orthogonally polarized probe. We further generalize the model to include coupling to an axion field. In the limit where the axion mass is much smaller than the typical photon energy, this yields dispersive corrections, and the axionic signature is found to be greatly enhanced for a circularly polarized pump as compared to a linearly polarized one. The formalism here presented points to a simplification of the DeLLight experiment [Sarazin et al., Eur. Phys. J. D 70, 13 (2016)] aiming to measure the deflection of a probe by a tightly focused laser pulse.
Feedup, Feedback, and Feedforward in Curve Mid-Air 3D Gestures

Issuing a mid-air gesture in a three-dimensional space intrinsically suffers for the lack of explicit direct representation of the gesture with which guidance and feedback can be offered. To address this challenge, we decompose the feedback problem into three components: feedup to constantly represent the goal of the gestural task, feedback to respond to what the end user already did related to the initial goal
Finite permutation groups with few orbits under the action on the power set

We study the orbits under the natural action of a permutation group $G \subseteq S_n$ on the powerset $\mathscr{P}(\{1, \dots , n\})$. The permutation groups having exactly $n+1$ orbits on the powerset can be characterized as set-transitive groups (see Definition 2.2) and were fully classified in \cite{BP55}. In this paper, we establish a general method that allows one to classify the permutation groups with $n+r$ set-orbits for a given $r$, and apply it to integers $2 \leq r \leq 11$ using the computer algebra system GAP.
Long-range, phase-and-polarization diversity coherent reflectometer.

A coherent, short-length reference arm reflectometer, which utilizes a 76-MHz repetition rate mode-locked fiber laser, was investigated experimentally for long fiber links (> 10 km). The reflectometer combines the advantages of optical time-domain and frequency-domain reflectometry without the need for high-speed photodetectors and electronics to achieve high spatial resolution of 2.5 mm at 10 km. To our knowledge, this is the highest resolution reported for fiber reflectometry at this distance for reflective type event using non-photon-counting detection. Phase and polarization diversity detection, combined with spectral compression using frequency-chirp, are proposed to improve sensitivity and discrimination against the fiber Rayleigh backscattering background.
Deep Learning Based Energy Disaggregation and On/Off Detection of Household Appliances

Energy disaggregation, a.k. a. Non-Intrusive Load Monitoring, aims to separate the energy consumption of individual appliances from the readings of a mains power meter measuring the total energy consumption of, e.g., a whole house. Energy consumption of individual appliances can be useful in many applications, e.g., providing appliance-level feedback to the end users to help them understand their energy consumption and ultimately save energy. Recently, with the availability of large-scale energy consumption datasets, various neural network models such as convolutional neural networks and recurrent neural networks have been investigated to solve the energy disaggregation problem. Neural network models can learn complex patterns from large amounts of data and have been shown to outperform the traditional machine learning methods such as variants of hidden Markov models. However, current neural network methods for energy disaggregation are either computational expensive or are not capable of handling long-term dependencies. In this article, we investigate the application of the recently developed WaveNet models for the task of energy disaggregation. Based on a real-world energy dataset collected from 20 households over 2 years, we show that WaveNet models outperforms the state-of-the-art deep learning methods proposed in the literature for energy disaggregation in terms of both error measures and computational cost. On the basis of energy disaggregation, we then investigate the performance of two deep-learning based frameworks for the task of on/off detection which aims at estimating whether an appliance is in operation or not. The first framework obtains the on/off states of an appliance by binarising the predictions of a regression model trained for energy disaggregation, while the second framework obtains the on/off states of an appliance by directly training a binary classifier with binarised energy readings of the appliance serving as the target values. Based on the same dataset, we show that for the task of on/off detection the second framework, i.e., directly training a binary classifier, achieves better performance in terms of F1 score.
A Caching Strategy Towards Maximal D2D Assisted Offloading Gain

Device-to-Device (D2D) communications incorporated with content caching have been regarded as a promising way to offload the cellular traffic data. In this paper, the caching strategy is investigated to maximize the D2D offloading gain with the comprehensive consideration of user collaborative characteristics as well as the physical transmission conditions. Specifically, for a given content, the number of interested users in different groups is different, and users always ask the most trustworthy user in proximity for D2D transmissions. An analytical expression of the D2D success probability is first derived, which represents the probability that the received signal to interference ratio is no less than a given threshold. As the formulated problem is nonconvex, the optimal caching strategy for the special unbiased case is derived in a closed form, and a numerical searching algorithm is proposed to obtain the globally optimal solution for the general case. To reduce the computational complexity, an iterative algorithm based on the asymptotic approximation of the D2D success probability is proposed to obtain the solution that satisfies the Karush-Kuhn-Tucker conditions. The simulation results verify the effectiveness of the analytical results and show that the proposed algorithm outperforms the existing schemes in terms of offloading gain.
Monitoring of people entering and exiting private areas using Computer Vision

Entry-Exit surveillance is a novel research problem that addresses security concerns when people attain absolute privacy in camera forbidden areas such as toilets and changing rooms that are basic amenities to the humans in public places such as Shopping malls, Airports, Bus and Rail stations. The objective is, if not inside these camera forbidden areas, from outside, the individuals are to be monitored to analyze the time spent by them inside and also the suspecting transformations in their appearances if any. In this paper, firstly, a pseudo-annotated dataset of a laboratory observation of people entering and exiting the camera forbidden area captured using two cameras in contrast to the state-of-the-art single-camera based EnEx dataset is presented. Conventionally the proposed dataset is named \textbf{\textit{EnEx2}}. Next, a spatial transition based event detection to determine the entry or exit of individuals is presented with standard results by evaluating the proposed model using the proposed dataset and the publicly available standard video surveillance datasets that are hypothesized to Entry-Exit surveillance scenarios. The proposed dataset is expected to enkindle active research in Entry-Exit Surveillance domain.
Scaled demagnetization models: susceptibility, resonant and relaxation frequency prediction compared to magnetic composite measurements.

Maxwell-Garnett Theory (MGT), Bruggeman Effective Medium Theory (BEMT), Coherent Model Approximation (CMA), Scaled Effective Medium Theory (ScEMT) and models of Schlomann (E. Schlomann, Phys. Rev. B, 182, 7, 632 (10 June 1969) and E. Schlomann, Conf. on Mag. and Mag. Materials, AIEE Spec. Publ. T-91, 600 (1956)) are applied to predict magnetic susceptibility, resonant and relaxation frequency in polymer-magnetic particle composites. Particulates had aspect ratios near unity; bulk low frequency susceptibilities ranging from approximately 5 to 4000 and particle volume fractions between 1 and 100%. Previous publications demonstrated that ScEMT improved the prediction of DC susceptibility as compared to classical models. This paper first modifies BEMT and ScEMT for volume fractions below about 10%. A ScEMT based model of composite resonant frequency is presented and compared to MGT, CMA models and measurement. Model and measurement comparisons of resonant frequency are followed by model-measurement comparisons of relaxation frequency. CMA, MGT, models of Schlomann, and volumetric scaled modifications of Schlomann are tested in the relaxation frequency study. The paper emphasizes the broad application of the models and therefore composite data for a wide range of particulate chemistries are presented ScEMT predictions of susceptibility and resonant frequency continue to show reasonable agreement with measurement and represent improvement over the CMA and MGT models. ScEMT modifications to Schlomann show overall best agreement with relaxation frequency measurement. CMA and MGT are most accurate for modest susceptibility (~ < 100) while ScEMT modified Schlomann models are most accurate for large susceptibility.
Origin of magnetic anisotropy in the spin ladder compound 
(C5H12N)2CuBr4

The $S=1/2$ spin ladder compound (C$_5$H$_{12}$N)$_2$CuBr$_4$ (BPCB) is studied by means of high-resolution inelastic neutron scattering. In agreement with previous studies we find a band of triplet excitations with a spin gap of $\sim0.8$~meV and a bandwidth of $\sim0.6$~meV. In addition, we observe a distinct splitting of the triplet band of $50(1)$~$\mu$eV or $40(2)$~$\mu$eV at the band minimum or maximum, respectively. By comparison to a strong coupling expansion calculation of the triplet dispersion for a spin ladder with anisotropic exchange, weakly anisotropic leg interactions are identified as the dominant source of magnetic anisotropy in BPCB. Based on these results, we discuss the nature of magnetic exchange anisotropy in BPCB and in related transition-metal insulators.
The buildup of strongly barred galaxies in the TNG100 simulation

We analyse the properties of strongly barred disc galaxies using the TNG100 simulation, a cosmological hydrodynamical realisation of the IllustrisTNG suite. We identify 270 disc galaxies at z = 0 in the stellar mass range $M_{*}=10^{10.4-11}\rm M_{\odot}$, of which 40 per cent are barred. Of the detected bars, more than half are strong. We find that the fraction of barred galaxies increases with stellar mass, in agreement with observational results. Strongly barred galaxies exhibit, overall, lower gas-to-stellar mass ratios compared to unbarred galaxies. The majority of barred galaxies are quenched (sSFR ∼10−11.7yr−1), whereas unbarred galaxies continue to be active (sSFR ∼10−10.3yr−1) on the main sequence of star-forming galaxies. We explore the evolution of strongly barred and unbarred galaxies to investigate their formation and quenching histories. We find that strong bars form between 0.5 < z < 1.5, with more massive galaxies hosting older bars. Strong bars form in galaxies with an early-established prominent disc component, undergoing periods of enhanced star formation and black hole accretion, possibly assisted by cosmological inflows. Unbarred galaxies, on the other hand, assemble most of their mass and disc component at late times. The nuclear region of strongly barred galaxies quenches shortly after bar formation, while unbarred galaxies remain active across time. Our findings are indicative of bar quenching, possibly assisted by nuclear feedback processes. We conclude that the cosmological environment, together with small scale feedback processes, determine the chances of a galaxy to form a bar and to rapidly quench its central region.
Adversarial Camera Alignment Network for Unsupervised Cross-Camera Person Re-Identification

In person re-identification (Re-ID), supervised methods usually need a large amount of expensive label information, while unsupervised ones are still unable to deliver satisfactory identification performance. In this paper, we introduce a novel person Re-ID task called unsupervised cross-camera person Re-ID, which only needs the within-camera (intra-camera) label information but not cross-camera (inter-camera) labels which are more expensive to obtain. In real-world applications, the intra-camera label information can be easily captured by tracking algorithms and few manual annotations. In this situation, the main challenge becomes the distribution discrepancy across different camera views, caused by the various body pose, occlusion, image resolution, illumination conditions, and background noises in different cameras. To address this situation, we propose a novel Adversarial Camera Alignment Network (ACAN) for unsupervised cross-camera person Re-ID. It consists of the camera-alignment task and the supervised within-camera learning task. To achieve the camera alignment, we develop a Multi-Camera Adversarial Learning (MCAL) to map images of different cameras into a shared subspace. Particularly, we investigate two different schemes, including the existing GRL (i.e., gradient reversal layer) scheme and the proposed scheme called “other camera equiprobability” (OCE), to conduct the multi-camera adversarial task. Based on this shared subspace, we then leverage the within-camera labels to train the network. Extensive experiments on five large-scale datasets demonstrate the superiority of ACAN over multiple state-of-the-art unsupervised methods that take advantage of labeled source domains and generated images by GAN-based models. In particular, we verify that the proposed multi-camera adversarial task does contribute to the significant improvement.
Efficient computation of counterfactual explanations of LVQ models

With the increasing use of machine learning in practice and because of legal regulations like EU's GDPR, it becomes indispensable to be able to explain the prediction and behavior of machine learning models. An example of easy to understand explanations of AI models are counterfactual explanations. However, for many models it is still an open research problem how to efficiently compute counterfactual explanations. We investigate how to efficiently compute counterfactual explanations of learning vector quantization models. In particular, we propose different types of convex and non-convex programs depending on the used learning vector quantization model.
Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection

A security threat to deep neural networks (DNN) is backdoor contamination, in which an adversary poisons the training data of a target model to inject a Trojan so that images carrying a specific trigger will always be classified into a specific label. Prior research on this problem assumes the dominance of the trigger in an image's representation, which causes any image with the trigger to be recognized as a member in the target class. Such a trigger also exhibits unique features in the representation space and can therefore be easily separated from legitimate images. Our research, however, shows that simple target contamination can cause the representation of an attack image to be less distinguishable from that of legitimate ones, thereby evading existing defenses against the backdoor infection. In our research, we show that such a contamination attack actually subtly changes the representation distribution for the target class, which can be captured by a statistic analysis. More specifically, we leverage an EM algorithm to decompose an image into its identity part (e.g., person, traffic sign) and variation part within a class (e.g., lighting, poses). Then we analyze the distribution in each class, identifying those more likely to be characterized by a mixture model resulted from adding attack samples to the legitimate image pool. Our research shows that this new technique effectively detects data contamination attacks, including the new one we propose, and is also robust against the evasion attempts made by a knowledgeable adversary.
Reflection-driven magnetohydrodynamic turbulence in the solar atmosphere and solar wind

We present three-dimensional direct numerical simulations and an analytic model of reflection-driven magnetohydrodynamic (MHD) turbulence in the solar wind. Our simulations describe transverse, non-compressive MHD fluctuations within a narrow magnetic flux tube that extends from the photosphere, through the chromosphere and corona and out to a heliocentric distance $r$ of 21 solar radii $(R_{\odot })$ . We launch outward-propagating ‘ $\boldsymbol{z}^{+}$ fluctuations’ into the simulation domain by imposing a randomly evolving photospheric velocity field. As these fluctuations propagate away from the Sun, they undergo partial reflection, producing inward-propagating ‘ $\boldsymbol{z}^{-}$ fluctuations’. Counter-propagating fluctuations subsequently interact, causing fluctuation energy to cascade to small scales and dissipate. Our analytic model incorporates dynamic alignment, allows for strongly or weakly turbulent nonlinear interactions and divides the $\boldsymbol{z}^{+}$ fluctuations into two populations with different characteristic radial correlation lengths. The inertial-range power spectra of $\boldsymbol{z}^{+}$ and $\boldsymbol{z}^{-}$ fluctuations in our simulations evolve toward a $k_{\bot }^{-3/2}$ scaling at $r>10R_{\odot }$ , where $k_{\bot }$ is the wave-vector component perpendicular to the background magnetic field. In two of our simulations, the $\boldsymbol{z}^{+}$ power spectra are much flatter between the coronal base and $r\simeq 4R_{\odot }$ . We argue that these spectral scalings are caused by: (i) high-pass filtering in the upper chromosphere; (ii) the anomalous coherence of inertial-range $\boldsymbol{z}^{-}$ fluctuations in a reference frame propagating outwards with the $\boldsymbol{z}^{+}$ fluctuations; and (iii) the change in the sign of the radial derivative of the Alfvén speed at $r=r_{\text{m}}\simeq 1. 7R_{\odot }$ , which disrupts this anomalous coherence between $r=r_{\text{m}}$ and $r\simeq 2r_{\text{m}}$ . At $r>1. 3R_{\odot }$ , the turbulent heating rate in our simulations is comparable to the turbulent heating rate in a previously developed solar-wind model that agreed with a number of observational constraints, consistent with the hypothesis that MHD turbulence accounts for much of the heating of the fast solar wind.
Multi-Scale Learned Iterative Reconstruction

Model-based learned iterative reconstruction methods have recently been shown to outperform classical reconstruction algorithms. Applicability of these methods to large scale inverse problems is however limited by the available memory for training and extensive training times, the latter due to computationally expensive forward models. As a possible solution to these restrictions we propose a multi-scale learned iterative reconstruction scheme that computes iterates on discretisations of increasing resolution. This procedure does not only reduce memory requirements, it also considerably speeds up reconstruction and training times, but most importantly is scalable to large scale inverse problems with non-trivial forward operators, such as those that arise in many 3D tomographic applications. In particular, we propose a hybrid network that combines the multi-scale iterative approach with a particularly expressive network architecture which in combination exhibits excellent scalability in 3D. Applicability of the algorithm is demonstrated for 3D cone beam computed tomography from real measurement data of an organic phantom. Additionally, we examine scalability and reconstruction quality in comparison to established learned reconstruction methods in two dimensions for low dose computed tomography on human phantoms.
Functional ratings in sports

Abstract In this paper, we present a new model for ranking sports teams. Our model uses all scoring data from all games to produce a functional rating by the method of least squares. The functional rating can be interpreted as a team average point differential adjusted for strength of schedule. Using two team’s functional ratings we can predict the expected point differential at any time in the game. We looked at three variations of our model accounting for home-court advantage in different ways. We use the 2018–2019 NCAA Division 1 men’s college basketball season to test the models and determined that home-court advantage is statistically important but does not differ between teams.
Optoelectronic systems for automatic vehicle counting and classification in rest areas

The following paper depicts an architectural layout and operational insights towards an optoelectronic system dedicated for vehicle counting and classification, designed to monitor occupancy of rest areas. The system is based upon an image cue acquisition by video cameras and advanced signal processing mechanism. In order to satisfy the appropriate realization of measurement procedure (counting and classification), a specific measurement configuration scheme alongside optimized video stream capturing parameters were set up. Concerning signal processing methods, the most effective and appropriate for the purpose of vehicle counting and classification were selected. Moreover, the article presents the outcomes of the designed measurement campaign conducted on the S7 expressway within the agenda of the National Center for Research and Development RID project. The results obtained with use of the designed methodology were compared to the outcomes of the manual method.
Captioning Near-Future Activity Sequences

Most of the existing works on human activity analysis focus on recognition or early recognition of the activity labels from complete or partial observations. Similarly, existing video captioning approaches focus on the observed events in videos. Predicting the labels and the captions of future activities where no frames of the predicted activities have been observed is a challenging problem, with important applications that require anticipatory response. In this work, we propose a system that can infer the labels and the captions of a sequence of future activities. Our proposed network for label prediction of a future activity sequence is similar to a hybrid Siamese network with three branches where the first branch takes visual features from the objects present in the scene, the second branch takes observed activity features and the third branch captures the last observed activity features. The predicted labels and the observed scene context are then mapped to meaningful captions using a sequence-to-sequence learning based method. Experiments on three challenging activity analysis datasets and a video description dataset demonstrate that both our label prediction framework and captioning framework outperforms the state-of-the-arts.