Miracle-dz/UniFAR-FTUs huggingface dataset:
Landscape allocation: stochastic generators and statistical inference

In agricultural landscapes, the composition and spatial configuration of cultivated and semi-natural elements strongly impact species dynamics, their interactions and habitat connectivity. To allow for landscape structural analysis and scenario generation, we here develop statistical tools for real landscapes composed of geometric elements including 2D patches but also 1D linear elements such as hedges. We design generative stochastic models that combine a multiplex network representation and Gibbs energy terms to characterize the distributional behavior of landscape descriptors for land-use categories. We implement Metropolis-Hastings for this new class of models to sample agricultural scenarios featuring parameter-controlled spatial and temporal patterns (e.g., geometry, connectivity, crop-rotation). Pseudolikelihood-based inference allows studying the relevance of model components in real landscapes through statistical and functional validation, the latter achieved by comparing commonly used landscape metrics between observed and simulated landscapes. Models fitted to subregions of the Lower Durance Valley (France) indicate strong deviation from random allocation, and they realistically capture small-scale landscape patterns. In summary, our approach of statistical modeling improves the understanding of structural and functional aspects of agro-ecosystems, and it enables simulation-based theoretical analysis of how landscape patterns shape biological and ecological processes.
Learning to Transfer Texture From Clothing Images to 3D Humans

In this paper, we present a simple yet effective method to automatically transfer textures of clothing images (front and back) to 3D garments worn on top SMPL, in real time. We first automatically compute training pairs of images with aligned 3D garments using a custom non-rigid 3D to 2D registration method, which is accurate but slow. Using these pairs, we learn a mapping from pixels to the 3D garment surface. Our idea is to learn dense correspondences from garment image silhouettes to a 2D-UV map of a 3D garment surface using shape information alone, completely ignoring texture, which allows us to generalize to the wide range of web images. Several experiments demonstrate that our model is more accurate than widely used baselines such as thin-plate-spline warping and image-to-image translation networks while being orders of magnitude faster. Our model opens the door for applications such as virtual try-on, and allows for generation of 3D humans with varied textures which is necessary for learning. Code will be available at https://virtualhumans.mpi-inf.mpg. de/pix2surf/.
Spin-wave Talbot effect in a thin ferromagnetic film

The Talbot effect has been known in optics since XIX century and found various technological applications. In this paper, we demonstrate with the help of micromagnetic simulations this self-imaging phenomenon for spin waves propagating in a thin ferromagnetic film magnetized out-of-plane. We show that the main features of the obtained Talbot carpets for spin waves can be described, to a large extent, by the approximate analytical formulas yielded by the general analysis of the wave phenomena. Our results indicate a route to a feasible experimental realisation of the Talbot effect at low and high frequencies and offer interesting effects and possible applications in magnonics.
The possible explanation of neutron lifetime beam anomaly

The results of measurements performed using UCN storing method are in good agreement. The latest most accurate measurements of the neutron decay asymmetry and neutron lifetime measurements by storage method are in agreement within the Standard Model. However, there is a significant discrepancy at $3.6\sigma$ (1% of decay probability) level with beam method experiment. This article discusses the possible causes of discrepancy in the measurements of the neutron lifetime with beam method experiment. The most probable cause, apparently, is the loss of protons in beam method experiment during storage in a magnetic trap due to charge exchange collisions of protons with the residual gas. The proton becomes neutral and leaves the trap, which leads to a decrease in the number of registered protons, i.e. to a decrease in the probability of neutron decay or to an increase in the measured neutron lifetime.
Digital synchronization for continuous-variable quantum key distribution

Continuous variable quantum key distribution (CV-QKD) is a promising emerging technology for the distribution of secure keys for symmetric encryption. It can be readily implemented using commercial off-the-shelf optical telecommunications components. A key requirement of the CV-QKD receiver is the ability to measure the quantum states at the correct time instance and rate using the correct orthogonal observables, referred to as synchronization. We propose a digital synchronization procedure for a modern CV-QKD system with locally generated local oscillator for coherent reception. Our proposed method is modulation format independent allowing it to be used in a variety of CV-QKD systems. We experimentally investigate its performance with a Gaussian modulated CV-QKD system operating over a 10–20 km span of standard single mode fibre. Since the procedure does not require hardware modifications it paves the way for cost-effective QKD solutions that can adapt rapidly to changing environmental conditions.
Deep Joint Transmission-Recognition for Power-Constrained IoT Devices

We propose a joint transmission-recognition scheme for efficient inference at the wireless network edge. Our scheme allows for reliable image recognition over wireless channels with significant computational load reduction at the sender side. We incorporate recently proposed deep joint source-channel coding (JSCC) scheme, and combine it with novel filter pruning strategies aimed at reducing the redundant complexity from neural networks. We evaluate our approach on a classification task, and show satisfactory results in both transmission reliability and workload reduction. This is the first work that combines deep JSCC with network pruning and applies it to images classification over wireless network.
Shaking photons from the vacuum: acceleration radiation from vibrating atoms

Acceleration radiation—or Unruh radiation—the thermal radiation observed by an ever accelerating observer or detector, although having similarities to Hawking radiation, so far has proved extremely challenging to observe experimentally. One recent suggestion is that, in the presence of a mirror, constant acceleration of an atom in its ground state can excite the atom while at the same time cause it to emit a photon in an Unruh-type process. In this work we show that merely by shaking the atom, in simple harmonic motion for example, can have the same effect. We calculate the transition rate for this in first order perturbation theory and consider harmonic motion of the atom in the presence of a stationary mirror, or within a cavity or just in empty vacuum. For the latter we propose a circuit-QED potential implementation that yields transition rates of ∼10−4 Hz, which may be detectable experimentally.
MOTS: Minimax Optimal Thompson Sampling

Thompson sampling is one of the most widely used algorithms for many online decision problems, due to its simplicity in implementation and superior empirical performance over other state-of-the-art methods. Despite its popularity and empirical success, it has remained an open problem whether Thompson sampling can achieve the minimax optimal regret $O(\sqrt{KT})$ for $K$-armed bandit problems, where $T$ is the total time horizon. In this paper, we solve this long open problem by proposing a variant of Thompson sampling called MOTS that adaptively clips the sampling result of the chosen arm at each time step. We prove that this simple variant of Thompson sampling achieves the minimax optimal regret bound $O(\sqrt{KT})$ for finite time horizon $T$, as well as the asymptotic optimal regret bound for Gaussian rewards when $T$ approaches infinity. To our knowledge, MOTS is the first Thompson sampling type algorithm that achieves minimax optimality for multi-armed bandit problems.
Magnetorotational Explosion of a Massive Star Supported by Neutrino Heating in General Relativistic Three-dimensional Simulations

We present results of three-dimensional (3D), radiation-magnetohydrodynamics (MHD) simulations of core-collapse supernovae in full general relativity (GR) with spectral neutrino transport. In order to study the effects of the progenitor’s rotation and magnetic fields, we compute three models, where the precollapse rotation rate and magnetic fields are included parametrically to a 20 M⊙ star. While we find no shock revival in our two nonmagnetized models during our simulation times (∼500 ms after bounce), the magnetorotational (MR) driven shock expansion immediately initiates after bounce in our rapidly rotating and strongly magnetized model. We show that the expansion of the MR-driven flows toward the polar directions is predominantly driven by the magnetic pressure, whereas the shock expansion toward the equatorial direction is supported by neutrino heating. Our detailed analysis indicates that the growth of the so-called kink instability may hinder the collimation of jets, resulting in the formation of broader outflows. Furthermore, we find a dipole emission of lepton number, only in the MR explosion model, whose asymmetry is consistent with the explosion morphology. Although it is similar to the lepton number emission self-sustained asymmetry (LESA), our analysis shows that the dipole emission occurs not from the proto–neutron star convection zone but from above the neutrino sphere, indicating that it is not associated with the LESA. We also report several unique neutrino signatures, which are significantly dependent on both the time and the viewing angle, if observed, possibly providing rich information regarding the onset of the MR-driven explosion.
Scaling MAP-Elites to deep neuroevolution

Quality-Diversity (QD) algorithms, and MAP-Elites (ME) in particular, have proven very useful for a broad range of applications including enabling real robots to recover quickly from joint damage, solving strongly deceptive maze tasks or evolving robot morphologies to discover new gaits. However, present implementations of ME and other QD algorithms seem to be limited to low-dimensional controllers with far fewer parameters than modern deep neural network models. In this paper, we propose to leverage the efficiency of Evolution Strategies (ES) to scale MAP-Elites to high-dimensional controllers parameterized by large neural networks. We design and evaluate a new hybrid algorithm called MAP-Elites with Evolution Strategies (ME-ES) for post-damage recovery in a difficult high-dimensional control task where traditional ME fails. Additionally, we show that ME-ES performs efficient exploration, on par with state-of-the-art exploration algorithms in high-dimensional control tasks with strongly deceptive rewards.
Siti Nurhaliza and purchase intention among university students in Malaysia

In this decade, advertising market has become more competitive and everywhere. Thus, in this article, we look into essential characteristics of an effective endorser. This study adapted the Ohanian's tri-component and model of celebrity endorsement. Although the tri-component of celebrity credibility scale has been widely used in European countries, but the information on the essential characteristics of a credible celebrity in Malaysia are still in its infancy and inadequate. Source Credibility which consists of attractiveness, trustworthiness and expertise while Source Attractiveness includes familiarity and likability are discussed and examined. An empirical study was carried out which hypothesized five-factor celebrity credibility scale and using a self-administration online questionnaire. Respondents were 200 undergraduate students from local universities in Malaysia. The sample were segmented into five zones, namely north, south, east, west (Peninsular Malaysia) and West Malaysia (Sabah & Sarawak). The celebrity chosen is Dato’ Siti Nurhaliza. Data were analyzed by using exploratory and confirmatory factor analyses. The final model shows that four-factor model consists of trustworthiness, expertise, attractiveness and likability. This study offers media practitioners a pave way for consumer responses to celebrity endorsement in Malaysian students market.
Risk-Aware Energy Scheduling for Edge Computing With Microgrid: A Multi-Agent Deep Reinforcement Learning Approach

In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that induces risk for energy demand estimations. As an energy supplier, a microgrid can facilitate seamless energy supply. However, the risk associated with energy supply is also increased due to unpredictable energy generation from renewable and non-renewable sources. Especially, the risk of energy shortfall is involved with uncertainties in both energy consumption and generation. In this article, we study a risk-aware energy scheduling problem for a microgrid-powered MEC network. First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the expected residual of scheduled energy for the MEC networks and we show this problem is an NP-hard problem. Second, we analyze our formulated problem using a multi-agent stochastic game that ensures the joint policy Nash equilibrium, and show the convergence of the proposed model. Third, we derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based asynchronous advantage actor-critic (A3C) algorithm with shared neural networks. This method mitigates the curse of dimensionality of the state space and chooses the best policy among the agents for the proposed problem. Finally, the experimental results establish a significant performance gain by considering CVaR for high accuracy energy scheduling of the proposed model than both the single and random agent models.
Reconstructed Discontinuous Approximation to Stokes Equation in A Sequential Least Squares Formulation

We propose a new least squares finite element method to solve the Stokes problem with two sequential steps. The approximation spaces are constructed by patch reconstruction with one unknown per element. For the first step, we reconstruct an approximation space consisting of piecewise curl-free polynomials with zero trace. By this space, we minimize a least squaresfunctional toobtain thenumericalapproximationstothe gradientof thevelocityand the pressure. In the second step, we minimize another least squares functional to give the solution to the velocity in the reconstructed piecewise divergence-free space. We derive error estimates for all unknowns under L2 norms and energy norms. Numerical results in two dimensions and three dimensions verify the convergence rates and demonstrate the great flexibility of our method.
HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing Enabled Embedding of n-gram Statistics

Recent advances in Deep Learning have led to a significant performance increase on several NLP tasks, however, the models become more and more computationally demanding. Therefore, this paper tackles the domain of computationally efficient algorithms for NLP tasks. In particular, it investigates distributed representations of $n$ -gram statistics of texts. The representations are formed using hyperdimensional computing enabled embedding. These representations then serve as features, which are used as input to standard classifiers. We investigate the applicability of the embedding on one large and three small standard datasets for classification tasks using nine classifiers. The embedding achieved on par $F_{1}$ scores while decreasing the time and memory requirements by several times compared to the conventional $n$ -gram statistics, e.g., for one of the classifiers on a small dataset, the memory reduction was 6.18 times; while train and test speed-ups were 4.62 and 3.84 times, respectively. For many classifiers on the large dataset, memory reduction was ca. 100 times and train and test speed-ups were over 100 times. Importantly, the usage of distributed representations formed via hyperdimensional computing allows dissecting strict dependency between the dimensionality of the representation and n-gram size, thus, opening a room for tradeoffs.
Generalized Gumbel-Softmax Gradient Estimator for Various Discrete Random Variables

Estimating the gradients of stochastic nodes is one of the crucial research questions in the deep generative modeling community, which enables the gradient descent optimization on neural network parameters. This estimation problem becomes further complex when we regard the stochastic nodes to be discrete because pathwise derivative techniques cannot be applied. Hence, the stochastic gradient estimation of discrete distributions requires either a score function method or continuous relaxation of the discrete random variables. This paper proposes a general version of the Gumbel-Softmax estimator with continuous relaxation, and this estimator is able to relax the discreteness of probability distributions including more diverse types, other than categorical and Bernoulli. In detail, we utilize the truncation of discrete random variables and the Gumbel-Softmax trick with a linear transformation for the relaxed reparameterization. The proposed approach enables the relaxed discrete random variable to be reparameterized and to backpropagated through a large scale stochastic computational graph. Our experiments consist of (1) synthetic data analyses, which show the efficacy of our methods; and (2) applications on VAE and topic model, which demonstrate the value of the proposed estimation in practices.
On the law of killed exponential functionals

For two independent Levy processes $\xi$ and $\eta$ and an exponentially distributed random variable $\tau$ with parameter $q>0$ that is independent of $\xi$ and $\eta$, the killed exponential functional is given by $V_{q,\xi,\eta} := \int_0^\tau \mathrm{e}^{-\xi_{s-}} \, \mathrm{d} \eta_s$. With the killed exponential functional arising as the stationary distribution of a Markov process, we calculate the infinitesimal generator of the process and use it to derive different distributional equations describing the law of $V_{q,\xi,\eta}$, as well as functional equations for its Lebesgue density in the absolutely continuous case. Various special cases and examples are considered, yielding more explicit information on the law of the killed exponential functional and illustrating the applications of the equations obtained. Interpreting the case $q=0$ as $\tau=\infty$ leads to the classical exponential functional $\int_0^\infty \mathrm{e}^{-\xi_{s-}} \, \mathrm{d} \eta_s$, allowing to extend many previous results to include killing.
Entropic uncertainty relations and the quantum-to-classical transition

Our knowledge of quantum mechanics can satisfactorily describe simple, microscopic systems, but is yet to explain the macroscopic everyday phenomena we observe. Here we aim to shed some light on the quantum-to-classical transition as seen through the analysis of uncertainty relations. We employ entropic uncertainty relations to show that it is only by the inclusion of imprecision in our model of macroscopic measurements that we can prepare a system with two simultaneously well-defined quantities, even if their associated observables do not commute. We also establish how the precision of measurements must increase in order to keep quantum properties, a desirable feature for large quantum computers.
A homological characterization of generalized multinomial coefficients related to the entropic chain rule

There is an asymptotic relationship between the multiplicative relations among multinomial coefficients and the (additive) recurrence property of Shannon entropy known as the chain rule. We show that both types of identities are manifestations of a unique algebraic construction: a $1$-cocycle condition in \emph{information cohomology}, an algebraic invariant of phesheaves of modules on \emph{information structures} (categories of observables). Baudot and Bennequin introduced this cohomology and proved that Shannon entropy represents the only nontrivial cohomology class in degree $1$ when the coefficients are a natural presheaf of probabilistic functionals. The author obtained later a $1$-parameter family of deformations of that presheaf, in such a way that each Tsallis $\alpha$-entropy appears as the unique $1$-cocycle associated to the parameter $\alpha$. In this article, we introduce a new presheaf of \emph{combinatorial functionals}, which are measurable functions of finite arrays of integers; these arrays represent \emph{histograms} associated to random experiments. In this case, the only cohomology class in degree $0$ is generated by the exponential function and $1$-cocycles are Fontene-Ward generalized multinomial coefficients. As a byproduct, we get a simple combinatorial analogue of the fundamental equation of information theory that characterizes the generalized binomial coefficients. The asymptotic relationship mentioned above is extended to a correspondence between certain generalized multinomial coefficients and any $\alpha$-entropy, that sheds new light on the meaning of the chain rule and its deformations.
Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters

Machine learning algorithms have made remarkable achievements in the field of artificial intelligence. However, most machine learning algorithms are sensitive to the hyper-parameters. Manually optimizing the hyper-parameters is a common method of hyper-parameter tuning. However, it is costly and empirically dependent. Automatic hyper-parameter optimization (autoHPO) is favored due to its effectiveness. However, current autoHPO methods are usually only effective for a certain type of problems, and the time cost is high. In this paper, we propose an efficient automatic parameter optimization approach, which is based on the mapping from data to the corresponding hyper-parameters. To describe such mapping, we propose a sophisticated network structure. To obtain such mapping, we develop effective network constrution algorithms. We also design strategy to optimize the result futher during the application of the mapping. Extensive experimental results demonstrate that the proposed approaches outperform the state-of-the-art apporaches significantly.
Development of the Quick-FAAM: A Preliminary Shortened Version of the Foot and Ankle Ability Measure for Chronic Ankle Instability

The study objective was to develop a shortened version of the Foot and Ankle Ability Measure (FAAM) for individuals with chronic ankle instability (CAI). Forty individuals with CAI completed the FAAM Activities of Daily Living and Sport subscales and the Short Form-12. Analyses were completed for item reduction followed by dimensionality, coverage redundancy, and internal consistency of a reduced-item instrument. Validity was examined through correlations with the original FAAM and Short Form-12. A 12-item FAAM was created which demonstrated strong internal consistency and convergent/divergent validity. The Quick-FAAM may provide an alternative patient-reported outcome for CAI which requires less administration time.