Miracle-dz/UniFAR-FTUs huggingface dataset:
Fixing the BMS frame of numerical relativity waveforms with BMS charges

The Bondi-van der Burg-Metzner-Sachs (BMS) group, which uniquely describes the symmetries of asymptotic infinity and therefore of the gravitational waves that propagate there, has become increasingly important for accurate modeling of waveforms. In particular, waveform models, such as post-Newtonian (PN) expressions, numerical relativity (NR), and black hole perturbation theory, produce results that are in different BMS frames. Consequently, to build a model for the waveforms produced during the merging of compact objects, which ideally would be a hybridization of PN, NR, and black hole perturbation theory, one needs a fast and robust method for fixing the BMS freedoms. In this work, we present the first means of fixing the entire BMS freedom of NR waveforms to match the frame of either PN waveforms or black hole perturbation theory. We achieve this by finding the BMS transformations that change certain charges in a prescribed way -- e.g., finding the center-of-mass transformation that maps the center-of-mass charge to a mean of zero. We find that this new method is 20 times faster, and more correct when mapping to the superrest frame, than previous methods that relied on optimization algorithms. Furthermore, in the course of developing this charge-based frame fixing method, we compute the PN expression for the Moreschi supermomentum to 3PN order without spins and 2PN order with spins. This Moreschi supermomentum is effectively equivalent to the energy flux or the null memory contribution at future null infinity $\mathscr{I}^{+}$. From this PN calculation, we also compute oscillatory ($m\not=0$ modes) and spin-dependent memory terms that have not been identified previously or have been missing from strain expressions in the post-Newtonian literature.
Novel collaborative optimization framework with a negotiation model for satellite system design

ABSTRACT This article proposes a novel collaborative optimization (NCO) framework that uses a persuasive multi-agent negotiation method for satellite system design. Satellite system design is a typical multi-disciplinary design optimization problem. The traditional collaborative optimization (CO) is a competitive method but has the disadvantage of inefficient convergence caused by the interdisciplinary compatibility constraints in system-level optimization. By introducing the multi-agent negotiation method, an NCO framework is proposed, in which the system-level and subsystem-level variable negotiations replace the system-level compatibility constraints in traditional CO. The negotiation introduces a modification for candidate values that do not satisfy the compatibility constraints, ensures that an alternative compatible candidate will be obtained and has high convergence efficiency. Two numerical benchmark functions and a multi-disciplinary satellite system design are used to analyse the performance of the proposed NCO. The simulation results show that NCO can efficiently ensure parameter interdisciplinary matching and advanced convergence performance.
Ridge Regularized Estimation of VAR Models for Inference and Sieve Approximation

Developments in statistical learning have fueled the analysis of high-dimensional time series. However, even in low-dimensional contexts the issues arising from ill-conditioned regression problems are well-known. Because linear time series modeling is naturally prone to such issues, I propose to apply ridge regression to the estimation of dense VAR models. Theoretical non-asymptotic results concerning the addition of a ridge-type penalty to the least squares estimator are discussed, while standard asymptotic and inference techniques are proven to be valid under mild conditions on the regularizer. The proposed estimator is then applied to the problem of sieve approximation of VAR(∞) processes under moderately harsh sample sizes. Simulation evidence is used to discuss the small sample properties of the ridge estimator (RLS) when compared to least squares and local projection approaches: I use a Monte Carlo exercise to argue that RLS with a lag-adapted cross-validated regularizer achieve meaningfully better performance in recovering impulse response functions and asymptotic confidence intervals than other common approaches.
Effective two-body approach to the hierarchical three-body problem: Quadrupole to 1PN

Many binary systems of interest for gravitational-wave astronomy are orbited by a third distant body, which can considerably alter their relativistic dynamics. Precision computations are needed to understand the interplay between relativistic corrections and three-body interactions. We use an effective field theory approach to derive the effective action describing the long time-scale dynamics of hierarchical three-body systems up to 1PN quadrupole order. At this level of approximation, computations are complicated by the backreaction of small oscillations on orbital time-scales as well as deviations from the adiabatic approximation. We address these difficulties by eliminating the fast modes through the method of near-identity transformations. This allows us to compute for the first time the complete expression of the 1PN quadrupole cross-terms in generic configurations of three-body systems. We numerically integrate the resulting equations of motion and show that 1PN quadrupole terms can affect the long term dynamics of relativistic three-body systems.
Universal consistency of the k-NN rule in metric spaces and Nagata dimension. II

We continue to investigate the k nearest neighbour (k-NN) learning rule in complete separable metric spaces. Thanks to the results of Cérou and Guyader (2006) and Preiss (1983), this rule is known to be universally consistent in every such metric space that is sigma-finite dimensional in the sense of Nagata. Here we show that the rule is strongly universally consistent in such spaces in the absence of ties. Under the tie-breaking strategy applied by Devroye, Gyorfi, Krzyzak, and Lugosi (1994) in the Euclidean setting, we manage to show the strong universal consistency in non-Archimedian metric spaces (that is, those of Nagata dimension zero). Combining the theorem of Cérou and Guyader with results of Assouad and Quentin de Gromard (2006), one deduces that the $k$-NN rule is universally consistent in metric spaces having finite dimension in the sense of de Groot. In particular, the $k$-NN rule is universally consistent in the Heisenberg group which is not sigma-finite dimensional in the sense of Nagata as follows from an example independently constructed by Korányi and Reimann (1995) and Sawyer and Wheeden (1992).
The GAPS programme at TNG

Context. Understanding stellar activity in M dwarfs is fundamental to improving our knowledge of the physics of stellar atmospheres and for planet search programmes. High levels of stellar activity (also frequently associated with flare events) can cause additional variations in the stellar emission that contaminate the signal induced by a planet and that need to be corrected. The study of activity indicators in active stars can improve our capability of modelling the signal generated by magnetic activity. Aims. In this work we present measurements of activity indicators at visible wavelength for a star with a high activity level, AD Leonis, observed with HARPS in 2006, and HARPS-N in 2018. Our aim is to understand the behaviour of stellar chromospheres of M stars, studying the more sensitive chromospheric activity indicators. We also focus on characterising their variability and on finding the correlations among these indicators to obtain information on the origin of the magnetic activity in low-mass stars. Methods. We performed a study of the main optical activity indicators (Ca II H&K, Balmer lines, Na I D1,2 doublet, He I D3, and other helium lines) measured for AD Leonis using the data provided by the HARPS-N high-resolution spectrograph at the Telescopio Nazionale Galileo in 2018, and by the HARPS instrument at La Silla observatory in 2006. Spectra were flux-calibrated in units of flux at the stellar surface. We measured excess flux of the selected activity indicators. The correlations between the different activity indicators as well as the temporal evolution of fluxes were analysed. A stellar flare was identified during the 2018 observing run and the Hα, Hβ, He I 4471 Å, and He I 5876 Å lines were analysed in detail by fitting the line profiles with two Gaussian components. Results. We found that the Ca II H&K flux excesses are strongly correlated with each other, but the Ca II H&K doublet is generally less correlated with the other indicators. Moreover, Hα is correlated with Na I doublet and helium lines. Analysing the time variability of flux of the studied lines, we found a higher level of activity of the star during the observations in 2018 than in 2006, while Ca II H&K showed more intense emission on spectra obtained during the observations in 2006. Thanks to a detailed analysis of selected line profiles, we investigated the flare evaluating the mass motion during the event.
PRECISION GATING: IMPROVING NEURAL NETWORK EFFICIENCY WITH DYNAMIC DUAL-PRECISION ACTI-

We propose precision gating (PG), an end-to-end trainable dual-precision quantization technique for deep neural networks. PG computes most features in a low precision and only a small proportion of important features in a higher precision. Precision gating is very lightweight and widely applicable to many neural network architectures. Experimental results show that precision gating can greatly reduce the average bitwidth of computations in both CNNs and LSTMs with negligible accuracy loss. Compared to state-of-the-art counterparts, PG achieves the same or better accuracy with 2.4× less compute on ImageNet. Compared to 8-bit uniform quantization, PG obtains a 1.2% improvement in perplexity per word with 2.8× computational cost reduction on LSTM on the Penn Tree Bank dataset. Precision gating has the potential to greatly reduce the execution costs of DNNs on both commodity and dedicated hardware accelerators. We implement the sampled dense-dense matrix multiplication kernel in PG on CPU, which achieves up to 8.3× wall clock speedup over the dense baseline.
Policy Optimization with Stochastic Mirror Descent

Improving sample efficiency has been a longstanding goal in reinforcement learning. In this paper, we propose the $\mathtt{VRMPO}$: a sample efficient policy gradient method with stochastic mirror descent. A novel variance reduced policy gradient estimator is the key of $\mathtt{VRMPO}$ to improve sample efficiency. Our $\mathtt{VRMPO}$ needs only $\mathcal{O}(\epsilon^{-3})$ sample trajectories to achieve an $\epsilon$-approximate first-order stationary point, which matches the best-known sample complexity. We conduct extensive experiments to show our algorithm outperforms state-of-the-art policy gradient methods in various settings.
Juno model rheometry and simulation

The experiment Waves aboard the Juno spacecraft, which will arrive at its target planet Jupiter in 2016, was devised to study the plasma and radio waves of the Jovian magnetosphere. We analyzed the Waves antennas, which consist of two nonparallel monopoles operated as a dipole. For this investigation we applied two independent methods: the experimental technique, rheometry, which is based on a downscaled model of the spacecraft to measure the antenna properties in an electrolytic tank and numerical simulations, based on commercial computer codes, from which the quantities of interest (antenna impedances and effective length vectors) are calculated. In this article we focus on the results for the low‐frequency range up to about 4 MHz, where the antenna system is in the quasi‐static regime. Our findings show that there is a significant deviation of the effective length vectors from the physical monopole directions, caused by the presence of the conducting spacecraft body. The effective axes of the antenna monopoles are offset from the mechanical axes by more than 30°, and effective lengths show a reduction to about 60% of the antenna rod lengths. The antennas' mutual capacitances are small compared to the self‐capacitances, and the latter are almost the same for the two monopoles. The overall performance of the antennas in dipole configuration is very stable throughout the frequency range up to about 4–5 MHz and therefore can be regarded as the upper frequency bound below which the presented quasi‐static results are applicable.
On eddy viscosity transport models with elliptic relaxation

ABSTRACT An eddy viscosity transport (EVT) model is formulated based on the transformation of the standard k–ϵ model without the assumption of equal diffusion coefficients. These terms that are usually neglected, due to their complexities and numerical difficulties, are regrouped so that an elliptic production term naturally emerges. This term forms the basis of an elliptic relaxation model that avoids the need for wall-damping functions. All of the EVT model's constants follow from the transformation, maintaining the closest relationship to its parent k–ϵ model. To assess the ability of the elliptic relaxation equation to account for the non-local wall effects, another version of the EVT model that neglects the diffusion terms and retains only the Laplace operator term, i.e., , is proposed and compared to Menter one-equation model, which utilises wall-damping functions. The performance of this version of the EVT model is also compared to the fully transformed EVT model in order to assess the importance of the neglected diffusion terms. All three models are tested in detail over a wide class of internal and external turbulent flows.
SAFEGUARDED LEARNED CONVEX OPTIMIZATION

Many applications require quickly and repeatedly solving a certain type of optimization problem, each time with new (but similar) data. However, state-of-theart general-purpose optimization methods may converge too slowly for real-time use. This shortcoming is addressed by learning to optimize (L2O) schemes, which construct neural networks from parameterized forms of the update operations of general-purpose methods. Inferences by each network form solution estimates, and networks are trained to optimize these estimates for a particular distribution of data. This results in task-specific algorithms (e.g., LISTA, ALISTA, and DLADMM) that can converge order(s) of magnitude faster than general-purpose counterparts. We provide the first general L2O convergence theory by wrapping all L2O schemes for convex optimization within a single framework. Existing L2O schemes form special cases, and we give a practical guide for applying our L2O framework to other problems. Using safeguarding, our theory proves, as the number of network layers increases, the distance between inferences and the solution set goes to zero, i.e., each cluster point is a solution. Our numerical examples demonstrate the efficacy of our approach for both existing and new L2O methods.
Fault-Tolerant Model Predictive Control with Flight-Test Results

This work presents a novel method of fault-tolerant model predictive control where the response of a reference closed-loop model is followed even in the presence of an actuator fault. This architecture is capable of redistributing the control efforts among healthy actuators in a stable manner, respecting their limitations. Also, a constrained guidance system that works in conjunction with the inner-loop fault-tolerant controller is proposed. The guidance law considers the calculated limitations of the inner-loop control system as input constraints in order to smooth the transition between two consecutive navigation legs defined by waypoints. A trajectory-tracking system composed of the constrained guidance and the fault-tolerant model predictive controller is demonstrated through numerical simulations and experimental results on an experimental midsize transport aircraft, showing adequate performance.
Corticosteroids in the treatment of dengue shock syndrome

Dengue infection causes significant morbidity and mortality in over 100 countries worldwide, and its incidence is on the rise. The pathophysiological basis for the development of severe dengue, characterized by plasma leakage and the “shock syndrome” are poorly understood. No specific treatment or vaccine is available, and careful monitoring and judicious administration of fluids forms the mainstay of management at present. It is postulated that vascular endothelial dysfunction, induced by cytokine and chemical mediators, is an important mechanism of plasma leakage. Although corticosteroids are potent modulators of the immune system, their role in pharmacological doses in modulating the purported immunological effects that take place in severe dengue has been a subject of controversy. The key evidence related to the role of corticosteroids for various manifestations of dengue are reviewed here. In summary, there is currently no high-quality evidence supporting the beneficial effects of corticosteroids for treatment of shock, prevention of serious complications, or increasing platelet counts. Non-randomized trials of corticosteroids given as rescue medication for severe shock have shown possible benefit. Nonetheless, the evidence base is small, and good-quality trials are lacking. We reiterate the need for well-designed and adequately powered randomized controlled trials of corticosteroids for the treatment of dengue shock.
Quantum sensing with Rydberg Schrödinger cat states

Rydberg atoms are highly excited states, in which the electron is orbiting far from the nucleus. Their large electric dipole makes them very sensitive to their electromagnetic environment. Using a combination of microwave and radio-frequency fields, we engineer non-classical quantum states specifically designed to exploit at best this sensitivity for electric and magnetic field metrology. In the first part, we prepare non-classical states, similar to Schrodinger cat states, superpositions of two orbitals with very different polarizabilities, that allow us to measure small variations of the static electric field with a sensitivity well beyond the standard quantum limit and close to the fundamental Heisenberg limit. We reach a single atom sensitivity of 30mV/m for a 200ns interrogation time. It makes our system one of the most sensitive electrometers to date. We then implement more complex manipulations of the atom. Using a spin-echo technique taking advantage of the full extent of the Rydberg manifold, we perform a correlation function measurement of the electric field with a MHz bandwidth.In the final part, we prepare a quantum superposition of two circular states with opposite magnetic quantum numbers. It corresponds to an electron rotating at the same time in opposite directions on the same orbit, a rather non-classical situation. The huge difference of magnetic moment between the two components of the superposition, in the order of 100muB, opens the way to the measurement of small variations of the magnetic field with a high bandwidth.
Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018)

This paper introduces the acoustic scene classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task, and evaluates the performance of a baseline system in the task. As in previous years of the challenge, the task is defined for classification of short audio samples into one of predefined acoustic scene classes, using a supervised, closedset classification setup. The newly recorded TUT Urban Acoustic Scenes 2018 dataset consists of ten different acoustic scenes and was recorded in six large European cities, therefore it has a higher acoustic variability than the previous datasets used for this task, and in addition to high-quality binaural recordings, it also includes data recorded with mobile devices. We also present the baseline system consisting of a convolutional neural network and its performance in the subtasks using the recommended cross-validation setup.
Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017)

Motivated by the recent success of deep learning techniques in various audio analysis tasks, this work presents a distributed sensor-server system for acoustic scene classification in urban environments based on deep convolutional neural networks (CNN). Stacked autoencoders are used to compress extracted spectrogram patches on the sensor side before being transmitted to and classified on the server side. In our experiments, we compare two state-of-theart CNN architectures subject to their classification accuracy under the presence of environmental noise, the dimensionality reduction in the encoding stage, as well as a reduced number of filters in the convolution layers. Our results show that the best model configuration leads to a classification accuracy of 75% for 5 acoustic scenes. We furthermore discuss which confusions among particular classes can be ascribed to particular sound event types, which are present in multiple acoustic scene classes.
Automatic Calibration Method for Driver's Head Orientation in Natural Driving Environment

Gaze tracking is crucial for studying driver's attention, detecting fatigue, and improving driver assistance systems, but it is difficult in natural driving environments due to nonuniform and highly variable illumination and large head movements. Traditional calibrations that require subjects to follow calibrators are very cumbersome to be implemented in daily driving situations. A new automatic calibration method, based on a single camera for determining the head orientation and which utilizes the side mirrors, the rear-view mirror, the instrument board, and different zones in the windshield as calibration points, is presented in this paper. Supported by a self-learning algorithm, the system tracks the head and categorizes the head pose in 12 gaze zones based on facial features. The particle filter is used to estimate the head pose to obtain an accurate gaze zone by updating the calibration parameters. Experimental results show that, after several hours of driving, the automatic calibration method without driver's corporation can achieve the same accuracy as a manual calibration method. The mean error of estimated eye gazes was less than 5° in day and night driving.
Pseudo-Conventional N-Gram Representation of the Discriminative N-Gram Model for LVCSR

The discriminative n-gram modeling approach re-ranks the N-best hypotheses generated during decoding and can effectively improve the performance of large-vocabulary continuous speech recognition (LVCSR). This work recasts the discriminative n-gram model as a pseudo-conventional n-gram model. The recast enables the power of discriminative n-gram modeling to be conveniently incorporated in a single-pass decoding procedure. We also propose an efficient method to apply the pseudo model to rescore the recognition lattices generated during decoding. Experimental results show that when the test data is similar in nature to the training data, applying the pseudo model to rescore the recognition lattices can achieve better performance and efficiency, when compared with discriminative N-best re-ranking (i.e., re-ranking the N-best hypotheses with the discriminative n-gram model). We demonstrate that in this case, applying the pseudo model in decoding can be even more advantageous. However, when the test data is different in nature from the training data, discriminative N -best re-ranking may offer greater benefits than pseudo-model based lattice rescoring or decoding. Based on the pseudo-conventional n-gram representation, we also investigate the feasibility of combining discriminative n-gram modeling with other recognition post-processes and demonstrate that cumulative performance improvements can be achieved.