Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimode CV-QKD with non-Gaussian operations

Non-Gaussian operations have been studied intensively in recent years due to their ability to increase the secret key rate for certain CV-QKD protocols. However, most previous studies on such protocols are carried out in a single-mode setting, even though in reality any quantum state contains multi-mode components in frequency space. In this work we investigate the use of non-Gaussian operations in a multi-mode CV-QKD system. Our main finding is that, contrary to single-mode CV-QKD systems, in generic multi-mode CV-QKD systems it is possible to use non-Gaussian operations to increase the optimized secret key rate. More specifically, we find that at losses of order 30dB, which represents a distance of order 160km and the effective maximum distance for CV-QKD, the key rate for multi-mode non-Gaussian operations can be orders of magnitude higher than single-mode operations. Our results are important for real-world CV-QKD systems especially those dependent on quantum error correction - a process that requires non-Gaussian effects.
Effect of foot supports on knee joint loading

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Exotic Higgs boson decays and the electroweak phase transition

Light new physics weakly coupled to the Higgs can induce a strong first-order electroweak phase transition (EWPT). Here, we argue that scenarios in which the EWPT is driven first-order by a light scalar with mass between ∼10 GeV − m_h/2 and small mixing with the Higgs will be conclusively probed by the high-luminosity LHC and future Higgs factories. Our arguments are based on analytic and numerical studies of the finite-temperature effective potential and provide a well-motivated target for exotic Higgs decay searches at the LHC and future lepton colliders.
Mechanism of universal conductance fluctuations

Universal conductance fluctuations are usually observed in the form of aperiodic oscillations in the magnetoresistance of thin wires as a function of the magnetic field B. If such oscillations are completely random at scales exceeding ξB, their Fourier analysis should reveal a white noise spectrum at frequencies below ξB−1. Comparison with the results for 1D systems suggests another scenario: according to it, such oscillations are due to the superposition of incommensurate harmonics and their spectrum should contain discrete frequencies. An accurate Fourier analysis of the classical experiment by Washburn and Webb reveals a purely discrete spectrum in agreement with the latter scenario. However, this spectrum is close in shape to the discrete white noise spectrum whose properties are similar to a continuous one.
Robustness Metrics for Real-World Adversarial Examples

We explore metrics to evaluate the robustness of real-world adversarial attacks, in particular adversarial patches, to changes in environmental conditions. We demonstrate how these metrics can be used to establish model baseline performance and uncover model biases to then compare against real-world adversarial attacks. We establish a custom score for an adversarial condition that is adjusted for different environmental conditions and explore how the score changes with respect to specific environmental factors. Lastly, we propose two use cases for confidence distributions in each environmental condition.
Study of efficiency and sensitivity of alarms of two hematology analyzers in a pediatric hospital

Con el objetivo de mejorar el flujo de trabajo en el Laboratorio de Hemocitología se evaluó el desempeño analítico y eficiencia del sistema de alarmas de dos contadores hematológicos de última generación, Coulter LH750 y Abbott CEL-DYN Ruby, para la resolución de muestras patológicas en una población de pacientes pediátricos, considerando como método de referencia la revisión microscópica y la realización del recuento diferencial leucocitario en forma manual de los extendidos de sangre periférica. Se procesaron 178 muestras seleccionadas de la carga de trabajo diario del Laboratorio en ambos contadores, se analizaron los reportes emitidos y se realizó el diferencial manual. Se encontró una excelente concordancia entre los dos instrumentos para el recuento de leucocitos, eritrocitos, hemoglobina, VCM y plaquetas, y entre el diferencial manual y el reportado por ambos equipos. La eficiencia de las alarmas en ambos analizadores fue similar, 77% para CELL-DYN Ruby y 71,3% para LH750. La tasa de falsos negativos fue de 3,4% y 4,0% respectivamente. La tasa de falsos positivos en ambos equipos fue 17%. Se puede concluir que los analizadores evaluados presentan un desempeño comparable. Cada laboratorio debe considerar las características de la población de pacientes que recibe para establecer criterios estrictos y sistemáticos para la revisión del frotis.
Exploiting Operation Importance for Differentiable Neural Architecture Search

Recently, differentiable neural architecture search (NAS) methods have made significant progress in reducing the computational costs of NASs. Existing methods search for the best architecture by choosing candidate operations with higher architecture weights. However, architecture weights cannot accurately reflect the importance of each operation, that is, the operation with the highest weight might not be related to the best performance. To circumvent this deficiency, we propose a novel indicator that can fully represent the operation importance and, thus, serve as an effective metric to guide the model search. Based on this indicator, we further develop a NAS scheme for “exploiting operation importance for effective NAS” (EoiNAS). More precisely, we propose a high-order Markov chain-based strategy to slim the search space to further improve search efficiency and accuracy. To evaluate the effectiveness of the proposed EoiNAS, we applied our method to two tasks: image classification and semantic segmentation. Extensive experiments on both tasks provided strong evidence that our method is capable of discovering high-performance architectures while guaranteeing the requisite efficiency during searching.
Bridging Disentanglement with Independence and Conditional Independence via Mutual Information for Representation Learning

Existing works on disentangled representation learning usually lie on a common assumption: all factors in disentangled representations should be independent. This assumption is about the inner property of disentangled representations, while ignoring their relation with external data. To tackle this problem, we propose another assumption to establish an important relation between data and its disentangled representations via mutual information: the mutual information between each factor of disentangled representations and data should be invariant to other factors. We formulate this assumption into mathematical equations, and theoretically bridge it with independence and conditional independence of factors. Meanwhile, we show that conditional independence is satisfied in encoders of VAEs due to factorized noise in reparameterization. To highlight the importance of our proposed assumption, we show in experiments that violating the assumption leads to dramatic decline of disentanglement. Based on this assumption, we further propose to split the deeper layers in encoder to ensure parameters in these layers are not shared for different factors. The proposed encoder, called Split Encoder, can be applied into models that penalize total correlation, and shows significant improvement in unsupervised learning of disentangled representations and reconstructions.
Fast Polynomial Kernel Classification for Massive Data

In the era of big data, it is highly desired to develop efficient machine learning algorithms to tackle massive data challenges such as storage bottleneck, algorithmic scalability, and interpretability. In this paper, we develop a novel efficient classification algorithm, called fast polynomial kernel classification (FPC), to conquer the scalability and storage challenges. Our main tools are a suitable selected feature mapping based on polynomial kernels and an alternating direction method of multipliers (ADMM) algorithm for a related non-smooth convex optimization problem. Fast learning rates as well as feasibility verifications including the convergence of ADMM and the selection of center points are established to justify theoretical behaviors of FPC. Our theoretical assertions are verified by a series of simulations and real data applications. The numerical results demonstrate that FPC significantly reduces the computational burden and storage memory of the existing learning schemes such as support vector machines and boosting, without sacrificing their generalization abilities much.
Robot Learning and Execution of Collaborative Manipulation Plans from YouTube Videos

People often watch videos on the web to learn how to cook new recipes, assemble furniture or repair a computer. We wish to enable robots with the very same capability. This is challenging; there is a large variation in manipulation actions and some videos even involve multiple persons, who collaborate by sharing and exchanging objects and tools. Furthermore, the learned representations need to be general enough to be transferable to robotic systems. On the other hand, previous work has shown that the space of human manipulation actions has a linguistic, hierarchical structure that relates actions to manipulated objects and tools. Building upon this theory of language for action, we propose a framework for understanding and executing demonstrated action sequences from full-length, unconstrained cooking videos on the web. The framework takes as input a cooking video annotated with object labels and bounding boxes, and outputs a collaborative manipulation action plan for one or more robotic arms. We demonstrate performance of the system in a standardized dataset of 100 YouTube cooking videos, as well as in three full-length Youtube videos that include collaborative actions between two participants. We additionally propose an open-source platform for executing the learned plans in a simulation environment as well as with an actual robotic arm.
Regularized and Smooth Double Core Tensor Factorization for Heterogeneous Data

We introduce a general tensor model suitable for data analytic tasks for heterogeneous data sets, wherein there are joint low-rank structures within groups of observations, but also discriminative structures across different groups. To capture such complex structures, a double core tensor (DCOT) factorization model is introduced together with a family of smoothing loss functions. By leveraging the proposed smoothing function, the model accurately estimates the model factors, even in the presence of missing entries. A linearized ADMM method is employed to solve regularized versions of DCOT factorizations, that avoid large tensor operations and large memory storage requirements. Further, we establish theoretically its global convergence, together with consistency of the estimates of the model parameters. The effectiveness of the DCOT model is illustrated on several real-world examples including image completion, recommender systems, subspace clustering and detecting modules in heterogeneous Omics multi-modal data, since it provides more insightful decompositions than conventional tensor methods.
A divergent horocycle in the horofunction compactification of the Teichmüller metric

We give an example of a horocycle in the Teichmuller space of the five-times-punctured sphere that does not converge in the Gardiner--Masur compactification, or equivalently in the horofunction compactification of the Teichmuller metric. As an intermediate step, we exhibit a simple closed curve whose extremal length is periodic but not constant along the horocycle. The example lifts to any Teichmuller space of complex dimension greater than one via covering constructions.
Theory of the Coherence of Topological Lasers

We present a theoretical study of the temporal and spatial coherence properties of a topological laser device built by including saturable gain on the edge sites of a Harper--Hofstadter lattice for photons. In the comoving frame of its chiral motion, the lasing edge mode behaves very similarly to a one-dimensional system, and the correlations of long-wavelength fluctuations display the peculiar Kardar-Parisi-Zhang (KPZ) scaling. At longer times, the finite size of the device starts to matter, and the functional form of the coherence decay changes from a KPZ stretched exponential to a pure exponential. Still, the nonlinear dynamics of KPZ fluctuations reflects into an enhanced linewidth as compared to a single-mode Schawlow-Townes phase diffusion. While all these features are general to extended laser arrays, the role of topology in protecting the coherence from static disorder is finally highlighted. This opens exciting possibilities and intriguing open questions both for fundamental studies of non-equilibrium statistical mechanics and for concrete applications to laser devices.
ForestFit : An R package for modeling tree diameter distributions

Modeling the diameter distribution of trees in forest stands is a common forestry task that supports key biologically and economically relevant management decisions. The choice of model used to represent the diameter distribution and how to estimate its parameters has received much attention in the forestry literature; however, accessible software that facilitates comprehensive comparison of the myriad modeling approaches is not available. To this end, we developed an R package called ForestFit that simplifies estimation of common probability distributions used to model tree diameter distributions, including the two- and three-parameter Weibull distributions, Johnson's SB distribution, Birnbaum-Saunders distribution, and finite mixture distributions. Frequentist and Bayesian techniques are provided for individual tree diameter data, as well as grouped data. Additional functionality facilitates fitting growth curves to height-diameter data. The package also provides a set of functions for computing probability distributions and simulating random realizations from common finite mixture models.
From power law to Anderson localization in nonlinear Schrödinger equation with nonlinear randomness.

We study the propagation of coherent waves in a nonlinearly induced random potential and find regimes of self-organized criticality and other regimes where the nonlinear equivalent of Anderson localization prevails. The regime of self-organized criticality leads to power-law decay of transport [Y. Sharabi et al., Phys. Rev. Lett. 121, 233901 (2018)PRLTAO0031-900710.1103/PhysRevLett.121. 233901], whereas the second regime exhibits exponential decay.
Facial Landmark Correlation Analysis

We present a facial landmark position correlation analysis as well as its applications. Although numerous facial landmark detection methods have been presented in the literature, few of them explicitly take into account the inherent relationship among landmarks. To reveal and interpret this relationship, we propose to analyze landmark correlation by using Canonical Correlation Analysis~(CCA). We experimentally show that the dense facial landmark annotations in current benchmarks are strongly correlated. We propose two applications based on this analysis. First, by analyzing the landmark correlation, we gain some interesting insights into the predictions of different landmark detection models (including random forests model and CNN models). We also demonstrate how CNNs progressively learn to predict facial landmarks. Second, we propose a few-shot learning method that allows to considerably reduce the manual effort for dense landmark annotation.
Thermodynamic Bethe Ansatz for Fishnet CFT

Benjamin Basso, ∗ Gwenaël Ferrando, 2, † Vladimir Kazakov, 3, ‡ and De-liang Zhong § Laboratoire de physique de l’École normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-Diderot, Sorbonne Paris Cité, 24 rue Lhomond, 75005 Paris, France Institut de Physique Théorique, Université Paris-Saclay, CNRS, CEA, 91191 Gif-sur-Yvette, France Theoretical Physics Department, CERN, 1211 Geneva 23, Switzerland School of Physics and Astronomy, Tel Aviv University, Ramat Aviv 69978, Israel (Dated: November 26, 2019)
The FLASHES Survey. I. Integral Field Spectroscopy of the CGM around 48 z ≃ 2.3–3.1 QSOs

We present the pilot study of the Fluorescent Lyman-Alpha Structures in High-z Environments Survey; the largest integral field spectroscopy survey to date of the circumgalactic medium at z = 2.3–3.1. We observed 48 quasar fields with the Palomar Cosmic Web Imager to an average (2σ) limiting surface brightness of 6 × 10−18 erg s−1 cm−2 arcsec−2 (in a 1″ aperture and ∼20 Å bandwidth). Extended H i Lyα emission is discovered around 37/48 of the observed quasars, ranging in projected radius from 14 to 55 proper kiloparsecs (pkpc), with one nebula exceeding 100 pkpc in effective diameter. The dimming-adjusted circularly averaged surface brightness profile peaks at 1 × 10−15 erg s−1 cm−2 arcsec−2 at R⊥ ∼ 20 pkpc and integrated luminosities range from 0.4 to 9.4 × 1043 erg s−1. The emission appears to have an eccentric morphology and an average covering factor of ∼30%–40% at small radii. On average, the nebular spectra are redshifted with respect to both the systemic redshift and Lyα peak of the quasar spectrum. The integrated spectra of the nebulae mostly have single- or double-peaked profiles with global dispersions ranging from 143 to 708 km s−1, though the individual Gaussian components of lines with complex shapes mostly have dispersions ≤400 km s−1, and the flux-weighted velocity centroids of the lines vary by thousands of km s−1 with respect to the QSO redshifts. Finally, the root-mean-square velocities of the nebulae are found to be consistent with those expected from gravitational motions in dark matter halos of mass Log 10 ( M h [ M ⊙ ] ) ≃ 12.2 − 1.2 + 0.7 . We compare these results to existing surveys at higher and lower redshift.
Unsupervised Neural Sensor Models for Synthetic LiDAR Data Augmentation

Data scarcity is a bottleneck to machine learning-based perception modules, usually tackled by augmenting real data with synthetic data from simulators. Realistic models of the vehicle perception sensors are hard to formulate in closed form, and at the same time, they require the existence of paired data to be learned. In this work, we propose two unsupervised neural sensor models based on unpaired domain translations with CycleGANs and Neural Style Transfer techniques. We employ CARLA as the simulation environment to obtain simulated LiDAR point clouds, together with their annotations for data augmentation, and we use KITTI dataset as the real LiDAR dataset from which we learn the realistic sensor model mapping. Moreover, we provide a framework for data augmentation and evaluation of the developed sensor models, through extrinsic object detection task evaluation using YOLO network adapted to provide oriented bounding boxes for LiDAR Bird-eye-View projected point clouds. Evaluation is performed on unseen real LiDAR frames from KITTI dataset, with different amounts of simulated data augmentation using the two proposed approaches, showing improvement of 6% mAP for the object detection task, in favor of the augmenting LiDAR point clouds adapted with the proposed neural sensor models over the raw simulated LiDAR.
On Perfect Matchings in k-Complexes

Keevash and Mycroft [ 19] developed a geometric theory for hypergraph matchings and characterized the dense simplicial complexes that contain a perfect matching. Their proof uses the hypergraph regularity method and the hypergraph blow-up lemma recently developed by Keevash. In this note we give a new proof of their results, which avoids these complex tools. In particular, our proof uses the lattice-based absorbing method developed by the author and a recent probabilistic argument of Kohayakawa, Person, and the author.
Generation of density waves in dipolar quantum gases by time-periodic modulation of atomic interactions

We study the emergence of density waves in dipolar Bose-Einstein condensates (BEC) when the strength of dipole-dipole atomic interactions is periodically varied in time. The proposed theoretical model, based on the evolution of small perturbations of the background density, allows to compute the growth rate of instability (gain factor) for arbitrary set of input parameters, thus to identify the regions of instability against density waves. We find that among other modes of the system the roton mode is most effectively excited due to the contribution of sub-harmonics of the excitation frequency. The frequency of temporal oscillations of emerging density waves coincides with the half of the driving frequency, this being the hallmark of the parametric resonance, is characteristic to Faraday waves. The possibility to create density waves in dipolar BECs, which can persist after the emergence, has been demonstrated. The existence of a stationary spatially periodic solution of the nonlocal Gross-Pitaevskii equation has been discussed. The effect of three-body atomic interactions, which is relevant to condensates with increased density, upon the properties of emerging waves has been analyzed too. Significant modification of the condensate's excitation spectrum owing to three-body effects is shown.
The route to chaos in routing games: When is price of anarchy too optimistic?

Routing games are amongst the most studied classes of games. Their two most well-known properties are that learning dynamics converge to equilibria and that all equilibria are approximately optimal. In this work, we perform a stress test for these classic results by studying the ubiquitous dynamics, Multiplicative Weights Update, in different classes of congestion games, uncovering intricate non-equilibrium phenomena. As the system demand increases, the learning dynamics go through period-doubling bifurcations, leading to instabilities, chaos and large inefficiencies even in the simplest case of non-atomic routing games with two paths of linear cost where the Price of Anarchy is equal to one. Starting with this simple class, we show that every system has a carrying capacity, above which it becomes unstable. If the equilibrium flow is a symmetric $50-50\%$ split, the system exhibits one period-doubling bifurcation. A single periodic attractor of period two replaces the attracting fixed point. Although the Price of Anarchy is equal to one, in the large population limit the time-average social cost for all but a zero measure set of initial conditions converges to its worst possible value. For asymmetric equilibrium flows, increasing the demand eventually forces the system into Li-Yorke chaos with positive topological entropy and periodic orbits of all possible periods. Remarkably, in all non-equilibrating regimes, the time-average flows on the paths converge exactly to the equilibrium flows, a property akin to no-regret learning in zero-sum games. These results are robust. We extend them to routing games with arbitrarily many strategies, polynomial cost functions, non-atomic as well as atomic routing games and heteregenous users. Our results are also applicable to any sequence of shrinking learning rates, e.g., $1/\sqrt{T}$, by allowing for a dynamically increasing population size.