Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time shaping of entangled photons by classical control and feedback

Shaping the classical laser that stimulates generation of entangled photons allows optimization of their quantum correlations. Quantum technologies hold great promise for revolutionizing photonic applications such as cryptography. Yet, their implementation in real-world scenarios is challenging, mostly because of sensitivity of quantum correlations to scattering. Recent developments in optimizing the shape of single photons introduce new ways to control entangled photons. Nevertheless, shaping single photons in real time remains a challenge due to the weak associated signals, which are too noisy for optimization processes. Here, we overcome this challenge and control scattering of entangled photons by shaping the classical laser beam that stimulates their creation. We discover that because the classical beam and the entangled photons follow the same path, the strong classical signal can be used for optimizing the weak quantum signal. We show that this approach can increase the length of free-space turbulent quantum links by up to two orders of magnitude, opening the door for using wavefront shaping for quantum communications.
Online data analysis system of the INTEGRAL telescope

In more than 17 years of operation in space INTEGRAL telescope has accumulated large data set that contains records of hard X-ray and soft gamma-ray astronomical sources. These data can be re-used in the context of multi-wavelength or multi-messenger studies of astronomical sources and have to be preserved on long time scales. We present a scientific validation of an interactive online INTEGRAL data analysis system, which generates publication-quality high-level data products: sky images, spectra and light-curves in response to user queries that define analysis parameters, such as source position, time and energy interval and binning. The data products can be requested via a web browser interface or via Application Programming Interface (API). The products for the ISGRI instrument of INTEGRAL are generated using the Offline Science Analysis (OSA) software which is provided by the instrument teams, but reorganized to preserve and re-use intermediate analysis products, ensuring that frequently requested results are available without delay. The platform is portable and can be deployed in any compatible infrastructure. We report the functionalities and performance of the online data analysis system by reproducing the benchmark INTEGRAL results on different types of sources, including bright steady and transient Galactic sources, and bright and weak variable extra-galactic sources. We compare the results obtained with the online data analysis system with previously published results on these sources. We consider the INTEGRAL online data analysis as a demonstrator of more general web-based "data analysis as a service" approach that provides a promising solution for preservation and maintenance of data analysis tools of astronomical telescopes on (multi)decade long time scales and facilitates combination of data in multi-wavelength and multi-messenger studies of astronomical sources.
A multi-layer approach to disinformation detection on Twitter

We tackle the problem of classifying news articles pertaining to disinformation vs mainstream news by solely inspecting their diffusion mechanisms on Twitter. Our technique is inherently simple compared to existing text-based approaches, as it allows to by-pass the multiple levels of complexity which are found in news content (e.g. grammar, syntax, style). We employ a multi-layer representation of Twitter diffusion networks, and we compute for each layer a set of global network features which quantify different aspects of the sharing process. Experimental results with two large-scale datasets, corresponding to diffusion cascades of news shared respectively in the United States and Italy, show that a simple Logistic Regression model is able to classify disinformation vs mainstream networks with high accuracy (AUROC up to 94%), also when considering the political bias of different sources in the classification task. We also highlight differences in the sharing patterns of the two news domains which appear to be country-independent. We believe that our network-based approach provides useful insights which pave the way to the future development of a system to detect misleading and harmful information spreading on social media.
Me, myself and I: A general theory of non-Markovian time-inconsistent stochastic control for sophisticated agents

We develop a theory for continuous-time non-Markovian stochastic control problems which are inherently time-inconsistent. Their distinguishing feature is that the classical Bellman optimality principle no longer holds. Our formulation is cast within the framework of a controlled non-Markovian forward stochastic differential equation, and a general objective functional setting. We adopt a game-theoretic approach to study such problems, meaning that we seek for \emph{sub-game perfect Nash equilibrium} points. As a first novelty of this work, we introduce and motivate a new definition of equilibrium that allows us to establish rigorously an \emph{extended dynamic programming principle}, in the same spirit as in the classical theory. This in turn allows us to introduce a system of backward stochastic differential equations analogous to the classical HJB equation. We prove that this system is fundamental, in the sense that its well-posedness is both necessary and sufficient to characterise the value function and equilibria. As a final step we provide an existence and uniqueness result. Some examples and extensions of our results are also presented.
Global Convergence and Geometric Characterization of Slow to Fast Weight Evolution in Neural Network Training for Classifying Linearly Non-Separable Data

In this paper, we study the dynamics of gradient descent in learning neural networks for classification problems. Unlike in existing works, we consider the linearly non-separable case where the training data of different classes lie in orthogonal subspaces. We show that when the network has sufficient (but not exceedingly large) number of neurons, (1) the corresponding minimization problem has a desirable landscape where all critical points are global minima with perfect classification; (2) gradient descent is guaranteed to converge to the global minima in this case. Moreover, we discovered a geometric condition on the network weights so that when it is satisfied, the weight evolution transitions from a slow phase of weight direction spreading to a fast phase of weight convergence. The geometric condition says that the convex hull of the weights projected on the unit sphere contains the origin.
A new approach to the generalized Springer correspondence

The Springer resolution of the nilpotent cone is used to give a geometric construction of the irreducible representations of Weyl groups. Borho and MacPherson obtain the Springer correspondence by applying the decomposition theorem to the Springer resolution, establishing an injective map from the set of irreducible Weyl group representations to simple equivariant perverse sheaves on the nilpotent cone. In this manuscript, we consider a generalization of the Springer resolution using a variety defined by the first author. Our main result shows that in the type A case, applying the decomposition theorem to this map yields all simple perverse sheaves on the nilpotent cone with multiplicity as predicted by Lusztig's generalized Springer correspondence.
Probably Approximately Correct Vision-Based Planning using Motion Primitives

This paper presents a deep reinforcement learning approach for synthesizing vision-based planners that provably generalize to novel environments (i.e., environments unseen during training). We leverage the Probably Approximately Correct (PAC)-Bayes framework to obtain an upper bound on the expected cost of policies across all environments. Minimizing the PAC-Bayes upper bound thus trains policies that are accompanied by a certificate of performance on novel environments. The training pipeline we propose provides strong generalization guarantees for deep neural network policies by (a) obtaining a good prior distribution on the space of policies using Evolutionary Strategies (ES) followed by (b) formulating the PAC-Bayes optimization as an efficiently-solvable parametric convex optimization problem. We demonstrate the efficacy of our approach for producing strong generalization guarantees for learned vision-based motion planners through two simulated examples: (1) an Unmanned Aerial Vehicle (UAV) navigating obstacle fields with an onboard vision sensor, and (2) a dynamic quadrupedal robot traversing rough terrains with proprioceptive and exteroceptive sensors.
Time Series Data Augmentation for Deep Learning: A Survey

Deep learning performs remarkably well on many time series analysis tasks recently. The superior performance of deep neural networks relies heavily on a large number of training data to avoid overfitting. However, the labeled data of many real-world time series applications may be limited such as classification in medical time series and anomaly detection in AIOps. As an effective way to enhance the size and quality of the training data, data augmentation is crucial to the successful application of deep learning models on time series data. In this paper, we systematically review different data augmentation methods for time series. We propose a taxonomy for the reviewed methods, and then provide a structured review for these methods by highlighting their strengths and limitations. We also empirically compare different data augmentation methods for different tasks including time series classification, anomaly detection, and forecasting. Finally, we discuss and highlight five future directions to provide useful research guidance.
Debye temperature in highly-compressed H3S and D3S

If near-room-temperature (NRT) superconductivity in hydrides/deuterides is originated from the electron-phonon coupling, then the superconducting transition temperature, Tc, and phonon spectrum characteristics (for instance, the Debye temperature, T$_D$, logarithmic phonon frequency, $\omega$$_{ln}$) should be linked through the electron-phonon coupling constant, $\lambda$$_{e-ph}$, which can be accurately computed by first-principles calculations. By utilizing relations between Tc, T$_D$ or $\omega$$_{ln}$, and $\lambda$$_{e-ph}$ (proposed by McMillan (1968), Allen and Dynes (1975)) it is possible to affirm/disprove the electron-phonon coupling mechanism in the superconductor. In this paper, we deduce T$_D$ for highly-compressed black phosphorous, boron, GeAs, SiH4, H3S and D3S by the fit of temperature-dependent resistivity data, R(T), to Bloch-Gruneisen equation. We show that computed $\lambda$$_{e-ph}$ for highly-compressed black phosphorous, boron, GeAs, and SiH4 are within BCS weak-coupling limit. We also find remarkable constancy of T$_D$=1531 K for H3S at different ageing stages. Due to phonon spectra in H3S and D3S have identical shape, we show that these isotopic counterparts obey the relation of T$_D$(H3S)/T$_D$(D3S)=Tc(H3S)/Tc(D3S)=$\omega$$_{ln}$(H3S)/$\omega$$_{ln}$(D3S). However, ratios of Tc(H3S)/Tc(D3S)=$\omega$$_{ln}$(H3S)/$\omega$$_{ln}$(D3S)=1.27 are largely different from deduced T$_D$(H3S)/T$_D$(D3S)=1.65. This alludes that NRT superconductivity in H3S-D3S system is originated from more than one mechanism, where the electron-phonon coupling lifts Tc in H3S vs D3S, but primary origin for NRT background of Tc~150 K in H3S and D3S remains to be discovered.
Nematic first order phase transition for liquid crystals in the van der Waals–Kac limit

In this paper we revisit and extend some mathematical aspects of Onsager's theory of liquid crystals that have been investigated in recent years by different communities (statistical mechanics, analysis and probability). We introduce a model of anisotropic molecules with three-dimensional orientations interacting via a Kac-type interaction. We prove that, in the limit in which the range of the interaction is sent to infinity after the thermodynamic limit, the free energy tends to the infimum of an effective energy functional a la Onsager. We then prove that, if the spherical harmonic transform of the angular interaction has a negative minimum, this effective free energy functional displays a first order phase transition as the total density of the system increases.
TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing

In this paper, we introduce TextBrewer, an open-source knowledge distillation toolkit designed for natural language processing. It works with different neural network models and supports various kinds of supervised learning tasks, such as text classification, reading comprehension, sequence labeling. TextBrewer provides a simple and uniform workflow that enables quick setting up of distillation experiments with highly flexible configurations. It offers a set of predefined distillation methods and can be extended with custom code. As a case study, we use TextBrewer to distill BERT on several typical NLP tasks. With simple configurations, we achieve results that are comparable with or even higher than the public distilled BERT models with similar numbers of parameters.
Magnetohydrodynamic Nonlinearities in Sunspot Atmospheres: Chromospheric Detections of Intermediate Shocks

The formation of shocks within the solar atmosphere remains one of the few observable signatures of energy dissipation arising from the plethora of magnetohydrodynamic waves generated close to the solar surface. Active region observations offer exceptional views of wave behavior and its impact on the surrounding atmosphere. The stratified plasma gradients present in the lower solar atmosphere allow for the potential formation of many theorized shock phenomena. In this study, using chromospheric Ca ii λ8542 line spectropolarimetric data of a large sunspot, we examine fluctuations in the plasma parameters in the aftermath of powerful shock events that demonstrate polarimetric reversals during their evolution. Modern inversion techniques are employed to uncover perturbations in the temperatures, line-of-sight velocities, and vector magnetic fields occurring across a range of optical depths synonymous with the shock formation. Classification of these nonlinear signatures is carried out by comparing the observationally derived slow, fast, and Alfvén shock solutions with the theoretical Rankine–Hugoniot relations. Employing over 200,000 independent measurements, we reveal that the Alfvén (intermediate) shock solution provides the closest match between theory and observations at optical depths of log 10 τ = − 4 , consistent with a geometric height at the boundary between the upper photosphere and lower chromosphere. This work uncovers first-time evidence of the manifestation of chromospheric intermediate shocks in sunspot umbrae, providing a new method for the potential thermalization of wave energy in a range of magnetic structures, including pores, magnetic flux ropes, and magnetic bright points.
SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural Network

Predicting the future trajectory of surrounding vehicles in a randomly varying traffic level is one of the most challenging problems in developing an autonomous vehicle. Since there is no pre-defined number of interacting vehicles participated in, the prediction network has to be scalable with respect to the number of vehicles in order to guarantee consistent performance in terms of both accuracy and computational load. In this paper, the first fully scalable trajectory prediction network, SCALE-Net, is proposed that can ensure both high prediction performance while keeping the computational load low regardless of the number of surrounding vehicles. The SCALE-Net employs the Edge-enhanced Graph Convolutional Neural Network (EGCN) for the inter-vehicular interaction embedding network. Since the proposed EGCN is inherently scalable with respect to the graph node (an agent in this study), the model can be operated independently from the total number of vehicles considered. We evaluated the scalability of the SCALE-Net on the publically available NGSIM datasets by comparing variations on computation time and prediction accuracy per single driving scene with respect to the varying vehicle number. The experimental test shows that both computation time and prediction performance of the SCALE-Net consistently outperform those of previous models regardless of the level of traffic complexities.
Brain-Inspired Model for Incremental Learning Using a Few Examples

Incremental learning attempts to develop a classifier which learns continuously from a stream of data segregated into different classes. Deep learning approaches suffer from catastrophic forgetting when learning classes incrementally. We propose a novel approach to incremental learning inspired by the concept learning model of the hippocampus that represents each image class as centroids and does not suffer from catastrophic forgetting. Classification of a test image is accomplished using the distance of the test image to the n closest centroids. We further demonstrate that our approach can incrementally learn from only a few examples per class. Evaluations of our approach on three class-incremental learning benchmarks: Caltech-101, CUBS-200-2011 and CIFAR-100 for incremental and few-shot incremental learning depict state-of-the-art results in terms of classification accuracy over all learned classes.
Robust wall modes in rapidly rotating Rayleigh-B\'enard convection

We show, using direct numerical simulations with experimentally realizable boundary conditions, that wall modes in Rayleigh-Benard convection in a rapidly rotating cylinder persist even very far from their linear onset. These nonlinear wall states survive in the presence of turbulence in the bulk and are robust with respect to changes in the shape of the boundary of the container. In this sense these modes behave much like the topologically protected states present in two-dimensional chiral systems even though rotating convection is a three-dimensional nonlinear driven dissipative system. We suggest that the robustness of this nonlinear mode may provide an explanation for the strong zonal flows observed recently in simulations and experiments on rapidly rotating convection at high Rayleigh number.
Intermittent hydrodynamic jets in collapsars do not produce GRBs

Strong variability is a common characteristic of the prompt emission of gamma-ray bursts (GRB). This observed variability is widely attributed to an intermittency of the central engine, through formation of strong internal shocks in the GRB-emitting jet expelled by the engine. In this paper we study numerically the propagation of hydrodynamic jets, injected periodically by a variable engine, through the envelope of a collapsed star. By post-processing the output of 3D numerical simulations, we compute the net radiative efficiency of the outflow. We find that all intermittent jets are subject to heavy baryon contamination that inhibits the emission at and above the photosphere well below detection limits. This is in contrast to continuous jets that, as shown recently, produce a highly variable gamma-ray photospheric emission with high efficiency, owing to the interaction of the jet with the stellar envelope. Our results challenge the variable engine model for hydrodynamic jets, and may impose constraints on the duty cycle of GRB engines. If such systems exist in nature, they are not expected to produce bright gamma-ray emission, but should appear as X-ray, optical and radio transients that resemble a delayed GRB afterglow signal.
Addressing $R_{D^{(*)}}$, $R_{K^{(*)}}$, muon $g-2$ and ANITA anomalies in a minimal $R$-parity violating supersymmetric framework

We analyze the recent hints of lepton flavor universality violation in both charged-current and neutral-current rare decays of $B$-mesons in an $R$-parity violating supersymmetric scenario. Motivated by simplicity and minimality, we had earlier postulated the third-generation superpartners to be the lightest (calling the scenario"RPV3") and explicitly showed that it preserves gauge coupling unification and of course has the usual attribute of naturally addressing the Higgs radiative stability. Here we show that both $R_{D^{(*)}}$ and $R_{K^{(*)}}$ flavor anomalies can be addressed in this RPV3 framework. Interestingly, this scenario may also be able to accommodate two other seemingly disparate anomalies, namely, the longstanding discrepancy in the muon $(g-2)$, as well as the recent anomalous upgoing ultra-high energy ANITA events. Based on symmetry arguments, we consider three different benchmark points for the relevant RPV3 couplings and carve out the regions of parameter space where all (or some) of these anomalies can be simultaneously explained. We find it remarkable that such overlap regions exist, given the plethora of precision low-energy and high-energy experimental constraints on the minimal model parameter space. The third-generation superpartners needed in this theoretical construct are all in the 1-10 TeV range, accessible at the LHC and/or next-generation hadron collider. We also discuss some testable predictions for the lepton-flavor-violating decays of the tau-lepton and $B$-mesons for the current and future $B$-physics experiments, such as LHCb and Belle II. Complementary tests of the flavor anomalies in the high-$p_T$ regime in collider experiments such as the LHC are also discussed.
HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression

The emerging edge computing has produced immense interests in compacting a neural network without sacrificing much accuracy. In this regard, low-rank tensor decomposition constitutes a powerful tool to compress convolutional neural networks (CNNs) by decomposing the 4-way kernel tensor into multi-stage smaller ones. Building on top of Tucker-2 decomposition, we propose a generalized Higher Order Tucker Articulated Kernels (HOT-CAKE) scheme comprising four steps: input channel decomposition, guided Tucker rank selection, higher order Tucker decomposition and fine-tuning. By subjecting each CONV layer to HOTCAKE, a highly compressed CNN model with graceful accuracy trade-off is obtained. Experiments show HOTCAKE can compress even pre-compressed models and produce state-of-the-art lightweight networks.