Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate separability of symmetrically penalized least squares in high dimensions: characterization and consequences

We show that the high-dimensional behavior of symmetrically penalized least squares with a possibly non-separable, symmetric, convex penalty in both (i) the Gaussian sequence model and (ii) the linear model with uncorrelated Gaussian designs nearly agrees with the behavior of least squares with an appropriately chosen separable penalty in these same models. This agreement is established by finite-sample concentration inequalities which precisely characterize the behavior of symmetrically penalized least squares in both models via a comparison to a simple scalar statistical model. The concentration inequalities are novel in their precision and generality. Our results help clarify that the role non-separability can play in high-dimensional M-estimation. In particular, if the empirical distribution of the coordinates of the parameter is known—exactly or approximately—there are at most limited advantages to use non-separable, symmetric penalties over separable ones. In contrast, if the empirical distribution of the coordinates of the parameter is unknown, we argue that non-separable, symmetric penalties automatically implement an adaptive procedure, which we characterize. We also provide a partial converse which characterizes the adaptive procedures which can be implemented in this way.
Non-Parallel Sequence-to-Sequence Voice Conversion With Disentangled Linguistic and Speaker Representations

This article presents a method of sequence-to-sequence (seq2seq) voice conversion using non-parallel training data. In this method, disentangled linguistic and speaker representations are extracted from acoustic features, and voice conversion is achieved by preserving the linguistic representations of source utterances while replacing the speaker representations with the target ones. Our model is built under the framework of encoder-decoder neural networks. A recognition encoder is designed to learn the disentangled linguistic representations with two strategies. First, phoneme transcriptions of training data are introduced to provide the references for leaning linguistic representations of audio signals. Second, an adversarial training strategy is employed to further wipe out speaker information from the linguistic representations. Meanwhile, speaker representations are extracted from audio signals by a speaker encoder. The model parameters are estimated by two-stage training, including a pre-training stage using a multi-speaker dataset and a fine-tuning stage using the dataset of a specific conversion pair. Since both the recognition encoder and the decoder for recovering acoustic features are seq2seq neural networks, there are no constrains of frame alignment and frame-by-frame conversion in our proposed method. Experimental results showed that our method obtained higher similarity and naturalness than the best non-parallel voice conversion method in Voice Conversion Challenge 2018. Besides, the performance of our proposed method was closed to the state-of-the-art parallel seq2seq voice conversion method.
Matrix product state algorithms for Gaussian fermionic states

While general quantum many-body systems require exponential resources to be simulated on a classical computer, systems of non-interacting fermions can be simulated exactly using polynomially scaling resources. Such systems may be of interest in their own right, but also occur as effective models in numerical methods for interacting systems, such as Hartree-Fock, density functional theory, and many others. Often it is desirable to solve systems of many thousand constituent particles, rendering these simulations computationally costly despite their polynomial scaling. We demonstrate how this scaling can be improved by adapting methods based on matrix product states, which have been enormously successful for low-dimensional interacting quantum systems, to the case of free fermions. Compared to the case of interacting systems, our methods achieve an exponential speedup in the entanglement entropy of the state. We demonstrate their use to solve systems of up to one million sites with an effective MPS bond dimension of 10^15.
Acoustic Modeling for Automatic Lyrics-to-Audio Alignment

Automatic lyrics to polyphonic audio alignment is a challenging task not only because the vocals are corrupted by background music, but also there is a lack of annotated polyphonic corpus for effective acoustic modeling. In this work, we propose (1) using additional speech and music-informed features and (2) adapting the acoustic models trained on a large amount of solo singing vocals towards polyphonic music using a small amount of in-domain data. Incorporating additional information such as voicing and auditory features together with conventional acoustic features aims to bring robustness against the increased spectro-temporal variations in singing vocals. By adapting the acoustic model using a small amount of polyphonic audio data, we reduce the domain mismatch between training and testing data. We perform several alignment experiments and present an in-depth alignment error analysis on acoustic features, and model adaptation techniques. The results demonstrate that the proposed strategy provides a significant error reduction of word boundary alignment over comparable existing systems, especially on more challenging polyphonic data with long-duration musical interludes.
RUBi: Reducing Unimodal Biases in Visual Question Answering

Visual Question Answering (VQA) is the task of answering questions about an image. Some VQA models often exploit unimodal biases to provide the correct answer without using the image information. As a result, they suffer from a huge drop in performance when evaluated on data outside their training set distribution. This critical issue makes them unsuitable for real-world settings. We propose RUBi, a new learning strategy to reduce biases in any VQA model. It reduces the importance of the most biased examples, i.e. examples that can be correctly classified without looking at the image. It implicitly forces the VQA model to use the two input modalities instead of relying on statistical regularities between the question and the answer. We leverage a question-only model that captures the language biases by identifying when these unwanted regularities are used. It prevents the base VQA model from learning them by influencing its predictions. This leads to dynamically adjusting the loss in order to compensate for biases. We validate our contributions by surpassing the current state-of-the-art results on VQA-CP v2. This dataset is specifically designed to assess the robustness of VQA models when exposed to different question biases at test time than what was seen during training. Our code is available: this http URL
Parametric Versus Semi and Nonparametric Regression Models

There are three common types of regression models: parametric, semiparametric and nonparametric regression. The model should be used to fit the real data depends on how much information is available about the form of the relationship between the response variable and explanatory variables, and the random error distribution that is assumed. Researchers need to be familiar with each modeling approach requirements. In this paper, differences between these models, common estimation methods, robust estimation, and applications are introduced. For parametric models, there are many known methods of estimation, such as least squares and maximum likelihood methods which are extensively studied but they require strong assumptions. On the other hand, nonparametric regression models are free of assumptions regarding the form of the response-explanatory variables relationships but estimation methods, such as kernel and spline smoothing are computationally expensive and smoothing parameters need to be obtained. For kernel smoothing there two common estimators: local constant and local linear smoothing methods. In terms of bias, especially at the boundaries of the data range, local linear is better than local constant estimator. Robust estimation methods for linear models are well studied, however the robust estimation methods in nonparametric regression methods are limited. A robust estimation method for the semiparametric and nonparametric regression models is introduced.
Semi-Supervised Learning with Self-Supervised Networks

Recent advances in semi-supervised learning have shown tremendous potential in overcoming a major barrier to the success of modern machine learning algorithms: access to vast amounts of human-labeled training data. Algorithms based on self-ensemble learning and virtual adversarial training can harness the abundance of unlabeled data to produce impressive state-of-the-art results on a number of semi-supervised benchmarks, approaching the performance of strong supervised baselines using only a fraction of the available labeled data. However, these methods often require careful tuning of many hyper-parameters and are usually not easy to implement in practice. In this work, we present a conceptually simple yet effective semi-supervised algorithm based on self-supervised learning to combine semantic feature representations from unlabeled data. Our models are efficiently trained end-to-end for the joint, multi-task learning of labeled and unlabeled data in a single stage. Striving for simplicity and practicality, our approach requires no additional hyper-parameters to tune for optimal performance beyond the standard set for training convolutional neural networks. We conduct a comprehensive empirical evaluation of our models for semi-supervised image classification on SVHN, CIFAR-10 and CIFAR-100, and demonstrate results competitive with, and in some cases exceeding, prior state of the art. Reference code and data are available at https://github.com/vuptran/sesemi.
Optical absorption of single-layer hexagonal boron nitride in the ultraviolet

In this paper we theoretically describe the absorption of hexagonal boron nitride (hBN) single layer. We develop the necessary formalism and present an efficient method for solving the Wannier equation for excitons. We give predictions for the absorption of hBN on quartz and on graphite. We compare our predictions with recently published results (Elias et al 2019 Nat. Commun. 10 2639) for a monolayer of hBN on graphite. The spontaneous radiative lifetime of excitons in hBN is also computed. We argue that the optical properties of hBN in the ultraviolet are very useful for the study of peptides and other biomolecules.
Advances and challenges in single-molecule electron transport

Electronic transport properties for single-molecule junctions have been widely measured by several techniques, including mechanically controllable break junctions, electromigration break junctions or by means of scanning tunneling microscopes. In parallel, many theoretical tools have been developed and refined for describing such transport properties and for obtaining numerical predictions. Most prominent among these theoretical tools are those based upon density functional theory. In this review, theory and experiment are critically compared and this confrontation leads to several important conclusions. The theoretically predicted trends nowadays reproduce the experimental findings quite well for series of molecules with a single well-defined control parameter, such as the length of the molecules. The quantitative agreement between theory and experiment usually is less convincing, however. Many reasons for quantitative discrepancies can be identified, from which one may decide that qualitative agreement is the best one may expect with present modeling tools. For further progress, benchmark systems are required that are sufficiently well-defined by experiment to allow quantitative testing of the approximation schemes underlying the theoretical modeling. Several key experiments can be identified suggesting that the present description may even be qualitatively incomplete in some cases. Such key experimental observations and their current models are also discussed here, leading to several suggestions for extensions of the models towards including dynamic image charges, electron correlations, and polaron formation.
Likely cavitation and radial motion of stochastic elastic spheres

The cavitation of solid elastic spheres is a classical problem of continuum mechanics. Here, we study this problem within the context of ‘stochastic elasticity’ where the constitutive parameters are characterised by probability density functions. We consider homogeneous spheres of stochastic neo-Hookean material, composites with two concentric stochastic neo-Hookean phases, and inhomogeneous spheres of locally neo-Hookean material with a radially varying parameter. In all cases, we show that the material at the centre determines the critical load at which a spherical cavity forms there. However, while under dead-load traction, a supercritical bifurcation, with stable cavitation, is obtained in a static sphere of stochastic neo-Hookean material, for the composite and radially inhomogeneous spheres, a subcritical bifurcation, with snap cavitation, is also possible. For the dynamic spheres, oscillatory motions are produced under suitable dead-load traction, such that a cavity forms and expands to a maximum radius, then collapses again to zero periodically, but not under impulse traction. Under a surface impulse, a subcritical bifurcation is found in a static sphere of stochastic neo-Hookean material and also in an inhomogeneous sphere, whereas in composite spheres, supercritical bifurcations can occur as well. Given the non-deterministic material parameters, the results can be characterised in terms of probability distributions.
On the intrinsic three-dimensionality of the flow normal to a circular disk

Direct numerical simulations are performed for the steady flow normal to a circular disk at the Reynolds number of 1000. Numerical simulations are conducted with different levels of simplification procedure by reducing the azimuthal extension of the disk. The full-disk, the half-disk, the quarter-disk, the eighth-disk and the two-dimensional (2D) cases with the identical grid resolution are considered. Intrinsic three-dimensionality is identified in the wake of the circular disk. Both of the instantaneous and mean flow quantities are influenced by the simplification level significantly. The mean drag coefficient obtained from the 2D case is about only 36% of that obtained from the three-dimensional (3D) simulation for the full-disk.
Widening Perspectives: The Intellectual and Social Benefits of Astrobiology, Big History, and the Exploration of Space

Astrobiology is the field of science devoted to searching for life elsewhere in the Universe. It is inherently interdisciplinary, integrating results from multiple fields of science, and in this respect has strong synergies with 'big history'. I argue that big history and astrobiology are both acting to widen human perspectives in intellectually and socially beneficial directions, especially by enhancing public awareness of cosmic and evolutionary worldviews. I will further argue that these perspectives have important implications for the social and political organisation of humanity, including the eventual political unification of our planet. Astrobiology and big history are also concerned with the future of humanity, and I will argue that this future will be culturally and intellectually enriched if it includes the exploration of the universe around us.
Reductions of Galois Representations and the Theta Operator

Let $p\ge 5$ be a prime, and let $f$ be an eigenform of weight at least $2$ and level coprime to $p$ of finite slope $\alpha$. Let $\bar{\rho}_f$ denote the mod $p$ Galois representation associated with $f$ and $\omega$ the mod $p$ cyclotomic character. Under some mild assumptions, we prove that there exists an eigenform $g$ of weight at least $2$ and level coprime to $p$ of slope $\alpha+1$ such that $$\bar{\rho}_f \otimes \omega \simeq \bar{\rho}_g,$$ up to semisimplification. The proof uses Hida-Coleman families and the theta operator acting on overconvergent forms. The structure of the reductions of the local Galois representations associated to cuspforms with slopes in the interval $[0,1)$ were determined by Deligne, Buzzard, Gee and for slopes in $[1,2)$ by Bhattacharya, Ghate, Rai and Rozensztajn. We show that these reductions, in spite of their somewhat complicated behavior, are compatible with the displayed equation above. Moreover, the displayed equation above allows us to predict the shape of the reductions of a class of Galois representations attached to eigenforms of slope larger than $2$. Finally, recall that Wan has given upper bounds on the radii of Coleman families. The methods of this paper allow us to obtain lower bounds on the radii of certain Coleman families.
Spectroscopic binaries RV Tauri and DF Cygni

Context. Some RV Tauri stars show a long-term photometric variability in their mean magnitudes. DF Cygni (DF Cyg), the only RV Tauri star in the original Kepler field, and the prototype RV Tauri (RV Tau) are two such stars. Aims. The focus of this paper is on two famous but still poorly understood RV Tauri stars: RV Tau and DF Cyg. We aim to confirm their suspected binary nature and derive their orbital elements to investigate the impact of their orbits on the evolution of these systems. This research is embedded in a wider endeavour to study binary evolution of low- and intermediate-mass stars. Methods. The high amplitude pulsations were cleaned from the radial-velocity data to better constrain the orbital motion, allowing us to obtain accurate orbital parameters. We also analysed the photometric time series of both stars using a Lomb-Scargle periodogram. We used Gaia Data Release 2 (DR2) parallaxes in combination with the spectral energy distributions (SEDs) to compute their luminosities. These luminosities were complemented with the ones we computed using a period-luminosity-colour (PLC) relation for RV Tauri stars. The ratio of the circumstellar infrared (IR) flux to the photospheric flux obtained from the SEDs was used to estimate the orbital inclination of each system. Results. DF Cyg and RV Tau are binaries with spectroscopic orbital periods of 784 ± 16 days and 1198 ± 17 days, respectively. These orbital periods are found to be similar to the long-term periodic variability in the photometry, indicating that binarity indeed explains the long-term photometric variability. The SEDs of these systems indicate the presence of a circumbinary disc. Our line of sight grazes the dusty disc, which causes the photometric flux from the star to extinct periodically with the orbital period. Our derived orbital inclinations enabled us to obtain accurate companion masses for DF Cyg and RV Tau, and these were found to be 0.6 ± 0.1 M⊙ and 0.7 ± 0.1 M⊙, respectively. The derived luminosities suggest that RV Tau is a post asymptotic giant branch (post-AGB) binary, while DF Cyg is likely a post red giant branch (post-RGB) binary. Analysis of the Kepler photometry of DF Cyg revealed a power spectrum with side lobes around the fundamental pulsation frequency. This modulation corresponds to the spectroscopic orbital period and hence to the long-term photometric period. Finally we report on the evidence of high velocity absorption features related to the Hα profile in both objects, indicating outflows launched from around the companion.
Probabilistic Error Analysis for Inner Products

Probabilistic models are proposed for bounding the forward error in the numerically computed inner product (dot product, scalar product) between two real n-vectors. We derive probabilistic perturbation bounds as well as probabilistic roundoff error bounds for the sequential accumulation of the inner product. These bounds are nonasymptotic, explicit, with minimal assumptions, and with a clear relationship between failure probability and relative error. The roundoffs are represented as bounded, zero-mean random variables that are independent or have conditionally independent means. Our probabilistic bounds are based on Azuma's inequality and its associated martingale, which mirrors the sequential order of computations. The derivation of forward error bounds "from first principles" has the advantage of producing condition numbers that are customized for the probabilistic bounds. Numerical experiments confirm that our bounds are more informative, often by several orders of magnitude, than traditional deterministic bounds-even for small vector dimensions n and very stringent success probabilities. In particular the probabilistic roundoff error bounds are functions of n rather than n, thus giving a quantitative confirmation of Wilkinson's intuition. The paper concludes with a critical assessment of the probabilistic approach.
Analog MIMO Radio-over-Copper: Prototype and Preliminary Experimental Results

Analog Multiple-Input Multiple-Output Radio-over-Copper (A-MIMO-RoC) is an effective all-analog FrontHaul (FH) architecture that exploits any pre-existing Local Area Network (LAN) cabling infrastructure of buildings to distribute Radio-Frequency (RF) signals indoors. A-MIMO-RoC, by leveraging a fully analog implementation, completely avoids any dedicated digital interface by using a transparent end-to-end system, with consequent latency, bandwidth and cost benefits. Usually, LAN cables are exploited mainly in the low-frequency spectrum portion, mostly due to the moderate cable attenuation and crosstalk among twisted-pairs. Unlike current systems based on LAN cables, the key feature of the proposed platform is to exploit more efficiently the huge bandwidth capability offered by LAN cables, that contain 4 twistedpairs reaching up to 500 MHz bandwidth/pair when the length is below 100 m. Several works proposed numerical simulations that assert the feasibility of employing LAN cables for indoor FH applications up to several hundreds of MHz, but an A-MIMO-RoC experimental evaluation is still missing. Here, we present some preliminary results obtained with an A-MIMO-RoC prototype made by low-cost all-analog/all-passive devices along the signal path. This setup demonstrates experimentally the feasibility of the proposed analog relaying of MIMO RF signals over LAN cables up to 400 MHz, thus enabling an efficient exploitation of the LAN cables transport capabilities for 5G indoor applications.
Modelling and optimization of industrial manufacturing processes subject to no-wait constraints

This paper deals with scheduling of no-wait manufacturing processes. The main features of these systems are analysed and an industrial application is described in detail. No standard solution procedure has been devised in the literature for this difficult scheduling problem. We exploit a heuristic decomposition approach which allows the reduction of the searchable solutions space and makes use of sequencing and timetabling modules. The timetabling can be performed by a polynomial time algorithm. To make the sequencing decision we propose several heuristic rules, a decomposition approach making use of fuzzy logic and an algorithm based on tabu search. The extensive computational results reported show the clear superiority of the tabu search approach.