Miracle-dz/UniFAR-FTUs huggingface dataset:
Synchronization and Multicluster Capabilities of Oscillatory Networks With Adaptive Coupling

We prove the existence of a multidimensional nontrivial invariant toroidal manifold for the Kuramoto network with adaptive coupling. The constructed invariant manifold corresponds to the multicluster behavior of the oscillators phases. Contrary to the static coupling, the adaptive coupling strengths exhibit quasiperiodic oscillations preserving zero phase-difference within clusters. The derived sufficient conditions for the existence of the invariant manifold provide a tradeoff between the natural frequencies of the oscillators, coupling plasticity parameters, and the interconnection structure of the network. Furthermore, we study the robustness of the invariant manifold with respect to the perturbations of the interconnection topology, and establish structural, and quantitative constraints on the perturbation adjacency matrix preserving the invariant manifold. Additionally, we demonstrate the application of the new results to the problem of interconnection topology design, which consists in endowing the desired multicluster behavior to the network by controlling its interconnection structure.
Driving Style Encoder: Situational Reward Adaptation for General-Purpose Planning in Automated Driving

General-purpose planning algorithms for automated driving combine mission, behavior, and local motion planning. Such planning algorithms map features of the environment and driving kinematics into complex reward functions. To achieve this, planning experts often rely on linear reward functions. The specification and tuning of these reward functions is a tedious process and requires significant experience. Moreover, a manually designed linear reward function does not generalize across different driving situations. In this work, we propose a deep learning approach based on inverse reinforcement learning that generates situation-dependent reward functions. Our neural network provides a mapping between features and actions of sampled driving policies of a model-predictive control-based planner and predicts reward functions for upcoming planning cycles. In our evaluation, we compare the driving style of reward functions predicted by our deep network against clustered and linear reward functions. Our proposed deep learning approach outperforms clustered linear reward functions and is at par with linear reward functions with a-priori knowledge about the situation.
Effect of therapeutic play on postoperative pain of hospitalized school age children in pediatric surgical ward

Effect of therapeutic play on postoperative pain of hospitalized school age children in pediatric surgical ward Baratee F 1 (MSM); Dabirian A 2* (MSM); Yoldashkhan M 3 (MD); Zaree F 4 (PhD); Rasouli M 1. Alumnus, Faculty of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran 2. Lecturer, Dept. of Pediatric, Faculty of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran 3. Lecturer, Dept. of Anestisiology, Faculty of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran 4. Assistant Professor, Dept. of Biostatistics, Faculty of Paramedical, Shahid Beheshti University of Medical Sciences, Tehran, Iran 5. A ssistant Professor , Dept. of pediatric, Faculty of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran Abstract Background and aim A majority of children experience moderate to severe postoperative pain. To ensure adequate pain relief in pediatric patients, non pharmacological techniques are needed for use in conjunction with pain medication. The aim of this study was to determine the effect of therapeutic play on postoperative pain of hospitalized school age children in surgical ward of Sheikh’s Pediatric Hospital affiliated to Mashhad Medical University in 2009-2010. Materials and methods In this randomized clinical trial, 40 hospitalized children in surgical ward between the age of 8 and 12 years old were selected and randomly assigned to the experimental group who participated in a session of therapeutic play that lasted between 30 and 45 minutes whereas those in the control group received routine care. Data collection tools included demographic questionnaire and Objective Numeric Pain Scales. The intensity of pain was assessed before (Phase1), immediately after (Phase2), and 15 minutes after (Phase3) intervention or routine care. The validity of ’Objective Pain Scales’ and ‘ Numeric Pain Scales’ were determined by concurrent validity (0/89) and between ‘Objective Pain Scales’ and ‘Face pain Scale’ ( 0/95). Reliability of ‘ Objective Pain Scales’ was measured by inter -rater reliability (0/95). Data were analyzed by SPSS v16. Findings The results showed that children in the experimental group reported having significantly less pain (p Conclusion The findings of this study have shown the positive effect of therapeutic play on postoperative pain of hospitalized school age children. Keywords: Postoperative pain, Therapeutic play, School age children. Corresponding Author : Akram Dabirian, Dept. of Pediatric, Faculty of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran . E-mail: a.dabirian@yahoo.com
New examples of non-Fourier–Mukai functors

A celebrated result by Orlov states that any fully faithful exact functor between the bounded derived categories of coherent sheaves on smooth projective varieties is of geometric origin, i.e. it is a Fourier–Mukai functor. In this paper we prove that any smooth projective variety of dimension $\ge 3$ equipped with a tilting bundle can serve as the source variety of a non-Fourier–Mukai functor between smooth projective schemes.
Approximate Factor Models with Strongly Correlated Idiosyncratic Errors

We consider the estimation of approximate factor models for time series data, where strong serial and cross-sectional correlations amongst the idiosyncratic component are present. This setting comes up naturally in many applications, but existing approaches in the literature rely on the assumption that such correlations are weak, leading to mis-specification of the number of factors selected and consequently inaccurate inference. In this paper, we explicitly incorporate the dependent structure present in the idiosyncratic component through lagged values of the observed multivariate time series. We formulate a constrained optimization problem to estimate the factor space and the transition matrices of the lagged values {\em simultaneously}, wherein the constraints reflect the low rank nature of the common factors and the sparsity of the transition matrices. We establish theoretical properties of the obtained estimates, and introduce an easy-to-implement computational procedure for empirical work. The performance of the model and the implementation procedure is evaluated on synthetic data and compared with competing approaches, and further illustrated on a data set involving weekly log-returns of 75 US large financial institutions for the 2001-2016 period.
Nonlocal stationary probability distributions and escape rates for an active Ornstein–Uhlenbeck particle

We evaluate the steady-state distribution and escape rate for an active Ornstein–Uhlenbeck particle (AOUP) using methods from the theory of large deviations. The calculation is carried out both for small and large memory times of the active force in one-dimension. We compare our results to those obtained in the literature about colored noise processes, and we emphasize their relevance for the field of active matter. In particular, we stress that contrary to equilibrium particles, the invariant measure of such an active particle is a non-local function of the potential. This fact has many interesting consequences, which we illustrate through two phenomena. First, active particles in the presence of an asymmetric barrier tend to accumulate on one side of the potential—a ratchet effect that was missing is previous treatments. Second, an active particle can escape over a deep metastable state without spending any time at its bottom.
No-Regret Exploration in Goal-Oriented Reinforcement Learning

Many popular reinforcement learning problems (e.g., navigation in a maze, some Atari games, mountain car) are instances of the episodic setting under its stochastic shortest path (SSP) formulation, where an agent has to achieve a goal state while minimizing the cumulative cost. Despite the popularity of this setting, the exploration-exploitation dilemma has been sparsely studied in general SSP problems, with most of the theoretical literature focusing on different problems (i.e., fixed-horizon and infinite-horizon) or making the restrictive loop-free SSP assumption (i.e., no state can be visited twice during an episode). In this paper, we study the general SSP problem with no assumption on its dynamics (some policies may actually never reach the goal). We introduce UC-SSP, the first no-regret algorithm in this setting, and prove a regret bound scaling as $\displaystyle \widetilde{\mathcal{O}}( D S \sqrt{ A D K})$ after $K$ episodes for any unknown SSP with $S$ states, $A$ actions, positive costs and SSP-diameter $D$, defined as the smallest expected hitting time from any starting state to the goal. We achieve this result by crafting a novel stopping rule, such that UC-SSP may interrupt the current policy if it is taking too long to achieve the goal and switch to alternative policies that are designed to rapidly terminate the episode.
Information density, structure and entropy in equilibrium and non-equilibrium systems

During a spontaneous change, a macroscopic physical system will evolve towards a macro-state with more realizations. This observation is at the basis of the statistical mechanical version of the second law of thermodynamics, and it provides an interpretation of entropy in terms of probabilities. However, we cannot rely on the statistical-mechanical expressions for entropy in systems that are far from equilibrium. In this paper, we compare various extensions of the definition of entropy, which have been proposed for non-equilibrium systems. It has recently been proposed that measures of information density may serve to quantify entropy in both equilibrium and non-equilibrium systems. We propose a new ‘bit-wise’ method to measure the information density for off-lattice systems. This method does not rely on coarse-graining of the particle coordinates. We then compare different estimates of the system entropy, based on information density and on the structural properties of the system, and check if the various entropies are mutually consistent and, importantly, whether they can detect non-trivial ordering phenomena. We find that, except for simple (one-dimensional) cases, the different methods yield answers that are at best qualitatively similar, and often not even that, although in several cases, the different entropy estimates do detect ordering phenomena qualitatively. Our entropy estimates based on bit-wise data compression contain no adjustable scaling factor, and show large quantitative differences with the thermodynamic entropy obtained from equilibrium simulations. Hence, our results suggest that, at present, there is not yet a single, structure-based entropy definition that has general validity for equilibrium and non-equilibrium systems.
Digital Twin: Acquiring High-Fidelity 3D Avatar from a Single Image

We present an approach to generate high fidelity 3D face avatar with a high-resolution UV texture map from a single image. To estimate the face geometry, we use a deep neural network to directly predict vertex coordinates of the 3D face model from the given image. The 3D face geometry is further refined by a non-rigid deformation process to more accurately capture facial landmarks before texture projection. A key novelty of our approach is to train the shape regression network on facial images synthetically generated using a high-quality rendering engine. Moreover, our shape estimator fully leverages the discriminative power of deep facial identity features learned from millions of facial images. We have conducted extensive experiments to demonstrate the superiority of our optimized 2D-to-3D rendering approach, especially its excellent generalization property on real-world selfie images. Our proposed system of rendering 3D avatars from 2D images has a wide range of applications from virtual/augmented reality (VR/AR) and telepsychiatry to human-computer interaction and social networks.
Thermal Conductivity of Oxide Tunnel Barriers in Magnetic Tunnel Junctions Measured by Ultrafast Thermoreflectance and Magneto-Optic Kerr Effect Thermometry

Spin-dependent charge transport in magnetic tunnel junctions (MTJs) can be manipulated by a temperature gradient, which can be utilized for spintronic and spin caloritronic applications. Evaluation of the thermally induced phenomena requires knowledge of the temperature differences across the oxide tunnel barrier adjacent to the ferromagnetic (FM) leads. However, it is challenging to accurately measure thermal properties of an oxide tunnel barrier consisting of only a few atomic layers. In this work, we experimentally interrogate the temperature evolutions in Ru/oxide/FM/seed/MgO (oxide=MgO, MgAl2O4; FM=Co, CoFeB; seed=Pt, Ta) structures having perpendicular magnetic anisotropy using ultrafast thermometry. The Ru layer is optically thick and heated by ultrafast laser pulses; the subsequent temperature changes are monitored using thermoreflectance of Ru and magneto-optic Kerr effect (MOKE) of the FM layers. We independently measure the response times of Co and CoFeB magnetism using quadratic MOKE and obtain {\tau}em=0.2 ps for Co and 2 ps for CoFeB. These time scales are much shorter than the time scale of heat transport through the oxide tunnel barrier, which occurs at 10-3000 ps. We determine effective thermal conductivities of MgO and MgAl2O4 tunnel barriers in the range of 0.4-0.6 W m-1 K-1, comparable to an estimate of the series conductance of the Ru/oxide and oxide/FM interfaces and an order of magnitude smaller than the thermal conductivity of MgO thin films. We find that the electron-phonon thermal conductance near the tunnel barrier is only a factor of 5-12 larger than the thermal conductance of the oxide tunnel barrier. Therefore, the drop in the electronic temperature is approximately 20-30% larger than the drop in the phonon temperature across the tunnel barrier.
Entanglement Renyi Negativity across a Finite Temperature Transition: A Monte Carlo Study.

Quantum entanglement is fragile to thermal fluctuations, which raises the question whether finite temperature phase transitions support long-range entanglement similar to their zero temperature counterparts. Here we use quantum Monte Carlo simulations to study the third Renyi negativity, a generalization of entanglement negativity, as a proxy of mixed-state entanglement in the 2D transverse field Ising model across its finite temperature phase transition. We find that the area-law coefficient of the Renyi negativity is singular across the transition, while its subleading constant is zero within the statistical error. This indicates that the entanglement is short-range at the critical point despite a divergent correlation length. Renyi negativity in several exactly solvable models also shows qualitative similarities to that in the 2D transverse field Ising model.
Element Level Differential Privacy: The Right Granularity of Privacy

Differential Privacy (DP) provides strong guarantees on the risk of compromising a user's data in statistical learning applications, though these strong protections make learning challenging and may be too stringent for some use cases. To address this, we propose element level differential privacy, which extends differential privacy to provide protection against leaking information about any particular "element" a user has, allowing better utility and more robust results than classical DP. By carefully choosing these "elements," it is possible to provide privacy protections at a desired granularity. We provide definitions, associated privacy guarantees, and analysis to identify the tradeoffs with the new definition; we also develop several private estimation and learning methodologies, providing careful examples for item frequency and M-estimation (empirical risk minimization) with concomitant privacy and utility analysis. We complement our theoretical and methodological advances with several real-world applications, estimating histograms and fitting several large-scale prediction models, including deep networks.
The Activation of Galactic Nuclei and Their Accretion Rates Are Linked to the Star Formation Rates and Bulge-types of Their Host Galaxies

We use bulge-type classifications of 809 representative Sloan Digital Sky Survey galaxies by Gadotti to classify a large sample of galaxies into real bulges (classical or elliptical) and pseudobulges using Random Forest. We use structural and stellar population predictors that can easily be measured without image decomposition. Multiple parameters—such as the central-mass density with 1 kpc, concentration index, Sérsic index, and velocity dispersion—result in accurate bulge classifications when combined together. We classify ∼44,500 face-on galaxies above stellar mass of 1010 M⊙ and redshift 0.02 < z < 0.07 into real bulges or pseudobulges with 93% ± 2% accuracy. We show that ∼75%–90% of active galactic nuclei (AGNs) identified by the optical line ratio diagnostic are hosted by real bulges. The pseudobulge fraction significantly decreases with AGN signature as the line ratios change from indicating pure star formation (∼54% ± 4%), to composite of star formation and AGN (∼18% ± 3%), and to AGN-dominated galaxies (∼5% ± 3%). Using the dust-corrected [O iii] luminosity as an AGN accretion indicator, and the stellar mass and radius as proxies for a black hole mass, we find that AGNs in real bulges have lower Eddington ratios than AGNs in pseudobulges. Real bulges have a wide range of AGN and star formation activities, although most of them are weak AGNs. For both bulge-types, their Eddington ratios are correlated with specific star formation rates (SSFR). Real bulges have a lower specific accretion rate but higher AGN fraction than pseudobulges do at similar SSFRs.
Explicit Sequential Equilibria in Linear Quadratic Games with Arbitrary Number of Exchangeable Players: A Non-Standard Riccati Equation

In this paper, we investigate a class of nonzero-sum dynamic stochastic games consisting of an arbitrary number of exchangeable players. The players have linear dynamics with quadratic cost functions and are coupled in both dynamics and cost through the empirical mean and covariance matrix of the states and actions. We study cooperative and non-cooperative games under three information structures: perfect sharing, aggregate sharing, and no sharing. For perfect and aggregate sharing information structures, we use a gauge transformation to solve for the best-response equations of players and identify a necessary and sufficient condition under which a unique subgame perfect Nash equilibrium exists. The equilibrium is proved to be fair and linear in the local state and aggregate state. The corresponding gains are obtained by solving a novel non-standard Riccati equation, whose dimension is independent of the number of players, thus making the solution scalable. Two approximate sequential Nash solutions are also proposed for the case of no-sharing information structure. To the best of our knowledge, this paper is the first to propose a unified framework to obtain the exact sequential Nash solution in closed form including both cooperative and non-cooperative linear quadratic games with an arbitrary number of exchangeable players. In addition, the main results are extended to the discounted and time-averaged infinite-horizon cost functions. Furthermore, the obtained results can naturally be generalized to the case with multiple exchangeable sub-populations. Finally, a numerical example is provided to demonstrate the efficacy of the obtained results.
Structured backward errors in linearizations

A standard approach to compute the roots of a univariate polynomial is to compute the eigenvalues of an associated confederate matrix instead, such as, for instance the companion or comrade matrix. The eigenvalues of the confederate matrix can be computed by Francis's QR algorithm. Unfortunately, even though the QR algorithm is provably backward stable, mapping the errors back to the original polynomial coefficients can still lead to huge errors. However, the latter statement assumes the use of a non-structure exploiting QR algorithm. In [J. Aurentz et al., Fast and backward stable computation of roots of polynomials, SIAM J. Matrix Anal. Appl. , 36(3), 2015] it was shown that a structure exploiting QR algorithm for companion matrices leads to a structured backward error on the companion matrix. The proof relied on decomposing the error into two parts: a part related to the recurrence coefficients of the basis (monomial basis in that case) and a part linked to the coefficients of the original polynomial. In this article we prove that the analysis can be extended to other classes of comrade matrices. We first provide an alternative backward stability proof in the monomial basis using structured QR algorithms; our new point of view shows more explicitly how a structured, decoupled error on the confederate matrix gets mapped to the associated polynomial coefficients. This insight reveals which properties must be preserved by a structure exploiting QR algorithm to end up with a backward stable algorithm. We will show that the previously formulated companion analysis fits in this framework and we will analyze in more detail Jacobi polynomials (Comrade matrices) and Chebyshev polynomials (Colleague matrices).
Density behaviour related to Lévy processes

Let $p_t(x)$, $f_t(x)$ and $q_t^*(x)$ be the densities at time $t$ of a real Levy process, its running supremum and the entrance law of the reflected excursions at the infimum. We provide relationships between the asymptotic behaviour of $p_t(x)$, $f_t(x)$ and $q_t^*(x)$, when $t$ is small and $x$ is large. Then for large $x$, these asymptotic behaviours are compared to this of the density of the Levy measure. We show in particular that, under mild conditions, if $p_t(x)$ is comparable to $t\nu(x)$, as $t\rightarrow0$ and $x\rightarrow\infty$, then so is $f_t(x)$.